MIT News - Drug development MIT News is dedicated to communicating to the media and the public the news and achievements of the students, faculty, staff and the greater MIT community. en Sat, 19 Oct 2019 23:59:59 -0400 Biological engineer Paul Blainey creates new tools to advance biomedical research His technology platforms have benefited genomics, diagnostics, and drug screening. Sat, 19 Oct 2019 23:59:59 -0400 Anne Trafton | MIT News Office <p>Microfluidics — the science of manipulating tiny amounts of fluid through channels — has been widely used in fields such as genomics, where it has helped to enable high-speed sequencing. Several years ago, Paul Blainey started to wonder why microfluidics was not used for drug screening, another application that requires analyzing huge amounts of samples quickly.</p> <p>That question led him and his students to develop a new type of microfluidics platform in which droplets are sealed within tiny wells, overcoming the problem of drug leakage that had stymied previous efforts. This system worked well for screening drugs, but it also ended up being useful for many other applications, far beyond what Blainey had originally envisioned.</p> <p>“That’s one of the things I love about science — you can have a thought about why doesn’t microfluidics do more for chemistry, and then you develop something that turns out to have all these really exciting uses and applications that no one imagined,” says Blainey, a member of the Broad Institute of MIT and Harvard and a newly tenured associate professor in the Department of Biological Engineering.</p> <p>Blainey’s lab takes a wide-ranging approach to solving technological problems, resulting in the development of many cutting-edge tools over the past several years, with applications in fields from genomics to diagnostics and drug development. He credits his students with helping to come up with ideas for novel technologies, and pursuing alternative directions until they find something that works.</p> <p>“The major research directions and technology platforms that the lab is known for today came out of this process where the students or I had a crazy idea, and then the lab executed on it, with all the twists and turns along the way,” he says.</p> <p><strong>Drawn to engineering</strong></p> <p>Growing up in Seattle, the son of a phone company technician and a nursing professor, Blainey had a natural affinity for engineering. “I was always that kid who was into building models,” he recalls. However, he began his academic career in the sciences, majoring in chemistry and mathematics at the University of Washington. He went on to earn a PhD in physical chemistry at Harvard University, but while pursuing his degrees, he was drawn to the aspects of science most closely related to engineering.</p> <p>“I really liked analytical chemistry, which is very much like an engineering discipline because it’s focused on instrumentation, measurement, and the quantitative aspects of chemistry,” he says.</p> <p>After finishing his PhD, he went to Stanford University to work as a postdoc in the lab of Stephen Quake, a professor of bioengineering. There, he worked with one of the first high-speed, next-generation genome sequencing machines installed in an academic lab, in 2007.</p> <p>“The result was that I learned sequencing technology and genomics, I learned a little bit of bacterial genetics, I learned microfluidic technology, and I really started to appreciate how these things could play together,” Blainey says.</p> <p>At Stanford, he performed single-cell genome sequencing of environmental microbes, but he wanted to turn his research focus toward biomedicine and studying human cells, so he applied for a faculty position at the Broad Institute. Before coming for his interview, he thought he would prefer living on the West Coast, but his visit to MIT changed his mind.</p> <p>“Despite having been at Harvard for graduate school, I knew very little about the Broad and very little about MIT,” he says. “I took the trip to Boston, which exceeded my expectations. The scientific and collaborative potential at the Broad Institute and surrounding institutions jumped out so clearly.”</p> <p>When Blainey became a member of the Broad Institute, he also joined MIT’s Department of Biological Engineering, renewing his longstanding interest in engineering. He launched his lab with the goal of developing biotechnologies that could strongly impact biomedical research and be broadly disseminated.</p> <p>“We were interested in identifying opportunities to develop technology that would fill crucial gaps in the life science research portfolio,” he says. “We had the opportunity to talk with people, see what the needs were, see where biological research was being well-served by technology, and try to find gaps that might overlap with our toolkit or new things we could invent.”</p> <p><strong>Filling the gaps</strong></p> <p>One area where Blainey saw a need for new technology was in screening potential drug compounds. One of the big challenges in screening drugs is making sure there is enough of each compound to test it against a huge number of single cells. Researchers weren’t using microfluidics to help with these screens because drug molecules tend to leak out of the tiny droplets used in microfluidic devices.</p> <p>One of Blainey’s graduate students, Tony Kulesa, came up with an idea for a new way to solve the problem, which was to seal nanoliter droplets into an array of tiny wells on a microfluidic chip. This prevented the drugs from leaking out, and enabled large-scale screens.&nbsp;</p> <p>This technology turned out to be very useful for screening individual drugs and also combinations of drugs. In a <a href="">paper</a> published in 2018, the researchers showed that this system could be used to <a href="">identify compounds</a> that help existing antibiotics to work better. The Broad Institute is now launching a new center funded by the National Institute of Allergy and Infectious Diseases, where this platform will be used to search for additional compounds with antimicrobial activity.</p> <p>It later turned out that this system could be useful for a variety of experiments that involve testing the interactions of many different combinations of cells or molecules.</p> <p>In <a href="">one project</a>, Blainey worked with Jeff Gore, an MIT associate professor of physics, to combine different strains of bacteria in droplets and study how they interact with each other. He also used it to create a new version of a CRISPR-based diagnostic technology called <a href="">Sherlock</a>, previously developed by several other labs at the Broad Institute. The droplet array platform allows the test to be carried out on many samples at a time, and to simultaneously test for many different diseases.</p> <p>Another technique Blainey recently developed, known as <a href="">optical pooled screening</a>, allows researchers to examine how genes affect complex cellular processes, with spatial and temporal resolution. This technique, described in <em>Cell</em> on Oct. 17, combines large-scale pooled genetic screens with image-based analysis of cell behavior.</p> <p>Blainey’s lab continues to seek out new areas that could benefit from technological innovation, while also pursuing potential applications for the tools they have already developed.</p> <p>“Our antennae are sensitive to these general types of technical barriers where if you can come up with robust and general solutions, it really unlocks a lot of stuff. But we’re also excited to dig further into the biology using tools we’ve already developed,” he says. “It’s a bit like grassroots politicking — you really have to get out there and pound the pavement and show how it can be used in different ways.”</p> “The major research directions and technology platforms that [my] lab is known for today came out of this process where the students or I had a crazy idea, and then the lab executed on it, with all the twists and turns along the way,” says Paul Blainey, associate professor of biological engineering.Image: M. Scott BrauerResearch, Biological engineering, Faculty, Broad Institute, School of Engineering, Profile, Imaging, Microfluidics, Genetics, Drug development Computer science in service of medicine Senior Kristy Carpenter aims to leverage artificial intelligence and other computational tools to develop new, more affordable drugs. Fri, 18 Oct 2019 00:00:00 -0400 Shafaq Patel | MIT News correspondent <p>MIT’s <a href="">Ray and Maria Stata Center</a> (Building 32), known for its striking outward appearance, is also designed to foster collaboration among the people inside. Sitting in the famous building’s amphitheater on a brisk fall day, Kristy Carpenter smiles as she speaks enthusiastically about how interdisciplinary efforts between the fields of computer science and molecular biology are helping accelerate the process of drug discovery and design.</p> <p>Carpenter, an MIT senior with a joint major in&nbsp;both subjects, said she didn’t want to specialize in only one or the other — it’s the intersection between both disciplines, and the application of that work to improving human health, that she finds compelling.</p> <p>“For me, to be really fulfilled in my work as a scientist, I want to have some tangible impact,” she says.&nbsp;</p> <p>Carpenter explains that artificial intelligence, which can help compute the combinations of compounds that would be better for a particular drug, can reduce trial-and-error time and ideally quicken the process of designing new medicines.</p> <p>“I feel like helping make drugs in a more efficient manner, or coming up with some new medicine or way to tackle cancer or Alzheimer’s or something, would really make me feel fulfilled,” she says.</p> <p>In the future, Carpenter hopes to get a PhD and pursue computational approaches to biomedicine, perhaps at one of the national laboratories or the National Institutes of Health. She also plans to continue advocating for diversity and inclusion in science, technology, engineering, and mathematics (STEM), throughout her career, drawing in part from her experiences as part of the leadership of the MIT chapter of the American Indian Science and Engineering Society (<a href="">AISES</a>) and the <a href="">MIT Women’s Independent Living Group</a>.</p> <p><strong>Finding her niche in STEM</strong></p> <p>Carpenter was first drawn to computer science and coding in middle school. She recalls becoming engrossed in a program called <a href="">Scratch</a>, spending hours in the computer lab playing with the block-based visual programming language, which, as it happens, was developed at MIT’s Media Lab.</p> <p>As an MIT student, Carpenter found her way into the computational biology major after a summer internship at Lawrence Livermore National Lab, where researchers were using computer simulations and physics to look at a particular protein implicated in tumors.</p> <p>Next, she got hooked on using computational biology for drug discovery and design during her sophomore year, as an intern at Massachusetts General Hospital. There, she learned that developing a new drug can be a very long, tedious, and&nbsp;complicated process that can take years, but that using machine learning and screening drugs virtually can help hasten this process.&nbsp;She followed that internship with an Undergraduate Research Opportunities Program (UROP) project in the lab of Professor Collin Stultz, within the MIT Research Laboratory of Electronics.</p> <p><strong>Building community </strong></p> <p>For Carpenter, who is part Japanese-American and part Alaskan Native and grew up outside of Seattle, the fact that there were Native American students at MIT, albeit just about a dozen of them, was an important factor in deciding where to attend college.&nbsp;</p> <p>Soon after Carpenter was admitted, a senior from MIT’s AISES chapter called her and told her about the organization.&nbsp;</p> <p>“They sort of recruited me before I even came here,” she recalls.&nbsp;</p> <p>Carpenter is now the vice president of the chapter. The people in the organization, which Carpenter describes as a cultural group at MIT, have become her close friends.&nbsp;</p> <p>“AISES has been a really important part of my time here,” Carpenter says. “At MIT, it’s mostly about having a community of Native students since it’s very easy for us to get isolated here. It’s hard to find people of a similar background, and so AISES is a place where we can all gather just to hang out, socialize, check in with each other.”</p> <p>The organization also puts on movie screenings and other events to “show that we exist and that there are Native people at MIT because a lot of people forget that.”</p> <p>Carpenter first became a member of the national AISES organization as a high school student, when she and her father made serious efforts to reconnect with their Alutiiq heritage. She began educating herself more about the history of Alaska Natives on Kodiak Island, and learning the Alutiiq language, which is severely endangered — just about a couple hundred people still speak it and even fewer speak it fluently.&nbsp;</p> <p>Carpenter started to teach herself the language and then took an online class in high school through Kodiak College.&nbsp;She said she learned very basic amounts and knows simple sentences and personal introductions.</p> <p>“I feel like learning the language was one of the best ways to connect to my culture and sort of legitimize myself in a way.&nbsp;Also, I knew it was important to keep the culture around,” she says.&nbsp;“I would always be telling my friends about it and trying to teach them what I was learning.”</p> <p>Carpenter has also built her MIT community through the Women’s Independent Living Group, one of the few all-women housing options at the Institute. She joined the group of about 40 women the spring semester of her sophomore year.</p> <p>“I really appreciate the group because there’s a lot of diversity in major and diversity in [graduation] year,” she says. “The living group is meant to be a strong community of women at MIT.”</p> <p>Carpenter is now the president of the living group, which has been a significant source of support for her. When she was trying to increase her iron intake so she could donate blood, her friends in the living group helped cook meals and cheered her on.</p> <p>Carpenter also hopes to rise in the ranks at the organizations where she ends up working after MIT, taking a leadership role in advocating for diversity, equity, and inclusion.</p> <p>“I don’t want to lose sight of where I came from or my heritage or being a woman in STEM,” Carpenter says. “Wherever I end up working, I hopefully will move up and keep my Native and Asian identity visible, to be an example for others.”</p> Kristy CarpenterImage: Jared CharneyStudents, Profile, Undergraduate, Electrical Engineering & Computer Science (eecs), Biology, Medicine, Health care, Drug development, Diversity and inclusion, Women in STEM, Artificial intelligence, Machine learning, Computer science and technology Detecting patients’ pain levels via their brain signals System could help with diagnosing and treating noncommunicative patients. Thu, 12 Sep 2019 00:00:00 -0400 Rob Matheson | MIT News Office <p>Researchers from MIT and elsewhere have developed a system that measures a patient’s pain level by analyzing brain activity from a portable neuroimaging device. The system could help doctors diagnose and treat pain in unconscious and noncommunicative patients, which could reduce the risk of chronic pain that can occur after surgery.</p> <p>Pain management is a surprisingly challenging, complex balancing act. Overtreating pain, for example, runs the risk of addicting patients to pain medication. Undertreating pain, on the other hand, may lead to long-term chronic pain and other complications. Today, doctors generally gauge pain levels according to their patients’ own reports of how they’re feeling. But what about patients who can’t communicate how they’re feeling effectively — or at all — such as children, elderly patients with dementia, or those undergoing surgery?</p> <p>In a paper presented at the International Conference on Affective Computing and Intelligent Interaction, the researchers describe a method to quantify pain in patients. To do so, they leverage an emerging neuroimaging technique called functional near infrared spectroscopy (fNIRS), in which sensors placed around the head measure oxygenated hemoglobin concentrations that indicate neuron activity.</p> <p>For their work, the researchers use only a few fNIRS sensors on a patient’s forehead to measure activity in the prefrontal cortex, which plays a major role in pain processing. Using the measured brain signals, the researchers developed personalized machine-learning models to detect patterns of oxygenated hemoglobin levels associated with pain responses. When the sensors are in place, the models can detect whether a patient is experiencing pain&nbsp;with around 87 percent accuracy.</p> <p>“The way we measure pain hasn’t changed over the years,” says Daniel Lopez-Martinez, a PhD student in the Harvard-MIT Program in Health Sciences and Technology and a researcher at the MIT Media Lab. “If we don’t have metrics for how much pain someone experiences, treating pain and running clinical trials becomes challenging. The motivation is to quantify pain in an objective manner that doesn’t require the cooperation of the patient, such as when a patient is unconscious during surgery.”</p> <p>Traditionally, surgery patients receive anesthesia and medication based on their age, weight, previous diseases, and other factors. If they don’t move and their heart rate remains stable, they’re considered fine. But the brain may still be processing pain signals while they’re unconscious, which can lead to increased postoperative pain and long-term chronic pain. The researchers’ system could provide surgeons with real-time information about an unconscious patient’s pain levels, so they can adjust anesthesia and medication dosages accordingly to stop those pain signals.</p> <p>Joining Lopez-Martinez on the paper are: Ke Peng of Harvard Medical School, Boston Children’s Hospital, and the CHUM Research Centre in Montreal; Arielle Lee and David Borsook, both of Harvard Medical School, Boston Children’s Hospital, and Massachusetts General Hospital; and Rosalind Picard, a professor of media arts and sciences and director of affective computing research in the Media Lab.</p> <p><strong>Focusing on the forehead</strong></p> <p>In their work, the researchers adapted the fNIRS system and developed new machine-learning techniques to make the system more accurate and practical for clinical use.</p> <p>To use fNIRS, sensors are traditionally placed all around a patient’s head. Different wavelengths of near-infrared light shine through the skull and into the brain. Oxygenated and deoxygenated hemoglobin absorb the wavelengths differently, altering their signals slightly. When the infrared signals reflect back to the sensors, signal-processing techniques use the altered signals to calculate how much of each hemoglobin type is present in different regions of the brain.</p> <p>When a patient is hurt, regions of the brain associated with pain will see a sharp rise in oxygenated hemoglobin and decreases in deoxygenated hemoglobin, and these changes can be detected through fNIRS monitoring. But traditional fNIRS systems place sensors all around the patient’s head. This can take a long time to set up, and it can be difficult for patients who must lie down. It also isn’t really feasible for patients undergoing surgery.</p> <p>Therefore, the researchers adapted the fNIRS system to specifically measure signals only from the prefrontal cortex. While pain processing involves outputs of information from multiple regions of the brain, studies have shown the prefrontal cortex integrates all that information. This means they need to place sensors only over the forehead.</p> <p><br /> Another problem with traditional fNIRS systems is they capture some signals from the skull and skin that contribute to noise. To fix that, the researchers installed additional sensors &nbsp;to capture and filter out those signals.</p> <p><strong>Personalized pain modeling</strong></p> <p>On the machine-learning side, the researchers trained and tested a model on a labeled pain-processing dataset they collected from 43 male participants. (Next they plan to collect a lot more data from diverse patient populations, including female patients — both during surgery and while conscious, and at a range of pain intensities — in order to better evaluate the accuracy of the system.)</p> <p>Each participant wore the researchers’ fNIRS device and was randomly exposed to an innocuous sensation and then about a dozen shocks to their thumb at two different pain intensities, measured on a scale of 1-10: a low level (about a 3/10) or high level (about 7/10). Those two intensities were determined with pretests: The participants self-reported the low level as being only strongly aware of the shock without pain, and the high level as the maximum pain they could tolerate.</p> <p><br /> In training, the model extracted dozens of features from the signals related to how much oxygenated and deoxygenated hemoglobin was present, as well as how quickly the oxygenated hemoglobin levels rose. Those two metrics — quantity and speed — give a clearer picture of a patient’s experience of pain at the different intensities.</p> <p>Importantly, the model also automatically generates “personalized” submodels that extract high-resolution features from individual patient subpopulations. Traditionally, in machine learning, one model learns classifications — “pain” or “no pain” — based on average responses of the entire patient population. But that generalized approach can reduce accuracy, especially with diverse patient populations.</p> <p>The researchers’ model instead trains on the entire population but simultaneously identifies shared characteristics among subpopulations within the larger dataset. For example, pain responses to the two intensities may differ between young and old patients, or depending on gender. This generates learned submodels that break off and learn, in parallel, patterns of their patient subpopulations. At the same time, however, they’re all still sharing information and learning patterns shared across the entire population. In short, they’re simultaneously leveraging fine-grained personalized information and population-level information to train better.</p> <p>The personalized models and a traditional model were evaluated in classifying pain or no-pain in a random hold-out set of participant brain signals from the dataset, where the self-reported pain scores were known for each participant. The personalized models outperformed the traditional model by about 20 percent, reaching about 87 percent accuracy.</p> <p>“Because we are able to detect pain with this high accuracy, using only a few sensors on the forehead, we have a solid basis for bringing this technology to a real-world clinical setting,” Lopez-Martinez says.</p> Researchers from MIT and elsewhere have developed a system that detects pain in patients by analyzing brain activity from a wearable neuroimaging device, which could help doctors diagnose and treat pain in unconscious and noncommunicative patients. Courtesy of the researchers, edited by MIT NewsResearch, Computer science and technology, Algorithms, Artificial intelligence, Machine learning, Behavior, Health, Health care, Health sciences and technology, Drug development, Neuroscience, Media Lab, Harvard-MIT Program in Health Sciences and Technology, School of Architecture and Planning Guided by AI, robotic platform automates molecule manufacture New system could free bench chemists from time-consuming tasks, may help inspire new molecules. Thu, 08 Aug 2019 14:04:00 -0400 Becky Ham | MIT News correspondent <p>Guided by artificial intelligence and powered by a robotic platform, a system developed by MIT researchers moves a step closer to automating the production of small molecules that could be used in medicine, solar energy, and polymer chemistry.</p> <p>The system, described in the August 8 issue of <em>Science</em>, could free up bench chemists from a variety of routine and time-consuming tasks, and may suggest possibilities for how to make new molecular compounds, according to the study co-leaders Klavs F. Jensen, the Warren K. Lewis Professor of Chemical Engineering, and Timothy F. Jamison, the Robert R. Taylor Professor of Chemistry and associate provost at MIT.</p> <p>The technology “has the promise to help people cut out all the tedious parts of molecule building,” including looking up potential reaction pathways and building the components of a molecular assembly line each time a new molecule is produced, says Jensen.</p> <p>“And as a chemist, it may give you inspirations for new reactions that you hadn’t thought about before,” he adds.</p> <p>Other MIT authors on the <em>Science</em> paper include Connor W. Coley, Dale A. Thomas III, Justin A. M. Lummiss, Jonathan N. Jaworski, Christopher P. Breen, Victor Schultz, Travis Hart, Joshua S. Fishman, Luke Rogers, Hanyu Gao, Robert W. Hicklin, Pieter P. Plehiers, Joshua Byington, John S. Piotti, William H. Green, and A. John Hart.</p> <p><strong>From inspiration to recipe to finished product</strong></p> <p>The new system combines three main steps. First, software guided by artificial intelligence suggests a route for synthesizing a molecule, then expert chemists review this route and refine it into a chemical “recipe,” and finally the recipe is sent to a robotic platform that automatically assembles the hardware and performs the reactions that build the molecule.</p> <p>Coley and his colleagues have been working for more than three years to develop the open-source software suite that suggests and prioritizes possible synthesis routes. At the heart of the software are several neural network models, which the researchers trained on millions of previously published chemical reactions drawn from the Reaxys and U.S. Patent and Trademark Office databases. The software uses these data to identify the reaction transformations and conditions that it believes will be suitable for building a new compound.</p> <p>“It helps makes high-level decisions about what kinds of intermediates and starting materials to use, and then slightly more detailed analyses about what conditions you might want to use and if those reactions are likely to be successful,” says Coley.</p> <p>“One of the primary motivations behind the design of the software is that it doesn’t just give you suggestions for molecules we know about or reactions we know about,” he notes. “It can generalize to new molecules that have never been made.”</p> <p>Chemists then review the suggested synthesis routes produced by the software to build a more complete recipe for the target molecule. The chemists sometimes need to perform lab experiments or tinker with reagent concentrations and reaction temperatures, among other changes.</p> <p>“They take some of the inspiration from the AI and convert that into an executable recipe file, largely because the chemical literature at present does not have enough information to move directly from inspiration to execution on an automated system,” Jamison says.</p> <p>The final recipe is then loaded on to a platform where a robotic arm assembles modular reactors, separators, and other processing units into a continuous flow path, connecting pumps and lines that bring in the molecular ingredients.</p> <p>“You load the recipe — that’s what controls the robotic platform — you load the reagents on, and press go, and that allows you to generate the molecule of interest,” says Thomas. “And then when it’s completed, it flushes the system and you can load the next set of reagents and recipe, and allow it to run.”</p> <p>Unlike the continuous flow system the researchers <a href="">presented last year</a>, which had to be manually configured after each synthesis, the new system is entirely configured by the robotic platform.</p> <p>“This gives us the ability to sequence one molecule after another, as well as generate a library of molecules on the system, autonomously,” says Jensen.</p> <p>The design for the platform, which is about two cubic meters in size — slightly smaller than a standard chemical fume hood — resembles a telephone switchboard and operator system that moves connections between the modules on the platform.</p> <p>“The robotic arm is what allowed us to manipulate the fluidic paths, which reduced the number of process modules and fluidic complexity of the system, and by reducing the fluidic complexity we can increase the molecular complexity,” says Thomas. “That allowed us to add additional reaction steps and expand the set of reactions that could be completed on the system within a relatively small footprint.”</p> <p><strong>Toward full automation</strong></p> <p>The researchers tested the full system by creating 15 different medicinal small molecules of different synthesis complexity, with processes taking anywhere between two hours for the simplest creations to about 68 hours for manufacturing multiple compounds.</p> <p>The team synthesized a variety of compounds: aspirin and the antibiotic secnidazole in back-to-back processes; the painkiller lidocaine and the antianxiety drug diazepam in back-to-back processes using a common feedstock of reagents; the blood thinner warfarin and the Parkinson’s disease drug safinamide, to show how the software could design compounds with similar molecular components but differing 3-D structures; and a family of five ACE inhibitor drugs and a family of four nonsteroidal anti-inflammatory drugs.</p> <p>“I’m particularly proud of the diversity of the chemistry and the kinds of different chemical reactions,” says Jamison, who said the system handled about 30 different reactions compared to about 12 different reactions in the previous continuous flow system.</p> <p>“We are really trying to close the gap between idea generation from these programs and what it takes to actually run a synthesis,” says Coley. “We hope that next-generation systems will increase further the fraction of time and effort that scientists can focus their efforts on creativity and design.”&nbsp;&nbsp;</p> <p>The research was supported, in part, by the U.S. Defense Advanced Research Projects Agency (DARPA) Make-It program.</p> Guided by artificial intelligence and powered by a robotic platform, a system developed by MIT researchers moves a step closer to automating the production of small molecules.Images: Connor Coley, Felice Frankel Research, Chemistry, Chemical engineering, School of Science, School of Engineering, Drug development, Artificial intelligence, Pharmaceuticals, Defense Advanced Research Projects Agency (DARPA) Model predicts cognitive decline due to Alzheimer’s, up to two years out Researchers hope the system can zero in on the right patients to enroll in clinical trials, to speed discovery of drug treatments. Thu, 01 Aug 2019 23:59:59 -0400 Rob Matheson | MIT News Office <p>A new model developed at MIT can help predict if patients at risk for Alzheimer’s disease will experience clinically significant cognitive decline due to the disease, by predicting their cognition test scores up to two years in the future.</p> <p>The model could be used to improve the selection of candidate drugs and participant cohorts for clinical trials, which have been notoriously unsuccessful thus far. It would also let patients know they may experience rapid cognitive decline in the coming months and years, so they and their loved ones can prepare. &nbsp;</p> <p>Pharmaceutical firms over the past two decades have injected hundreds of billions of dollars into Alzheimer’s research. Yet the field has been plagued with failure: Between 1998 and 2017, there were 146 unsuccessful attempts to develop drugs to treat or prevent the disease, according to a 2018 report from the Pharmaceutical Research and Manufacturers of America. In that time, only four new medicines were approved, and only to treat symptoms. More than 90 drug candidates are currently in development.</p> <p>Studies suggest greater success in bringing drugs to market could come down to recruiting candidates who are in the disease’s early stages, before symptoms are evident, which is when treatment is most effective. In a paper to be presented next week at the Machine Learning for Health Care conference, MIT Media Lab researchers describe a machine-learning model that can help clinicians zero in on that specific cohort of participants.</p> <p>They first trained a “population” model on an entire dataset that included clinically significant cognitive test scores and other biometric data from Alzheimer’s patients, and also healthy individuals, collected between biannual doctor’s visits. From the data, the model learns patterns that can help predict how the patients will score on cognitive tests taken between visits. In new participants, a second model, personalized for each patient, continuously updates score predictions based on newly recorded data, such as information collected during the most recent visits.</p> <p>Experiments indicate accurate predictions can be made looking ahead six, 12, 18, and 24 months. Clinicians could thus use the model to help select at-risk participants for clinical trials, who are likely to demonstrate rapid cognitive decline, possibly even before other clinical symptoms emerge. Treating such patients early on may help clinicians better track which antidementia medicines are and aren’t working.</p> <p>“Accurate prediction of cognitive decline from six to 24 months is critical to designing clinical trials,” says Oggi Rudovic, a Media Lab researcher. “Being able to accurately predict future cognitive changes can reduce the number of visits the participant has to make, which can be expensive and time-consuming. Apart from helping develop a useful drug, the goal is to help reduce the costs of clinical trials to make them more affordable and done on larger scales.”</p> <p>Joining Rudovic on the paper are: Yuria Utsumi, an undergraduate student, and Kelly Peterson, a graduate student, both in the Department of Electrical Engineering and Computer Science; Ricardo Guerrero and Daniel Rueckert, both of Imperial College London; and Rosalind Picard, a professor of media arts and sciences and director of affective computing research in the Media Lab.</p> <p><strong>Population to personalization</strong></p> <p>For their work, the researchers leveraged the world’s largest Alzheimer’s disease clinical trial dataset, called Alzheimer's Disease Neuroimaging Initiative (ADNI). The dataset contains data from around 1,700 participants, with and without Alzheimer’s, recorded during semiannual doctor’s visits over 10 years.</p> <p>Data includes their AD Assessment Scale-cognition sub-scale (ADAS-Cog13) scores, the most widely used cognitive metric for clinical trials of Alzheimer’s disease drugs. The test assesses memory, language, and orientation on a scale of increasing severity up to 85 points. The dataset also includes MRI scans, demographic and genetic information, and cerebrospinal fluid measurements.</p> <p>In all, the researchers trained and tested their model on a sub-cohort of 100 participants, who made more than 10 visits and had less than 85 percent missing data, each with more than 600 computable features. Of those participants, 48 were diagnosed with Alzheimer’s disease. But data are sparse, with different combinations of features missing for most of the participants. &nbsp;</p> <p>To tackle that, the researchers used the data to train a population model powered by a “nonparametric” probability framework, called Gaussian Processes (GPs), which has flexible parameters to fit various probability distributions and to process uncertainties in data. This technique measures similarities between variables,&nbsp;such as patient data points, to predict a value for an unseen data point —&nbsp;such as a cognitive score. The output also contains an estimate for how certain it is about the prediction. The model works robustly even when analyzing datasets with missing values or lots of noise from different data-collecting formats.</p> <p>But, in evaluating the model on new patients from a held-out portion of participants, the researchers found the model’s predictions weren’t as accurate as they could be. So, they personalized the population model for each new patient. The system would then progressively fill in data gaps with each new patient visit and update the ADAS-Cog13 score prediction accordingly, by continuously updating the previously unknown distributions of the GPs. After about four visits, the personalized models significantly reduced the error rate in predictions. It also outperformed various traditional machine-learning approaches used for clinical data.</p> <p><strong>Learning how to learn</strong></p> <p>But the researchers found the personalized models’ results were still suboptimal. To fix that, they invented a novel “metalearning” scheme that learns to automatically choose which type of model, population or personalized, works best for any given participant at any given time, depending on the data being analyzed. Metalearning has been used before for computer vision and machine translation tasks to learn new skills or adapt to new environments rapidly with a few training examples. But this is the first time it’s been applied to tracking cognitive decline of Alzheimer’s patients, where limited data is a main challenge, Rudovic says.</p> <p>The scheme essentially simulates how the different models perform on a given task — such as predicting an ADAS-Cog13 score — and learns the best fit. During each visit of a new patient, the scheme assigns the appropriate model, based on the previous data. With patients with noisy, sparse data during early visits, for instance, population models make more accurate predictions. When patients start with more data or collect more through subsequent visits, however, personalized models perform better.</p> <p>This helped reduce the error rate for predictions by a further 50 percent. “We couldn’t find a single model or fixed combination of models that could give us the best prediction,” Rudovic says. “So, we wanted to learn how to learn with this metalearning scheme. It’s like a model on top of a model that acts as a selector, trained using metaknowledge to decide which model is better to deploy.”</p> <p>Next, the researchers are hoping to partner with pharmaceutical firms to implement the model into real-world Alzheimer’s clinical trials. Rudovic says the model can also be generalized to predict various metrics for Alzheimer’s and other diseases.</p> A model developed at MIT predicts the cognitive decline of patients at risk for Alzheimer’s disease by forecasting their cognition test scores up to two years in the future, which could help zero in on the right patients to select for clinical trials.Image: Christine Daniloff, MITResearch, Computer science and technology, Algorithms, Alzheimer’s, Artificial intelligence, Behavior, Machine learning, Health, Health care, Drug development, Media Lab, Electrical Engineering & Computer Science (eecs), School of Architecture and Planning, School of Engineering Speeding up drug discovery for brain diseases Whitehead Institute team finds drugs that activate a key brain gene; initial tests in cells and mice show promise for rare, untreatable neurodevelopmental disorder. Wed, 31 Jul 2019 14:25:01 -0400 Nicole Davis <p>A research team led by Whitehead Institute scientists has identified 30 distinct chemical compounds — 20 of which are drugs undergoing clinical trial or have already been approved by the FDA — that boost the protein production activity of a critical gene in the brain and improve symptoms of Rett syndrome, a rare neurodevelopmental condition that often provokes autism-like behaviors in patients. The new study, conducted in human cells and mice, helps illuminate the biology of an important gene, called KCC2, which is implicated in a variety of brain diseases, including autism, epilepsy, schizophrenia, and depression. The researchers’ findings, published in the July 31 online issue of <em>Science Translational Medicine</em>, could help spur the development of new treatments for a host of devastating brain disorders.</p> <p>“There’s increasing evidence that KCC2 plays important roles in several different disorders of the brain, suggesting that it may act as a common driver of neurological dysfunction,” says senior author <a href="">Rudolf</a><a href=""> Jaenisch</a>, a founding member of Whitehead Institute and professor of biology at MIT. “These drugs we’ve identified may help speed up the development of much-needed treatments.”</p> <p>KCC2 works exclusively in the brain and spinal cord, carrying ions in and out of specialized cells known as neurons. This shuttling of electrically charged molecules helps maintain the cells’ electrochemical makeup, enabling neurons to fire when they need to and to remain idle when they don’t. If this delicate balance is upset, brain function and development go awry.</p> <p>Disruptions in KCC2 function have been linked to several human brain disorders, including Rett syndrome (RTT), a progressive and often debilitating disorder that typically emerges early in life in girls and can involve disordered movement, seizures, and communication difficulties. Currently, there is no effective treatment for RTT.</p> <p>Jaenisch and his colleagues, led by first author Xin Tang, devised a high-throughput screen assay to uncover drugs that increase KCC2 gene activity. Using CRISPR/Cas9 genome editing and stem cell technologies, they engineered human neurons to provide rapid readouts of the amount of KCC2 protein produced. The researchers created these so-called reporter cells from both healthy human neurons as well as RTT neurons that carry disease-causing mutations in the MECP2 gene. These reporter neurons were then fed into a drug-screening pipeline to find chemical compounds that can enhance KCC2 gene activity.</p> <p>Tang and his colleagues screened over 900 chemical compounds, focusing on those that have been FDA-approved for use in other conditions, such as cancer, or have undergone at least some level of clinical testing. “The beauty of this approach is that many of these drugs have been studied in the context of non-brain diseases, so the mechanisms of action are known,” says Tang. “Such molecular insights enable us to learn how the KCC2 gene is regulated in neurons, while also identifying compounds with potential therapeutic value.”</p> <p>The Whitehead Institute team identified a total of 30 drugs with KCC2-enhancing activity. These compounds, referred to as KEECs (short for KCC2 expression-enhancing compounds), work in a variety of ways. Some block a molecular pathway, called FLT3, which is found to be overactive in some forms of leukemia. Others inhibit the GSK3b pathway that has been implicated in several brain diseases. Another KEEC acts on SIRT1, which plays a key role in a variety of biological processes, including aging.</p> <p>In followup experiments, the researchers exposed RTT neurons and mouse models to KEEC treatment and found that some compounds can reverse certain defects associated with the disease, including abnormalities in neuronal signaling, breathing, and movement. These efforts were made possible by a collaboration with <a href="">Mriganka Sur’s</a> group at the Picower Institute for Learning and Memory, in which Keji Li and colleagues led the behavioral experiments in mice that were essential for revealing the drugs’ potency.</p> <p>“Our findings illustrate the power of an unbiased approach for discovering drugs that could significantly improve the treatment of neurological disease,” says Jaenisch. “And because we are starting with known drugs, the path to clinical translation is likely to be much shorter.”</p> <p>In addition to speeding up drug development for Rett syndrome, the researchers’ unique drug-screening strategy, which harnesses an engineered gene-specific reporter to unearth promising drugs, can also be applied to other important disease-related genes in the brain. “Many seemingly distinct brain diseases share common root causes of abnormal gene expression or disrupted signaling pathways,” says Tang. “We believe our method has broad applicability and could help catalyze therapeutic discovery for a wide range of neurological conditions.”</p> <p>Support for this work was provided by the National Institutes of Health, the Simons Foundation Autism Research Initiative, the Simons Center for the Social Brain at MIT, the Rett Syndrome Research Trust, the International Rett Syndrome Foundation, the Damon Runyon Cancer Foundation, and the National Cancer Institute.</p> Image: Steven Lee/Whitehead InstituteWhitehead Institute, Picower Institute, School of Science, Behavior, Biology, Brain and cognitive sciences, CRISPR, Development, Disease, Genetics, Mental health, National Institutes of Health (NIH), Pharmaceuticals, Research, Proteins, Drug development MIT “Russian Doll” tech lands $7.9M international award to fight brain tumors Researchers from MIT&#039;s Koch Institute will work with teams in the UK and Europe to use nanoparticles to carry multiple drug therapies to treat glioblastoma. Fri, 26 Jul 2019 13:30:01 -0400 Koch Institute <p>Cancer Research UK awarded $7.9 million to MIT researchers as part of an international team to identify combinations of drugs that could effectively tackle glioblastoma — the most aggressive and deadly type of brain tumor. The team will then use tiny “Russian doll-like” particles — a technology developed at MIT — to deliver those combinations to brain tumors.</p> <p>The MIT team, based at the Koch Institute for Integrative Cancer Research, includes Paula Hammond, the David H. Koch Professor of Engineering and head of the Department of Chemical Engineering; Michael Yaffe, the David H. Koch Professor of Science and director of the MIT Center for Precision Cancer Medicine; and Forest White, the Ned C. and Janet Bemis Rice Professor of Biological Engineering.</p> <p>Brain tumors represent one of the hardest types of cancer to treat. There are just a few drugs approved to treat glioblastoma, but none of them are curative. Just last year, around 24,200 people in the United States were diagnosed with brain tumors, with around 17,500 deaths from brain tumors in the same year. Patients diagnosed with disease have a median life expectancy of less than 15 months.</p> <p>Treating glioblastoma is challenging in part because, like many other cancers, it can quickly develop resistance to cancer drugs. Some drug combinations deliver a powerful one-two punch that can overcome cancer cells’ ability to adapt to treatment.</p> <p>The international team aims to find potential drug combinations and targets using high-throughput small molecules and CRISPRi-based screens, mass spectrometry proteomic analysis, and computational modeling platforms for systems pharmacology developed at MIT for predicting the development and reversal of drug resistance in glioblastomas. The team will then test the effectiveness of newly-identified drug combinations in cell and mouse models, including two promising combinations already identified by researchers at the Koch Institute and the University of Edinburgh.</p> <p>Drugs that have already been approved, as well as experimental drugs that have passed initial safety testing in people, will be prioritized. Because of this, if an effective drug combination is found, the team won’t have to navigate the initial regulatory hurdles needed to get them into clinical testing, which could help get promising treatments to patients faster.</p> <p>But glioblastoma presents an additional obstacle to treatment: Even if the researchers find potential new treatments, the drugs must cross the blood-brain barrier, a structure that keeps a tight check on anything trying to get into the brain, drugs included. The team will deploy nanoparticles developed by Hammond at MIT to ferry new drug treatments across this barrier. The nanoparticles — one-thousandth the width of a human hair — are coated in a protein called transferrin, which helps them cross the blood-brain barrier.</p> <p>Not only are the nanoparticles able to access hard-to-reach areas of the brain, they have also been designed to carry multiple cancer drugs at once by holding them inside layers, similarly to the way Russian dolls fit inside one another.</p> <p>To make the nanoparticles even more effective, they will carry signals on their surface so that they are preferentially taken up by brain tumor cells. This means that healthy cells should be left untouched, which will minimize the side effects of treatment.</p> <p><a href="" target="_blank">Early research</a> by the Hammond and Yaffe labs has already shown that nanoparticles loaded with two different drugs were able to shrink glioblastomas in mice.</p> <p>“Glioblastoma is particularly challenging because we want to get highly effective but toxic drug combinations safely across the blood-brain barrier, but also want our nanoparticles to avoid healthy brain cells and only target the cancer cells," Hammond says. "We are very excited about this alliance between the MIT Koch Institute and our colleagues at Edinburgh and Oxford to address these critical challenges.”</p> <p>The MIT group and their collaborators in the UK are one of three international teams to have been given Cancer Research UK Brain Tumor Awards — in partnership with The Brain Tumour Charity — receiving $7.9 million of funding. The awards are designed to accelerate the pace of brain tumor research. Altogether, teams were awarded a total of $23 million.</p> <p>“The Cancer Research UK Brain Tumor Award provides us with a unique opportunity to unite perspectives in biology and engineering to create better options for patients with glioblastoma,” says Yaffe. “Each member of this international team brings a deep well of expertise— in the biology of brain tumors, signaling proteomics, high-throughput screening, drug combinations and systems pharmacology, and drug delivery technologies — that will be vital to overcoming the challenges of developing effective therapies for glioblastoma.”</p> <p><em>This article has been updated to reflect additional specificity regarding the project and its collaborators.</em></p> Cancer cells targeted with nanoparticles built in the Hammond laboratoryImage: Stephen Morton, Kevin Shopsowitz, Peter DeMuthKoch Institute, Chemical engineering, School of Engineering, Biological engineering, Faculty, Cancer, Medicine, Funding, Nanoscience and nanotechnology, Drug development, Pharmaceuticals Drug makes tumors more susceptible to chemo Compound that knocks out a DNA repair pathway enhances cisplatin treatment and helps prevent drug-resistance. Thu, 06 Jun 2019 10:59:59 -0400 Anne Trafton | MIT News Office <p>Many chemotherapy drugs kill cancer cells by severely damaging their DNA. However, some tumors can withstand this damage by relying on a DNA repair pathway that not only allows them to survive, but also introduces mutations that helps cells become resistant to future treatment.</p> <p>Researchers at MIT and Duke University have now discovered a potential drug compound that can block this repair pathway. “This compound increased cell killing with cisplatin and prevented mutagenesis, which is was what we expected from blocking this pathway,” says Graham Walker, the American Cancer Society Research Professor of Biology at MIT, a Howard Hughes Medical Institute Professor, and one of the senior authors of the study.</p> <p>When they treated mice with this compound along with cisplatin, a DNA-damaging drug, tumors shrank much more than those treated with cisplatin alone. Tumors treated with this combination would be expected not to develop new mutations that could make them drug-resistant.</p> <p>Cisplatin, which is used as the first treatment option for at least a dozen types of cancer, often successfully destroys tumors, but they frequently grow back following treatment. Drugs that target the mutagenic DNA repair pathway that contributes to this recurrence could help to improve the long-term effectiveness of not only cisplatin but also other chemotherapy drugs that damage DNA, the researchers say.</p> <p>“We’re trying to make the therapy work better, and we also want to make the tumor recurrently sensitive to therapy upon repeated doses,” says Michael Hemann, an associate professor of biology, a member of MIT’s Koch Institute for Integrative Cancer Research, and a senior author of the study.</p> <p>Pei Zhou, a professor of biochemistry at Duke University, and Jiyong Hong, a professor of chemistry at Duke, are also senior authors of the paper, which appears in the June 6 issue of <em>Cell</em>. The lead authors of the paper are former Duke graduate student Jessica Wojtaszek, MIT postdoc Nimrat Chatterjee, and Duke research assistant Javaria Najeeb.</p> <p><strong>Overcoming resistance</strong></p> <p>Healthy cells have several repair pathways that can accurately remove DNA damage from cells. As cells become cancerous, they sometimes lose one of these accurate DNA repair systems, so they rely heavily on an alternative coping strategy known as translesion synthesis (TLS).</p> <p>This process, which Walker has been studying in a variety of organisms for many years, relies on specialized TLS DNA polymerases. Unlike the normal DNA polymerases used to replicate DNA, these TLS DNA polymerases can essentially copy over damaged DNA, but the copying they perform is not very accurate. This enables cancer cells to survive treatment with a DNA-damaging agent such as cisplatin, and it leads them to acquire many additional mutations that can make them resistant to further treatment.</p> <p>“Because these TLS DNA polymerases are really error-prone, they are accountable for nearly all of the mutation that is induced by drugs like cisplatin,” Hemann says. “It’s very well-established that with these frontline chemotherapies that we use, if they don’t cure you, they make you worse.”</p> <p>One of the key TLS DNA polymerases required for translesion synthesis is Rev1, and its primary function is to recruit a second TLS DNA polymerase that consists of a complex of the Rev3 and Rev7 proteins. Walker and Hemann have been searching for ways to disrupt this interaction, in hopes of derailing the repair process.</p> <p>In a pair of <a href="">studies published in 2010</a>, the researchers showed that if they used RNA interference to reduce the expression of Rev1, cisplatin treatment became much more effective against lymphoma and lung cancer in mice. While some of the tumors grew back, the new tumors were not resistant to cisplatin and could be killed again with a new round of treatment.</p> <p>After showing that interfering with translesion synthesis could be beneficial, the researchers set out to find a small-molecule drug that could have the same effect. Led by Zhou, the researchers performed a screen of about 10,000 potential drug compounds and identified one that binds tightly to Rev1, preventing it from interacting with Rev3/Rev7 complex.</p> <p>The interaction of Rev1 with the Rev7 component of the second TLS DNA polymerase had been considered “undruggable” because it occurs in a very shallow pocket of Rev1, with few features that would be easy for a drug to latch onto. However, to the researchers’ surprise, they found a molecule that actually binds to two molecules of Rev1, one at each end, and brings them together to form a complex called a dimer. This dimerized form of Rev1 cannot bind to the Rev3/Rev7 TLS DNA polymerase, so translesion synthesis cannot occur.</p> <p>Chatterjee tested the compound along with cisplatin in several types of human cancer cells and found that the combination killed many more cells than cisplatin on its own. And, the cells that survived had a greatly reduced ability to generate new mutations.</p> <p>“Because this novel translesion synthesis inhibitor targets the mutagenic ability of cancer cells to resist therapy, it can potentially address the issue of cancer relapse, where cancers continue to evolve from new mutations and together pose a major challenge in cancer treatment,” Chatterjee says.</p> <p><strong>A powerful combination</strong></p> <p>Chatterjee then tested the drug combination in mice with human melanoma tumors and found that the tumors shrank much more than tumors treated with cisplatin alone. They now hope that their findings will lead to further research on compounds that could act as translesion synthesis inhibitors to enhance the killing effects of existing chemotherapy drugs.</p> <p>Zhou’s lab at Duke is working on developing variants of the compound that could be developed for possible testing in human patients. Meanwhile, Walker and Hemann are further investigating how the drug compound works, which they believe could help to determine the best way to use it.</p> <p>“That’s a future major objective, to identify in which context this combination therapy is going to work particularly well,” Hemann says. “We would hope that our understanding of how these are working and when they’re working will coincide with the clinical development of these compounds, so by the time they’re used, we’ll understand which patients they should be given to.”</p> <p>The research was funded, in part, by an Outstanding Investigator Award from the National Institute of Environmental Health Sciences to Walker, and by grants from the National Cancer Institute, the Stewart Trust, and the Center for Precision Cancer Medicine at MIT.</p> MIT biologists have identified a drug that blocks a DNA repair pathway used by cancer cells, making them more susceptible to chemotherapy drugs that damage DNA.Image: Knight Cancer Institute, edited by MIT NewsResearch, Biology, Cancer, DNA, Drug development, Drug resistance, Koch Institute, School of Science, Medicine, Health sciences and technology Merging cell datasets, panorama style Algorithm stitches multiple datasets into a single “panorama,” which could provide new insights for medical and biological studies. Mon, 06 May 2019 10:59:58 -0400 Rob Matheson | MIT News Office <p>A new algorithm developed by MIT researchers takes cues from panoramic photography to merge massive, diverse cell datasets into a single source that can be used for medical and biological studies.</p> <p>Single-cell datasets profile the gene expressions of human cells —&nbsp;such as a neurons, muscles, and immune cells — to gain insight into human health and treating disease. Datasets are produced by a range of labs and technologies, and contain extremely diverse cell types. Combining these datasets into a single data pool could open up new research possibilities, but that’s difficult to do effectively and efficiently.</p> <p>Traditional methods tend to cluster cells together based on nonbiological patterns — such as by lab or technologies used — or accidentally merge dissimilar cells that appear the same. Methods that correct these mistakes don’t scale well to large datasets, and require all merged datasets share at least one common cell type.</p> <p>In a paper published today in <em>Nature Biotechnology</em>, the MIT researchers describe an algorithm that can efficiently merge more than 20 datasets of vastly differing cell types into a larger “panorama.” The algorithm, called “Scanorama,” automatically finds and stitches together shared cell types between two datasets — like combining overlapping pixels in images to generate a panoramic photo.</p> <p>As long as any other dataset shares one cell type with any one dataset in the final panorama, it can also be merged. But all of the datasets don’t need to have a cell type in common. The algorithm preserves all cell types specific to every dataset.</p> <p>“Traditional methods force cells to align, regardless of what the cell types are. They create a blob with no structure, and you lose all interesting biological differences,” says Brian Hie, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and a researcher in the Computation and Biology group. “You can give Scanorama datasets that shouldn’t align together, and the algorithm will separate the datasets according to biological differences.”</p> <p>In their paper, the researchers successfully merged more than 100,000 cells from 26 different datasets containing a wide range of human cells, creating a single, diverse source of data. With traditional methods, that would take roughly a day’s worth of computation, but Scanorama completed the task in about 30 minutes. The researchers say the work represents the highest number of datasets ever merged together.</p> <p>Joining Hie on the paper are: Bonnie Berger, the Simons Professor of Mathematics at MIT, a professor of electrical engineering and computer science, and head of the Computation and Biology group; and Bryan Bryson, an MIT assistant professor of biological engineering.</p> <p><strong>Linking “mutual neighbors”</strong></p> <p>Humans have hundreds of categories and subcategories of cells, and each cell expresses a diverse set of genes. Techniques such as RNA sequencing capture that information in sprawling multidimensional space. Cells are points scattered around the space, and each dimension corresponds to the expression of a different gene.</p> <p>Scanorama runs a modified computer-vision algorithm, called “mutual nearest neighbors matching,” which finds the closest (most similar) points in two computational spaces. Developed at CSAIL, the algorithm was initially used to find pixels with matching features —&nbsp;such as color levels — in dissimilar photos. That could help computers match a patch of pixels representing an object in one image to the same patch of pixels in another image where the object’s position has been drastically altered. It could also be used for stitching vastly different images together in a panorama.</p> <p>The researchers repurposed the algorithm to find cells with overlapping gene expression — instead of overlapping pixel features — and in multiple datasets instead of two. The level of gene expression in a cell determines its function and, in turn, its location in the computational space. If stacked on top of one another, cells with similar gene expression, even if they’re from different datasets, will be roughly in the same locations.</p> <p>For each dataset, Scanorama first links each cell in one dataset to its closest neighbor among all datasets, meaning they’ll most likely share similar locations. But the algorithm only retains links where cells in both datasets are each other’s nearest neighbor — a mutual link. For instance, if Cell A’s nearest neighbor is Cell B, and Cell B’s is Cell A, it’s a keeper. If, however, Cell B’s nearest neighbor is a separate Cell C, then the link between Cell A and B will be discarded.</p> <p>Keeping mutual links increases the likelihood that the cells are, in fact, the same cell types. Breaking the nonmutual links, on the other hand, prevents cell types specific to each dataset from merging with incorrect cell types. Once all mutual links are found, the algorithm stitches all dataset sequences together. In doing so, it combines the same cell types but keeps cell types unique to any datasets separated from the merged cells. “The mutual links form anchors that enable [correct] cell alignment across datasets,” Berger says.</p> <p><strong>Shrinking data, scaling up</strong></p> <p>To ensure Scanorama scales to large datasets, the researchers incorporated two optimization techniques. The first reduces the dataset dimensionality. Each cell in a dataset could potentially have up to 20,000 gene expression measurements and as many dimensions. The researchers leveraged a mathematical technique that summarizes high-dimensional data matrices with a small number of features while retaining vital information. Basically, this led to a 100-fold reduction in the dimensions.</p> <p>They also used a popular hashing technique to find nearest mutual neighbors more quickly. Traditionally, computing on even the reduced samples would take hours. But the hashing technique basically creates buckets of nearest neighbors by their highest probabilities. The algorithm need only search the highest probability buckets to find mutual links, which reduces the search space and makes the process far less computationally intensive. &nbsp;&nbsp;&nbsp;</p> <p>In separate work, the researchers combined Scanorama with another <a href="">technique they developed</a> that generates comprehensive samples — or “sketches” —&nbsp;of massive cell datasets that reduced the time of combining more than 500,000 cells from two hours down to eight minutes. To do so, they generated the “geometric sketches,” ran Scanorama on them, and extrapolated what they learned about merging the geometric sketches to the larger datasets. This technique itself derives from <a href="">compressive genomics</a>, which was developed by Berger’s group.</p> <p>“Even if you need to sketch, integrate, and reapply that information to the full datasets, it was still an order of magnitude faster than combining entire datasets,” Hie says.</p> A new algorithm developed by MIT researchers takes cues from panoramic photography to merge massive, diverse cell datasets into a single source that can be used for medical and biological studies.Image courtesy of the researchers Research, Computer science and technology, Algorithms, Biology, Data, Health sciences and technology, Drug development, Medicine, Machine learning, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), Mathematics, Biological engineering, School of Engineering, School of Science New approach could accelerate efforts to catalog vast numbers of cells Data-sampling method makes “sketches” of unwieldy biological datasets while still capturing the full diversity of cell types. Thu, 02 May 2019 00:00:00 -0400 Rob Matheson | MIT News Office <p>Artistic sketches can be used to capture details of a scene in a simpler image. MIT researchers are now bringing that concept to computational biology, with a novel method that extracts comprehensive samples — called “sketches” —&nbsp;of massive cell datasets that are easier to analyze for biological and medical studies.</p> <p>Recent years have seen an explosion in profiling single cells from a diverse range of human tissue and organs —&nbsp;such as a neurons, muscles, and immune cells — to gain insight into human health and treating disease. The largest datasets contain anywhere from around 100,000 to 2 million cells, and growing. The long-term goal of the Human Cell Atlas, for instance, is to profile about 10 billion cells. Each cell itself contains tons of data on RNA expression, which can provide insight about cell behavior and disease progression.</p> <p>With enough computation power, biologists can analyze full datasets, but it takes hours or days. Without those resources, it’s impractical. Sampling methods can be used to extract small subsets of the cells for faster, more efficient analysis, but they don’t scale well to large datasets and often miss less abundant cell types.</p> <p>In a paper being presented next week at the Research in Computational Molecular Biology conference, the MIT researchers describe a method that captures a fully comprehensive “sketch” of an entire dataset that can be shared and merged easily with other datasets. Instead of sampling cells with equal probability, it evenly samples cells from across the diverse cell types present in the dataset.</p> <p>“These are like sketches on paper, where an artist will try to preserve all the important features of a main image,” says Bonnie Berger, the Simons Professor of Mathematics at MIT, a professor of electrical engineering and computer science, and head of the Computation and Biology group.</p> <p>In experiments, the method generated sketches from datasets of millions of cells in a few minutes — as opposed to a few hours —&nbsp;that had far more equal representation of rare cells from across the datasets. The sketches even captured, in one instance, a rare subset of inflammatory macrophages that other methods missed.</p> <p>“Most biologists analyzing single-cell data are just working on their laptops,” says Brian Hie, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and a researcher in the Computation and Biology group. “Sketching gives a compact summary of a very large dataset that tries to preserve as much biological information as possible … so people don’t need to use so much computational power.”</p> <p>Joining Hie and Berger on the paper are: CSAIL PhD student Hyunghoon Cho; Benjamin DeMeo, a graduate student at MIT and Harvard Medical School; and Bryan Bryson, an MIT assistant professor of biological engineering.</p> <p><strong>Plaid coverings</strong></p> <p>Humans have hundreds of categories and subcategories of cells, and each cell expresses a diverse set of genes. Techniques such as RNA sequencing capture all cell information in massive tables, where each row represents a cell and each column represents some measurement of gene expression. Cells are points scattered around a sprawling multidimensional space where each dimension corresponds to the expression of a different gene.</p> <p>As it happens, cell types with similar gene diversity — both common and rare — form similar-sized clusters that take up roughly the same space. But the density of cells within those clusters varies greatly: 1,000 cells may reside in a common cluster, while the equally diverse rare cluster will contain 10 cells. That’s a problem for traditional sampling methods that extract a target-size sample of single cells.</p> <p>“If you take a 10-percent sample, and there are 10 cells in a rare cluster and 1,000 cells in a common cluster, you’re more likely to grab tons of common cells, but miss all rare cells,” Hie says. “But rare cells can lead to important biological discoveries.”</p> <p>The researchers modified a class of algorithm that lays shapes over datasets. Their algorithm covers the entire computational space with what they call a “plaid covering,” which is like a grid of equal-sized squares but in many dimensions. It only lays these multidimensional squares where there’s at least one cell, and skips over any empty regions. In the end, the grid’s empty columns will be much wider or skinnier than occupied columns — hence the “plaid” description. That technique saves tons of computation to help the covering scale to massive datasets.</p> <p><strong>Capturing rare cells</strong></p> <p>Occupied squares may contain only one cell or 1,000 cells, but they will all have the exact same sampling weight. The algorithm then finds a target sample — of, say, 20,000 cells —&nbsp;by selecting a set number of cells from each occupied square uniformly, at random. The resulting sketch contains a far more equal distribution of cell types — for example, 10 common cells from a cluster of 100 and eight rare cells from a cluster of 10.</p> <p>“We take advantage of these cell types occupying similar volumes of space,” Hie says. “Because we sample according to volume, instead of density, we get a more even coverage of the biological space … and we’re naturally preserving the rare cell types.”</p> <p>They applied their sketching method to a dataset of around 250,000 umbilical cord cells that contained two subsets of a rare macrophages — inflammatory and anti-inflammatory. All other traditional sampling methods clustered both subsets together, while the sketching method separated them. Additional in-depth studies of these macrophage subpopulations could help reveal insight into inflammation and how to modulate inflammatory processes in response to disease, the researchers say.</p> <p>“That’s a benefit in working at the interface of fields,” Berger says. “We’re trained as mathematicians, but we understand what biological data science problems are, so we can bring the best technologies to their analysis.”</p> <p>“Geometric sketching is a promising tool for many practical applications of single-cell technology,” says Teresa Przytycka, a senior investigator in the Algorithmic Methods in Computational and Systems Biology group at the National Institutes of Health. “Thanks to the [single-cell RNA sequencing] technologies, many new cell types have been already discovered in several human tissues. Analyzes of even larger single-cell data have the potential to reveal additional rare cell subpopulations. … [S]ingle-cell data tends to be quite complex and its analysis often calls for applying advanced and computationally demanding algorithms. Geometric sketching will allow efficient application of such algorithms by reducing the number of data points while preserving the most relevant information.”</p> MIT researchers have developed a method for analyzing massive sets of data on single cells, that captures comprehensive samples — called “sketches” — while retaining retain cell diversity. Research, Computer science and technology, Algorithms, Biology, Data, Health sciences and technology, Drug development, Medicine, Machine learning, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), Mathematics, Biological engineering, School of Engineering, School of Science The fluid that feeds tumor cells The substance that bathes tumors in the body is quite different from the medium used to grow cancer cells in the lab, biologists report. Tue, 16 Apr 2019 00:00:00 -0400 Anne Trafton | MIT News Office <p>Before being tested in animals or humans, most cancer drugs are evaluated in tumor cells grown in a lab dish. However, in recent years, there has been a growing realization that the environment in which these cells are grown does not accurately mimic the natural environment of a tumor, and that this discrepancy could produce inaccurate results.</p> <p>In a new study, MIT biologists analyzed the composition of the interstitial fluid that normally surrounds pancreatic tumors, and found that its nutrient composition is different from that of the culture medium normally used to grow cancer cells. It also differs from blood, which feeds the interstitial fluid and removes waste products.</p> <p>The findings suggest that growing cancer cells in a culture medium more similar to this fluid could help researchers better predict how experimental drugs will affect cancer cells, says Matthew Vander Heiden, an associate professor of biology at MIT and a member of the Koch Institute for Integrative Cancer Research.</p> <p>“It’s kind of an obvious statement that the tumor environment is important, but I think in cancer research the pendulum had swung so far toward genes, people tended to forget that,” says Vander Heiden, one of the senior authors of the study.</p> <p>Alex Muir, a former Koch Institute postdoc who is now an assistant professor at the University of Chicago, is also a senior author of the paper, which appears in the April 16 edition of the journal <em>eLife</em>. The lead author of the study is Mark Sullivan, an MIT graduate student.</p> <p><strong>Environment matters</strong></p> <p>Scientists have long known that cancer cells metabolize nutrients differently than most other cells. This alternative strategy helps them to generate the building blocks they need to continue growing and dividing, forming new cancer cells. In recent years, scientists have sought to develop drugs that interfere with these metabolic processes, and one such drug was approved to treat leukemia in 2017.</p> <p>An important step in developing such drugs is to test them in cancer cells grown in a lab dish. The growth medium typically used to grow these cells includes carbon sources (such as glucose), nitrogen, and other nutrients. However, in the past few years, Vander Heiden’s lab has found that cancer cells grown in this medium respond differently to drugs than they do in mouse models of cancer.</p> <p>David Sabatini, a member of the Whitehead Institute and professor of biology at MIT, has also found that drugs affect cancer cells differently if they are grown in a medium that resembles the nutrient composition of human plasma, instead of the traditional growth medium.</p> <p>“That work, and similar results from a couple of other groups around the world, suggested that environment matters a lot,” Vander Heiden says. “It really was a wake up call for us that to really know how to find the dependencies of cancer, we have to get the environment right.”</p> <p>To that end, the MIT team decided to investigate the composition of interstitial fluid, which bathes the tissue and carries nutrients that diffuse from blood flowing through the capillaries. Its composition is not identical to that of blood, and in tumors, it can be very different because tumors often have poor connections to the blood supply.</p> <p>The researchers chose to focus on pancreatic cancer in part because it is known to be particularly nutrient-deprived. After isolating interstitial fluid from pancreatic tumors in mice, the researchers used mass spectrometry to measure the concentrations of more than 100 different nutrients, and discovered that the composition of the interstitial fluid is different from that of blood (and from that of the culture medium normally used to grow cells). Several of the nutrients that the researchers found to be depleted in tumor interstitial fluid are amino acids that are important for immune cell function, including arginine, tryptophan, and cystine.</p> <p>Not all nutrients were depleted in the interstitial fluid — some were more plentiful, including the amino acids glycine and glutamate, which are known to be produced by some cancer cells.</p> <p><strong>Location, location, location</strong></p> <p>The researchers also compared tumors growing in the pancreas and the lungs and found that the composition of the interstitial fluid can vary based on tumors’ location in the body and at the site where the tumor originated. They also found slight differences between the fluid surrounding tumors that grew in the same location but had different genetic makeup; however, the genetic factors tested did not have as big an impact as the tumor location.</p> <p>“That probably says that what determines what nutrients are in the environment is heavily driven by interactions between cancer cells and noncancer cells within the tumor,” Vander Heiden says.</p> <p>Scientists have previously discovered that those noncancer cells, including supportive stromal cells and immune cells, can be recruited by cancer cells to help remake the environment around the tumor to promote cancer survival and spread.</p> <p>Vander Heiden’s lab and other research groups are now working on developing a culture medium that would more closely mimic the composition of tumor interstitial fluid, so they can explore whether tumor cells grown in this environment could be used to generate more accurate predictions of how cancer drugs will affect cells in the body.</p> <p>The research was funded by the National Institutes of Health, the Lustgarten Foundation, the MIT Center for Precision Cancer Medicine, Stand Up to Cancer, the Howard Hughes Medical Institute, and the Ludwig Center at MIT.</p> Pancreatic cancer cells (nuclei in blue) growing as a sphere encased in membranes (red). By growing cancer cells in the lab, researchers can study factors that promote and prevent the formation of deadly tumors.Image: Min Yu (Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC) Research, Biology, Cancer, Koch Institute, School of Science, National Institutes of Health (NIH), Drug development Model learns how individual amino acids determine protein function Technique could improve machine-learning tasks in protein design, drug testing, and other applications. Fri, 22 Mar 2019 13:46:35 -0400 Rob Matheson | MIT News Office <p>A machine-learning model from MIT researchers computationally breaks down how segments of amino acid chains determine a protein’s function, which could help researchers design and test new proteins for drug development or biological research.&nbsp;</p> <p>Proteins are linear chains of amino acids, connected by peptide bonds, that fold into exceedingly complex three-dimensional structures, depending on the sequence and physical interactions within the chain. That structure, in turn, determines the protein’s biological function. Knowing a protein’s 3-D structure, therefore, is valuable for, say, predicting how proteins may respond to certain drugs.</p> <p>However, despite decades of research and the development of multiple imaging techniques, we know only a very small fraction of possible protein structures — tens of thousands out of millions. Researchers are beginning to use machine-learning models to predict protein structures based on their amino acid sequences, which could enable the discovery of new protein structures. But this is challenging, as diverse amino acid sequences can form very similar structures. And there aren’t many structures on which to train the models.</p> <p>In a paper being presented at the International Conference on Learning Representations in May, the MIT researchers develop a method for “learning” easily computable representations of each amino acid position in a protein sequence, initially using 3-D protein structure as a training guide. Researchers can then use those representations as inputs that help machine-learning models predict the functions of individual amino acid segments — without ever again needing any data on the protein’s structure.</p> <p>In the future, the model could be used for improved protein engineering, by giving researchers a chance to better zero in on and modify specific amino acid segments. The model might even steer researchers away from protein structure prediction altogether.</p> <p>“I want to marginalize structure,” says first author Tristan Bepler, a graduate student in the Computation and Biology group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We want to know what proteins do, and knowing structure is important for that. But can we predict the function of a protein given only its amino acid sequence? The motivation is to move away from specifically predicting structures, and move toward [finding] how amino acid sequences relate to function.”</p> <p>Joining Bepler is co-author Bonnie Berger, the Simons Professor of Mathematics at MIT with a joint faculty position in the Department of Electrical Engineering and Computer Science, and head of the Computation and Biology group.</p> <p><strong>Learning from structure</strong></p> <p>Rather than predicting structure directly — as traditional models attempt — the researchers encoded predicted protein structural information directly into representations. To do so, they use known structural similarities of proteins to supervise their model, as the model learns the functions of specific amino acids.</p> <p>They trained their model on about 22,000 proteins from the Structural Classification of Proteins (SCOP) database, which contains thousands of proteins organized into classes by similarities of structures and amino acid sequences. For each pair of proteins, they calculated a real similarity score, meaning how close they are in structure, based on their SCOP class.</p> <p>The researchers then fed their model random pairs of protein structures and their amino acid sequences, which were converted into numerical representations called embeddings by an encoder. In natural language processing, embeddings are essentially tables of several hundred numbers combined in a way that corresponds to a letter or word in a sentence. The more similar two embeddings are, the more likely the letters or words will appear together in a sentence.</p> <p>In the researchers’ work, each embedding in the pair contains information about how similar each amino acid sequence is to the other. The model aligns the two embeddings and calculates a similarity score to then predict how similar their 3-D structures will be. Then, the model compares its predicted similarity score with the real SCOP similarity score for their structure, and sends a feedback signal to the encoder.</p> <p>Simultaneously, the model predicts a “contact map” for each embedding, which basically says how far away each amino acid is from all the others in the protein’s predicted 3-D structure —&nbsp;essentially, do they make contact or not? The model also compares its predicted contact map with the known contact map from SCOP, and sends a feedback signal to the encoder. This helps the model better learn where exactly amino acids fall in a protein’s structure, which further updates each amino acid’s function.</p> <p>Basically, the researchers train their model by asking it to predict if paired sequence embeddings will or won’t share a similar SCOP protein structure. If the model’s predicted score is close to the real score, it knows it’s on the right track; if not, it adjusts.</p> <p><strong>Protein design</strong></p> <p><a name="_gjdgxs"></a>In the end, for one inputted amino acid chain, the model will produce one numerical representation, or embedding, for each amino acid position in a 3-D structure. Machine-learning models can then use those sequence embeddings to accurately predict each amino acid’s function based on its predicted 3-D structural “context” — its position and contact with other amino acids.</p> <p>For instance, the researchers used the model to predict which segments, if any, pass through the cell membrane. Given only an amino acid sequence, the researchers’ model predicted all transmembrane and non-transmembrane segments more accurately than state-of-the-art models.</p> <p>“The work by Bepler and Berger is a significant advance in representing the local structural properties of a protein sequence,” says Serafim Batzoglou, a professor of computer science at Stanford University. “The representation is learned using state-of-the-art deep learning methods, which have made major strides in protein structure prediction in systems such as RaptorX and AlphaFold. This work has ultimate application in human health and pharmacogenomics, as it facilitates detection of deleterious mutations that disrupt protein structures.”</p> <p>Next, the researchers aim to apply the model to more prediction tasks, such as figuring out which sequence segments bind to small molecules, which is critical for drug development. They’re also working on using the model for protein design. Using their sequence embeddings, they can predict, say, at what color wavelengths a protein will fluoresce.</p> <p>“Our model allows us to transfer information from known protein structures to sequences with unknown structure. Using our embeddings as features, we can better predict function and enable more efficient data-driven protein design,” Bepler says. “At a high level, that type of protein engineering is the goal.”</p> <p>Berger adds: “Our machine learning models thus enable us to learn the ‘language’ of protein folding — one of the original ‘Holy Grail’ problems — from a relatively small number of known structures.”</p> A new model developed by MIT researchers creates richer, more easily computable representations of how individual amino acids determine a protein’s function, which could be used for designing and testing new proteins.Research, Computer science and technology, Algorithms, Biology, Data, Health science and technology, Drug development, Medicine, Machine learning, Mathematics, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), School of Engineering, School of Science Using machine learning for medical solutions Master’s student and Marshall Scholar Kyle Swanson uses computer science to help make drug development more efficient. Tue, 19 Mar 2019 00:00:00 -0400 Gina Vitale | MIT News correspondent <p>Pharmaceutical companies spend a lot of time testing potential drugs, and they end up wasting &nbsp;much of that effort on candidates that don’t pan out. Kyle Swanson wants to change that.</p> <p>A master’s student in computer science and engineering, Swanson is working on a project that involves feeding a computer information about chemical compounds that have or have not worked as drugs in the past. From this input, the machine “learns” to predict which kinds of new compounds have the most promise as drug candidates, potentially saving money and time otherwise spent on testing. Several prominent companies have already adopted the software as their new model.</p> <p>“Our model is never going to be perfect … but the hope is that by doing this prediction phase first, the molecules that they actually test in the lab have a much higher chance of being viable drugs,” says Swanson, who graduated from MIT in 2018 with a BS in computer science and engineering, a BS in mathematics, and a minor in music.</p> <p>Swanson’s overall aim is to use his skills in computer science and machine learning for real-world science applications. He’ll work toward that goal as a Marshall Scholar for the next two years, attending Cambridge University to pursue a pair of master’s degrees, one in mathematical statistics and the other in computational biology.</p> <p>“I think the ultimate goal is to do something very similar to what I’m doing right now,” he says. “I feel like it’s a great mix of doing interesting computer science research and pushing the field of machine learning forward, while also having practical applications in the sciences.”</p> <p><strong>Researcher and survivor</strong></p> <p>Swanson’s first experience researching medical applications for machine learning was as an undergraduate in the lab of Regina Barzilay, the Delta Electronics Professor in the Computer Science and Artificial Intelligence Laboratory and the Department of Electrical Engineering and Computer Science. Swanson worked on a system designed to identify the presence of breast cancer from mammogram images. While the original goal of cancer detection proved to be difficult, the tool was successful at a related task. The algorithm is still used to analyze mammogram images, but rather than identifying cancer, it identifies whether patients are at greater risk for cancer, depending on <a href="">the density</a> of their breast tissue.</p> <p>While he was already interested in machine learning, Swanson entered cancer research for a very personal reason. One day, he noticed he had a little cough, which he attributed to catching a cold from his roommate. But while his roommate’s cough subsided, Swanson’s didn’t. Walking home one night a few weeks later, he found a lump above his collarbone. It turned out to be Hodgkin’s lymphoma.</p> <p>“My approach is to try and laugh it off as much as possible. I feel like if I were to take it seriously, it would just be so awful I wouldn’t be able to handle it,” Swanson says. “I mean, obviously there were times when I actually was very distraught about the whole thing. … The way I’ve tried to handle it is just to be as positive as possible.”</p> <p>He asked to join Barzilay’s lab not only because he found her research important, but also because she’d been through a similar scare with breast cancer. He felt that she understood what he was going through. Even now, as he’s working on that pharmaceutical machine learning project, she is still his advisor.</p> <p>“She’s been a role model for the kind of person I want to be both professionally and personally, and I hope that one day I can be in a similar position, making a real difference in the lives of others through my research,” he says.</p> <p>After several rounds of treatment, Swanson’s most recent PET scans indicate that he’s now cancer free.</p> <p><strong>A symphony for all seasons</strong></p> <p>Swanson first went to music school in Scarsdale, New York, when he was 2 years old. He picked up the flute in third grade, and later the piccolo. With many hours of practice, he became a skilled classical musician. He’s been in the MIT Symphony Orchestra for five straight years, and he’s played in a number of other ensembles as well.</p> <p>“The great thing about MIT is that I’ve been able to continue that interest. …The music program here is really excellent,” Swanson says. “I’ve enjoyed all the classes I’ve taken, and the ensembles are great as well.”</p> <p>His favorite experience in the music department is one to be rivaled. His first-year roommate, Bertrand Stone, also a mathematics major and musician, is a very talented composer. Before the summer of 2016, Swanson joked that Stone should use some of his free time outside of class to write a flute piece for him. When he returned in the fall, Stone handed him a 135-page, fully composed 20-minute flute concerto. Stone had already shown the piece to the MIT symphony conductor for input during the composition process, and Swanson was asked to perform it with the orchestra.</p> <p>“That was my favorite by far,” Swanson says.</p> <p>Music still takes up most of Swanson’s free time. But when he’s not practicing on some sort of woodwind, he enjoys pounding the pavement with MIT’s Running Club and spending time with friends. His undergraduate fraternity, Alpha Epsilon Pi, is still a big part of his life. He met many of his closest friends there, including one of his current roommates, and they played a key supportive role for him when he was wrestling with cancer.</p> <p>“They’re just some of the smartest and nicest people I know on campus,” Swanson says.</p> <p><strong>A master of degrees</strong></p> <p>By the time Swanson leaves Cambridge, he’ll have three master’s degrees. “Really, I want to just have a better understanding of the fields that I’m going to be applying machine learning to,” he says.</p> <p>As for his future after that, he’s not exactly sure. He will most likely go back to school for a PhD, and then he’ll decide if he wants to enter industry or academia. The important thing for him is that he’s applying his knowledge of machine learning to science that has a real impact on human lives.</p> <p>“If I were to keep doing what I’m doing right now, I think I would be very happy. I love machine learning and I love the way it can do such amazing things,” he says. “But I also specifically like seeing the difference that I’m making in the world.”</p> Kyle SwansonImage: Ian MacLellanProfile, Students, Graduate, postdoctoral, Drug development, Cancer, Artifical intelligence, Machine learning, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Copmuter Science (eecs), Computer science and technology, School of Engineering, Music, Medicine A new approach to drugging a difficult cancer target Study suggests an alternative way to treat tumors that are dependent on the cancer-promoting Myc protein. Thu, 14 Mar 2019 11:00:00 -0400 Anne Trafton | MIT News Office <p>One of the most common cancer-promoting genes, known as Myc, is also one of the most difficult to target with drugs. Scientists have long tried to develop drugs that block the Myc protein, but so far their efforts have not been successful.</p> <p>Now, using an alternative strategy, MIT researchers have discovered a compound that can reduce Myc activity by tying up the protein that is Myc’s usual binding partner, leaving Myc partnerless and unable to perform its usual functions.</p> <p>The research team, led by Angela Koehler, an assistant professor of biological engineering and a member of MIT’s Koch Institute for Integrative Cancer Research, found that the compound they developed could suppress tumor growth in mice with certain types of cancer. The compound has been licensed by an MIT spinout that is now seeking to develop more powerful versions that could potentially be tested in human patients.</p> <p>Koehler is the senior author of the study, which appears online in the journal <em>Cell Chemical Biology </em>on March 14. MIT postdoc Nicholas Struntz and graduate student Andrew Chen are the lead authors of the study, and the research team also includes authors from the Broad Institute of MIT and Harvard, Stanford University, Baylor College of Medicine, Brigham and Women’s Hospital, and Dana-Farber Cancer Institute.</p> <p><strong>A new approach</strong></p> <p>For decades, cancer researchers have been trying to find ways to shut off Myc, which is a transcription factor — a protein that controls the expression of other genes. Known as a “master regulator,” Myc controls many genes involved in basic cellular functions such as growth and metabolism. When it becomes overexpressed, as it does in about 70 percent of cancers, it drives uncontrolled cell growth and proliferation.</p> <p>Myc usually forms a structure known as a heterodimer with the Max protein, and these proteins together bind to DNA to turn on gene transcription. Drug development efforts have traditionally focused on disrupting the interaction of Myc and Max, which has proven difficult. Most of the compounds that researchers have tested have proven too weak, or not specific enough to the Myc-Max interaction.</p> <p>Koehler encountered similar difficulties, but several years ago, she decided to pursue a different strategy, based on the Max protein. The idea was to try to find compounds that would interact with Max, and then see if they had any effect on Myc’s ability to drive cell growth.</p> <p>Using a technology developed by Koehler known as a microarray binding assay, the researchers screened a library of about 20,000 compounds, including both natural products and a collection of compounds synthesized by the Broad Institute, as possible drug candidates. The top six hits, in terms of ability to bind to Max and inhibit Myc transcriptional activity in another assay, all came from the Broad Institute collection.</p> <p>The researchers tested the compounds in several different cancer cell lines and identified one that appeared to be most effective at halting cell growth.</p> <p>At first, the researchers were unsure how this compound was blocking Myc activity, but experiments revealed that it was stabilizing a structure in which two molecules of Max bind together, forming a structure called a homodimer. This reduces the formation of the Myc-Max heterodimer and leads to a decrease in Myc levels, which the researchers believe may be the result of the unpartnered protein being broken down within cells.</p> <p><strong>Shrinking tumors</strong></p> <p>The researchers found that the compound slowed cell growth in a variety of Myc-dependent human cancer cells, including models for hepatocellular carcinoma, T-cell acute lymphoblastic leukemia, and Burkitt’s lymphoma.</p> <p>They also tested the compound in mice, and found that even though the compound they originally identified was not optimized for maximum potency, it could slow tumor progression in mouse models of hepatocellular carcinoma and T-cell acute lymphoblastic leukemia.</p> <p>“The discovery and detailed validation of a small molecule targeting Max homodimers represents a significant advance over previous attempts to directly inhibit either Myc itself or Myc-Max dimerization,” says Robert Eisenman, a principal investigator at the Fred Hutchinson Cancer Research Center, who was not involved in the study. “It not only provides new insight into how Myc functions but reveals what is likely to be an important exploitable vulnerability in Myc-driven cancers.”</p> <p>Kronos Bio, the company that has licensed the rights to the compound described in this paper, is now working to optimize it to be more potent and more efficient. Koehler’s lab is also working on learning more about how this compound works, as well as determining the structure of the complex that it forms with the Max homodimer, in hopes of potentially developing better versions.</p> <p>“This particular compound isn’t going to be a drug — it’s really just a tool to clarify the relevance of stabilizing Max homodimers as a strategy to perturb Myc function,” Koehler says. “That can guide people in the pharmaceutical industry who are thinking about trying to drug Myc, to maybe think about other ways to find Max homodimer stabilizers.”</p> <p>Her lab is also pursuing other ways to target Myc, such as finding ways to stabilize a homodimer of two Myc molecules, which would likely end up being degraded within the cell.</p> <p>“There may be different ways to stabilize biomolecular interactions within the Myc-Max network that could lead to different ways of perturbing Myc function,” she says.</p> <p>The research was funded, in part, by the National Cancer Institute, including the Koch Institute Support (core) Grant, the National Institutes of Health, the Leukemia and Lymphoma Society, the Ono Pharma Foundation, the MIT Deshpande Center for Technological Innovation, the MIT Center for Precision Cancer Medicine, the AACR-Bayer Innovation and Discovery Grant, and the Merkin Institute Fellows Program at the Broad Institute.</p> MIT researchers have discovered a way to manipulate the interactions of the proteins Myc and Max, which regulate gene transcription. At left, Myc interacts with Max, at center, Max is alone, at right, two molecules of Max.Image: Courtesy of the researchers, edited by MIT NewsResearch, Biological engineering, Cancer, Koch Institute, School of Engineering, National Institutes of Health (NIH), Drug development Tissue model reveals how RNA will act on the liver Studies could speed the development of new treatments for liver disease. Tue, 05 Mar 2019 10:59:59 -0500 Anne Trafton | MIT News Office <p>Novel therapies based on a process known as <a href="">RNA interference</a> (RNAi) hold great promise for treating a variety of diseases by blocking specific genes in a patient’s cells. Many of the earliest RNAi treatments have focused on diseases of the liver, because RNA-carrying particles tend to accumulate in that organ.</p> <p>MIT researchers have now shown that an engineered model of human liver tissue can be used to investigate the effects of RNAi, helping to speed up the development of such treatments. In a paper appearing in the journal <em>Cell Metabolism </em>on March 5, the researchers showed with the model that they could use RNAi to turn off a gene that causes a rare hereditary disorder. And using RNA molecules that target a different gene expressed by human liver cells, they were able to reduce malaria infections in the model’s cells.</p> <p>“We showed that you could look at how this new class of nucleic acid therapies, especially RNAi, could affect rare genetic diseases and infectious diseases,” says Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and Electrical Engineering and Computer Science, a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science, and the senior author of the study.</p> <p>The liver tissue model can also be used to manipulate metabolic enzyme levels, which could help researchers to predict how different patients would metabolize drugs, allowing them to identify possible side effects earlier in the drug development process.</p> <p>MIT research scientist Liliana Mancio-Silva is the lead author of the paper. Other authors include Heather Fleming, director of research for Bhatia’s lab; Alex Miller, an MIT graduate student; and Stuart Milstein, Abigail Liebow, Patrick Haslett, and Laura Sepp-Lorenzino of Alnylam Pharmaceuticals.</p> <p><strong>Liver model</strong></p> <p>Human liver tissue is notoriously difficult to grow outside of the human body, making it difficult to study how experimental drugs will affect the liver. Several years ago, Bhatia and her colleagues first demonstrated that they could grow human hepatocytes, the main type of liver cell, on special micropatterned surfaces, surrounded by supportive cells. This precision-engineered architecture creates a microenvironment in which human liver cells function much the same way as they do in humans.</p> <p>Since then, they have used this model to test small-molecule drugs for malaria and other diseases that affect the liver. In their new paper, they decided to try to demonstrate the model’s usefulness for testing the delivery of nucleic acids such as RNA. Through RNA interference, short strands of RNA can be used to block the expression of specific disease-causing genes.</p> <p>To explore RNA-based therapies, the researchers decided to model two different types of disease: genetic disorders and infectious diseases. As a model genetic disorder, the researchers chose alpha-1 antitrypsin-associated liver disease. This rare disease causes the alpha-1 antitrypsin protein to misfold and accumulate in hepatocytes, damaging them.</p> <p>They found that the RNA they delivered to the cells of the liver model could reduce expression of the implicated protein by about 95 percent. Dozens of other disorders of the liver could benefit from genetic manipulation, Bhatia says.</p> <p>The researchers also tested an RNAi treatment designed to treat infectious diseases by turning down genes expressed by the host, which the pathogen normally exploits to infect the host. In this case, they delivered RNA that interferes with a gene that encodes a cell surface receptor that the malaria parasite requires to get into liver cells and infect them.</p> <p>Other host genes could be targeted to treat infectious diseases such as hepatitis B. In some patient settings, Bhatia says, this kind of treatment could be preferable to having patients take daily pills over a long period of time, because a single shot of RNA has been shown to turn down gene expression for several weeks.</p> <p><strong>Drug screening</strong></p> <p>The researchers also showed that this model could be useful for testing the possible side effects of traditional small-molecule drugs. The liver is responsible for metabolizing such drugs, and liver damage from these drugs is one of the biggest reasons that clinical trials fail.</p> <p>To make matters more complicated, different people can express varying levels of the metabolic enzymes used to break down drugs, so potential drugs need to be tested under different conditions. This is usually done in human liver tissue treated with drugs that inhibit certain metabolic enzymes. However, these drugs are not highly specific and can block multiple metabolic pathways at once.</p> <p>In this study, the researchers used RNA interference to reduce levels of two metabolic enzymes that belong to a family known as cytochromes P450. They were then able to test how the liver cells metabolized acetaminophen (Tylenol) and atorvastatin (Lipitor), which can damage the liver in some cases. They showed that the tissue model accurately replicated how these drugs are broken down when varying levels of metabolic enzymes are present.</p> <p>This kind of drug screening could make it easier for researchers to test the potential responses of many different types of people, using cells from just one donor, manipulated with RNAi, Bhatia says.</p> <p>In future studies, the researchers plan to study whether this model could be useful for studying gene therapy, which involves delivering DNA encoding a gene that is missing or defective. For example, hemophilia could be treated by delivering the gene for the clotting factor that hemophilia patients lack.</p> <p>The research was funded by the Bill and Melinda Gates Foundation, Alnylam Pharmaceuticals, and the Koch Institute Support (core) Grant from the National Cancer Institute.</p> MIT researchers have developed an engineered liver tissue model that can be manipulated with RNA interference (RNAi).Credit: Liliana Mancio-SilvaResearch, Disease, RNA, Drug development, Koch Institute, Health sciences and technology, Electrical Engineering & Computer Science (eecs), School of Engineering A better way to measure cell survival New test rapidly evaluates the effect of drugs and potentially toxic compounds on cells. Tue, 05 Feb 2019 11:00:00 -0500 Anne Trafton | MIT News Office <p>Measuring the toxic effects of chemical compounds on different types of cells is critical for developing cancer drugs, which must be able to kill their target cells. Analyzing cell survival is also an important task in fields such as environmental regulation, to test industrial and agricultural chemicals for possible harmful effects on healthy cells.</p> <p>MIT biological engineers have now devised a new toxicity test that can measure chemical effects on cell survival with much greater sensitivity than some of the most popular tests used today. It is also much faster than the gold-standard test, which is not widely used because it takes two to three weeks to yield results. The new test could thus help drug companies and academic researchers identify and evaluate new drugs more rapidly.</p> <p>“Cytotoxicity assays are one of the most commonly used assays in life sciences,” says Bevin Engelward, a professor of biological engineering at MIT and the senior author of the study.</p> <p>Le Ngo, a former MIT graduate student and postdoc, is the lead author of the paper, which appears in the Feb. 5 issue of <em>Cell Reports</em>. Other authors include Tze Khee Chan, a former graduate student at the Singapore-MIT Alliance for Research and Technology (SMART); Jing Ge, a former MIT graduate student; and Leona Samson, Ngo’s co-advisor and an MIT professor emerita of biological engineering.</p> <p><strong>Measuring survival</strong></p> <p>The traditional test for measuring cell survival, known as the colony formation assay, involves growing cell colonies in tissue culture dishes for two to three weeks after exposing the cells to a chemical compound or another harmful agent such as radiation. A researcher then counts the number of colonies to determine how the treatment affected the cells’ survival.</p> <p>Part of Engelward’s motivation for this study was the memory of the long hours she spent counting such colonies as a graduate student.</p> <p>“The counting is really laborious and painfully difficult because you have to constantly make judgement calls as to what is a colony versus debris,” she says. “Few people use the colony formation assay anymore because it’s difficult, way too slow, and requires huge amounts of cell growth media, so you need a lot of the compound being tested.”</p> <p>In recent years, scientists have begun using other methods that are faster but not as accurate and sensitive as the colony formation assay. These tests do not measure cell growth directly but instead analyze mitochondrial function.</p> <p>Engelward and colleagues set out to develop a test that could generate results in just a few days while still matching the accuracy and sensitivity of the colony formation assay. The system they invented, which they call the MicroColonyChip, consists of tiny wells on a plate. Treated and untreated cells are placed into these wells and begin to form very small colonies in a grid pattern. Within just a few days, before the colonies become visible to the naked eye, the researchers can use a microscope to image the cells’ DNA, which is fluorescently labeled.</p> <p>By modifying code originally developed by former MIT postdoc David Wood and MIT Professor Sangeeta Bhatia, the researchers created a software program that measures the amount of fluorescent DNA in each well and then calculates how much cell growth occurred. By comparing the growth of treated and untreated cells, the researchers can determine the toxicity of whatever compound they are studying.</p> <p>“We have an automatic scanning system to do the fluorescent imaging, and afterward, the image analysis is completely automated,” Ngo says.</p> <p>The researchers compared their new test to the gold-standard colony formation assay and found that the results were indistinguishable. They were also able to precisely reproduce data on the effects of gamma radiation on human lymphoblastoid cells, collected 20 years ago using the colony formation assay. Using the MicroColonyChip, the researchers obtained their data in three days, instead of three weeks.</p> <p>“We were able reproduce radiation studies from 20 years ago, using a process much easier than what they did,” Engelward says.</p> <p><strong>Greater sensitivity</strong></p> <p>The researchers also compared their new test to the two toxicity tests that are most commonly used by researchers and pharmaceutical companies, known as XTT and CellTiter-Glo (CTG). Both of these tests are indirect measures of cell viability: XTT measures cells’ ability to break down tetrazolium, a key step in cellular metabolism, and CTG measures intracellular levels of ATP, molecules that cells use to store energy.</p> <p>“The MicroColonyChip is much more sensitive than the XTT assay, so it really gives you the ability to see subtle changes in cell survival, and it is as sensitive as the CTG assay while being more robust to artifacts,” Engelward says.</p> <p>Using the new test, the researchers examined the effects of two DNA-damaging drugs used for chemotherapy and found that they could accurately reproduce the results obtained using the traditional colony formation assay. “We now plan to expand those studies in hopes of demonstrating that the test works for many more types of drugs and cells,” Ngo says.</p> <p>In addition to being useful for drug development, this test could also be helpful for environmental regulatory agencies responsible for testing chemical compounds for potential harmful effects, Engelward says. Another possible application is in personalized medicine, where it could be used to test a variety of drugs on a patient’s cells before a treatment is chosen.</p> <p>The researchers have filed for a patent on their technology. The research was funded by the National Institute of Environmental Health Sciences, including the NIEHS Superfund Basic Research Program, and the National Institutes of Health.</p> MIT biological engineers have developed a way to rapidly measure cell survival rates by growing many cell colonies and imaging their fluorescently labeled DNA.Image: Le NgoResearch, Biological engineering, Biology, Cancer, Drug development, Medicine, Invention, School of Engineering, National Institutes of Health (NIH) Julia Lab joins team to speed up drug approval process Health Analytics Collective uses real-world evidence to modernize health and drug development decisions. Fri, 25 Jan 2019 11:00:01 -0500 Sandi Miller | Department of Mathematics <p>The timeline to market a new drug or medical device, from the point of discovery to U.S. Food and Drug Administration approval, can stretch to a decade. By pooling its industry experience and technology, a new health research supergroup led by the <a href="" target="_blank">Julia Lab</a> within the MIT Computer Science and Artificial Intelligence Laboratory aims to significantly shorten the approval process for pharmaceutical and health care groups.</p> <p>The team aims to leverage real-world evidence,&nbsp;observational data that are generated during routine clinical practice, and&nbsp;patient health care databases to augment label claims and/or support new drug applications with leading-edge software and algorithms&nbsp;and a depth&nbsp;of&nbsp;regulatory&nbsp;and&nbsp;clinical&nbsp;experience.&nbsp;</p> <p>Calling themselves the&nbsp;<a href="" target="_blank">Health Analytics Collective</a>, the team includes&nbsp;<a href="" target="_blank">MMS Holdings</a>&nbsp;of Canton, Michigan, a data-focused&nbsp;contract research organization (CRO) to the pharmaceutical, biotechnology, and&nbsp;medical&nbsp;device industries; and the&nbsp;<a href="" target="_blank">Center for Translational Medicine</a>&nbsp;(CTM) at the University of Maryland School of Pharmacy, where the team will be based.&nbsp;&nbsp;&nbsp;</p> <p>To cut down on the number of required clinical trials, the team compares effectiveness claims for similar drugs in development, examines available evidence from existing data, assesses available treatments, identifies treatment gaps, and evaluates patient risks. To process all of these data, the team relies on Julia, an MIT-incubated programming language designed to solve massive computational problems quickly and accurately, and the use of real-world evidence.&nbsp;</p> <p>“The field of using real-world evidence in pharmaceuticals is new and the methods are still evolving,” says <a href="" target="_blank">Alan Edelman</a>, Julia co-founder and professor of applied mathematics. “We created this collective to forecast the future in health care and make critical decisions, giving data a longer life of more than just one use.” &nbsp;&nbsp;</p> <p><a href="">Joga Gobburu</a>, CTM’s director and a former U.S. Food and Drug Administration scientist, has spent years analyzing experiments and clinical trials to advise key drug development decisions. “[Real-world evidence] is new and challenging,” he says. “There is much research required to inform technical methodology and regulatory policy.”</p> <p>The team members expect to create unique insights from data curation, data analytics, reporting, and regulatory submissions services for pharmaceutical companies, hospital and health care systems, and universities. By combining real-world evidence with their knowledge of areas lacking regulatory precedence, the team is available to guide a company’s business decisions with insights into an asset’s life-cycle management and due-diligence efforts in order to slash the development period of lifesaving drugs.</p> <p>“Using real-world evidence for the purposes of regulatory drug approvals is an innovative approach that can be applied to support a wide variety of healthcare decisions,” says <a href="">Uma Sharma</a>, MMS’s chief scientific officer.&nbsp;“This group’s combined efforts will give sponsors of clinical trials the ability to bring safe, life-changing therapies to patients much more quickly.”</p> <p>The team is riding a wave of big-data investments in the health care and pharmaceutical industries from hardware, software, and professional services, with revenues&nbsp;expected to grow at a compound annual growth rate of more than 15 percent annually, rising to $5.8 billion by the end of 2020.</p> Members of the Health Analytics Collective: (left to right) Mohamad Zahreddine, Michelle Gayari, Duane Robinson, Uma Sharma, Vijay Ivaturi, Kelly Hill, Joga Gobburu, Alan Edelman, and Prasad Koppolu, gather at MMS Holdings in Canton, Michigan. Photo: Don McLean/MMSMathematics, Computer science and technology, Health care, Computer Science and Artificial Intelligence Laboratory (CSAIL), Programming languages, Software, Medicine, School of Science, Analytics, Data, Drug development Stephen Buchwald awarded 2019 Wolf Prize for chemistry Honor shared with John Hartwig of the University of California at Berkeley for their development of a process to improve the synthesis of large organic molecules. Wed, 16 Jan 2019 16:25:01 -0500 Danielle Randall Doughty | Department of Chemistry <p>Camille Dreyfus Professor of Chemistry Stephen L. Buchwald has been named one of seven laureates across five categories honored with Israel’s 2019 Wolf Prize. Buchwald shares the award in chemistry with Professor John Hartwig of the University of California at Berkeley for their development of the Buchwald-Hartwig amination, a process used to improve the synthesis of large organic molecules.</p> <p>“This award is due to the hard work and creativity of the graduate students and postdoctoral coworkers that I have been fortunate enough to have in my group during my over 30 years at MIT,” said Buchwald. “It also reflects the importance of funding basic research. In this case, the key finding came from work that had no practical application. However, based what we learned, we (and others) were able to develop new chemistry that is now widely used in industry as well as in academia.”</p> <p>Buchwald received his BS from Brown University in 1977, and his PhD from Harvard University in 1982. Following a postdoctoral fellowship at Caltech, he joined the faculty at MIT in 1984, and was promoted to professor of chemistry in 1993. Among Buchwald’s numerous honors are the Linus Pauling Award, the Roger Adams Award, the Arthur C. Cope Award, BBVA Frontiers of Knowledge Award in Basic Sciences, and the Gustavus J. Esselen Award.</p> <p>Research in the Buchwald Group combines elements of organic synthesis, physical organic chemistry, and organometallic chemistry to devise catalytic processes of use in solving problems of fundamental importance.</p> <p>Ricardo Wolf, a German-born inventor who served as Cuba’s ambassador to Israel, established the Wolf Foundation in 1975 and the Wolf Prize, given in recognition of “achievements in the interest of mankind and friendly relations among peoples, irrespective of nationality, race, color, religion, sex or political view”, in 1978. Winners are selected by an international prize committee comprised of renowned experts in each field. The prestigious $100,000 awards are given in the fields of agriculture, chemistry, mathematics, medicine, physics, and art, and, within the filed of chemistry are widely regarded as second only to the Nobel Prize in terms of their stature.</p> <p>Israeli President Reuven Rivlin will present Buchwald and the other 2019 laureates with their awards this May at a ceremony held at the Knesset Building (Israel’s Parliament) in Jerusalem.</p> Stephen BuchwaldPhoto: Justin KnightAwards, honors and fellowships, Chemistry, Faculty, Drug development, School of Science MIT engineers repurpose wasp venom as an antibiotic drug Altered peptides from a South American wasp’s venom can kill bacteria but are nontoxic to human cells. Fri, 07 Dec 2018 04:59:59 -0500 Anne Trafton | MIT News Office <p>The venom of insects such as wasps and bees is full of compounds that can kill bacteria. Unfortunately, many of these compounds are also toxic for humans, making it impossible to use them as antibiotic drugs.</p> <p>After performing a systematic study of the antimicrobial properties of a toxin normally found in a South American wasp, researchers at MIT have now created variants of the peptide that are potent against bacteria but nontoxic to human cells.</p> <p>In a study of mice, the researchers found that their strongest peptide could completely eliminate <em>Pseudomonas aeruginosa</em>, a strain of bacteria that causes respiratory and other infections and is resistant to most antibiotics.</p> <p>“We’ve repurposed a toxic molecule into one that is a viable molecule to treat infections,” says Cesar de la Fuente-Nunez, an MIT postdoc. “By systematically analyzing the structure and function of these peptides, we’ve been able to tune their properties and activity.”</p> <p>De la Fuente-Nunez is one of the senior authors of the paper, which appears in the Dec. 7 issue of the journal <em>Nature Communications Biology</em>. Timothy Lu, an MIT associate professor of electrical engineering and computer science and of biological engineering, and Vani Oliveira, an associate professor at the Federal University of ABC in Brazil, are also senior authors. The paper’s lead author is Marcelo Der Torossian Torres, a former visiting student at MIT.</p> <p><strong>Venomous variants</strong></p> <p>As part of their immune defenses, many organisms, including humans, produce peptides that can kill bacteria. To help fight the emergence of antibiotic-resistant bacteria, many scientists have been trying to adapt these peptides as potential new drugs.</p> <p>The peptide that de la Fuente-Nunez and his colleagues focused on in this study was isolated from a wasp known as <em>Polybia paulista</em>. This peptide is small enough — only 12 amino acids — that the researchers believed it would be feasible to create some variants of the peptide and test them to see if they might become more potent against microbes and less harmful to humans.</p> <p>“It’s a small enough peptide that you can try to mutate as many amino acid residues as possible to try to figure out how each building block is contributing to antimicrobial activity and toxicity,” de la Fuente-Nunez says.</p> <p>Like many other antimicrobial peptides, this venom-derived peptide is believed to kill microbes by disrupting bacterial cell membranes. The peptide has an alpha helical structure, which is known to interact strongly with cell membranes.</p> <p>In the first phase of their study, the researchers created a few dozen variants of the original peptide and then measured how those changes affected the peptides’ helical structure and their hydrophobicity, which also helps to determine how well the peptides interact with membranes. They then tested these peptides against seven strains of bacteria and two of fungus, making it possible to correlate their structure and physicochemical properties with their antimicrobial potency.</p> <p>Based on the structure-function relationships they identified, the researchers then designed another few dozen peptides for further testing. They were able to identify optimal percentages of hydrophobic amino acids and positively charged amino acids, and they also identified a cluster of amino acids where any changes would impair the overall function of the molecule.</p> <p><strong>Fighting infection</strong></p> <p>To measure the peptides’ toxicity, the researchers exposed them to human embryonic kidney cells grown in a lab dish. They selected the most promising compounds to test in mice infected with <em>Pseudomonas aeruginosa</em>, a common source of respiratory and urinary tract infections, and found that several of the peptides could reduce the infection. One of them, given at a high dose, could eliminate it completely.</p> <p>“After four days, that compound can completely clear the infection, and that was quite surprising and exciting because we don’t typically see that with other experimental antimicrobials or other antibiotics that we’ve tested in the past with this particular mouse model,” de la Fuente-Nunez says.</p> <p>“This represents a beautiful model of how work of this type should be done,” says Michael Zasloff, a professor of surgery and pediatrics scientific director at the MedStar Georgetown Transplant Institute, who was not involved in the research. “It’s a very comprehensive approach to redesigning a naturally occurring antimicrobial peptide.”</p> <p>The researchers have begun creating additional variants that they hope will be able to clear infections at lower doses. De la Fuente-Nunez also plans to apply this approach to other types of naturally occurring antimicrobial peptides when he joins the faculty of the University of Pennsylvania next year.&nbsp;</p> <p>“I do think some of the principles that we’ve learned here can be applicable to other similar peptides that are derived from nature,” he says. “Things like helicity and hydrophobicity are very important for a lot of these molecules, and some of the rules that we’ve learned here can definitely be extrapolated.”</p> <p>The research was funded, in part, by the Ramon Areces Foundation and the Defense Threat Reduction Agency.</p> MIT engineers have developed new antimicrobial peptides based on a naturally occurring peptide produced by a South American wasp.Image: Wikimedia, Charles J. SharpResearch, Microbes, Biological engineering, Electrical Engineering & Computer Science (eecs), School of Engineering, Medicine, Bacteria, Genetics, Antibiotics, Drug development Chemical synthesis could produce more potent antibiotics Simple method for linking molecules could help overcome drug resistant infections. Mon, 05 Nov 2018 10:59:59 -0500 Anne Trafton | MIT News Office <p>Using a novel type of chemical reaction, MIT researchers have shown that they can modify antibiotics in a way that could potentially make them more effective against drug-resistant infections.</p> <p>By chemically linking the antibiotic vancomycin to an antimicrobial peptide, the researchers were able to dramatically enhance the drug’s effectiveness against two strains of drug-resistant bacteria. This kind of modification is simple to perform and could be used to create additional combinations of antibiotics and peptides, the researchers say.</p> <p>“Typically, a lot of steps would be needed to get vancomycin in a form that would allow you to attach it to something else, but we don’t have to do anything to the drug,” says Brad Pentelute, an MIT associate professor of chemistry and the study’s senior author. “We just mix them together and we get a conjugation reaction.”</p> <p>This strategy could also be used to modify other types of drugs, including cancer drugs, Pentelute says. Attaching such drugs to an antibody or another targeting protein could make it easier for the drugs to reach their intended destinations.</p> <p>Pentelute’s lab worked with Stephen Buchwald, the Camille Dreyfus Professor of Chemistry at MIT; Scott Miller, a professor of chemistry at Yale University; and researchers at Visterra, a local biotech company, on the paper, which appears in the Nov. 5 issue of <em>Nature Chemistry</em>. The paper’s lead authors are former MIT postdoc Daniel Cohen, MIT postdoc Chi Zhang, and MIT graduate student Colin Fadzen.</p> <p><strong>A simple reaction</strong></p> <p>Several years ago, Cohen made the serendipitous discovery that an amino acid called selenocysteine can spontaneously react with complex natural compounds without the need for a metal catalyst. Cohen found that when he mixed electron-deficient selenocysteine with the antibiotic vancomycin, the selenocysteine attached itself to a particular spot — an electron-rich ring of carbon atoms within the vancomycin molecule.</p> <p>This led the researchers to try using selenocysteine as a “handle” that could be used to link peptides and small-molecule drugs. They incorporated selenocysteine into naturally occurring antimicrobial peptides — small proteins that most organisms produce as part of their immune defenses. Selenocysteine, a naturally occurring amino acid that includes an atom of selenium, is not as common as the other 20 amino acids but is found in a handful of enzymes in humans and other organisms.</p> <p>The researchers found that not only were these peptides able to link up with vancomycin, but the chemical bonds consistently occurred at the same location, so all of the resulting molecules were identical. Creating such a pure product is difficult with existing methods for linking complex molecules. Furthermore, doing this kind of reaction with previously existing methods would likely require 10 to 15 steps just to chemically modify vancomycin in a way that would allow it to react with a peptide, the researchers say.</p> <p>“That’s the beauty of this method,” Zhang says. “These complex molecules intrinsically possess regions that can be harnessed to conjugate to our protein, if the protein possesses the selenocysteine handle that we developed. It can greatly simplify the process.”</p> <p>Dan Mandell, CEO of GRO Biosciences, says the new approach also overcomes another obstacle to this type of reaction, which is that when drugs are chemically modified to enable attachment to selenocysteine, it can weaken them.</p> <p>“This paper&nbsp;provides an important advance on this technology by allowing attachment of unmodified drugs to targeting proteins,” says Mandell, who was not involved in the research. “This approach can help usher in a new wave of&nbsp;selenocysteine-mediated drug conjugates, where targeting proteins deliver potent drugs to the site of disease in a predictable fashion.”</p> <p>The researchers tested conjugates of vancomycin and a variety of antimicrobial peptides (AMPs). They found that one of these molecules, a combination of vancomycin and the AMP dermaseptin, was five times more powerful than vancomycin alone against a strain of bacteria called <em>E. faecalis</em>. Vancomycin linked to an AMP called RP-1 was able to kill the bacterium <em>A. baumannii</em>, even though vancomycin alone has no effect on this strain. Both of these strains have high levels of drug resistance and often cause infections acquired in hospitals.</p> <p><strong>Modified drugs</strong></p> <p>This approach should work for linking peptides to any complex organic molecule that has the right kind of electron-rich ring, the researchers say. They have tested their method with about 30 other molecules, including serotonin and resveratrol, and found that they could be easily joined to peptides containing selenocysteine. The researchers have not yet explored how these modifications might affect the drugs’ activity.</p> <p>In addition to modifying antibiotics, as they did in this study, the researchers believe they could use this technique for creating targeted cancer drugs. Scientists could use this approach to attach antibodies or other proteins to cancer drugs, helping the drugs to reach their destination without causing side effects in healthy tissue.</p> <p>Adding selenocysteine to small peptides is a fairly straightforward process, the researchers say, but they are now working on adapting the method so that it can be used for larger proteins. They are also experimenting with the possibility of performing this type of conjugation reaction using the more common amino acid cysteine as a handle instead of selenocysteine.</p> <p>The research was funded by the National Institutes of Health, a Damon Runyon Cancer Research Foundation Award, and a Sontag Distinguished Scientist Award.</p> By chemically linking the antibiotic vancomycin to two different antimicrobial peptides, MIT researchers were able to dramatically enhance the drug’s effectiveness against two strains of drug-resistant bacteria: A. baumannii and E. faecalis (shown here)Image: U.S. Centers for Disease Control and PreventionResearch, Antibiotics, Chemistry, School of Science, National Institutes of Health (NIH), Medicine, Bacteria, Drug development Cryptographic protocol enables greater collaboration in drug discovery Neural network that securely finds potential drugs could encourage large-scale pooling of sensitive data. Thu, 18 Oct 2018 14:00:00 -0400 Rob Matheson | MIT News Office <p>MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.</p> <p>Datasets of drug-target interactions (DTI), which show whether candidate compounds act on target proteins, are critical in helping researchers develop new medications. Models can be trained to crunch datasets of known DTIs and then, using that information, find novel drug candidates.</p> <p>In recent years, pharmaceutical firms, universities, and other entities have become open to pooling pharmacological data into larger databases that can greatly improve training of these models. Due to intellectual property matters and other privacy concerns, however, these datasets remain limited in scope. Cryptography methods to secure the data are so computationally intensive they don’t scale well to datasets beyond, say, tens of thousands of DTIs, which is relatively small.</p> <p>In a paper published today in <em>Science</em>, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) describe a neural network securely trained and tested on a dataset of more than a million DTIs. The network leverages modern cryptographic tools and optimization techniques to keep the input data private, while running quickly and efficiently at scale.</p> <p>The team’s experiments show the network performs faster and more accurately than existing approaches; it can process massive datasets in days, whereas other cryptographic frameworks would take months. Moreover, the network identified several novel interactions, including one between the leukemia drug imatinib and an enzyme ErbB4 — mutations of which have been associated with cancer — which could have clinical significance.</p> <p>“People realize they need to pool their data to greatly accelerate the drug discovery process and enable us, together, to make scientific advances in solving important human diseases, such as cancer or diabetes. But they don’t have good ways of doing it,” says corresponding author Bonnie Berger, the Simons Professor of Mathematics and a principal investigator at CSAIL. “With this work, we provide a way for these entities to efficiently pool and analyze their data at a very large scale.”</p> <p>Joining Berger on the paper are co-first authors Brian Hie and Hyunghoon Cho, both graduate students in electrical engineering and computer science and researchers in CSAIL’s Computation and Biology group.</p> <p><strong>“Secret sharing” data</strong></p> <p>The new paper builds on previous <a href="">work</a> by the researchers in protecting patient confidentiality in genomic studies, which find links between particular genetic variants and incidence of disease. That genomic data could potentially reveal personal information, so patients can be reluctant to enroll in the studies. In that work, Berger, Cho, and a former Stanford University PhD student developed a protocol based on a cryptography framework called “secret sharing,” which securely and efficiently analyzes datasets of a million genomes. In contrast, existing proposals could handle only a few thousand genomes.</p> <p>Secret sharing is used in multiparty computation, where sensitive data is divided into separate “shares” among multiple servers. Throughout computation, each party will always have only its share of the data, which appears fully random. Collectively, however, the servers can still communicate and perform useful operations on the underlying private data. At the end of the computation, when a result is needed, the parties combine their shares to reveal the result.</p> <p>“We used our previous work as a basis to apply secret sharing to the problem of pharmacological collaboration, but it didn’t work right off the shelf,” Berger says.</p> <p>A key innovation was reducing the computation needed in training and testing. Existing predictive drug-discovery models represent the chemical and protein structures of DTIs as graphs or matrices. These approaches, however, scale quadratically, or squared, with the number of DTIs in the dataset. Basically, processing these representations becomes extremely computationally intensive as the size of the dataset grows. “While that may be fine for working with the raw data, if you try that in secure computation, it’s infeasible,” Hie says.</p> <p>The researchers instead trained a neural network that relies on linear calculations, which scale far more efficiently with the data. “We absolutely needed scalability, because we’re trying to provide a way to pool data together [into] much larger datasets,” Cho says.</p> <p>The researchers trained a neural network on the STITCH dataset, which has 1.5 million DTIs, making it the largest publicly available dataset of its kind. In training, the network encodes each drug compound and protein structure as a simple vector representation. This essentially condenses the complicated structures as 1s and 0s that a computer can easily process. From those vectors, the network then learns the patterns of interactions and noninteractions. Fed new pairs of compounds and protein structures, the network then predicts if they’ll interact.</p> <p>The network also has an architecture optimized for efficiency and security. Each layer of a neural network requires some activation function that determines how to send the information to the next layer. In their network, the researchers used an efficient activation function called a rectified linear unit (ReLU). This function requires only a single, secure numerical comparison of an interaction to determine whether to send (1) or not send (0) the data to the next layer, while also never revealing anything about the actual data. This operation can be more efficient in secure computation compared to more complex functions, so it reduces computational burden while ensuring data privacy.</p> <p>“The reason that’s important is we want to do this within the secret sharing framework … and we don’t want to ramp up the computational overhead,” Berger says. In the end, “no parameters of the model are revealed and all input data — the drugs, targets, and interactions —&nbsp;are kept private.”</p> <p><strong>Finding interactions</strong></p> <p>The researchers pitted their network against several state-of-the-art, plaintext (unencrypted) models on a portion of known DTIs from DrugBank, a popular dataset containing about 2,000 DTIs. In addition to keeping the data private, the researchers’ network outperformed all of the models in prediction accuracy. Only two baseline models could reasonably scale to the STITCH dataset, and the researchers’ model achieved nearly double the accuracy of those models.</p> <p>The researchers also tested drug-target pairs with no listed interactions in STITCH, and found several clinically established drug interactions that weren’t listed in the database but should be. In the paper, the researchers list the top strongest predictions, including: droloxifene and an estrogen receptor, which reached phase III clinical trials as a treatment for breast cancer; and seocalcitol and a vitamin D receptor to treat other cancers. Cho and Hie independently validated the highest-scoring novel interactions via contract research organizations.</p> <p>The work could be "revolutionizing” for predictive drug discovery, says Artemis Hatzigeorgiou, a professor of bioinformatics at the University of Thessaly in Greece. “Having entered the era of big data in pharmacogenetics, it is possible for the first time to retrieve a dataset of this unprecedented big size from patient data. Similar to the learning procedure of a human brain, artificial neural networks need a critical mass of data in order to provide confident decisions,” Hatzigeorgiou says. “Now is possible the use of millions of data to train an artificial neural network toward the identification of unknown drug-target interactions. Under such conditions, it is not a surprise that this trained model outperforms all existing methods on drug discovery.”</p> <p>Next, the researchers are working with partners to establish their collaborative pipeline in a real-world setting. “We are interested in putting together an environment for secure computation, so we can run our secure protocol with real data,” Cho says.</p> MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets while keeping the data private, which could enable broader pooling of sensitive pharmacological data for predictive drug discovery.Image: Hie, Cho, BergerResearch, Cryptography, Privacy, Biology, Data, Health science and technology, Drug development, Machine learning, Computer science and technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Mathematics, Electrical Engineering & Computer Science (eecs), School of Engineering, School of Science, Medicine Computer model offers more control over protein design New approach generates a wider variety of protein sequences optimized to bind to drug targets. Mon, 15 Oct 2018 15:03:46 -0400 Anne Trafton | MIT News Office <p>Designing synthetic proteins that can act as drugs for cancer or other diseases can be a tedious process: It generally involves creating a library of millions of proteins, then screening the library to find proteins that bind the correct target.</p> <p>MIT biologists have now come up with a more refined approach in which they use computer modeling to predict how different protein sequences will interact with the target. This strategy generates a larger number of candidates and also offers greater control over a variety of protein traits, says Amy Keating, a professor of biology, a member of the Koch Institute, and the leader of the research team.</p> <p>“Our method gives you a much bigger playing field where you can select solutions that are very different from one another and are going to have different strengths and liabilities,” she says. “Our hope is that we can provide a broader range of possible solutions to increase the throughput of those initial hits into useful, functional molecules.”</p> <p>In a paper appearing in the <em>Proceedings of the National Academy of Sciences</em> the week of Oct. 15, Keating and her colleagues used this approach to generate several peptides that can target different members of a protein family called Bcl-2, which help to drive cancer growth.</p> <p>Recent PhD recipients Justin Jenson and Vincent Xue are the lead authors of the paper. Other authors are postdoc Tirtha Mandal, former lab technician Lindsey Stretz, and former postdoc Lothar Reich.</p> <p><strong>Modeling interactions</strong></p> <p>Protein drugs, also called biopharmaceuticals, are a rapidly growing class of drugs that hold promise for treating a wide range of diseases. The usual method for identifying such drugs is to screen millions of proteins, either randomly chosen or selected by creating variants of protein sequences already shown to be promising candidates. This involves engineering viruses or yeast to produce each of the proteins, then exposing them to the target to see which ones bind the best.</p> <p>“That is the standard approach: Either completely randomly, or with some prior knowledge, design a library of proteins, and then go fishing in the library to pull out the most promising members,” Keating says.</p> <p>While that method works well, it usually produces proteins that are optimized for only a single trait: how well it binds to the target. It does not allow for any control over other features that could be useful, such as traits that contribute to a protein’s ability to get into cells or its tendency to provoke an immune response.</p> <p>“There’s no obvious way to do that kind of thing — specify a positively charged peptide, for example — using the brute force library screening,” Keating says.</p> <p>Another desirable feature is the ability to identify proteins that bind tightly to their target but not to similar targets, which helps to ensure that drugs do not have unintended side effects. The standard approach does allow researchers to do this, but the experiments become more cumbersome, Keating says.</p> <p>The new strategy involves first creating a computer model that can relate peptide sequences to their binding affinity for the target protein. To create this model, the researchers first chose about 10,000 peptides, each 23 amino acids in length and helical in structure, and tested their binding to three different members of the Bcl-2 family. They intentionally chose some sequences they already knew would bind well, plus others they knew would not, so the model could incorporate data about a range of binding abilities.</p> <p>From this set of data, the model can produce a “landscape” of how each peptide sequence interacts with each target. The researchers can then use the model to predict how other sequences will interact with the targets, and generate peptides that meet the desired criteria.</p> <p>Using this model, the researchers produced 36 peptides that were predicted to tightly bind one family member but not the other two. All of the candidates performed extremely well when the researchers tested them experimentally, so they tried a more difficult problem: identifying proteins that bind to two of the members but not the third. Many of these proteins were also successful.</p> <p>“This approach represents a shift from posing a very specific problem and then designing an experiment to solve it, to investing some work up front to generate this landscape of how sequence is related to function, capturing the landscape in a model, and then being able to explore it at will for multiple properties,” Keating says.</p> <p>Sagar Khare, an associate professor of chemistry and chemical biology at Rutgers University, says the new approach is impressive in its ability to discriminate between closely related protein targets.</p> <p>“Selectivity of drugs is critical for minimizing off-target effects, and often selectivity is very difficult to encode because there are so many similar-looking molecular competitors that will also bind the drug apart from the intended target. This work shows how to encode this selectivity in the design itself,” says Khare, who was not involved in the research. “Applications in the development of therapeutic peptides will almost certainly ensue.”&nbsp;</p> <p><strong>Selective drugs</strong></p> <p>Members of the Bcl-2 protein family play an important role in regulating programmed cell death. Dysregulation of these proteins can inhibit cell death, helping tumors to grow unchecked, so many drug companies have been working on developing drugs that target this protein family. For such drugs to be effective, it may be important for them to target just one of the proteins, because disrupting all of them could cause harmful side effects in healthy cells.</p> <p>“In many cases, cancer cells seem to be using just one or two members of the family to promote cell survival,” Keating says. “In general, it is acknowledged that having a panel of selective agents would be much better than a crude tool that just knocked them all out.”</p> <p>The researchers have filed for patents on the peptides they identified in this study, and they hope that they will be further tested as possible drugs. Keating’s lab is now working on applying this new modeling approach to other protein targets. This kind of modeling could be useful for not only developing potential drugs, but also generating proteins for use in agricultural or energy applications, she says.</p> <p>The research was funded by the National Institute of General Medical Sciences, National Science Foundation Graduate Fellowships, and the National Institutes of Health.</p> Using a computer modeling approach that they developed, MIT biologists identified three different proteins that can bind selectively to each of three similar targets, all members of the Bcl-2 family of proteins.Image: Vincent XueResearch, Drug development, Pharmaceuticals, Biology, Biological engineering, School of Science, School of Engineering, National Science Foundation (NSF), National Institutes of Health (NIH), Koch Institute A new way to manufacture small batches of biopharmaceuticals on demand System can be rapidly reconfigured to produce a variety of protein drugs. Mon, 01 Oct 2018 11:00:00 -0400 Anne Trafton | MIT News Office <p>Biopharmaceuticals, a class of drugs comprising proteins such as antibodies and hormones, represent a fast-growing sector of the pharmaceutical industry. They’re increasingly important for “precision medicine” — drugs tailored toward the genetic or molecular profiles of particular groups of patients.</p> <p>Such drugs are normally manufactured at large facilities dedicated to a single product, using processes that are difficult to reconfigure. This rigidity means that manufacturers tend to focus on drugs needed by many patients, while drugs that could help smaller populations of patients may not be made.</p> <p>To help make more of these drugs available, MIT researchers have developed a new way to rapidly manufacture biopharmaceuticals on demand. Their system can be easily reconfigured to produce different drugs, enabling flexible switching between products as they are needed.</p> <p>“Traditional&nbsp;biomanufacturing relies on unique processes for each new molecule that is produced,” says J. Christopher Love, a professor of chemical engineering at MIT and a member of MIT’s Koch Institute for Integrative Cancer Research. “We’ve demonstrated a single hardware configuration that can produce different recombinant proteins in a fully automated, hands-free manner.”</p> <p>The researchers have used this manufacturing system, which can fit on a lab benchtop, to produce three different biopharmaceuticals, and showed that they are of comparable quality to commercially available versions.</p> <p>Love is the senior author of the study, which appears in the XX issue of the journal <em>Nature Biotechnology</em>. The paper’s lead authors are graduate students Laura Crowell and Amos Lu, and research scientist Kerry Routenberg Love.</p> <p><strong>A streamlined process</strong></p> <p>Biopharmaceuticals, which usually have to be injected, are often used to treat cancer, as well as other diseases including cardiovascular disease and autoimmune disorders. Most of these drugs are produced in “bioreactors” where bacteria, yeast, or mammalian cells churn out large quantities of a single drug. These drugs must be purified before use, so the entire production process can include dozens of steps, many of which require human intervention. As a result, it can take weeks to months to produce a single batch of a drug.</p> <p>The MIT team wanted to come up with a more agile system that could be easily reprogrammed to rapidly produce a variety of different drugs on demand. They also wanted to create a system that would require very little human oversight while maintaining the high quality of protein required for use in patients.</p> <p>“Our goal was to make the entire process automated, so once you set up our system, you press ‘go’ and then you come back a few days later and there’s purified, formulated drug waiting for you,” Crowell says.</p> <p>One key element of the new system is that the researchers used a different type of cell in their bioreactors — a strain of yeast called <em>Pichia pastoris</em>. Yeast can begin producing proteins much faster than mammalian cells, and they can grow to higher population densities. Additionally, <em>Pichia pastoris</em> secretes only about 150 to 200 proteins of its own, compared to about 2,000 for Chinese hamster ovary (CHO) cells, which are often used for biopharmaceutical production. This makes the purification process for drugs produced by <em>Pichia pastoris</em> much simpler.&nbsp;</p> <p>The researchers also greatly reduced the size of the manufacturing system, with the ultimate goal of making it portable. Their system consists of three connected modules: the bioreactor, where yeast produce the desired protein; a purification module, where the drug molecule is separated from other proteins using chromatography; and a module in which the protein drug is suspended in a buffer that preserves it until it reaches the patient.</p> <p>In this study, the researchers used their new technology to produce three different drugs: human growth hormone; interferon alpha 2b, which is used to treat cancer; and granulocyte colony-stimulating factor (GCSF), which is used to boost the immune systems of patients receiving chemotherapy.</p> <p>They found that for all three molecules, the drugs produced with the new process had the same biochemical and biophysical traits as the commercially manufactured versions. The GCSF product behaved comparably to a licensed product from Amgen when tested in animals.</p> <p>Reconfiguring the system to produce a different drug requires simply giving the yeast the genetic sequence for the new protein and replacing certain modules for purification. With colleagues at Rensselaer Polytechnic Institute, the researchers also designed software that helps to come up with a new purification process for each drug they want to produce. Using this approach, they can come up with a new procedure and begin manufacturing a new drug within about three months. In contrast, developing a new industrial manufacturing process can take 18 to 24 months.</p> <p><strong>Decentralized manufacturing</strong></p> <p>The ease with which the system switches between production of different drugs could enable many different applications. For one, it could be useful for producing drugs to treat rare diseases. Currently, such diseases have few treatments available, because it’s not worthwhile for drug companies to devote an entire factory to producing a drug that is not widely needed. With the new MIT technology, small-scale production of such drugs could be easily achieved, and the same machine could be used to produce a wide variety of such drugs.</p> <p>Another potential use is producing small quantities of drugs needed for “precision medicine,” which involves giving patients with cancer or other diseases drugs that are specific to a genetic mutation or other feature of their particular disease. Many of these drugs are also needed in only small quantities.</p> <p>“This paper is an important breakthrough in the possibility to produce and develop biotherapeutics at the point of care, and makes personalized medicine a reality,” says Huub Schellekens, a professor of medical biotechnology at Utrecht University in the Netherlands, who was not involved in the research.</p> <p>These machines could also be deployed to regions of the world that do not have large-scale drug manufacturing facilities.</p> <p>“Instead of centralized manufacturing, you can move to decentralized manufacturing, so you can have a couple of systems in Africa, and then it’s easier to get those drugs to those patients rather than making everything in North America, shipping it there, and trying to keep it cold,” Crowell says.</p> <p>This type of system could also be used to rapidly produce drugs needed to respond to an outbreak such as Ebola.</p> <p>The researchers are now working on making their device more modular and portable, as well as experimenting with producing other therapies, including vaccines. The system could also be deployed to speed up the process of developing and testing new drugs, the researchers say.</p> <p>“You could be prototyping many different molecules because you can really build processes that are simple and fast to deploy. We could be looking in the clinic at a lot of different assets and making decisions about which ones perform the best clinically at an early stage, since we could potentially achieve the quality and quantity necessary for those studies,” Routenberg Love says.</p> <p>The research was funded by the Defense Advanced Research Projects Agency, SPAWAR Systems Center Pacific, and the Koch Institute Support (core) Grant from the National Cancer Institute.</p> MIT chemical engineers have devised a new desktop machine that can be easily reconfigured to manufacture small amounts of different biopharmaceutical drugs.Image: Felice Frankel, Christine Daniloff, MIT Research, Medicine, Chemical engineering, Koch Institute, School of Engineering, Drug development, Pharmaceuticals, National Institutes of Health (NIH), Defense Advanced Research Projects Agency (DARPA) Plug-and-play technology automates chemical synthesis System makes it easier to produce new molecules for myriad applications. Thu, 20 Sep 2018 14:06:30 -0400 Anne Trafton | MIT News Office <p>Designing a new chemical synthesis can be a laborious process with a fair amount of drudgery involved — mixing chemicals, measuring temperatures, analyzing the results, then starting over again if it doesn’t work out.</p> <p>MIT researchers have now developed an automated chemical synthesis system that can take over many of the more tedious aspects of chemical experimentation, freeing up chemists to spend more time on the more analytical and creative aspects of their research.</p> <p>“Our goal was to create an easy-to-use system that would allow scientists to come up with the best conditions for making their molecules of interest — a general chemical synthesis platform with as much flexibility as possible,” says Timothy F. Jamison, head of MIT’s Department of Chemistry and one of the leaders of the research team.</p> <p>This system could cut the amount of time required to optimize a new reaction, from weeks or months down to a single day, the researchers say. They have patented the technology and hope that it will be widely used in both academic and industrial chemistry labs.</p> <p>“When we set out to do this, we wanted it to be something that was generally usable in the lab and not too expensive,” says Klavs F. Jensen, the Warren K. Lewis Professor of Chemical Engineering at MIT, who co-led the research team. “We wanted to develop technology that would make it much easier for chemists to develop new reactions.”</p> <p>Former MIT postdoc Anne-Catherine Bédard and former MIT research associate Andrea Adamo are the lead authors of the paper, which appears in the Sept. 20 online edition of <em>Science</em>.</p> <p><strong>Going with the flow</strong></p> <p>The new system makes use of a type of chemical synthesis known as continuous flow. With this approach, the chemical reagents flow through a series of tubes, and new chemicals can be added at different points. Other processes such as separation can also occur as the chemicals flow through the system.</p> <p>In contrast, traditional “batch chemistry” requires performing each step separately, and human intervention is required to move the reagents along to the next step.</p> <p>A few years ago, Jensen and Jamison developed a continuous flow system that can rapidly produce <a href="">pharmaceuticals on demand</a>. They then turned their attention to smaller-scale systems that could be used in research labs, in hopes of eliminating much of the repetitive manual experimentation needed to develop a new process to synthesize a particular molecule.</p> <p>To achieve that, the team designed a plug-and-play system with several different modules that can be combined to perform different types of synthesis. Each module is about the size of a large cell phone and can be plugged into a port, just as computer components can be connected via USB ports. Some of modules perform specific reactions, such as those catalyzed by light or by a solid catalyst, while others separate out the desired products. In the current system, five of these components can be connected at once.</p> <p>The person using the machine comes up with a plan for how to synthesize a desired molecule and then plugs in the necessary modules. The user then tells the machine what reaction conditions (temperature, concentration of reagents, flow rate, etc.) to start with. For the next day or so, the machine uses a general optimization program to explore different conditions and ultimately to determine which conditions generate the highest yield of the desired product.</p> <p>Meanwhile, instead of manually mixing chemicals together and then isolating and testing the products, the researcher can go off to do something else.</p> <p>“While the optimizations are being performed, the users could be talking to their colleagues about other ideas, they could be working on manuscripts, or they could be analyzing data from previous runs. In other words, doing the more human aspects of research,” Jamison says.</p> <p><strong>Rapid testing</strong></p> <p>In the new study, the researchers created about 50 different organic compounds, and they believe the technology could help scientists more rapidly design and produce compounds that could be tested as potential drugs or other useful products. This system should also make it easier for chemists to reproduce reactions that others have developed, without having to reoptimize every step of the synthesis.</p> <p>“If you have a machine where you just plug in the components, and someone tries to do the same synthesis with a similar machine, they ought to be able to get the same results,” Jensen says.</p> <p>The researchers are now working on a new version of the technology that could take over even more of the design work, including coming up with the order and type of modules to be used.&nbsp;</p> <p>The research was funded by the Defense Advanced Research Projects Agency (DARPA).</p> MIT researchers have developed an automated chemical synthesis system that can take over many of the more tedious aspects of chemical experimentation, freeing up chemists to spend more time on the more analytical and creative aspects of their research.Image: Anne-Catherine Bédard edited by MIT NewsSchool of Science, School of Engineering, Research, Drug development, Pharmaceuticals, Chemistry, Chemical engineering, Defense Advanced Research Projects Agency (DARPA) Sensor could help doctors select effective cancer therapy Hydrogen peroxide-sensing molecule reveals whether chemotherapy drugs are having their intended effects. Tue, 07 Aug 2018 04:59:59 -0400 Anne Trafton | MIT News Office <p>MIT chemical engineers have developed a new sensor that lets them see inside cancer cells and determine whether the cells are responding to a particular type of chemotherapy drug.</p> <p>The sensors, which detect hydrogen peroxide inside human cells, could help researchers identify new cancer drugs that boost levels of hydrogen peroxide, which induces programmed cell death. The sensors could also be adapted to screen individual patients’ tumors to predict whether such drugs would be effective against them.</p> <p>“The same therapy isn’t going to work against all tumors,” says Hadley Sikes, an associate professor of chemical engineering at MIT. “Currently there’s a real dearth of quantitative, chemically specific tools to be able to measure the changes that occur in tumor cells versus normal cells in response to drug treatment.”</p> <p>Sikes is the senior author of the study, which appears in the Aug. 7 issue of <em>Nature Communications</em>. The paper’s first author is graduate student Troy Langford; other authors are former graduate students Beijing Huang and Joseph Lim and graduate student Sun Jin Moon.</p> <p><strong>Tracking hydrogen peroxide</strong></p> <p>Cancer cells often have mutations that cause their metabolism to go awry and produce abnormally high fluxes of hydrogen peroxide. When too much of the molecule is produced, it can damage cells, so cancer cells become highly dependent on antioxidant systems that remove hydrogen peroxide from cells.</p> <p>Drugs that target this vulnerability, which are known as “redox drugs,” can work by either disabling the antioxidant systems or further boosting production of hydrogen peroxide. Many such drugs have entered clinical trials, with mixed results.</p> <p>“One of the problems is that the clinical trials usually find that they work for some patients and they don’t work for other patients,” Sikes says. “We really need tools to be able to do more well-designed trials where we figure out which patients are going to respond to this approach and which aren’t, so more of these drugs can be approved.”</p> <p>To help move toward that goal, Sikes set out to design a sensor that could sensitively detect hydrogen peroxide inside human cells, allowing scientists to measure a cell’s response to such drugs.</p> <p>Existing hydrogen peroxide sensors are based on proteins called transcription factors, taken from microbes and engineered to fluoresce when they react with hydrogen peroxide. Sikes and her colleagues tried to use these in human cells but found that they were not sensitive in the range of hydrogen peroxide they were trying to detect, which led them to seek human proteins that could perform the task.</p> <p>Through studies of the network of human proteins that become oxidized with increasing hydrogen peroxide, the researchers identified an enzyme called peroxiredoxin that dominates most human cells’ reactions with the molecule. One of this enzyme’s many functions is sensing changes in hydrogen peroxide levels.</p> <p>Langford then modified the protein by adding two fluorescent molecules to it — a green fluorescent protein at one end and a red fluorescent protein at the other end. When the sensor reacts with hydrogen peroxide, its shape changes, bringing the two fluorescent proteins closer together. The researchers can detect whether this shift has occurred by shining green light onto the cells: If no hydrogen peroxide has been detected, the glow remains green; if hydrogen peroxide is present, the sensor glows red instead.</p> <p><strong>Predicting success</strong></p> <p>The researchers tested their new sensor in two types of human cancer cells: one set that they knew was susceptible to a redox drug called piperlongumine, and another that they knew was not susceptible. The sensor revealed that hydrogen peroxide levels were unchanged in the resistant cells but went up in the susceptible cells, as the researchers expected.</p> <p>Sikes envisions two major uses for this sensor. One is to screen libraries of existing drugs, or compounds that could potentially be used as drugs, to determine if they have the desired effect of increasing hydrogen peroxide concentration in cancer cells. Another potential use is to screen patients before they receive such drugs, to see if the drugs will be successful against each patient’s tumor. Sikes is now pursuing both of these approaches.</p> <p>“You have to know which cancer drugs work in this way, and then which tumors are going to respond,” she says. “Those are two separate but related problems that both need to be solved for this approach to have practical impact in the clinic.”</p> <p>The research was funded by the Haas Family Fellowship in Chemical Engineering, the National Science Foundation, a Samsung Fellowship, and a Burroughs Wellcome Fund Career Award at the Scientific Interface.</p> Research, Chemical engineering, Cancer, Chemotherapy, Medicine, Drug development, School of Engineering, National Science Foundation (NSF) Chemists synthesize millions of proteins not found in nature New technology could lead to development of novel “xenoprotein” drugs against infectious diseases. Mon, 21 May 2018 15:00:00 -0400 Anne Trafton | MIT News Office <p>MIT chemists have devised a way to rapidly synthesize and screen millions of novel proteins that could be used as drugs against Ebola and other viruses.</p> <p>All proteins produced by living cells are made from the 20 amino acids that are programmed by the genetic code. The MIT team came up with a way to assemble proteins from amino acids not used in nature, including many that are mirror images of natural amino acids.</p> <p>These proteins, which the researchers call “xenoproteins,” offer many advantages over naturally occurring proteins. They are more stable, meaning that unlike most protein drugs, they don’t require refrigeration, and may not provoke an immune response.</p> <p>“There is no other technological platform that can be used to create these xenoproteins because people haven’t worked through the ability to use completely nonnatural sets of amino acids throughout the entire shape of the molecule,” says Brad Pentelute, an MIT associate professor of chemistry and the senior author of the paper, which appears in the <em>Proceedings of the National Academy of Sciences</em> the week of May 21.</p> <p>Zachary Gates, an MIT postdoc, is the lead author of the paper. Timothy Jamison, head of MIT’s Department of Chemistry, and members of his lab also contributed to the paper.</p> <p><strong>Nonnatural proteins</strong></p> <p>Pentelute and Jamison launched this project four years ago, working with the Defense Advanced Research Projects Agency (DARPA), which asked them to come up with a way to create molecules that mimic naturally occurring proteins but are made from nonnatural amino acids.</p> <p>“The mission was to generate discovery platforms that allow you to chemically manufacture large libraries of molecules that don’t exist in nature, and then sift through those libraries for the particular function that you desired,” Pentelute says.</p> <p>For this project, the research team built on technology that Pentelute’s lab had previously developed for <a href="">rapidly synthesizing protein chains</a>. His tabletop machine can perform all of the chemical reactions needed to string together amino acids, synthesizing the desired proteins within minutes.</p> <p>As building blocks for their xenoproteins, the researchers used 16 “mirror-image” amino acids. Amino acids can exist in two different configurations, known as L and D. The L and D versions of a particular amino acid have the same chemical composition but are mirror images of each other. Cells use only L amino acids.</p> <p>The researchers then used synthetic chemistry to assemble tens of millions of proteins, each about 30 amino acids in length, all of the D configuration. These proteins all had a similar folded structure that is based on the shape of a naturally occurring protein known as a trypsin inhibitor.</p> <p>Before this study, no research group had been able to create so many proteins made purely of nonnatural amino acids.</p> <p>“Significant effort has been devoted to development of methods for the incorporation of nonnatural amino acids into protein molecules, but these are generally limited with regard to the number of nonnatural amino acids that can simultaneously be incorporated into a protein molecule,” Gates says.</p> <p>After synthesizing the xenoproteins, the researchers screened them to identify proteins that would bind to an IgG antibody against an influenza virus surface protein. The antibodies were tagged with a fluorescent molecule and then mixed with the xenoproteins. Using a system called fluorescence-activated cell sorting, the researchers were able to isolate xenoproteins that bind to the fluorescent IgG molecule.</p> <p>This screen, which can be done in only a few hours, revealed several xenoproteins that bind to the target. In other experiments, not published in the <em>PNAS</em> paper, the researchers have also identified xenoproteins that bind to anthrax toxin and to a glycoprotein produced by the Ebola virus. This work is in collaboration with John Dye, Spencer Stonier, and Christopher Cote at the U.S. Army Medical Research Institute of Infectious Diseases.</p> <p>“This is an extremely important first step in finding a good way of rapidly screening complex mirror image proteins,” says Stephen Kent, a professor of chemistry at the University of Chicago, who was not involved in the research. “Being able to use chemistry to make a library of mirror image proteins, with their high stability and specificity for a given target, is obviously of potential therapeutic interest.”</p> <p><strong>Built on demand</strong></p> <p>The researchers are now working on synthesizing proteins modeled on different scaffold shapes, and they are searching for xenoproteins that bind to other potential drug targets. Their long-term goal is to use this system to rapidly synthesize and identify proteins that could be used to neutralize any type of emerging infectious disease.</p> <p>“The hope is that we can discover molecules in a rapid manner using this platform, and we can chemically manufacture them on demand. And after we make them, they can be shipped all over the place without refrigeration, for use in the field,” Pentelute says.</p> <p>In addition to potential drugs, the researchers also hope to develop “xenozymes” — xenoproteins that can act as enzymes to catalyze novel types of chemical reactions.</p> <p>The research was funded by DARPA and a STAR Postdoctoral Fellowship from Novo Nordisk.</p> Zachary P. Gates (left) and Brad Pentelute with their ‘Xenoprotein’ etching.Image: Rachael FullerResearch, Chemistry, Biological engineering, School of Science, Disease, Drug development, Defense Advanced Research Projects Agency (DARPA) A single-injection vaccine for the polio virus Nanoparticles could offer a new way to help eradicate the disease worldwide. Mon, 21 May 2018 14:59:59 -0400 Anne Trafton | MIT News Office <p>A new nanoparticle vaccine developed by MIT researchers could assist efforts to eradicate polio worldwide. The vaccine, which delivers multiple doses in just one injection, could make it easier to immunize children in remote regions of Pakistan and other countries where the disease is still found.&nbsp;</p> <p>While the number of reported cases of polio dropped by 99 percent worldwide between 1988 and 2013, according to the Centers for Disease Control, the disease has not been completely eradicated, in part because of the difficulty in reaching children in remote areas to give them the two to four polio vaccine injections required to build up immunity.</p> <p>“Having a one-shot vaccine that can elicit full protection could be very valuable in being able to achieve eradication,” says Ana Jaklenec, a research scientist at MIT’s Koch Institute for Integrative Cancer Research and one of the senior authors of the paper. &nbsp;</p> <p>Robert Langer, the David H. Koch Institute Professor at MIT, is also a senior author of the study, which appears in the <em>Proceedings of the National Academy of Sciences</em> the week of May 21. Stephany Tzeng, a former MIT postdoc who is now a research associate at Johns Hopkins University School of Medicine, is the paper’s lead author.</p> <p>“We are very excited about the approaches and results in this paper, which I hope will someday lead to better vaccines for patients around the world,”&nbsp;Langer says.</p> <p><strong>Global eradication</strong></p> <p>There are no drugs against poliovirus, and in about 1 percent of cases, it enters the nervous system, where it can cause paralysis. The first polio vaccine, also called the Salk vaccine, was developed in the 1950s. This vaccine consists of an inactivated version of the virus, which is usually given as a series of two to four injections, beginning at 2 months of age. In 1961, an oral vaccine was developed, which offers some protection with only one dose but is more effective with two to three doses.</p> <p>The oral vaccine, which consists of a virus that has reduced virulence but is still viable, has been phased out in most countries because in very rare cases, it can mutate to a virulent form and cause infection. It is still used in some developing countries, however, because it is easier to administer the drops than to reach children for multiple injections of the Salk vaccine.</p> <p>For polio eradication efforts to succeed, the oral vaccine must be completely phased out, to eliminate the chance of the virus reactivating in an immunized person. Several years ago, Langer’s lab received funding from the Bill and Melinda Gates Foundation to try to develop an injectable vaccine that could be given just once but carry multiple doses.</p> <p>“The goal is to ensure that everyone globally is immunized,” Jaklenec says. “Children in some of these hard-to-reach developing world locations tend to not get the full series of shots necessary for protection.”</p> <p>To create a single-injection vaccine, the MIT team encapsulated the inactivated polio vaccine in a biodegradable polymer known as PLGA. This polymer can be designed to degrade after a certain period of time, allowing the researchers to control when the vaccine is released.</p> <p>“There’s always a little bit of vaccine that’s left on the surface or very close to the surface of the particle, and as soon as we put it in the body, whatever is at the surface can just diffuse away. That’s the initial burst,” Tzeng says. “Then the particles sit at the injection site and over time, as the polymer degrades, they release the vaccine in bursts at defined time points, based on the degradation rate of the polymer.”</p> <p>The researchers had to overcome one major obstacle that has stymied previous efforts to use PLGA for polio vaccine delivery: The polymer breaks down into byproducts called glycolic acid and lactic acid, and these acids can harm the virus so that it no longer provokes the right kind of antibody response.</p> <p>To prevent this from happening, the MIT team added positively charged polymers to their particles. These polymers act as “proton sponges,” sopping up extra protons and making the environment less acidic, allowing the virus to remain stable in the body.</p> <p>“I think the beauty of&nbsp;the paper is that the traditional microparticle formulation design principles for antigen stabilization are applicable to the delivery of a very complex antigen system. Single dose vaccination for developing world applications is a Holy Grail, and they are getting close,” says David Putnam, a professor of biomedical engineering and chemical and biomolecular engineering at Cornell University who was not involved in the research.</p> <p><strong>Successful immunization</strong></p> <p>In the <em>PNAS</em> study, the researchers designed particles that would deliver an initial burst at the time of injection, followed by a second release about 25 days later. They injected the particles into rats, then sent blood samples from the immunized rats to the Centers for Disease Control for testing. Those studies revealed that the blood samples from rats immunized with the single-injection particle vaccine had an antibody response against poliovirus just as strong as, or stronger than, antibodies from rats that received two injections of Salk polio vaccine.</p> <p>To deliver more than two doses, the researchers say they could design particles that release vaccine at injection and one month later, and mix them with particles that release at injection and two months later, resulting in three overall doses, each a month apart. The polymers that the researchers used in the vaccines are already FDA-approved for use in humans, so they hope to soon be able to test the vaccines in clinical trials.</p> <p>The researchers are also working on applying this approach to create stable, single-injection vaccines for other viruses such as Ebola and HIV.</p> <p>The research was funded by the Bill and Melinda Gates Foundation.</p> MIT researchers developed these polymer microspheres containing polio vaccine that can be released in two separate bursts.Courtesy of the researchersResearch, Chemical engineering, Nanoscience and nanotechnology, Koch Institute, School of Engineering, Drug delivery, Drug development, Developing countries, Medicine, Disease, Vaccines, Microbes Virtual drug trials boost results Bringing drug trials to the virtual realm, Belinda Tan ’96 has found a way to cut entire process in half. Tue, 01 May 2018 13:50:00 -0400 Julie Barr | MIT Alumni Association <p>Bringing a new drug to market can cost more than two billion dollars. Not to mention years of work spent developing and testing for scientists, researchers, doctors, and trial participants. By bringing trials to the virtual realm, Belinda H. Tan ’96 has found a way to cut all that in half.</p> <p>Her company, <a href="">Science 37</a>, is moving the entire drug trial process online, allowing patients anywhere in the U.S. to participate — with ease of access via phone, email, and video chat. Tan and her staff of about 180 provide end-to-end decentralized trial support, including&nbsp;coordinators, project managers, and doctors.</p> <p>Tan, who got her MD/PhD at the University of California at Los Angeles, and her co-founder first got the idea to start their venture in 2014 while working for a dermatology telemedicine company, and a drug company requested their help recruiting more people for their trials. They quickly saw the opportunity to be a one-stop-shop for virtual trials.</p> <p>“There are a lot of inefficiencies with how trials are run when they require a lot of different sites — different lawyers, different contracts, and it all slows things down,” says Tan. “Ultimately our mission is to accelerate biomedical research by putting patients first.”</p> <p>Science 37 is conducting trials in a range of therapeutic areas, including dermatology, neurology, diabetes, psychiatry, and oncology, and patient participation varies based on what is being tested. “When we did a trial for AOBiome testing a topical live bacterial drug, patients took photos of the acne on their face over the course of a three-month treatment period,” says Tan. The photos were taken on a phone given to them for the trial and they sent them through NORA, Science 37’s software platform. Study investigators, who were all dermatologists, evaluated the photos to assess effectiveness of the treatment.</p> <p>Tan says they are using technology to democratize science. By doing drug trials remotely, people without the ability to travel to a trial or who don't have&nbsp;exposure to information about drug trials now have the opportunity to participate.</p> <p>“A lot of science research and drug development is confined to a very small group of institutions and participants, major universities and hospital centers,” says Tan. “We’ve built a technology platform to support doing clinical research so that trials can be centered around patients in their homes. Participants don’t need to drive four hours to a university to go to a trial every other week. They can just stay at home and an investigator, a doctor who is part of the Science 37 team, will take care of that participant during the trial remotely.”</p> <p>While it may seem that not all trials would be conducive to this model, Science 37 has innovative ways to make virtual trials work. For trials that require blood work or other lab tests beyond photos, the company uses a mobile nurse who can travel to participants’ homes and bring back samples to a medical team for evaluation.</p> <p>Not only does the virtual system help streamline the whole process and remove obstacles, it also allows trials to represent a more diverse population, something that is severely lacking in current trials. “Typically in trials in the U.S. today, less than 10 percent of the average trial will be non-white minority,” says Tan. “In our trial, it was about 40 percent, which is really great in many ways. The science is better when you have a more diverse patient population and you have a better indication that your treatment can work for more people.”</p> <p>Science 37 recently completed the first end-to-end entirely virtual drug trial, a huge accomplishment and a benchmark for what can be achieved when this model is applied, Tan says.</p> <p>“Typically, that kind of trial would have required dozens of clinic sites at hospitals and universities in order to recruit almost 400 patients. We did it with our one site in half the time projected by other companies,” she says. “By accelerating trials, it makes them cheaper and also means the drug goes to market faster, patients get better treatment faster, and they have more resources to test different types of treatment.”</p> <p>Although she says there are no other companies doing exactly what they are doing, Tan&nbsp;says she welcomes others to enter the field and even hopes to make their platform software, NORA, available for licensing.</p> <p>“We don’t see ourselves as the sole company doing virtual drug trials,” she says.&nbsp;“We want to enable other interested physicians and scientists to use our tools too.”</p> Belinda H. Tan '96 says virtual drug trials can result in more diverse testing populations and boost non-white participation, which currently averages less than 10 percent.Image courtesy of Belinda TanSchool of Science, Biology, Alumni/ae, Medicine, Pharmaceuticals, Drug development, Startups, Health sciences and technology, Diversity and inclusion Inaugural class of MIT-GSK Gertrude B. Elion Research Fellows selected New fellowship program honoring trailblazing Nobel laureate awards four MIT postdocs focused on drug discovery and development. Tue, 17 Apr 2018 12:00:27 -0400 Erin Edwards | Institute for Medical Engineering and Science <p>Jay Mahat from the Sharp Lab at the Koch Institute for Integrative Cancer Research, Benjamin Mead from the Shalek Lab at the Institute for Medical Engineering and Science, Nicholas Struntz from the Koehler Lab at the Koch Institute, and Sarvesh Varma from the Biological Microtechnology and BioMEMS Group at the Research Laboratory of Electronics have been awarded two-year postdoctoral fellowships through the MIT-GSK Gertrude B. Elion Research Fellowship Program for Drug Discovery and Disease.</p> <p>The fellowship program is a new initiative between MIT and GlaxoSmithKline (GSK) that aims to promote basic research while introducing young scientists to key aspects of pharmaceutical research and development. It honors <a href="" target="_blank">Gertrude Belle Elion</a> (1918-1999), an early leader in the field of chemotherapeutic agents who worked for many years at Burroughs Wellcome, which became Glaxo Wellcome in 1995 and GlaxoSmithKline in 2000. Although Elion never finished a PhD due to her need to work full-time, she eventually received at least 25 honorary doctorate degrees and numerous awards in recognition of her scientific achievements. In 1988, she shared the Nobel Prize in physiology or medicine for the discoveries of important principles for drug treatment in developing compounds to treat conditions such as leukemia, viral and bacterial infections, malaria, and gout. In 1991, she was awarded the National Medal of Science and was the first woman inducted into the National Inventors Hall of Fame, and in 1997, she was awarded the Lemelson-MIT Lifetime Achievement Award for her groundbreaking work in developing therapies for cancer and leukemia.</p> <p>The Gertrude B. Elion Research Fellows are basic or applied scientists and engineers at MIT who are interested in innovative technology and/or platforms that can enable transformative advances in drug discovery. They will receive funding for salary and benefits, lab supplies, and indirect costs for two years to conduct research in the laboratory of a principal investigator at MIT, and they will have ancillary mentorship from a GSK mentor. A critical component of the program will be ongoing communication and exchange of information amongst the fellow, MIT principal investigator, and GSK mentor.</p> <p>The next call for applications for the MIT-GSK Gertrude B. Elion Research Fellowship Program for Drug Discovery and Disease will occur in 2019.</p> Left to right: Jay Mahat, Nicholas Struntz, Benjamin Mead, and Sarvesh Varma are the first recipients of the MIT-GSK Gertrude B. Elion Research Fellowship Program for Drug Discovery and Disease.Photo: Riley DrakeAwards, honors and fellowships, Graduate, postdoctoral, Women in STEM, History of science, Drug discovery, Drug development, Pharmaceuticals, Collaboration, Industry, Health sciences and technology, Koch Institute, Research Laboratory of Electronics, School of Engineering, Institute for Medical Engineering and Science (IMES), School of Science Featured video: Magical Bob A fascination with magic leads Institute Professor Robert Langer to solve world problems using the marvels of chemical engineering. Tue, 27 Mar 2018 09:30:00 -0400 MIT News Office <div class="cms-placeholder-content-video"></div> <p>As a child, Institute Professor Robert S. Langer was captivated by the “magic” of the chemical reactions in a toy chemistry set. Decades later, he continues to be enchanted by the potential of chemical engineering. He is the most cited engineer in the world, and shows no signs of slowing down, despite four decades of ground-breaking work in drug delivery and polymer research.</p> <p>Langer explains, “For me, magic has been discovering and inventing things. Discovering substances that can stop blood vessels from growing in the body, which can ultimately lead to treatments for cancer and blindness.”</p> <p>The Langer Lab has had close to 1,000&nbsp;students and postdocs go through its doors. Hundreds are now professors around the world. Many have started companies.</p> <p>“I’m very proud of all of them,” says Langer. “I hope that I help them a little bit. That’s what we try to do.”</p> <p><em>Submitted by: Melanie Miller Kaufman /</em>&nbsp;<em>Department of Chemical Engineering </em>| <em>Video by: Lillie Paquette / School of Engineering </em>| <em>1 min, 26 sec</em></p> A fascination with magic leads MIT Professor Robert Langer to solve world problems using the marvels of chemical engineering.Photo: Lillie Paquette / MIT School of EngineeringFaculty, Featured video, Chemical engineering, Chemistry, Drug delivery, Bioengineering and biotechnology, Biological engineering, School of Engineering, Drug development “Body on a chip” could improve drug evaluation Human tissue samples linked by microfluidic channels replicate interactions of multiple organs. Wed, 14 Mar 2018 05:59:59 -0400 Anne Trafton | MIT News Office <p>MIT engineers have developed new technology that could be used to evaluate new drugs and detect possible side effects before the drugs are tested in humans. Using a microfluidic platform that connects engineered tissues from up to 10 organs, the researchers can accurately replicate human organ interactions for weeks at a time, allowing them to measure the effects of drugs on different parts of the body.</p> <p>Such a system could reveal, for example, whether a drug that is intended to treat one organ will have adverse effects on another.</p> <p>“Some of these effects are really hard to predict from animal models because the situations that lead to them are idiosyncratic,” says Linda Griffith, the School of Engineering Professor of Teaching Innovation, a professor of biological engineering and mechanical engineering, and one of the senior authors of the study. “With our chip, you can distribute a drug and then look for the effects on other tissues, and measure the exposure and how it is metabolized.”</p> <p>These chips could also be used to evaluate antibody drugs and other immunotherapies, which are difficult to test thoroughly in animals because they are designed to interact with the human immune system.</p> <p>David Trumper, an MIT professor of mechanical engineering, and Murat Cirit, a research scientist in the Department of Biological Engineering, are also senior authors of the paper, which appears in the journal <em>Scientific Reports</em>. The paper’s lead authors are former MIT postdocs Collin Edington and Wen Li Kelly Chen.</p> <p><strong>Modeling organs</strong></p> <p>When developing a new drug, researchers identify drug targets based on what they know about the biology of the disease, and then create compounds that affect those targets. Preclinical testing in animals can offer information about a drug’s safety and effectiveness before human testing begins, but those tests may not reveal potential side effects, Griffith says. Furthermore, drugs that work in animals often fail in human trials.</p> <p>“Animals do not represent people in all the facets that you need to develop drugs and understand disease,” Griffith says. “That is becoming more and more apparent as we look across all kinds of drugs.”</p> <p>Complications can also arise due to variability among individual patients, including their genetic background, environmental influences, lifestyles, and other drugs they may be taking. “A lot of the time you don’t see problems with a drug, particularly something that might be widely prescribed, until it goes on the market,” Griffith says.</p> <p>As part of a project spearheaded by the Defense Advanced Research Projects Agency (DARPA), Griffith and her colleagues decided to pursue a technology that they call a “physiome on a chip,” which they believe could offer a way to model potential drug effects more accurately and rapidly. To achieve this, the researchers needed new equipment — a platform that would allow tissues to grow and interact with each other — as well as engineered tissue that would accurately mimic the functions of human organs.</p> <p>Before this project was launched, no one had succeeded in connecting more than a few different tissue types on a platform. Furthermore, most researchers working on this kind of chip were working with closed microfluidic systems, which allow fluid to flow in and out but do not offer an easy way to manipulate what is happening inside the chip. These systems also require external pumps.</p> <p>The MIT team decided to create an open system, which essentially removes the lid and makes it easier to manipulate the system and remove samples for analysis. Their system, adapted from technology they previously developed and commercialized through U.K.-based CN BioInnovations, also incorporates several on-board pumps that can control the flow of liquid between the “organs,” replicating the circulation of blood, immune cells, and proteins through the human body. The pumps also allow larger engineered tissues, for example tumors within an organ, to be evaluated.</p> <p><strong>Complex interactions</strong></p> <p>The researchers created several versions of their chip, linking up to 10 organ types: liver, lung, gut, endometrium, brain, heart, pancreas, kidney, skin, and skeletal muscle. Each “organ” consists of clusters of 1 million to 2 million cells. These tissues don’t replicate the entire organ, but they do perform many of its important functions. Significantly, most of the tissues come directly from patient samples rather than from cell lines that have been developed for lab use. These so-called “primary cells” are more difficult to work with but offer a more representative model of organ function, Griffith says.</p> <p>Using this system, the researchers showed that they could deliver a drug to the gastrointestinal tissue, mimicking oral ingestion of a drug, and then observe as the drug was transported to other tissues and metabolized. They could measure where the drugs went, the effects of the drugs on different tissues, and how the drugs were broken down. In a related publication, the researchers modeled how drugs can cause unexpected stress on the liver by making the gastrointestinal tract “leaky,” allowing bacteria to enter the bloodstream and produce inflammation in the liver.</p> <p>Kevin Healy, a professor of bioengineering and materials science and engineering at the University of California at Berkeley, says that this kind of system holds great potential for accurate prediction of complex adverse drug reactions.</p> <p>“While microphysiological systems (MPS) featuring single organs can be of great use for both pharmaceutical testing and basic organ-level studies, the huge potential of MPS technology is revealed by connecting multiple organ chips in an integrated system for <em>in vitro</em> pharmacology. This study beautifully illustrates that multi-MPS “physiome-on-a-chip” approaches, which combine the genetic background of human cells with physiologically relevant tissue-to-media volumes, allow accurate prediction of drug pharmacokinetics and drug absorption, distribution, metabolism, and excretion,” says Healy, who was not involved in the research.</p> <p>Griffith believes that the most immediate applications for this technology involve modeling two to four organs. Her lab is now developing a model system for Parkinson’s disease that includes brain, liver, and gastrointestinal tissue, which she plans to use to investigate the hypothesis that bacteria found in the gut can influence the development of Parkinson’s disease.</p> <p>Other applications include modeling tumors that metastasize to other parts of the body, she says.</p> <p>“An advantage of our platform is that we can scale it up or down and accommodate a lot of different configurations,” Griffith says. “I think the field is going to go through a transition where we start to get more information out of a three-organ or four-organ system, and it will start to become cost-competitive because the information you’re getting is so much more valuable.”</p> <p>The research was funded by the U.S. Army Research Office and DARPA.</p> MIT engineers have designed a microfluidic platform that connects engineered tissue from up to 10 organs, allowing them to replicate human-organ interactions.Image: Felice FrankelResearch, Biological engineering, Mechanical engineering, School of Engineering, Health, Medicine, Microfluidics, Drug development New study solves an arthritis drug mystery MIT biological engineers discover why a promising drug failed in clinical trials. Tue, 06 Mar 2018 13:59:59 -0500 Anne Trafton | MIT News Office <p>Pharmaceutical companies once considered a protein called p38 a very attractive target for treating rheumatoid arthritis. Arthritis patients usually have elevated activity of this inflammation-producing protein, and in lab studies p38 inhibitors appeared to soothe inflammation. However, these drugs failed in several clinical trials.</p> <p>A new study from MIT sheds light on just why these drugs did not work for arthritis. By untangling the complex interactions between different cell pathways involved in inflammation, the researchers discovered that shutting off p38 triggers other inflammatory pathways.</p> <p>The findings demonstrate the importance of studying a potential drug’s impact on complex cellular systems, says Doug Lauffenburger, head of MIT’s Department of Biological Engineering and the senior author of the study. It’s also important to do these studies under environmental conditions that match those found in diseased tissue, he adds.</p> <p>“You’ve got to make sure you understand the complexity of the intracellular networks, and beyond that, you need to think about the environment you put the cells in,” Lauffenburger says. “It’s easy to get different results in different contexts, so you need to study them under many different conditions.”</p> <p>Former MIT postdoc Doug Jones is the lead author of the paper, which appears in the March 6 issue of <em>Science Signaling</em>.</p> <p><strong>A promising target</strong></p> <p>Rheumatoid arthritis, which afflicts more than 1 million Americans, is an autoimmune disorder that produces swollen and painful joints, primarily affecting the wrists and hands. This pain results from inflammation in the lining of the joints. Cells called synovial fibroblasts, which typically provide structural support for the joint lining, promote the inflammation and swelling in arthritic conditions.</p> <p>Several years ago, scientists seeking new treatments for arthritis discovered that synovial fibroblasts from arthritis patients had very high levels of p38, and many pharmaceutical companies began working on p38 inhibitors. “The activity of this pathway was so strong that people tended to think that it was the best one to inhibit,” Lauffenburger says.</p> <p>Despite their promise, p38 inhibitors failed in phase II clinical trials run by at least eight pharmaceutical companies. One of those companies, Boehringer Ingelheim, asked Lauffenburger to help them figure out why. Lauffenburger’s lab focuses on systems biology, a field that involves measuring the interactions of many cell components and then performing computational modeling of those measurements to predict cell behavior.</p> <p>The researchers’ analysis revealed that the inflammatory pathway controlled by p38 interacts with several other pathways that can cause inflammation. These pathways, known collectively as stress pathways, produce inflammatory cytokines in response to events such as infection or injury.</p> <p>The MIT team found that when p38 is extremely elevated, it suppresses the activity of these other inflammatory pathways. Therefore, when it gets turned off, the brake on the other pathways is released. Under these circumstances, inflammation remains high — the difference is that now it is controlled by other stress pathways.</p> <p>“This is an insightful paper on redundancy in signaling and the need to understand compensatory mechanisms before spending billions on drug development. In that sense, it is a far more important insight than ‘just’ p38 inhibitors, and it makes clear again that animal efficacy models have severe limitations as tools to predict human efficacy,” says David De Graaf, CEO and president of Syntimmune, who was not involved in the research. “This paper outlines one very thoughtful and generic approach to answer complex questions about efficacy in <em>ex vivo</em> human model systems.”</p> <p><strong>Environment matters</strong></p> <p>Why was the MIT team able to see this phenomenon when others had not? Lauffenburger says one key is the environment in which the synovial fibroblast cells were studied.</p> <p>Normally, cells studied in the lab are grown in a culture medium that offers them nutrients and molecules called growth factors, which keep the cells alive and proliferating. However, the MIT team found that under these conditions, a pro-growth pathway called MEK actually keeps p38 levels lower than in cells under stress. Because p38 is not as high, it doesn’t inhibit the other stress pathways as strongly, so when the cells are exposed to p38 inhibitors, the other pathways don’t soar into action and overall inflammation goes down.</p> <p>“It looks like p38 inhibitors work well, if cells are in these growth factor environments,” Lauffenburger says.</p> <p>However, the MIT team found that synovial fluid from arthritis patients is not a pro-growth environment but is full of inflammatory cytokines. They then decided to expose synovial fibroblasts taken from patients with arthritis and from healthy individuals to this inflammatory environment. In both healthy and diseased cells, p38 levels skyrocketed, producing more inflammation and shutting off other stress pathways.</p> <p>One question still to be answered is whether p38 inhibitors could work against other diseases such as cancer, in which the cells targeted would likely be in a pro-growth environment. They are also being considered as potential treatments for other inflammatory diseases such as multiple sclerosis and Alzheimer’s. Lauffenburger says that their success will likely depend on what kind of environment the affected cells are in.</p> <p>“A p38 inhibitor could work; you just have to know what the context is that the target cells are in. If you have the same kind of inflammatory cytokines there, then you might encounter the same problem” seen in arthritis, he says.</p> <p>It’s also possible that p38 inhibitors could work against arthritis or other diseases if given along with drugs that shut off other stress pathways, but more research would be needed to investigate that possibility, Lauffenburger says.</p> <p>The research was funded by the National Institutes of Health, the Army Research Office, and Boehringer Ingelheim Inc. The project was undertaken in collaboration with Professor Peter Sorger at Harvard Medical School; Brian Joughin at MIT and Anne Jenney at Harvard were also significantly involved in the work.</p> Rheumatoid arthritis, which afflicts more than 1 million Americans, is an autoimmune disorder that produces swollen and painful joints, primarily affecting the wrists and hands.Research, Biological engineering, School of Engineering, National Institutes of Health (NIH), Drug development, Pharmaceuticals, Disease Fine-tuning cancer medicine New cancer research initiative eyes individualized treatment for patients. Thu, 01 Feb 2018 14:45:01 -0500 MIT Center for Precision Cancer Medicine <p>Details matter — perhaps most noticeably in the fight against cancer. Some patients respond to a given anticancer therapy, and some do not. A new initiative at MIT takes aim at those details, and the name of the game is precision.</p> <p>The recently launched MIT Center for Precision Cancer Medicine&nbsp;(CPCM) is housed within MIT’s Koch Institute for Integrative Cancer Research and headed by physician-scientist Michael B. Yaffe, the David H. Koch Professor of Science and professor of biology and biological engineering. The center brings together leading Institute faculty members to focus on key research themes to accelerate the clinical translation of novel cancer discoveries, treatments, and technologies.</p> <p><strong>Engineering approaches to the clinic</strong></p> <p>While other institutions have begun efforts in precision medicine as well, the MIT Center for Precision Cancer Medicine stands out for using engineering approaches to solve complex clinical challenges in cancer treatment that are rooted in biology. In particular, the CPCM combines understandings of biological circuitry — along with engineering, computational, and mathematical techniques (as well as genomic ones) — to focus on signaling networks and pathways that are aberrantly regulated in cancer cells. This strategy is supported by the fact that most state-of-the-art molecularly targeted cancer therapies are focused on these key pathways.</p> <p>At its core, the CPCM is driven by both internal and external collaboration, and is devoted to translational research to help the substantial number of patients who do not respond well to traditional cancer therapies — for example, those with triple-negative breast cancer, ovarian cancer, non-small cell lung cancer, or advanced prostate cancer.</p> <p>To improve outcomes for these patients, CPCM investigators are focused on four key areas of research. First among these is identifying and targeting the processes, signals, and mechanisms that determine an individual patient’s response to chemotherapy. Recent discoveries by CPCM researchers include mechanisms that cancer cells use to repair chemotherapy damage that should have killed them, to hide from drugs in protected “niches” in the body, or to grow when and where they should not.</p> <p>CPCM members are also working on a second research pillar, which involves finding ways to use existing FDA-approved cancer drugs more effectively, particularly in carefully designed combinations. Combination therapies are currently used in the clinic to treat some cancers, yet the discovery process for these has been largely empirical. By contrast, CPCM investigators are integrating their knowledge of cancer biology, understandings of drugs’ mechanisms of action, and sophisticated analytical techniques, to identify or design specific combinations that work synergistically to disarm and then destroy cancer cells.</p> <p>“We believe we can significantly alter cancer patients’ outcomes by determining the right combination of therapies and the right sequence of drugs for the right patients,” says Yaffe. “We’re also concentrating on innovative ways to give these drugs, like time-staggered dosages and nanoparticle delivery.” He notes that, as part of their analyses of drugs and combination regimens currently administered in the clinic, CPCM members expect to identify combinations of drugs that are not as efficacious when given simultaneously as when given sequentially, at specific intervals. Yaffe stresses that these will be important findings that could help reduce the toxicity of treatment by not exposing people to multiple drug toxicities at the same time.</p> <p>In parallel with their efforts to use existing drugs more effectively, CPCM investigators are also working to identify compounds, materials, and approaches that can engage key “undruggable” genetic and molecular targets and disrupt processes driving drug resistance. The “undruggable” label often refers to the fact that a target protein or molecule lacks a site to which drugs can bind, and thus is not considered a good drug target by the pharmaceutical industry. However, using novel chemistry approaches, CPCM researchers have made early inroads against several such high-value cancer targets, including specific transcription factors and RNA-binding proteins. The center will continue and expand these efforts as the third part of its research platform, including collaborations with industry.</p> <p>Finally, the fourth component of the CPCM’s efforts will be harnessing MIT’s particular expertise in big data analysis and tools to begin new and expedite existing cancer research efforts. For example, the researchers plan to use data analytics to identify selective panels of biomarkers that can be used to prioritize which of their drug combinations, treatment protocols, and formulations are best suited to a particular patient’s tumor.</p> <p><strong>Getting discoveries out the door</strong></p> <p>“Patients will be the ultimate beneficiaries of the work of the new MIT Center for Precision Cancer Medicine,” says Tyler Jacks, director of the Koch Institute and the David H. Koch Professor of Biology. “This research is, by its nature, imminently and rapidly translatable. By concentrating efforts on which patients will benefit from particular existing drugs or combinations of drugs, there is a relatively small step from laboratory to a treatment that is benefitting a cancer patient.”</p> <p>While work on combinations of approved therapies, like that at the CPCM, may be more rapidly translatable than other cancer research, it can be challenging for industry to pursue, particularly when those drugs hail from multiple companies. Overcoming this disjuncture is one of the goals behind the establishment of the MIT Center for Precision Cancer Medicine, which was made possible by a generous gift from an anonymous donor.</p> <p>Yaffe and his CPCM colleagues are committed to finding viable routes to move their cancer research into the clinic, particularly through collaborations between CPCM members, hospitals, and industry. Logistically, this means more work for the center’s research groups, including advanced laboratory and preclinical studies, safety and scale-up studies, and clinical-grade manufacturing, as well as staff to carry it out. Woven into these efforts, CPCM investigators will tap into MIT’s celebrated tradition of entrepreneurship and, even more so, the Institute’s expanding network of clinical collaborators. The philanthropic investment behind the center will provide stable financial support for the researchers’ endeavors.</p> <p><strong>The new hub in town</strong></p> <p>In addition to supporting the research of member investigators, the CPCM offers a robust training ground for young engineers and scientists interested in precision medicine. Moreover, it will serve as the hub of precision cancer medicine research at MIT and beyond, connecting with researchers across the MIT campus and partnering with clinical investigators in Greater Boston’s noted health care centers and around the country.</p> <p>Five outstanding cancer researchers make up the center’s founding faculty:</p> <ul> <li><a href="" target="_blank">Michael B. Yaffe</a>, MD, PhD, director, MIT Center for Precision Cancer Medicine; David H. Koch Professor of Science, professor of biology and biological engineering</li> <li><a href="" target="_blank">Michael Hemann</a>, PhD, associate professor of biology</li> <li><a href="" target="_blank">Angela Koehler</a>, PhD, Karl Van Tassel (1925) Career Development Associate Professor, assistant professor of biological engineering</li> <li><a href="" target="_blank">Matthew Vander Heiden</a>, MD, PhD, associate professor of biology, associate director, Koch Institute for Integrative Cancer Research</li> <li><a href="" target="_blank">Forest M. White</a>, PhD, professor of biological engineering</li> </ul> <p>Efforts are currently underway to recruit an assistant director and a scientific advisory board.</p> <p>As part of its charge, and key to spurring the new collaborations in precision cancer medicine that are its focus, the MIT Center for Precision Cancer Medicine will also convene lectures, events, and scientific exchanges and symposia, the first of which is slated for the fall.</p> Five cancer researchers make up the MIT Center for Precision Cancer Medicine’s founding faculty. Left to right: Angela Koehler, Michael Hemann, Michael Yaffe (director), Forest White, and Matthew Vander Heiden.Photo: Kelsey MontgomeryKoch Institute, Medicine, Research, Biology, Biological engineering, Cancer, School of Science, School of Engineering, Innovation and Entrepreneurship (I&E), Health care, Drug delivery, Drug development, Drug resistance, Technology and society, Biomedicine Startup makes labs smarter Platform connects individual pieces of lab equipment, compiles data in the cloud for speedier, more accurate research. Thu, 25 Jan 2018 16:30:00 -0500 Rob Matheson | MIT News Office <p>Although Internet-connected “smart” devices have in recent years penetrated numerous industries and private homes, the technological phenomenon has left the research lab largely untouched. Spreadsheets, individual software programs, and even pens and paper remain standard tools for recording and sharing data in academic and industry labs.</p> <p>TetraScience, co-founded by Spin Wang SM ’15, a graduate of electrical engineering and computer science,&nbsp;has developed a data-integration platform that connects disparate types of lab equipment and software systems, in-house and at outsourced drug developers and manufacturers. It then unites the data from all these sources in the cloud for speedier and more accurate research, cost savings, and other benefits.</p> <p>“Software and hardware systems [in labs] cannot communicate with each other in a consistent way,” says Wang, TetraScience’s chief technology officer, who co-founded the startup with former Harvard University postdocs Salvatore Savo and Alok Tayi. “Data flows through systems in a very fragmented manner and there are a lot of siloed data sets [created] in the life sciences. Humans must manually copy and paste information or write it down on paper, [which] is a lengthy manual process that’s error prone.”</p> <p>TetraScience has developed an Internet of Things (IoT) hub that plugs into most lab equipment, including freezers, ovens, incubators, scales, pH meters, syringe pumps, and autoclaves. The hub can also continuously collect relevant data — such as humidity, temperature, gas concentration and oxygen levels, vibration, light intensity, and mass air flow — and shoot it to TetraScience’s centralized data-integration platform in the cloud. TetraScience also has custom integration methods for more complicated instruments and software.</p> <p>In the cloud dashboard, researchers can monitor equipment in real time and set alerts if any equipment deviates from ideal conditions. Data appears as charts, graphs, percentages, and numbers — somewhat resembling the easily readable Google Analytics dashboard. Equipment can be tracked for usage and efficiency over time to determine if, say, a freezer is slowly warming and compromising samples. Researchers can also comb through scores of archived data, all located in one place.</p> <p>“Our technology is establishing a ‘data highway’ system between different entities, software and hardware, within life sciences labs. We make facilitating data seamless, faster, more accurate, and more efficient,” says Wang, who was named to this year’s <em>Forbes</em> 30 Under 30 list of innovators for his work with TetraScience.</p> <p>More than 70 major pharmaceutical and biotech firms, including many in Cambridge, Massachusetts, use the platform. Numerous labs at MIT and Harvard are users, as well.</p> <p><strong>Pain in the lab</strong></p> <p>For Wang and his TetraScience co-founders, building their smart solution was personal.</p> <p>As a Cornell University undergraduate, Wang worked in the Cornell Semiconducting RF Lab&nbsp;on high-energy physics research. Frustrated by the time and effort required to manually record data, he developed his own system that connected and controlled more than 10 instruments, such as a signal generator, power meter, frequency counter, and power amplifier.</p> <p>Years later, as an MIT master’s student studying microelectromechanical systems, Wang worked on sensing technologies and processing of radio frequency signals under the guidance of Professor Dana Weinstein, now at Purdue University. During his final year, he wound up at the MIT Media Lab, working on a 3-D printing project with Tayi, who had spent his academic career toiling away in materials science, chemistry, and other labs. Tayi and Savo were already conducting market research around potential opportunities for IoT in labs.</p> <p>All three bonded over a shared dislike for data-collecting tools that have remained relatively unchanged in labs for a half-century. “We felt the pain of manually tracking data and not having a consistent interface for all our equipment,” Wang says.</p> <p>This is especially troublesome at scale. Large pharmaceutical or biotechnology firms, for instance, can have several hundreds or thousands of instruments, all with different hardware running on different software. Humans must record data and input it manually into dozens of separate recording systems, which leads to errors. People also must be physically in a lab to control experiments. Smart labs were the new frontier, Wang, Savo, and Tayi agreed.</p> <p>In 2014, the three launched TetraScience to build a platform that connected equipment and pooled data into a single place in the cloud —&nbsp;similar to the one Wang created at Cornell, but more advanced. Back then, they used a slightly modified Raspberry Pi as their “hub,” while they refined their software and hardware.</p> <p>For early-stage startup advice, the startup turned to the Industrial Liaison Program and MIT’s Venture Mentoring Service, and leveraged MIT’s vast alumni network for feedback on their technology and business plan. “We definitely benefited from MIT,” Wang says.</p> <p><strong>Saving time and money</strong></p> <p>An early trial for the platform was with the Media Lab, where researchers used the platform to monitor not equipment, but beehives. The researchers were studying how hives could be implemented into building infrastructure and how design and materials could promote bee health. As bees are sensitive to changes in environment, the researchers needed to constantly monitor temperature and humidity around hives over several months, which would be challenging if done manually.</p> <p>Using TetraScience’s platform, the researchers were captured all the necessary data for their project, without suiting up and approaching all the hives daily — saving “hundreds of hours … and 686 bee stings,” according to the startup. Testing at MIT, Wang says, “helped us gain an understanding of the industry and value proposition.”</p> <p>From there, the TetraScience platform found its way into more biotech companies and into more than 60 percent of the world’s top 20 pharmaceutical companies, according to the startup. Benefits of today’s TetraScience platform include speeding up research, improving compliance, producing better-quality data and, ultimately, saving millions of dollars and countless hours of work, Wang says.</p> <p>Numerous <a href="">case studies</a>, listed on the startup’s webpage, showcase the platform’s efficacy and value at major pharmaceutical firms and cancer research centers, and at Harvard and MIT.</p> <p>For example, in the final stages of approval of a multibillion-dollar drug, a large pharmaceutical firm conducted an accelerated lifetime test, where any prolonged deviation from preset conditions would require restarting the experiment, at the cost of millions of dollars, weeks of unusable data, and delayed commercialization. Within a few weeks of the test’s conclusion, a major deviation in one experiment occurred late at night. Within seconds, according to the study, TetraScience’s platform detected the deviation and alerted scientists, who caught it immediately, stopping any significant damage.</p> <p>The platform also offers benefits for determining equipment efficiency and usage. In a 2017 case study with another pharmaceutical firm, TetraScience monitored 70 pieces of equipment. The startup flagged 23 instruments as “heavily underused.” The firm used that data to reduce service contracts for 14 instruments and sell nine instruments, leading to improved efficiency and hundreds of thousands of dollars in savings that could be put toward more research and development.&nbsp;</p> <p>Although the startup’s focus is on pharmaceutical and biotechnology industries, the platform could also be used in oil and gas, brewing, and food and chemistry industries to see similar benefits. “Those industries all use similar instruments [as life science labs] and produce the same kind of data, such as monitoring the pH of beer, so we will get into those industries in the future,” Wang says.</p> Startups, Innovation and Entrepreneurship (I&E), Alumni/ae, Electrical Engineering & Computer Science (eecs), School of Engineering, Media Lab, School of Architecture and Planning, Sensors, Software, Drug development, Pharmaceuticals, internet of things, Bioengineering and biotechnology, Big data New drug capsule may allow weekly HIV treatment Replacing daily pills with a weekly regimen could help patients stick to their dosing schedule. Tue, 09 Jan 2018 10:59:59 -0500 Anne Trafton | MIT News Office <p>Researchers at MIT and Brigham and Women’s Hospital have developed a capsule that can deliver a week’s worth of HIV drugs in a single dose. This advance could make it much easier for patients to adhere to the strict schedule of dosing required for the drug cocktails used to fight the virus, the researchers say.</p> <p>The new capsule is designed so that patients can take it just once a week, and the drug will release gradually throughout the week. This type of delivery system could not only improve patients’ adherence to their treatment schedule but also be used by people at risk of HIV exposure to help prevent them from becoming infected, the researchers say.</p> <p>“One of the main barriers to treating and preventing HIV is adherence,” says Giovanni Traverso, a research affiliate at MIT’s Koch Institute for Integrative Cancer Research and a gastroenterologist and biomedical engineer at Brigham and Women’s Hospital. “The ability to make doses less frequent stands to improve adherence and make a significant impact at the patient level.”</p> <p>Traverso and Robert Langer, the David H. Koch Institute Professor at MIT, are the senior authors of the study, which appears in the Jan. 9 issue of <em>Nature Communications</em>. MIT postdoc Ameya Kirtane and visiting scholar Omar Abouzid are the lead authors of the paper.</p> <p>Scientists from Lyndra, a company that was launched to develop this technology, also contributed to the study. Lyndra is now working toward performing a clinical trial using this delivery system.</p> <p>“We are all very excited about how this new drug-delivery system can potentially help patients with HIV/AIDS, as well as many other diseases,” Langer says.</p> <div class="cms-placeholder-content-video"></div> <p><strong>“A pillbox in a capsule”</strong></p> <p>Although the overall mortality rate of HIV has dropped significantly since the introduction of antiretroviral therapies in the 1990s, there were 2.1 million new HIV infections and 1.2 million HIV-related deaths in 2015.</p> <p>Several large clinical trials have evaluated whether antiretroviral drugs can prevent HIV infection in healthy populations. These trials have had mixed success, and one major obstacle to preventative treatment is the difficulty in getting people to take the necessary pills every day.</p> <p>The MIT/BWH team believed that a <a href="">drug delivery capsule they developed</a> in 2016 might help to address this problem. Their capsule consists of a star-shaped structure with six arms that can be loaded with drugs, folded inward, and encased in a smooth coating. After the capsule is swallowed, the arms unfold and gradually release their cargo.</p> <p>In a previous study, the researchers found that these capsules could remain in the stomach for up to two weeks, gradually releasing the malaria drug ivermectin. The researchers then set out to adapt the capsule to deliver HIV drugs.</p> <p>In their original version, the entire star shape was made from one polymer that both provides structural support and carries the drug payload. This made it more difficult to design new capsules that would release drugs at varying rates, because any changes to the polymer composition might disrupt the capsule’s structural integrity.</p> <p>To overcome that, the researchers designed a new version in which the backbone of the star structure is still a strong polymer, but each of the six arms can be filled with a different drug-loaded polymer. This makes it easier to design a capsule that releases drugs at different rates.</p> <p>“In a way, it’s like putting a pillbox in a capsule. Now you have chambers for every day of the week on a single capsule,” Traverso says.</p> <p>Tests in pigs showed that the capsules were able to successfully lodge in the stomach and release three different HIV drugs over one week. The capsules are designed so that after all of the drug is released, the capsules disintegrate into smaller components that can pass through the digestive tract.</p> <p>Daniel Kuritzkes, a professor of medicine at Harvard Medical School and the chief of infectious diseases at Brigham and Women’s Hospital, says that with further safety studies and tests of different drug combinations, this approach could provide another tool to help doctors treat HIV infections and prevent new ones.</p> <p>“It’s a very interesting approach and certainly something that’s worth further development, and potentially human trials to see how workable this is,” says Kuritzkes, who was not involved in the research.</p> <p><strong>Preventing infection</strong></p> <p>Working with the Institute for Disease Modeling in Bellevue, Washington, the researchers tried to predict how much impact a weekly drug could have on preventing HIV infections. They calculated that going from a daily dose to a weekly dose could improve the efficacy of HIV preventative treatment by approximately 20 percent. When this figure was incorporated into a computer model of HIV transmission in South Africa, the model showed that 200,000 to 800,000 new infections could be prevented over the next 20 years.</p> <p>“A longer-acting, less invasive oral formulation could be one important part of our future arsenal to stop the HIV/AIDS pandemic,” says Anthony Fauci, director of the National Institute of Allergy and Infectious Disease, which partly funded the research.</p> <p>“Substantial progress has been made to advance antiretroviral therapies, enabling a person living with HIV to achieve a nearly normal lifespan and reducing the risk of acquiring HIV. However, lack of adherence to once-daily therapeutics for infected individuals and pre-exposure prophylaxis (PrEP) for uninfected at-risk people remain a key challenge. New and improved tools for HIV treatment and prevention, along with wider implementation of novel and existing approaches, are needed to end the HIV pandemic as we know it.&nbsp;Studies such as this help us move closer to achieving this goal,” Fauci says.</p> <p>The MIT/BWH team is now working on adapting this technology to other diseases that could benefit from weekly drug dosing. Because of the way that the researchers designed the polymer arms of the capsule, it is fairly easy to swap different drugs in and out, they say.</p> <p>“To put other drugs onto the system is significantly easier because the core system remains the same,” Kirtane says. “All we need to do is change how slowly or how quickly it will be released.”</p> <p>The researchers are also working on capsules that could stay in the body for much longer periods of time.</p> <p>The research was also funded by the Bill and Melinda Gates Foundation, Bill and Melinda Gates through the Global Good Fund, the National Institutes of Health, and the Division of Gastroenterology at Brigham and Women’s Hospital.</p> Researchers at MIT and Brigham and Women’s Hospital have developed a capsule that can deliver a week’s worth of HIV drugs in a single dose. The new capsule is designed so that patients can take it just once a week, and the drug will release gradually throughout the week. Courtesy of the researchers Research, HIV/AIDS, Chemical engineering, Medicine, Drug delivery, Drug development, Koch Institute, School of Engineering, National Institutes of Health (NIH) Drug manufacturing that’s out of this world Continuous-flow chemistry device used for drug production could find use in long-duration space missions. Fri, 05 Jan 2018 00:00:00 -0500 Rob Matheson | MIT News Office <p>Liquid-liquid separation and chemical extraction are key processes in drug manufacturing&nbsp;and many other industries, including oil and gas, fragrances, food, wastewater filtration, and biotechnology.</p> <p>Three years ago, MIT spinout Zaiput Flow Technologies launched a novel continuous-flow liquid-liquid separator that makes those processes faster, easier, and more efficient. Today, nine pharmaceutical giants and a growing number of academic labs and small companies use the separator.</p> <p>Having proved its efficacy on Earth, the separator is now being tested as a tool for manufacturing drugs and synthesizing chemicals in outer space.</p> <p>In 2015, Zaiput won a Galactic Grant from the Center for the Advancement of Science in Space that allows companies to test technologies on the International Space Station (ISS). On Dec. 15, after two years of development and preparation, Zaiput launched its separator in a SpaceX rocket as part of the CRS-13 cargo resupply mission that will last one month.</p> <p>As long-duration space travel and extraterrestrial habitation becomes a potential reality, it’s important to find ways to synthesize chemicals for drugs, food, fuels, and other products in space that may be important for those missions, says Zaiput co-founder and CEO Andrea Adamo SM ’03, who co-invented the separator in the lab of Klavs Jensen, the Warren K. Lewis Professor of Chemical Engineering. Notably, Zaiput’s separator, called SEP-10, separates liquids without the need for gravity, which is a trademark of traditional methods.</p> <p>“When people go on deep space explorations, or maybe to Mars, these are multiyear missions,” Adamo says. “But how do you synthesize chemicals for drugs and other products without gravity? We have that answer. Testing our unit in space will show that what we have done on Earth is fully exportable to space.”</p> <p>Results from the ISS experiments will prove that the device indeed functions in zero-gravity, which is basically impossible to verify on Earth. And, they will help the startup refine the device, Adamo says: “MIT strives for excellence and we inherited that model — we’re still striving for excellence.”</p> <p><strong>Surface forces</strong></p> <p>In traditional liquid-liquid separators, a mixture of two liquids of different densities is fed into a funnel-shaped settling tank. The heavier liquid sinks and can be drained out through a valve, away from the lighter liquid, which stays on top. But the separation process is time-consuming, and some chemicals can decay or become unstable while sitting in the tank.</p> <p>Instead of leveraging gravity, Zaiput’s separator uses surface forces to attract or repel a liquid from a membrane. As an example, consider a nonstick pan: Oil spreads on the pan, but water beads up because it has an affinity to bond with the polymer covering the pan, while oil does not.</p> <p>Zaiput’s separator uses the same principle. A mixture of liquids is pumped through a feed tube and travels to a porous polymer membrane. One liquid is drawn to the surface of the membrane, while the other is repelled. An internal mechanical pressure controller maintains a slight pressure differential between one side of the membrane and the other. This differential is just enough to push the attracted liquid through the porous membrane without pushing the repelled one. The attracted liquid then goes out through one tube, while allowing the repelled liquid to flow out through a separate tube. Flow rates range from 0 to 12 milliliters per minute.</p> <p>“If you want to use this for a continuous operation in a reliable way, you have to carefully control pressure conditions across membranes,” Adamo says. “You want a little bit of pressure, so the chemical goes through, but not too much to push through the unwanted liquid. The internal controller ensures this happens at all times.”</p> <p>Zaiput’s separator also improves chemical extraction, which is different from liquid separation. Imagine working with a mixture of wine and oil. Liquid separation means separating the mixture into individual flows, of wine and oil. Extraction, however, means removing the ethanol chemical from the wine, along with separating the liquids, which is of interest to chemists.</p> <p>For chemical extraction, a “feed” liquid that contains a target chemical for extraction and a “solvent” — which is incapable of mixing with the feed liquid —&nbsp;are combined in a tube that flows toward the separation device. The solvent captures the target chemical from the feed because the chemical is soluble in it; the separation devices then separate two streams, with the solvent containing the target chemical. In the wine-oil example, the ethanol would be removed by the oil solvent.</p> <p>Zaiput units can be equipped with different types of membranes to achieve specific effects, or connected in a series of units.</p> <p>Importantly, Adamo says, Zaiput’s continuous-flow, membrane-based separator allows for separation of emulsions, whereby small droplets of one liquid end up in the other liquid, never fully separating. “We don’t have that issue, because we don’t need to wait for liquids to settle,” Adamo says. “We are the only technology that provides continuous separation, can readily separate emulsions, and is also designed for safety, so if you’re dealing with explosive or toxic substances, you can process them quickly.”</p> <p><strong>Beautifying and scaling up</strong></p> <p>Adamo came to MIT in the early 2000s as a civil engineer. Conducting research at MIT and being exposed to the Institute’s entrepreneurial ecosystem, however, “changed my horizons,” he says. “I wanted to be in a field where I could bring technology to the world through a startup.”</p> <p>Civil engineering had some limits in that regard, so Adamo started experimenting in the fast-moving field of microfluidics, working as a researcher in the lab of Jensen, a pioneer of flow chemistry. Inspired by Jensen’s previous research into surface forces, Adamo began designing a small, membrane-based separation device equipped with a precise pressure controller that maintained exact conditions for separation. This first prototype consisted of two bulky plastic pieces bolted together. “It was really ugly,” Adamo says.</p> <p>But showcasing the prototype to colleagues at MIT, he found that despite its unaesthetic appearance, the device had commercial potential. “The innovation was not just good for the lab, but also for general public,” he says. “I started looking into business propositions.” (So far, the research has also produced <a href="">two</a> <a href="">papers</a> co-authored by Jensen, Adamo, and other MIT researchers in <em>Industrial &amp; Engineering Chemistry</em> <em>Research</em>.)</p> <p>In 2013, Adamo co-founded Zaiput with partner and Harvard University biochemist Jennifer Baltz, now Zaiput’s chief operating officer, with help from MIT’s Venture Mentoring Service and other MIT services.</p> <p>The startup designed a far more appealing product. Growing up in Italy, Adamo says, he was always surrounded by beautiful, colorful scenery and objects. He used that background as inspiration for the separator’s design, turning the prototype into a series of handheld, colorful blocks. Lab units are orange; larger units are purple, gold, or lime green. There is also color coding for different devices that are made of different materials.</p> <p>“Customers visit labs and these devices pop out,” Adamo says. “Function is key, but when you take an object in your hands, it has to feel nice. It has to be pleasing to the eye and, in a commercial sense, distinctive.”</p> <p>Currently, Zaiput is developing a production-scale device with a flow rate of 3,000 milliliters per minute, for larger-scale drug manufacturing. The startup is also hoping to more efficiently tackle very complex chemical extractions. Today, this involves repeating chemical extraction processes multiple times in massive columns, about 100 feet high, to ensure as much of the target chemical has been extracted from a liquid. But Zaiput hopes it can do the same with a small system of combined modular units. Additionally, the startup hopes to bring the device to traditional batch-separation users, notably those who still work with settling tanks.</p> <p>“The next challenges are bigger-scale development, more complex extraction, and reaching out to traditional users to empower them with new technologies,” Adamo says.</p> In the continuous-flow liquid-liquid separator developed by MIT spinout Zaiput Flow Technologies, liquid mix (blue and pink) is pumped through a feed tube to a porous polymer membrane (dotted line). One liquid (pink) is drawn to the surface of the membrane, while the other (blue) is repelled. An internal mechanical pressure controller maintains a slight pressure differential between the two sides of the membrane. This pushes the attracted liquid through the membrane without the repelled one, sending each liquid through separate tubes.Courtesy of Zaiput Flow TechnologiesInnovation and Entrepreneurship (I&E), Startups, Alumni/ae, Chemical engineering, School of Engineering, Chemistry, Drug development, Pharmaceuticals, Manufacturing, Oil and gas, Industry, Food, Water, Agriculture, Biological engineering, Space, astronomy and planetary science Pioneering a health care innovation ecosystem to better serve patients NEWDIGS Initiative at MIT leads multi-stakeholder collaboration to design and pilot a sustainable, patient-centered innovation ecosystem for a target disease. Fri, 15 Dec 2017 08:55:01 -0500 Eric Norman | Center for Biomedical Innovation <p>The MIT Center for Biomedical Innovation on Dec. 12 announced its New Drug Development Paradigms (NEWDIGS) initiative to pilot a next-generation health care innovation ecosystem. This pilot is designed to deliver more value from new medicines to patients at a faster pace, in ways best suited for all parties. Current NEWDIGS collaborative members GlaxoSmithKline, Merck, and Sanofi are providing the $500,000 startup funding, with other corporate and nonprofit members contributing in-kind resources.</p> <p>One component of the NEWDIGS approach is the Learning Ecosystems for Accelerating Patient-Centered and Sustainable Innovation (LEAPS) Project, which focuses on connecting knowledge generation across the silos of drug development and patient care through platform clinical trials linked with a real-world, evidence learning engine — a system for managing and sharing knowledge across stakeholders. The first pilot in LEAPS will leverage Massachusetts as a statewide test bed.</p> <p>“While pharmaceutical research and development is a global enterprise, the value of new medicines is assessed and driven locally. This has always been true in other countries, but is increasingly the case in the U.S.,” says Gigi Hirsch, executive director of the MIT Center for Biomedical Innovation and of the NEWDIGS initiative. “Our goal is to integrate emerging but fragmented innovations in policy, process, and technology into a system that works better for everyone, and especially for patients.”</p> <p>LEAPS will leverage NEWDIGS methods and tools for collaborative systems engineering involving patients, providers, payers, biotechnology and pharmaceutical companies, information technology firms, regulators, payers, public health officials, and academic researchers.</p> <p>“It is critically important that we align priorities in pharmaceutical drug development with unmet public health needs,” says Massachusetts Health and Human Services Secretary Marylou Sudders. “By engaging the entire health care system and its key stakeholders, this pilot project has the potential to serve as a model for person-centered health care and break down barriers that currently exist when linking patients with timely, essential treatments.”</p> <p>The LEAPS project will launch in January 2018. Target diseases under consideration for the pilot are rheumatoid arthritis, Type 2 diabetes, Alzheimer’s disease, and opioid addiction. Objectives, beyond improving patient outcomes, include the following:</p> <ul> <li>Enhancing the value of the growing array of disparate data and evidence from electronic medical records and insurance claims to mobile apps and longitudinal patient and disease registries;</li> <li>Accelerating health care insights from data analytics tools such as artificial intelligence, machine learning, and blockchain technologies; and</li> <li>Establishing community hospitals and clinics as key elements of the broader innovation ecosystem.</li> </ul> <p>“Massachusetts is uniquely suited to serve as the test bed for this pilot project, which offers an exciting opportunity to better serve patients by connecting the unparalleled strengths of the state’s biocluster, world renowned provider systems, and payers, who play an increasingly important role in access to new products,” says Robert K. Coughlin, president and CEO of the Massachusetts Biotechnology Council.</p> <p>“We look forward to building on the work we have done with the NEWDIGS collaborative to design and pilot a next-generation biomedical innovation system in Massachusetts. Done well, we believe this effort can help transform the way new therapies are developed and delivered, and serve as a model to replicate in other states, and for other diseases,” says Susan Shiff, senior vice president and head of the Center for Observational and Real-World Evidence at Merck.</p> <p>Elements of the strategic vision for LEAPS were explored in the Next Wave Forum, hosted by NEWDIGS on Dec. 12-13, in Cambridge, Massachusetts. The event included keynote speakers Janet Woodcock (Food and Drug Administration), Hans-Georg Eichler (European Medicines Agency), Trent Haywood (Blue Cross Blue Shield), Donald Berwick (formerly Centers for Medicare and Medicaid, and Institute for Healthcare Improvement), and MIT’s Alex “Sandy” Pentland, Jonathan Gruber, and Michael Cusumano.</p> <p>Further details on MIT NEWDIGS LEAPS are available at <a href=""></a>.</p> Project leader Gigi Hirsch, executive director of the MIT Center for Biomedical Innovation and NEWDIGS Initiative, announced LEAPS at the Next Wave Forum on Dec. 12. Also pictured, left to right: Krystyn, J. Van Vliet, associate provost at MIT; Kourtney Davis, global head of real world data and analytics at GlaxoSmithKline; Felipe Dolz, vice president of global regulatory science and policy at Sanofi; and Elizabeth J. Cobbs, executive director, HTA strategy at Merck and Co., Inc.Photo: Eric NormanCambridge, Boston and region, Center for Biomedical Innovation, Disease, Health care, Health sciences and technology, Medicine, Pharmaceuticals, Public health, Drug development, Innovation and Entrepreneurship (I&E) Boosting the antibiotic arsenal New strategy could enable existing drugs to kill bacteria that cause chronic infections. Thu, 07 Dec 2017 12:00:00 -0500 Anne Trafton | MIT News Office <p>MIT researchers have discovered a way to make bacteria more vulnerable to a class of antibiotics known as quinolones, which include ciprofloxacin and are often used to treat infections such as <em>Escherichia coli</em> and <em>Staphylococcus aureus</em>.</p> <p>The new strategy overcomes a key limitation of these drugs, which is that they often fail against infections that feature a very high density of bacteria. These include many chronic, difficult-to-treat infections, such as <em>Pseudomonas aeruginosa</em>, often found in the lungs of cystic fibrosis patients, and methicillin-resistant <em>Staphylococcus aureus</em> (MRSA).</p> <p>“Given that the number of new antibiotics being developed is diminishing, we face challenges in treating these infections. So efforts such as this could enable us to expand the efficacy of existing antibiotics,” says James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering and the senior author of the study.</p> <p>Arnaud Gutierrez, a former MIT postdoc, and Saloni Jain, a recent Boston University PhD recipient, are the lead authors of the study, which appears in the Dec. 7 online edition of <em>Molecular Cell</em>.</p> <p><strong>Overcoming bacterial defenses</strong></p> <p>Bacteria that have become tolerant to a drug enter a physiological state that allows them to evade the drug’s action. (This is different from bacterial resistance, which occurs when microbes acquire genetic mutations that protect them from antibiotics.) “Tolerance is not well-understood, and we don’t have the means to circumvent it or overcome it,” Collins says.</p> <p>In a study published in 2011, Collins and his colleagues found that they could increase the ability of antibiotics known as aminoglycosides to kill drug-tolerant bacteria by delivering a type of sugar along with the drug. The sugar helps to boost the metabolism of the bacteria, making it more likely that the microbes will undergo cell death in response to the DNA damage caused by the antibiotic.</p> <p>However, aminoglycosides can have serious side effects, so they are not widely used. In their new study, Collins and his colleagues decided to explore whether they could use a similar approach to boost the effectiveness of quinolones, a class of antibiotics used more often than aminoglycosides. Quinolones work by interfering with bacterial enzymes called topoisomerases, which help with DNA replication and repair.</p> <p>With quinolones, the researchers found that it wasn’t enough to add just sugar; they also had to add a type of molecule known as a terminal electron acceptor. Electron acceptors play an essential role in cellular respiration, the process that bacteria use to extract energy from sugar. In cells, the electron acceptor is usually oxygen, but other molecules, including fumarate, an acidic organic compound that is used as a food additive, can also be used.</p> <p>In tests in high-density bacterial colonies grown in a lab dish, the researchers found that delivering quinolones along with glucose and fumarate could eliminate several types of bacteria, including <em>Pseudomonas aeruginosa</em>, <em>Staphylococcus aureus, </em>and<em> Mycobacterium smegmatis, </em>a close relative of the bacterium that causes tuberculosis.</p> <p>“If you simply add a carbon source like glucose, that’s not enough to enable the quinolone to kill. If you simply add oxygen, or another terminal electron acceptor, that by itself is not enough to cause killing either. But if you combine the two, you can eradicate the tolerant infection,” Collins says.</p> <p><strong>Metabolic state</strong></p> <p>The findings suggest that high-density bacterial infections rapidly consume nutrients and oxygen from their environment, which then provokes them to enter a starvation state that helps them to survive. In this state, they greatly reduce their metabolic activity, which allows them to avoid the cell death pathway that is normally triggered when DNA is damaged by antibiotics.</p> <p>“This finding highlights that the metabolic state of the bug significantly influences how the antibiotic will impact the bug. And, for the antibiotic to be effective as a killing agent, it requires downstream cellular respiration as part of the process,” Collins says.</p> <p>The researchers now hope to test this approach in bacterial infections in animals, and they are also exploring how to best deliver the drug combination for different types of infections. A topical treatment could work well for <em>Staphylococcus aureus</em> infections, while an inhaled version could be used to treat <em>Pseudomonas aeruginosa</em> infections of the lungs, Collins says.</p> <p>Collins also hopes to test this approach with other types of antibiotics, including the class that includes penicillin and ampicillin.</p> <p>“This study encourages work to find new ways to stimulate bacterial respiration and thereby enhance the production of reactive oxygen (or even non-oxygen) species during antibiotic treatment, for better eradication of bacterial pathogens, particularly those having low metabolic activity that may render them tolerant to antimicrobials,” says Karl Drlica, a professor at the Public Health Research Institute at Rutgers New Jersey Medical School, who was not involved in the research.</p> <p>The research was funded by the Defense Threat Reduction Agency, the Broad Institute of MIT and Harvard, and a gift from Anita and Josh Bekenstein.</p> MIT researchers have discovered a way to make bacteria more vulnerable to a class of antibiotics known as quinolones, which include ciprofloxacin and are often used to treat infections such as Escherichia coli and Staphylococcus aureus. Image: Chelsea Turner/MITResearch, Biological engineering, Institute for Medical Engineering and Science (IMES), Bacteria, Microbes, Medicine, Drug development MIT NEWDIGS Design Lab advances work on medical treatment financing strategies Industry leaders, patient advocates, and policymakers collaborate to make groundbreaking new therapies economically sustainable. Thu, 09 Nov 2017 16:00:01 -0500 Eric Norman | Center for Biomedical Innovation <p>The MIT NEW Drug Development ParadIGmS (NEWDIGS) initiative, an international biomedical innovation “think-and-do tank” involving a range of stakeholders in the health care system, hosted a collaborative design lab to advance work on innovative financing models that could make potentially curative — but expensive — therapies accessible to patients and affordable to payers, while ensuring sustainable innovation by industry.</p> <p>The design lab, held October 18-20 at MIT, was the third in an ongoing NEWDIGS project called Financing and Reimbursement of Cures in the U.S. (FoCUS). Eighty-one people, including senior leaders from a number of biotechnology and pharmaceutical companies, as well as patient advocates, public and private payers, clinicians, academic researchers, and venture capitalists, among others, participated.</p> <p>“The FoCUS program’s goal is not to assess value, but rather to develop innovative approaches to finance value,” said Gigi Hirsch, executive director of the MIT Center for Biomedical Innovation, which runs NEWDIGS. “Our current payment systems were not designed for this. We have to find ways to finance therapies with a high, one time cost up front, whose clinical benefits will accrue over time.”</p> <p>This meeting focused on pressure-testing financing models emerging from past sessions’ work. Case studies included a gene therapy and a portfolio of ultra-orphan products — all currently under development in the industry — and a hypothetical product designed from a blending of characteristics of a group of oncology products. These cases were chosen because they represent classes of innovative therapies that, while suggesting dramatic benefit to patients, may be too expensive to succeed in current financing and reimbursement systems. The project teams are working toward real-world pilots designed to validate the financing models, and gain insights important for scalability.</p> <p>The NEWDIGS Design Labs are distinguished by their format, which fosters candid dialogue and exploration of issues within a safe haven environment, under the strict terms of case-specific confidentiality agreements as well as the Chatham House Rule. Participants are encouraged to not just identify problems, but also to propose solutions — and cautioned against making assumptions about other stakeholders’ motivations.</p> <p>“Working together to pressure test solutions is crucial, because we really need to get at stakeholders’ true needs,” said one participant. “We can’t have pilots fail because of knowable but unanticipated constraints on any party’s part.”</p> <p>“We’re hoping to bring a new medicine to market, but we’re concerned the financing system won’t support it,” said a delegate of the biotech company developing one of the case study medicines. “Payers are scratching their heads, too; no single company can solve it. This week we’re hoping for model recommendations that think through the mechanics and critical decisions from each stakeholder’s point of view.”</p> <p>“[The FoCUS project] is interesting on several levels,” said a public health expert working for a national patient advocacy organization. “It’s a chance to interact with people with vastly different backgrounds and roles and I think there's a real chance to potentially influence future public policy with some of the outputs of the process.”&nbsp;</p> <p>Another participant, a program lead in a major pharmaceutical company, underscored the diverse participants’ shared purpose. “I have seen the identification of financing tools based on the willingness of stakeholders to collaborate for the greater good of patients,” she said.</p> <p>“We’re making great progress in FoCUS, and are developing a design toolkit as we go that everyone can use,” said Hirsch. “In fact, the confidence we have in the project to date has inspired us to take steps toward a new project — Financing of Cures Internationally, or FoCI — so that we can address these challenges on a global scale.”</p> Drug development, Health care, Medicine, Systems design, Finance, Insurance, Pharmaceuticals, Center for Biomedical Innovation, Innovation and Entrepreneurship (I&E) MIT research laid groundwork for promising Alzheimer’s-fighting drink Studies by Richard Wurtman have led to development of nutrient mix shown to slow cognitive impairment in early stages of the disease. Mon, 30 Oct 2017 19:30:00 -0400 Rob Matheson | MIT News Office <p>Much of Professor Emeritus Richard Wurtman’s career in MIT’s Department of Brain and Cognitive Sciences revolved around developing new treatments for diseases and conditions by modifying chemicals produced in the brain.</p> <p>Since coming to MIT in 1970, Wurtman,&nbsp;the Cecil H. Green Distinguished Professor Emeritus, and his research group have generated more than 1,000 research articles and 200 patents, laying the groundwork for numerous successful medical products.</p> <p>For example, the 3 million people in the United States who take melatonin as a sleeping aid are using a product that derives from research in Wurtman’s lab. “I’m very interested in using basic knowledge to ameliorate the human condition, to make living better,” says Wurtman, who is also a medical doctor.</p> <p>Now a nutrient mix based on essential research contributions by Wurtman has shown promise in treating the early stages of Alzheimer’s disease, according to a new clinical trial funded by the European Union.</p> <p>In the mid-2000s, Wurtman developed a nutrient cocktail aimed at treating what he considers “the root cause” of Alzheimer’s: loss of brain synapses. The mixture increases production of new synapses and restores connectivity between brain regions, improving memory and other cognitive functions. A French company then combined this research with a multinutrient it was developing along with the <a href="">LipiDiDiet consortium</a> —&nbsp;a European collaboration of 16 universities and research centers — to create a drink, called Souvenaid, for Alzheimer’s patients.</p> <p>Over the years, Souvenaid has been the focus on <a href="">several</a> <a href="">clinical</a> <a href="">trials</a> to validate its efficacy. The mixture is not yet available in the United States, but it is being sold as a “medical food” — a category of regulated and safe foods that are designed for dietary management of diseases — in a number of countries across the globe.</p> <p>In the new clinical trial, published in today’s issue of <em>Lancet Neurology</em>, patients with prodromal Alzheimer’s — the predementia stage of Alzheimer’s with mild symptoms — were given either Souvenaid or a placebo. Compared to people who drank the placebo, patients who drank Souvenaid throughout the trial showed less worsening in everyday cognitive and functional performance and significantly less atrophy of the hippocampus, which is caused early in Alzheimer’s by brain tissue loss.</p> <p>“It feels like science-fiction, where you can take a drink of Souvenaid and you get more synapses … for improved cognitive function,” Wurtman says. “But it works.”</p> <p>The co-authors of the study are from the University of Eastern Finland, Kuopio University Hospital, Karolinska Institutet and Karolinska University Hospital, the University of Masstricht, the VU University Medical Centre, Pentara Corporation, the University of Gothenburg, Sahlgrenska University Hospital, and Saarland University and the LipiDiDiet study group.</p> <p>Other results of the study were mixed. The researchers say larger studies, involving more patients over a longer period of time, are still needed to determine if Souvenaid can actually slow progression of Alzheimer’s.</p> <p><strong>Making Souvenaid</strong></p> <p>Souvenaid’s popularity may be on the rise today, but the product would not be possible without years of MIT research, Wurtman says.</p> <p>In the mid-2000s, Wurtman’s research led him to seek the mechanisms behind the body’s production of phosphatides, a class of lipids that, along with proteins, form biological membranes. Production of these phosphatides, Wurtman discovered, depends on a set of nutrient precursors.</p> <p>Specifically, Wurtman homed in on three naturally occurring dietary compounds: choline, uridine, and the omega-3 fatty acid DHA. Choline is found in meats, nuts, and eggs. Fish, flaxseeds, and certain meats contain omega-3 fatty acids. Uridine is mostly produced in the liver.</p> <p>All those compounds taken simultaneously boost production of phosphatides, encouraging membrane development, which is critical in creating new synapses. Knowing that Alzheimer’s-affected brains continuously lose synapses, Wurtman patented the work through MIT’s Technology Licensing Office in hopes of using some version of the cocktail to treat Alzheimer’s and any disease that leads to loss of synapses.</p> <p>Then, in 2003, Wurtman presented the work at a meeting in Europe. Attending the event was a representative from Nutricia — a unit of Danone, a French company known as Dannon in the United States — which was experienced in making medical foods. Wurtman was invited to the company’s headquarters, where a deal was hashed out to combine Wurtman’s findings with a multinutrient the company was working on to create a new treatment for Alzheimer’s.</p> <p>By 2008, Danone had licensed the patent and Souvenaid was already a product. But Wurtman and several graduate students continued basic research behind Souvenaid, which gave the product a boost. “We were much more able to do the basic research at MIT,” Wurtman says. “As soon as we found something in the research, we’d patent it. We never had the lag time. If you work in entrepreneurship and innovation that lag time could be the downfall of a prospective product.”</p> <p>Among the group’s key discoveries was the finding that Souvenaid boosted the number of structures called dendritic spines, found in brain cells. When spines from one neuron contact another, a synapse is formed.</p> <p>A 2010 <a href="">study</a> detailing those findings in<em> Alzheimer’s and Dementia</em> indicated that Souvenaid improved verbal memory in patients with mild Alzheimer’s. A 2012 <a href="">study</a> published in the <em>Journal of Alzheimer’s Disease</em> confirmed and expanded these findings. Over six months, patients with mild Alzheimer’s were given Souvenaid or a placebo. Patients taking the placebo deteriorated in their verbal-memory performance in the final three months of the study, while the Souvenaid patients continued to improve. Both trials were conducted by Philip Scheltens of the Alzheimer Center of the VU University Medical Centre in Amsterdam.</p> <p><strong>Future of Souvenaid</strong></p> <p>In the new clinical trial by the LipiDiDiet consortium, researchers conducted a 24-month trial, where more than 300 patients with prodromal Alzheimer’s were randomly assigned Souvenaid or a placebo. The patients taking Souvenaid showed about 45 percent less cognitive decline than people taking the placebo, according to a measure known as the clinical dementia rating sum of boxes.</p> <p>But the surprising finding, Wurtman says, is that the patients taking Souvenaid showed a substantial reduction is the loss of hippocampal volume. In early stages of Alzheimer’s, the hippocampus — which plays an important role in memory — shrinks as tissue is destroyed. But rates of deterioration for those taking Souvenaid were about 26 percent lower than the control group.</p> <p>“That’s remarkable,” Wurtman says. “I never would have guessed that something like that could happen. But if you suppress the loss of the hippocampus, it makes sense that you’d have better retention of cognitive function.”</p> <p>The results indicate that Souvenaid may be able to slow or stop full progression of very early Alzheimer’s into full-blown Alzheimer’s, Wurtman says.</p> <p>With this new study, Wurtman has high hopes for Souvenaid. First, he says the findings could encourage more researchers to view synapse restoration as a treatment for Alzheimer’s, which isn’t a popular area of study. Most research today, he says, focuses on reducing the accumulation of amyloid plaques or minimizing damage caused by toxic metabolites in Alzheimer’s-affected brains.</p> <p>“Everyone who writes about Alzheimer’s knows there’s a synapse deficiency, and this impairs connections between brain regions,” he says. “Even if the amyloid or another problem gets solved, one way or another, you’ll have to replace these synapses.”</p> <p>Wurtman also hopes the study will “catalyze the rapid appearance of Souvenaid in the American market” and become a very early treatment for suspected Alzheimer’s patients. Several potential biomarkers are being studied as indicators of early Alzheimer’s. But it’s somewhat useless to detect these biomarkers if nothing can be done about the disease at that point, Wurtman says. With Souvenaid, he says, that can change.</p> <p>“Most people don’t do a biomarker test, because … there’s been nowhere to go from there. Now, it will be possible, I believe, for a doctor to tell a patient that, even though they have early Alzheimer’s, they can take Souvenaid chronically to suppress the development of the disease.”</p> A nutrient mix based in part on research from the lab of MIT Professor Emeritus Richard Wurtman has shown promise in treating the early stages of Alzheimer’s disease. Image: Donna CoveneyResearch, Innovation and Entrepreneurship (I&E), Brain and cognitive sciences, School of Science, Biology, Food, Health, Medicine, Disease, Alzheimer's, Memory, Drug development, Drug delivery One vaccine injection could carry many doses Microparticles created by new 3-D fabrication method could release drugs or vaccines long after injection. Thu, 14 Sep 2017 13:59:59 -0400 Anne Trafton | MIT News Office <p>MIT engineers have invented a new 3-D fabrication method that can generate a novel type of drug-carrying particle that could allow multiple doses of a drug or vaccine to be delivered over an extended time period with just one injection.</p> <p>The new microparticles resemble tiny coffee cups that can be filled with a drug or vaccine and then sealed with a lid. The particles are made of a biocompatible, FDA-approved polymer that can be designed to degrade at specific times, spilling out the contents of the “cup.”</p> <p>“We are very excited about this work because, for the first time, we can create a library of tiny, encased vaccine particles, each programmed to release at a precise, predictable time, so that people could potentially receive a single injection that, in effect, would have multiple boosters already built into it. This could have a significant impact on patients everywhere, especially in the developing world where patient compliance is particularly poor,” says Robert Langer, the David H. Koch Institute Professor at MIT.</p> <p>Langer and Ana Jaklenec, a research scientist at MIT’s Koch Institute for Integrative Cancer Research, are the senior authors of the paper, which appears online in <em>Science</em> on Sept. 14. The paper’s lead authors are postdoc Kevin McHugh and former postdoc Thanh D. Nguyen, now an assistant professor of mechanical engineering at the University of Connecticut.</p> <p><img alt="" src="/sites/" style="width: 595px; height: 446px;" /></p> <p><span style="font-size:10px;"><em>After the lids are deposited onto the cups, the particles are heated slightly to form a tight seal between the lids and cups. (Courtesy of the Langer lab)</em></span></p> <p><strong>Sealed cups</strong></p> <p>Langer’s lab began working on the new drug delivery particles as part of a project funded by the Bill and Melinda Gates Foundation, which was seeking a way to deliver multiple doses of a vaccine over a specified period of time with just one injection. That could allow babies in developing nations, who might not see a doctor very often, to get one injection after birth that would deliver all of the vaccines they would need during the first one or two years of life.</p> <p>Langer has previously developed polymer particles with drugs embedded throughout the particle, allowing them to be gradually released over time. However, for this project, the researchers wanted to come up with a way to deliver short bursts of a drug at specific time intervals, to mimic the way a series of vaccines would be given.</p> <p>To achieve their goal, they set out to develop a sealable polymer cup made from PLGA, a biocompatible polymer that has already been approved for use in medical devices such as implants, sutures, and prosthetic devices. PLGA can also be designed to degrade at different rates, allowing for the fabrication of multiple particles that release their contents at different times.</p> <p>Conventional 3-D printing techniques proved unsuitable for the material and size that the researchers wanted, so they had to invent a new way to fabricate the cups, drawing inspiration from computer chip manufacturing.</p> <p>Using photolithography, they created silicon molds for the cups and the lids. Large arrays of about 2,000 molds are fit onto a glass slide, and these molds are used to shape the PLGA cups (cubes with edge lengths of a few hundred microns) and lids. Once the array of polymer cups has been formed, the researchers employed a custom-built, automated dispensing system to fill each cup with a drug or vaccine. After the cups are filled, the lids are aligned and lowered onto each cup, and the system is heated slightly until the cup and lid fuse together, sealing the drug inside.</p> <p>“Each layer is first fabricated on its own, and then they’re assembled together,” Jaklenec says. “Part of the novelty is really in how we align and seal the layers. In doing so we developed a new method that can make structures which current 3-D printing methods cannot. This new method called SEAL (StampEd Assembly of polymer Layers) can be used with any thermoplastic material and allows for fabrication of microstructures with complex geometries that could have broad applications, including injectable pulsatile drug delivery, pH sensors, and 3-D microfluidic devices.”</p> <p>Leon Bellan, an assistant professor of mechanical engineering and biomedical engineering at Vanderbilt University, says the approach offers an impressive level of control for constructing 3-D microparticles.</p> <p>“It’s a new take on a 3-D printing process, and an elegant solution to building macroscopic 3-D structures out of materials that are relevant for biomedical applications,” says Bellan, who was not involved in the research.</p> <p><img alt="An automated dispensing system can be used to load drugs into the 3-D microparticles. Courtesy of the Langer lab " src="" style="width: 594px; height: 396px;" /></p> <p><em><span style="font-size:10px;">An automated dispensing system can be used to load drugs into the 3-D microparticles. (Courtesy of the Langer lab)</span></em></p> <p><strong>Long-term delivery</strong></p> <p>The molecular weight of the PLGA polymer and the structure of the polymer molecules’ “backbone” determine how fast the particles will degrade after injection. The degradation rate determines when the drug will be released. By injecting many particles that degrade at different rates, the researchers can generate a strong burst of drug or vaccine at predetermined time points. “In the developing world, that might be the difference between not getting vaccinated and receiving all of your vaccines in one shot,” McHugh says.</p> <p>In mice, the researchers showed that particles release in sharp bursts, without prior leakage, at 9, 20, and 41 days after injection. They then tested particles filled with ovalbumin, a protein found in egg whites that is commonly used to experimentally stimulate an immune response. Using a combination of particles that released ovalbumin at 9 and 41 days after injection, they found that a single injection of these particles was able to induce a strong immune response that was comparable to that provoked by two conventional injections with double the dose.</p> <p>The researchers have also designed particles that can degrade and release hundreds of days after injection. One challenge to developing long-term vaccines based on such particles, the researchers say, is making sure that the encapsulated drug or vaccine remains stable at body temperature for a long period before being released. They are now testing these delivery particles with a variety of drugs, including existing vaccines, such as inactivated polio vaccine, and new vaccines still in development. They are also working on strategies to stabilize the vaccines. &nbsp;</p> <p>“The SEAL technique could provide a new platform that can create nearly any tiny, fillable&nbsp;object with nearly any material, which could provide unprecedented opportunities in manufacturing in medicine and other areas,” Langer says. These particles could also be useful for delivering drugs that have to be given on a regular basis, such as allergy shots, to minimize the number of injections.</p> <p>Other authors on the paper are Allison Linehan, David Yang, Adam Behrens, Sviatlana Rose, Zachary Tochka, Stephanie Tzeng, James Norman, Aaron Anselmo, Xian Xu, Stephanie Tomasic, Matthew Taylor, Jennifer Lu, and Rohiverth Guarecuco.</p> “We are very excited about this work because, for the first time, we can create a library of tiny, encased vaccine particles, each programmed to release at a precise, predictable time,” says professor Robert Langer. Courtesy of the Langer lab Research, Chemical engineering, Koch Institute, School of Engineering, Drug delivery, Medicine, Drug development, Developing countries Fikile Brushett and Florence Wagner named to Chemical and Engineering News “Talented 12” MIT affiliates recognized for their innovative approaches to energy storage and drug discovery. Fri, 01 Sep 2017 14:00:01 -0400 Melanie Miller Kaufman | Department of Chemical Engineering <p>Professor Fikile Brushett of the MIT Department of Chemical Engineering and Florence Wagner, institute scientist at the Broad Institute of MIT and Harvard, have been selected as two of 2017's “Talented 12” by <em>Chemical and Engineering News (C&amp;EN),</em> the weekly magazine of the American Chemical Society. Brushett is recognized for his innovative approach to economical and sustainable energy storage and the magazine calls him the “<a href="" target="_blank">Baron of Batteries</a>.” Wagner is the “<a href="" target="_blank">Drug Discovery Dynamo</a>,” as her work in targeted psychiatric therapies has shown potential to upend the field of psychiatric drug discovery.</p> <p>Brushett, the Raymond A. (1921) and Helen E. St. Laurent Career Development Professor of Chemical Engineering, is developing new ways of storing energy from sustainable sources such as wind and sunlight. He is particularly interested in understanding and controlling the fundamental processes that define the performance, cost, and lifetime of present day and next-generation electrochemical systems. His laboratory is presently pursuing research on redox flow batteries for grid storage and on electrochemical upgrading of low-value feedstocks. <a href="" target="_blank">As described by </a><em><a href="" target="_blank">C&amp;EN</a>,</em> “a major focus of his lab is understanding how chemical structure affects the function of redox active molecules, with the goal of expanding the toolbox for engineering batteries. In addition, his lab is developing new electrochemical reactors to improve battery performance.”</p> <p>Wagner, director of the medicinal chemistry group in the Broad’s Stanley Center for Psychiatric Research, focuses on designing and implementing strategies that will enable development of novel therapeutic strategies for central nervous system-related psychiatric disorders, such as schizophrenia, bipolar disorder, autism, and neurodevelopmental disorders. These strategies include the rational design and development of novel, potent, and highly selective small molecules suitable for clinical development and the development of translatable biomarkers. <a href="" target="_blank"><em>C&amp;EN</em> explains</a>, “Recently, Wagner and her colleagues developed molecules that can selectively inhibit each of the two forms of an enzyme called glycogen synthase kinase 3 (GSK3), a possible target of the bipolar disorder treatment lithium. Previous inhibitors out of industry hit both forms of GSK3 and caused serious side effects in human studies. Wagner and her colleagues showed that selectively inhibiting either of the two forms avoided that toxicity in cells.”</p> <p>To find its annual Talented 12, <em>C&amp;EN</em> called on a panel of industry advisers, <em>C&amp;EN’s </em>advisory board, and Talented 12 alumni to nominate prospects aged 42 or younger who are pushing the boundaries in their fields. They also accepted nominations from readers through an online form. Finally, they researched and evaluated the more than 150 candidates amassed during this process to zero in on the 12 most "path-paving" individuals.</p> Images courtesy of Chemical and Engineering News.Awards, honors and fellowships, Faculty, Staff, Chemical engineering, Chemistry, Energy, Broad Institute, School of Engineering, Psychiatric disorders, Drug development, Batteries New way to test antibiotics could lead to better drugs Study finds bacterial response to drugs varies in different environments. Thu, 31 Aug 2017 12:00:00 -0400 Anne Trafton | MIT News Office <p>MIT and Harvard University researchers have engineered <em>E. coli</em> cells that can be used to study how bacteria at an infection site respond to antibiotic treatment, allowing scientists to learn more about how existing antibiotics work and potentially help them to develop new drugs.</p> <p>In the new study, which appears in the Aug. 31 issue of <em>Cell Host and Microbe</em>, the researchers found evidence that some existing hypotheses about how bacteria respond to antibiotics are not correct.</p> <p>“Our study shows that using engineered organisms can give you a window into infection sites and expand our understanding of what antibiotics are actually doing. This work indicates that some of our assumptions may be wrong,” says James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering and the senior author of the study.</p> <p>The paper’s lead author is Laura Certain, a clinical fellow at Harvard’s Wyss Institute for Biologically Inspired Engineering.</p> <p><strong>Engineering bacteria</strong></p> <p>Much of the research in Collins’ lab focuses on trying to understand how antibiotics work, in hopes of designing more effective drugs. For the new study, Collins wanted to apply synthetic biology — the construction of novel genetic circuits in living cells — to design bacteria that could be used to study antibiotics and infection.</p> <p>Most studies of how antibiotics work are done with bacterial cells grown in a lab dish. However, Collins and Certain suspected that these drugs’ effects could be different in living animals because that environment, including available nutrients and other conditions, is very different from a lab dish.</p> <p>To allow them to study antibiotics under more realistic conditions, the researchers engineered a strain of <em>E. coli</em> bacteria that expresses a genetic “toggle switch” that flips only under certain conditions. Such switches can be incorporated into bacteria to allow them to record events such as exposure to a chemical.</p> <p>In this case, the researchers designed the bacteria to reveal whether they were dividing or not, allowing them to explore how antibiotics affect cells in either state. Previous studies done in bacteria grown in a lab dish have found that most antibiotics work better on cells that are dividing, while non-replicating cells are much harder to kill.</p> <p>The researchers delivered the bacteria to mice along with a small orthopedic implant, to mimic the infections that often occur at the sites of medical implants. The mice were then treated with the antibiotic levofloxacin. Before and after treatment, the cells were removed and treated with ATC, a molecule that turns on the toggle switch, but only in cells that are replicating.</p> <p>Scientists have hypothesized from previous studies that chronic infections usually consist largely of non-dividing bacteria. However, in this study, the researchers found that before antibiotic treatment, about half of the bacteria were still actively dividing.</p> <p>They also found that levofloxacin appeared to be highly and perhaps even more effective against non-dividing cells, contrary to what has been seen in cells grown in a dish. The researchers noted that the percentage of replicating cells increased after treatment, suggesting that levofloxacin did not kill all of the replicating cells.</p> <p>Another surprising finding contradicted scientists’ hypothesis that chronic persistent infections consisting of non-dividing bacteria are highly tolerant to antibiotics: They found the infections were still susceptible to antibiotics, when given at large enough doses.</p> <p><strong>More to learn</strong></p> <p>Collins says this study demonstrates that there is much more for scientists to learn about how antibiotics work, and suggests that engineered organisms could be useful for further investigating their effects.</p> <p>“This is going to challenge people to rethink what antibiotics are doing at an infection site,” Collins says. “I think that eventually these synthetic biology tools could also be quite useful in antibiotic development, to see whether the antibiotics are getting to the pathogens of interest, how effective they are, and what they are actually doing at the site.”</p> <p>He adds that the genetic toggle switch could be easily transferred to other types of bacteria, and could also be designed to test for other features such as how bacteria interact with immune cells at an infection site. This approach could also be used to study biofilms — sticky sheets of bacterial cells that can be very difficult to remove — and other pathogens such as fungi.</p> <p>The research was funded by the Paul G. Allen Frontiers Group, the Defense Threat Reduction Agency, and the Wyss Institute.</p> A new method allows scientists to learn more about how existing antibiotics work and potentially help them to develop new drugs. Image: MIT NewsResearch, Drug development, Biological engineering, Synthetic biology, Microbes, Disease, Institute for Medical Engineering and Science (IMES), School of Engineering How cytoplasm “feels” to a cell’s components In study that may guide drug design, researchers find organelles encounter varying levels of resistance. Mon, 21 Aug 2017 15:00:00 -0400 Jennifer Chu | MIT News Office <p>Under a microscope, a cell’s cytoplasm can resemble a tiny underwater version of New York’s Times Square: Thousands of proteins swarm through a cytoplasm’s watery environment, coming together and breaking apart like a cytoskeletal flash mob.</p> <p>Organelles such as mitochondria and lysosomes must traverse this crowded, ever-changing cytoplasmic space to deliver materials to various parts of a cell.</p> <p>Now engineers at MIT have found that these organelles and other intracellular components may experience the surrounding cytoplasm as very different environments as they travel. For instance, a cell’s nucleus may “feel” the cytoplasm as a fluid, honey-like material, while mitochondria may experience it more like toothpaste.</p> <p>The team, led by Ming Guo, the Brit and Alex d'Arbeloff Career Development Assistant Professor in MIT’s Department of Mechanical Engineering, found that an organelle feels a certain resistance in cytoplasm, depending on that organelle’s size and the speed at which it moves through a cell. In particular, these characteristics determine how easily it can push against a cytoplasm’s surrounding water and move through its ever-changing web of cytoskeletal protein structures.</p> <p>Certain organelles may have to work harder to make their way through cytoplasm, and may therefore feel more resistance. The researchers found that the resistance that any major organelle may feel ranges from that of a viscous fluid to an elastic, rubbery solid.</p> <p>Guo and his colleagues have drawn up a phase diagram to describe the type of material that a cytoplasm would resemble, from the perspective of an organelle, given the organelle’s size and speed.</p> <p>“Our main goal was to provide the most fundamental understanding of living cells as a material,” Guo says. “With this phase diagram, as long as you tell me the size and speed at which an organelle moves, I can tell you what mechanical environment it sees.”</p> <p>The results, published this week in the <em>Proceedings of the National Academy of Sciences</em>, may help guide pharmaceutical designs. For instance, with the team’s phase diagram, scientists can tailor a drug’s size to enable it to travel within a cell with a certain amount of ease.</p> <p>“A drug with a 100-nanometer diameter will feel a very different resistance than something that is 500 nanometers wide,” Guo says. “This can be a guide to understanding how a drug is delivered and transported inside a cell.”</p> <p>The study’s lead author is Jiliang Hu, a former visiting student at MIT, who will join Guo’s lab as a graduate student this fall. Other co-authors include Yulong Han, a postdoc in Guo’s lab; and Alan Grodzinsky, professor of biological engineering, electrical engineering and computer science, and mechanical engineering at MIT; along with Somaye Jafari and Shengqiang Cai at the University of California at San Diego.</p> <p><strong>What a drag</strong></p> <p>Most scientists who study the transport of materials within a cell have focused on the drivers of that transport — namely, molecular motors, a family of biological agents that actively convert a cell’s energy into mechanical work to move cargo across a cell.</p> <p>“But as mechanical engineers, we think the driving force is not the only part of this transport process, but that resistance of the surrounding material is actually equally important,” Guo says. “For example, it’s not just your own energy that determines how you move through a crowd — the mechanical resistance of the crowd itself can also affect your movement.”</p> <p>In the case of living cells, Guo wondered whether the surrounding cytoplasm would have a similar crowding effect on the movement of major organelles such as mitochondria and lysosomes.</p> <p>To test his hypothesis, he and his colleagues carried out experiments on living mammalian cells, into which they injected tiny plastic beads ranging in size from 0.5 to 1.5 microns — a range that covers most major organelles. They then dragged each bead across a cell using optical tweezers, a technique that employs a highly focused laser beam to physically move microscopic objects.</p> <p>The researchers trapped and pulled each bead toward the cell edge at a constant speed and measured the force required to drag the bead a certain distance. They interpreted that force as the mechanical resistance of the surrounding cytoplasm.</p> <p>They then assumed that a cytoplasm’s mechanical resistance stems from two main sources: poroelasticity and viscoelasticity. Poroelasticity originates from how fast cytoplasm can diffuse water out of a region. The group reasoned that the more poroelastic cytoplasm is, the more effort an object such as an organelle needs to make to push water out of its way.</p> <p>Viscoelasticity, in the context of cytoplasm, is how fast its cytoskeleton, or web of proteins, changes configuration. A cell’s cytoskeleton serves as a sort of scaffold, made from thousands of proteins that are constantly assembling, disassembling, and reassembling. This dynamic network can feel like both an elastic solid and a viscous fluid. The faster a cytoskeleton rearranges itself, the more fluid-like it is. The researchers reasoned that an organelle would feel less resistance while moving through a more fluid-like, frequently changing cytoskeleton.</p> <p><strong>It’s all about perspective</strong></p> <p>Guo and his colleagues analyzed their experimental results and found that a bead’s size and speed were related to the type of resistance that it encountered as it was dragged across a cell. In general, the larger the beads, the more they met with poroelastic resistance, as large beads with greater surface area have to push against more water to move themselves through.</p> <p>On the other hand, the faster a bead was dragged, the more it met with a solid-like resistance. As Guo explains it, “the faster you move, the more permanent [cytoskeletal] structures you would see and feel resistance to.”</p> <p>The researchers drew up their phase diagram based on their experimental results. They then looked through the scientific literature for speed and size measurements, made by others, of actual organelles in living cells. They plotted these measurements onto the diagram and found that, given their size and speed, these organelles should experience a range of resistances within cytoplasm.</p> <p>“If you ask a nucleus, they would tell you the cytoplasm is like honey, because they are really large and slow, and they don’t feel cytoskeletal structures — they only feel the viscous disassembled protein solution, and have very small resistance,” Guo says. “But mitochondria would say it’s like toothpaste, because they are smaller and faster, and are sometimes blocked by these constantly changing structures. A lysosome, which is even smaller and faster, would tell you the cytoplasm is actually Jell-O, because they are moving so fast, they are constantly bouncing off these structures and meeting with resistance, like rubber. So their views are limited by their own size and speed.”</p> <p>Guo hopes scientists will use the group’s phase diagram to characterize other cellular components, to understand how they see their cytoplasmic surroundings.</p> <p>“People can use other parameters to find out what section of the phase diagram different organelles should belong to,” Guo says. “This will tell you what kind of distinct material they would feel.”</p> A phase diagram of living mammalian cytoplasm, developed by MIT researchers, describes the type of material an organelle feels as it moves through cytoplasm, given its size and speed. Image courtesy of the researchersBiology, Cells, Drug delivery, Drug development, Mechanical engineering, Research, Materials Science and Engineering, School of Engineering Ram Sasisekharan wins Agilent Thought Leader Award Honor recognizes bioengineer’s advance of analytical techniques for characterization of biopharmaceuticals. Tue, 18 Jul 2017 13:25:01 -0400 School of Engineering <p>MIT Professor Ram Sasisekharan has received an Agilent Thought Leader Award in recognition of his contributions in the field of biologics characterization.</p> <p>Sasisekharan, the Alfred H. Caspary Professor of Biological Engineering and Health Sciences and Technology in the Department of Biological Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research, employs multidisciplinary strategies to develop and integrate technologies to further study complex polysaccharides important to a host of disease processes. A primary goal of his lab is to develop novel therapeutic approaches to alleviate suffering from disease and promote overall human health.</p> <p>Comprised of financial support, Agilent products, and expertise, the Agilent Thought Leader Award will help Sasisekharan’s lab further refine an approach that aims to shorten the development time between biopharmaceutical product design and clinical use. This approach represents a new paradigm, characterizing and optimizing both the product and the process in a highly rich, parallel, and multilayered fashion.</p> <p>“I am gratified by this honor,” Sasisekharan says. “Having secure, reliable funding is ever more important, and we are pleased that Agilent has stepped up to support our ongoing work in the area of biopharmaceutical characterization and development."</p> <p>Specifically, Sasisekharan and his team will focus on the advancement of analytical techniques for monoclonal antibody (mAb) characterization to explore the utilization of critical quality attribute measurements, earlier in the clone selection and drug development process, as a strategy to bring biologics to market faster. The efficient development of mAbs, against novel cancer targets and emerging pathogens (such as pandemic influenza, Ebola virus, Zika virus, and multi-drug resistant bacteria) is becoming ever more critical, as the typical timeframe to develop these biopharmaceuticals from discovery to human clinical trials can be up to several years.</p> <p>“We are very excited to be working with Dr. Sasisekharan and the entire team at MIT on these innovative methods and approaches. Fundamentally improving the biopharmaceutical development process and timelines will ultimately result in more effective therapies being available sooner,” says Todd Christian, general manager of Agilent’s Cell Analysis Division, and executive sponsor of this award.</p> <p>The Agilent Thought Leader Award promotes fundamental scientific advances by contributing financial support, products, and expertise to the research of influential thought leaders in the life sciences, diagnostics, and applied chemical markets. Information about previous award recipients is available on the Agilent Thought Leader Award <a href="" target="_blank">web page</a>.</p> MIT Professor Ram SasisekharanPhoto: Bryce VickmarkAwards, honors and fellowships, Funding, Grants, Faculty, Biological engineering, Drug development, Medicine, Health sciences and technology, Industry, Disease, School of Engineering STEX event showcases innovations in fitness technology and science Entrepreneurs, researchers, and industry experts build connections at workshop. Mon, 26 Jun 2017 16:00:00 -0400 Rob Matheson | MIT News Office <p>Many MIT-affiliated startups are innovating in the burgeoning fitness technology and science space, aiming to promote healthier lifestyles and help optimize athletic performance.</p> <p>Novel products from these startups include a smart chair that fights back pain and diabetes, a sleeve that monitors muscle-movement data that users can share in the cloud, a wristband that tracks blood oxygen levels for greater performance, and even a so-called anti-aging pill.</p> <p>A <a href="">workshop</a> hosted June 22 by the MIT Office Corporate Relations' (OCR) <a href="">MIT Startup Exchange (STEX) program</a> brought together some of these MIT entrepreneurs and industry experts to showcase their innovations and foster connections that could lead to new business opportunities.</p> <p>Held throughout the year, the three-hour STEX workshops include lightning presentations from MIT-connected startups; brief talks from academic innovators, industry experts, government representatives, and venture capitalists; startup presentation and demonstration sessions; and an interactive panel discussion.</p> <p>At last week’s event, eight entrepreneurs pitched their fitness-tech products — several rooted in MIT research — to a crowd of around 80 entrepreneurs, researchers, and industry experts in the OCR headquarters on Main Street, in Cambridge, Massachusetts. The academic keynote speaker was MIT Novartis Professor of Biology Leonard Guarente, who took the opportunity to demystify the science behind his startup Elysium Health’s “anti-aging pill,” which is made of compounds that aim to thwart age-related cell damage, which can lead to inflammatory and heart diseases, osteoporosis, and diabetes.</p> <p>STEX events aim to stimulate discussion, foster collaboration, and build partnerships between MIT-connected startups and member companies of MIT's Industrial Liaison Program (ILP). The series covers a broad range of topics: a recent workshop focused on energy storage, while upcoming events will focus on synthetic biology, robotics and drones, cancer therapies, renewable energy, world water issues, and 3-D printing.</p> <p>“Fitness, wellness and nutrition are very exciting areas, and MIT founders are very active in the space. We certainly have industry coming to campus interested in all of these technologies and products coming from them,” Trond Undheim, who directs STEX and is the organizer of the event, said in his opening remarks.</p> <p>Presenter Simon Hong, a researcher in the McGovern Institute for Brain Research at MIT, and CEO of smart-chair startup Robilis, said last week’s STEX workshop provided “an opportunity to interact with potential stakeholders.”</p> <p>Based on neuroscience research, Robilis developed StandX, a chair with two automated moving halves, side by side. The halves alternate — one dropping down and the other staying straight — making the user sit down on one half while standing on the opposite leg. The frequent alternation prevents stress on the spine caused by sitting in one position for extended periods, and the chair’s design encourages proper posture. The movement also interrupts prolonged sitting, which is associated with diabetes.</p> <p>During a startup demonstration session midway through the event, Hong’s station was crowded with attendees looking to try out the chair. In the end, he walked away with a few contacts interested in helping with production and in introducing him to potential investors. “I was quite satisfied with the event,” Hong told <em>MIT News</em>. “It is in a way a networking event, and good things tend to happen quite unexpectedly during many, many interactions with people.”</p> <p>Apart from providing a venue to spread the word about his wearables, the event enabled Alessandro Babini MBA ’15, co-founder of Humon, to connect with larger organizations in the space. Humon, a wearable targeted at endurance athletes, attaches to a muscle, where it monitors blood oxygen levels by shining a light into the skin and analyzing changes in the light that indicate less or more oxygen.</p> <p>“It was interesting to get an understanding about what big brands seek in partners, what they’re looking to invest in, and what they’re working on now,” Babini told <em>MIT News</em>. “Big corporations have a lot of customers and a big influence on where the market is going.”</p> <p>Another interesting MIT spinout, figure8, presented a wearable that captures 3-D body movement that can be analyzed by the user or shared with an online community — like a “YouTube” of movement data.</p> <p>The wearable is a small sleeve made from novel sensor-woven fabric that fits over the arm or leg to track joint and muscle movement. It lets users map the movement of muscle, bone, and ligaments. Put on a knee, for instance, the wearable can map individual ligaments, which is valuable for, say, monitoring the <em>anterior cruciate ligament</em> (ACL). One application is in physical therapy, so athletes can track injuries as they heal.</p> <p>Users can also map their movement to others. Dancers, for instance, can use the sensor to match their movements to those of others during training. The startup is also developing a platform that lets users upload and share that data in the cloud.</p> <p>“Before YouTube, no one thought about video as something you can share, upload, and download as a commodity,” said co-founder and CEO Nan-Wei Gong, an MIT Media Lab researcher, during her presentation. “We’re trying to create a system for everyone to collect this motion [data] they can upload and download.”</p> <p>Other startups that presented included: <a href="">Kitchology</a>, <a href="">Fitnescity</a>, <a href="">Digital Nutrition</a>, <a href="">Food for Sleep</a>, and <a href="">SplitSage</a>.</p> <p>In his keynote, Guarente explained the science and history behind Elysium’s “anti-aging” pill, called Basis, which he himself has been taking for three years. He noted the pill doesn’t necessarily make people feel more youthful or healthier, especially if they’re already healthy. “You should just fall apart more slowly,” Guarente said to laughter from the audience.</p> <p>Years ago, Guarente and other MIT researchers identified a group of genes called sirtuins that have been demonstrated to slow the aging process in microbes, fruit flies, and mice. For instance, calorie-restricted diets, long known to extend lifespans and prevent many diseases in mammals, is key in activating sirtuins. “It turns out there are compounds that can do the same thing,” Guarente said.</p> <p>It was later discovered that one of those compounds is abundant in blueberries and that an enzyme called nicotinamide adenine dinucleotide (NAD) is essential in carrying out the activity of sirtuins. But NAD deteriorates with age. “If there’s not enough NAD, you don’t activate sirtuins. Metabolism and DNA-repair goes awry, and a lot of things go wrong,” he said.</p> <p>However, in the NAD synthesis pathway, NAD’s precursor, called <em>nicotinamide riboside</em> (NR), can be injected into an organism, where it would move efficiently into cells and be converted into NAD.</p> <p>Basis is a combination of NR and the sirtuin-activating compound from blueberries.</p> <p>Last year, Elysium conducted a 120-person trial. The results indicated that the pills were safe and led to an increase and sustainability of NAD levels. More trials are on the way, and the startup is growing its pipeline of products. It has not yet been shown whether Basis can extend life-span in humans.</p> <p>“We could really make a difference in people’s health,” Guarente said at the conclusion of his talk. “And it would add to all the … medical devices and DNA analysis and motion sensors, so that people can begin to do what they want to do, which is to take charge of their health.”</p> <p>The investor speaker was David T. Thibodeau, managing director of Wellvest Capital, an investment banking company specializing in healthy living and wellness. The industry speaker was Matthew Decker, global technical leader in the Comfort and Biophysics Group of W.L. Gore and Associates, the manufacturing company best known for Gore-Tex fabrics.</p> <p>Panelists were Guarente, Decker, Thibodeau, and Josh Sarmir, co-founder and CEO of SplitSage, an MIT spinout that is developing an analytics platform that can detect “sweet spots” and “blind spots” in people’s fields of vision to aid in sports performance, online advertising, and work safety, among other applications.&nbsp;&nbsp;</p> <p>STEX has a growing database of roughly 1,200 MIT-affiliated startups. Last year, OCR, in close partnership with ILP, created <a href="">STEX25</a>, an accelerator for 25 startups at any time that focuses on high-level, high-quality introductions. The first cohort of 14 startups have gone through the accelerator, gaining industry partnerships that have led to several pilots, partnerships, and lead client relationships.</p> Innovation and Entrepreneurship (I&E), Startups, Alumni/ae, Special events and guest speakers, Industry, Biology, Research, Invention, Faculty, Staff, Students, Health, Disease, Drug development, Technology and society, Sensors, Sports, McGovern Institute MIT Corporation life member and biotech pioneer Henri Termeer dies at 71 Termeer built Genzyme from a startup into a biotech powerhouse, while becoming an innovator in rare-disease drug development. Wed, 17 May 2017 11:00:00 -0400 Rob Matheson | MIT News Office <p>MIT Corporation life member and biotechnology pioneer Henri A. Termeer, who led the iconic Genzyme Corporation for nearly three decades, with a strong focus on combating rare diseases, died on Friday night at his home in Marblehead, Massachusetts. He was 71.</p> <p>Widely regarded as a founder of Boston’s biotech industry, Termeer joined a fledgling Genzyme in the early 1980s, when the biotech industry itself was still in its infancy. Serving as Genzyme’s chairman, president, and CEO from 1983 until 2011, Termeer nurtured the company from a startup with fewer than 20 employees into one of the world’s largest biotech companies, employing more than 12,500 people worldwide.</p> <p>At Genzyme, Termeer waged a therapeutic battle against rare genetic diseases that had few, if any, effective treatments available. Under his leadership, Genzyme developed a group of rare-disease medicines that have now reached thousands of patients —&nbsp;including the enzyme-replacement drugs Cerezyme for Gaucher disease and Fabrazyme for Fabry disease, as well as Lemtrada, a multiple sclerosis drug. Consequently, Termeer rose to prominence as an innovator of rare-disease drugs (called “orphan drugs”), which is fast becoming a lucrative market.</p> <p>In 2011, Genzyme sold to French drug giant Sanofi SA for more than $20 billion — the second-largest acquisition deal in the history of the biotech industry, thanks largely to Termeer’s vision and business acumen.</p> <p>In 2006, Termeer joined the MIT Corporation — the Institute’s board of trustees — as a term member, and was re-elected in 2011. In 2013, Termeer was named an MIT Corporation life member.</p> <p>“Henri Termeer was a gentleman — and a giant,” MIT President L. Rafael Reif says. “Brilliantly creative, wise, charming, gracious, and brave, he showed us all, in everything he did, how to live and how to lead. At Genzyme, by developing treatments for patients struggling with rare diseases, he built a thriving company, created thousands of jobs, and offered hope and health to countless individuals who otherwise had neither. In the process, he helped launch the biotechnology revolution in Greater Boston. And he built a parallel career as one of the region’s leading citizens, offering his insight and vision to help guide many of our most important institutions.”</p> <p>“At MIT alone,” Reif continues, “he was a life member of the MIT Corporation and served on its Executive Committee; he chaired its Risk and Audit Committee; and he was crucial to shaping the Institute for Medical Engineering and Science. We will miss Henri terribly — as an advisor, an inspiration, and a friend. Our hearts go out to his wife, Belinda, to all the Termeers, and to the broader Genzyme family.”</p> <p>“Henri was an extraordinarily wise man who gave MIT the wisdom of his experience, judgement, and perspective, such that the Institute could better serve its mission,” MIT Corporation Chair Robert B. Millard ’73 says. “He tangibly guided and amplified the work of MIT. He was one of the most respected and admired people we had the honor of knowing.”</p> <p>Termeer’s impact on the local and global biotech industry was enormous. Scores of biotech leaders from around the world considered Termeer a mentor. In numerous articles published online about Termeer’s passing over the weekend, a number of those leaders mourned the loss of their former friend, praising Termeer as a mentor and biotech pioneer, and noting his magnetism and warmth as a business leader.</p> <p>In a <em>Boston Business Journal</em> <a href="">story</a>, Josh Boger, founding CEO of Vertex Pharmaceuticals, called Termeer an “example for how to run a biotech firm” as a human- and community-centric organization: “I really was tutored by Henri Termeer about how [being a biotech CEO] is not just something that’s added to who you are. ... He was such a wonderful advocate for the patients, and an advocate for the industry he was a part of.”</p> <p>Echoing that sentiment was David Meeker, now president of Sanofi Genzyme, in an <a href="">article</a> about Termeer in <em>The Boston Globe</em>. Termeer, Meeker told the <em>Globe</em>, was “the dean of the biotech community.” “His vision was to cure rare diseases, and he always had time to meet with the people dependent on our treatments,” he said. “He had the ability to forge such an intense relationship with everyone he met, and he made everyone in the industry feel like he cared about them as an individual.”</p> <p>Many also attribute the rise of the Boston and Massachusetts biotech scene to Termeer’s work at Genzyme. In the <em>Globe</em> article, Bob Coughlin, president of the Massachusetts Biotechnology Council, called Termeer “a true visionary and an exemplary leader,” noting that Termeer and Genzyme had helped mold Massachusetts “into the best biotech hub in the world.”</p> <p>After leaving Genzyme, Termeer stayed active in the biotech industry and never lost his love of launching and building companies. In 2011, Termeer and his wife, Belinda, donated $10 million to launch the Henri and Belinda Termeer Center for Targeted Therapies at Massachusetts General Hospital, where he served on the board of trustees. The center is designing a fast-growing pipeline of targeted treatments for rare tumors that have had few effective treatments.</p> <p>Until his passing, Termeer also served on the boards of directors of several biotech companies, including X4 Pharmaceuticals, ProQR Therpeutics, and Lysosomal Therapeutics, all of which he co-founded, as well as Moderna Therapeutics, Verastem, Aveo Oncology, and others.</p> <p>Termeer was also a board member for Partners Health Care, Harvard Medical School, Project HOPE, the Pharmaceutical Research and Manufacturers of America, and the Biotechnology Industry Organization, the world’s largest biotech trade association that he helped launch. Termeer also helped launch the Network for Excellence in Health Innovation, a Cambridge-based nonpartisan national health policy institute. From 2007 to 2011, Termeer served as a member of the board of the Federal Reserve Bank of Boston and as chairman from 2010 to 2011.</p> <p>Born in the Netherlands in 1946, Termeer studied economics at the Economische Hogeschool, Erasmus University. In 1973, he earned his MBA at the Darden School of Business at the University of Virginia, and started working for Baxter International, a medical device company in Deerfield, Illinois, where he quickly climbed the ranks. From 1976 to 1979, he served as general manager for Travenol GmbH in Munich, Germany, and then as vice president of the Hyland Therapeutics division of Baxter Travenol in Glendale, California, from 1979 to 1981.</p> <p>In 1983, Termeer was named president of a relatively unknown two-year-old biotech startup, Genzyme, located in Boston’s Chinatown, then called the “Combat Zone” — a former red-light district that Termeer had wryly described once as “the most romantic of neighborhoods.” Coincidentally, that year saw the passing of the Orphan Drug Act, a U.S. law that facilitated the development of drugs to treat rare diseases — such as Huntington’s Disease, amyotrophic lateral sclerosis, and muscular dystrophy — that then had limited treatment options.</p> <p>In 1985, Termeer was appointed as Genzyme CEO and, in 1988, was named as chairman. By its 2011 acquisition, Genzyme, with Termeer at the helm, had become the third largest company of its kind, with more than 12,500 employees spanning more than 70 offices and plants worldwide. Much of the company’s value has been credited to Termeer’s leadership and focus on developing orphan drugs with unique biochemical designs to fight rare diseases. The National Organization for Rare Disorders estimates that 30 million Americans suffer from 7,000 rare diseases. In recent years, experts have projected that the steady-growing global orphan drug market will reach anywhere from $150 billion to $200 billion by 2020.</p> <p>In a 2011 <a href="">article</a>, Termeer fondly recalled his fateful first introduction to Genzyme nearly three decades prior — an unassuming company with infinite promise: “One day I got a call to visit whatever it was at Genzyme. And it was, of course, not much. It had the right ingredients. I liked that the direction hadn’t been really established. It was an open book. I could go where I wanted to go. I gave up my job, gave up half my income, and moved here. It was a shot in the dark, and it was magnificent.”</p> <p>Termeer’s survivors include his wife, Belinda, and their daughter, Adriana, of Marblehead; his son, Nicholas, who lives in England; two sisters, Ineke of France and Marlies Verduijn of the Netherlands; and three brothers, Bert, Paul, Roel, all of the Netherlands.</p> <p>A celebration of Termeer’s life will be held on Saturday, May 20, at 11 a.m. in Kresge Auditorium. Parking is available in the West Garage on Vassar Street.</p> Henri Termeer Courtesy of GenzymeObituaries, MIT Corporation, Innovation and Entrepreneurship (I&E), Startups, Bioengineering and biotechnology, Industry, Drug development, Pharmaceuticals, Disease, Health, Health care, Health sciences and technology Study helps explain varying outcomes for cancer, Down Syndrome Differences in chromosome number may underlie variation among genetically identical individuals. Thu, 06 Apr 2017 12:00:00 -0400 Helen Knight | MIT News correspondent <p>Aneuploidy is a condition in which cells contain an abnormal number of chromosomes, and is known to be the cause of many types of cancer and genetic disorders, including Down Syndrome. The condition is also the leading cause of miscarriage.</p> <p>Disorders caused by aneuploidy are unusual in that the severity of their effects often varies widely from one individual to another.</p> <p>For example, nearly 90 percent of fetuses with three copies of chromosome 21, the cause of Down Syndrome, will miscarry before birth. In other cases, people with the condition will live until they are over 60 years old.</p> <p>Researchers have previously believed that this variation is the result of differences in the genetic makeup of those individuals with the condition.</p> <p>But in a paper published today in the journal <em>Cell</em>, researchers at the Koch Institute for Integrative Cancer Research at MIT reveal that aneuploidy alone can cause this significant variability in traits, in otherwise genetically identical cells.</p> <p>The finding could have significant implications for cancer treatment, since it could explain why genetically identical cancer cells may respond differently to the same therapy.</p> <p><strong>An immediate impact</strong></p> <p>Aneuploidy originates during cell division, when the chromosomes do not separate properly or are not equally partitioned between the two daughter cells. This leads the cells, which in humans would normally have 46 chromosomes, to develop with either too many or too few chromosomes.</p> <p>To study the effects of the condition, the researchers induced either chromosome loss or gain in genetically identical baker’s yeast cells. They chose baker’s yeast because the cells behave in a very similar way to human cells, according to Angelika Amon, the Kathleen and Curtis Marble Professor of Cancer Research and a member of the Koch Institute.</p> <p>The induced changes had an immediate impact on the cells.</p> <p>“We induced aneuploidy, and we found that the response was very variable from cell to cell,” Amon says. “Some cells slowed down their cycle completely, so that they could no longer divide, whereas others kept dividing quite normally and only experienced a small effect.”</p> <p>The researchers carried out a systematic analysis, investigating the effect on the cells of gaining or losing a variety of different chromosomes. They found that in each case, even though individual cells had gained or lost the same chromosome, they behaved very differently from each other.</p> <p>“So that really suggested that every single chromosome gained or lost had this effect, in that the responses (in each case) were quite variable,” Amon says.</p> <p><strong>Beyond cell division</strong></p> <p>The researchers also investigated the impact of aneuploidy on other biological pathways, such as transcription, the first stage of gene expression in which a segment of DNA is copied into RNA.</p> <p>They found that here too, the effects of aneuploidy were varied across otherwise identical cells.</p> <p>The cells’ response to environmental changes also varied considerably, suggesting that aneuploidy has an impact on the robustness of many, if not all, biological processes.</p> <p>To ensure the response is not an effect that is unique to baker’s yeast cells, the researchers then studied the impact of aneuploidy on mice, and found the same levels of variability, Amon says.</p> <p>“This suggests that the aneuploidy state itself could create variability, and that could provide an additional explanation of why diseases that are caused by aneuploidy are so variable,” Amon says.</p> <p>Tumors, for example, are known to contain different populations of cells, some of which are quite different to each other in their genetic makeup. These genetic differences have often been blamed when chemotherapy or other treatments have been unsuccessful, as it was believed that the therapy may not have targeted all of the cells within the tumor.</p> <p>“Unfortunately our paper suggests that tumors don’t even need to be heterogeneous genetically, the very fact that they have aneuploidy could lead to very variable outcomes, and that represents a significant challenge for cancer therapy,” Amon says.</p> <p>Understanding the consequences of aneuploidy on cellular phenotypes is a fundamental question that has important implications for the treatment of several diseases, such as cancer and Down Syndrome, according to Giulia Rancati of the Institute of Medical Biology at the Agency for Science, Technology and Research (A*STAR) in Singapore, who was not involved in the research.</p> <p>“This new exciting work adds an additional layer of understanding of how aneuploidy causes phenotypic variation, by revealing an unexpectedly high cell-to-cell variability between cells harboring the same aneuploidy karyotype,” Rancati says. “It would be interesting to test if this property of the aneuploid state might positively contribute to the evolution of cancer cells, which are known to develop drug resistance at high frequency.”</p> <p>The researchers are now hoping to carry out further studies to investigate the origins of the variability, Amon says.</p> <p>The results suggest that subtle changes in gene dosage across many genes, caused by the change in chromosome numbers, can promote alternate behaviors.</p> <p>“We’re now trying to track down which the key genes are, and which the key pathways are,” she says. “Once we can understand what the key pathways are that cause this variability, we can start to think about targeting those pathways, to combat alternate outcomes in cancer treatment, for example.”</p> A deconvolved wide-field fluorescence microscope image of human HeLa cancer cells, during late anaphase/early telophase, showing a lagging chromosome. If this fails to get into a daughter cell, it may lead to aneuploidy. Image: Iain M Porter/University of Dundee, Wellcome ImagesResearch, Biology, Cancer, Disease, DNA, Genetics, Medicine, Koch Institute, School of Science, Drug development MIT receives $7.5 million to enhance structural biology research Beckman Foundation grant helps secure cryo-electron microscopy at MIT.nano facility. Tue, 04 Apr 2017 11:00:00 -0400 Julia C. Keller | School of Science <p>MIT will receive a $2.5 million gift from the Arnold and Mabel Beckman Foundation to help develop a state-of-the-art cryo-electron microscopy (cryo-Em) center to be housed at the MIT.nano facility. In addition, the Institute also received an anonymous donation of $5 million to support the purchase of a synergistic high-resolution cryo-EM instrument.</p> <p>Cryo-EM is fast outpacing traditional X-ray crystallography techniques for understanding large biological structures. In X-ray crystallography, X-rays are scattered through a crystallized protein, and the resulting diffraction pattern allows scientists to determine the position of atoms in a biomolecule. Though this technique has resulted in major scientific discoveries, including DNA’s double helix structure, it also has limitations.</p> <p>Some marcomolecules and proteins don’t easily crystallize and, if the molecules can be crystallized, they are locked into a single conformation. With cryo-EM, researchers can look at protein structures in many different conformations and gain better insight into the protein’s mechanisms — leading to biomedical applications, such as more efficient drug development, or to increased understanding of chemotherapy efficacies.</p> <p>By using cryo-EM techniques, researchers such as Thomas Schwartz, the Boris Magasanik Professor of Biology, can gain new insights into communication within the cell. Schwartz is particularly interested in a large protein assembly called the Nuclear Pore Complex (NPC), which mediates how <a href="">signals and molecules traverse the nuclear envelope</a> from nucleus to cytoplasm and back again.</p> <p>“Cryo-EM allows us to not only see fragments and subcomplexes of the NPC, but also allows us to see their different conformations to understand how the complex may be carrying out its functions,” says Schwartz, adding that understanding the mechanism of the cellular machinery for nucleus-cytoplasm communication could allow development of treatments for when this communication malfunctions.</p> <p>“This revolutionary technology will enable ground-breaking innovations and insights in structural biology and therefore affect many areas of human health and disease,” says Alan Grossman, the Praecis Professor and head of the Department of Biology.</p> <p>The Beckman Foundation funding and the anonymous donation will allow for the purchase of the Talos Arctica and Titan Krios cryo-EM instruments, which will enhance several core facilities already present on the MIT campus, including traditional electron microscopy laboratories, the Department of Biology’s X-ray crystallography facilities, and the Francis Bitter Magnet Laboratory, which uses nuclear magnetic resonance spectroscopy.</p> <p>“Coupled with other emerging imaging and characterization tools, the cryo-EM instruments will provide a synergy across many research areas within MIT.nano and beyond,” says Vladimir Bulović, faculty head of the <a href="">MIT.nano facility</a>, a professor of electrical engineering, and the Fariborz Maseeh Chair in Emerging Technology. “The Beckman grant helps us clear that final hurdle in solidifying our nanoscale bio-imaging facilities and provide the research capabilities to turn scientific discoveries into breakthrough technologies.”</p> <p>“While the expense can make acquiring this technology via federal grants prohibitive, we as a private foundation are in a unique position to support major infrastructure investments to enable broader deployment of this new tool and increase access for young scientists to this exciting field of study,” says Anne Hultgren, executive director of the Beckman Foundation.</p> <p>Some of these researchers include Gabriela Schlau-Cohen, an assistant professor of chemistry and a 2016 recipient of the Beckman Young Investigator award.</p> <p>“This new facility will be transformative for the research programs of many faculty in the biology and chemistry departments, in particular for junior faculty with burgeoning research groups," says Timothy Jamison, the Robert R. Taylor Professor of Chemistry and head of the Department of Chemistry.</p> <p>The Beckman Foundation made similar instrumentation grants to Johns Hopkins University School of Medicine, University of Pennsylvania’s Perelman School of Medicine, University of Utah, and University of Washington School of Medicine.</p> Collaborative research by Bradley Pentelute, the Pfizer-Laubach Career Development Associate Professor in Chemistry, and scientists at UCLA and Harvard Medical School, used cryo-electron microscopy to understand the mechanisms behind anthrax bacteria toxin delivery system (shown here). Courtesy of Bradley PenteluteResearch, Funding, Grants, Facilities, MIT.nano, Nanoscience and nanotechnology, School of Science, School of Engineering, Drug development, Biology, Chemistry, Disease, Health Progress toward a Zika vaccine Researchers program RNA nanoparticles that could protect against the virus. Wed, 29 Mar 2017 00:00:00 -0400 Anne Trafton | MIT News Office <p>Using a new strategy that can rapidly generate customized RNA vaccines, MIT researchers have devised a new vaccine candidate for the Zika virus.</p> <p>The vaccine consists of strands of genetic material known as messenger RNA, which are packaged into a nanoparticle that delivers the RNA into cells. Once inside cells, the RNA is translated into proteins that provoke an immune response from the host, but the RNA does not integrate itself into the host genome, making it potentially safer than a DNA vaccine or vaccinating with the virus itself.</p> <p>“It functions almost like a synthetic virus, except it’s not pathogenic and it doesn’t spread,” says Omar Khan, a postdoc at MIT’s Koch Institute for Integrative Cancer Research and an author of the new study. “We can control how long it’s expressed, and it’s RNA so it will never integrate into the host genome.”</p> <p>This research also yielded a new benchmark for evaluating the effectiveness of other Zika vaccine candidates, which could help others who are working toward the same goal.</p> <p>Jasdave Chahal, a postdoc at MIT’s Whitehead Institute for Biomedical Research, is the first author of the paper, which appears in <em>Scientific Reports</em>. The paper’s senior author is Hidde Ploegh, a former MIT biology professor and Whitehead Institute member who is now a senior investigator in the Program in Cellular and Molecular Medicine at Boston Children’s Hospital.</p> <p>Other authors of the paper are Tao Fang and Andrew Woodham, both former Whitehead Institute postdocs in the Ploegh lab; Jingjing Ling, an MIT graduate student; and Daniel Anderson, an associate professor in MIT’s Department of Chemical Engineering and a member of the Koch Institute and MIT’s Institute for Medical Engineering and Science (IMES).</p> <p><strong>Programmable vaccines</strong></p> <p>The MIT team first reported its new approach to <a href="">programmable RNA vaccines</a> last year. RNA vaccines are appealing because they induce host cells to produce many copies of the proteins encoded by the RNA. This provokes a stronger immune reaction than if the proteins were administered on their own. However, finding a safe and effective way to deliver these vaccines has proven challenging.</p> <p>The researchers devised an approach in which they package RNA sequences into a nanoparticle made from a branched molecule that is based on fractal-patterned dendrimers. This modified-dendrimer-RNA structure can be induced to fold over itself many times, producing a spherical particle about 150 nanometers in diameter. This is similar in size to a typical virus, allowing the particles to enter cells through the same viral entry mechanisms. In their 2016 paper, the researchers used this nanoparticle approach to generate experimental vaccines for Ebola, H1N1 influenza, and the parasite <em>Toxoplasma gondii</em>.</p> <p>In the new study, the researchers tackled Zika virus, which emerged as an epidemic centered in Brazil in 2015 and has since spread around the world, causing serious birth defects in babies born to infected mothers. Since the MIT method does not require working with the virus itself, the researchers believe they might be able to explore potential vaccines more rapidly than scientists pursuing a more traditional approach.</p> <p>Instead of using viral proteins or weakened forms of the virus as vaccines, which are the most common strategies, the researchers simply programmed their RNA nanoparticles with the sequences that encode Zika virus proteins. Once injected into the body, these molecules replicate themselves inside cells and instruct cells to produce the viral proteins.</p> <p>The entire process of designing, producing, and testing the vaccine in mice took less time than it took for the researchers to obtain permission to work with samples of the Zika virus, which they eventually did get.</p> <p>“That’s the beauty of it,” Chahal says. “Once we decided to do it, in two weeks we were ready to vaccinate mice. Access to virus itself was not necessary.”</p> <p><strong>Measuring response</strong></p> <p>When developing a vaccine, researchers usually aim to generate a response from both arms of the immune system — the adaptive arm, mediated by T cells and antibodies, and the innate arm, which is necessary to amplify the adaptive response. To measure whether an experimental vaccine has generated a strong T cell response, researchers can remove T cells from the body and then measure how they respond to fragments of the viral protein.</p> <p>Until now, researchers working on Zika vaccines have had to buy libraries of different protein fragments and then test T cells on them, which is an expensive and time-consuming process. Because the MIT researchers could generate so many T cells from their vaccinated mice, they were able to rapidly screen them against this library. They identified a sequence of eight amino acids that the activated T cells in the mouse respond to. Now that this sequence, also called an epitope, is known, other researchers can use it to test their own experimental Zika vaccines in the appropriate mouse models.</p> <p>“We can synthetically make these vaccines that are almost like infecting someone with the actual virus, and then generate an immune response and use the data from that response to help other people predict if their vaccines would work, if they bind to the same epitopes,” Khan says. The researchers hope to eventually move their Zika vaccine into tests in humans.</p> <p>“The identification and characterization of CD8 T cell epitopes in mice immunized with a Zika RNA vaccine is a very useful reference for all those working in the field of Zika vaccine development,” says Katja Fink, a principal investigator at the A*STAR Singapore Immunology Network. “RNA vaccines have received much attention in the last few years, and while the big breakthrough in humans has not been achieved yet, the technology holds promise to become a flexible platform that could provide rapid solutions for emerging viruses.”</p> <p>Fink, who was not involved in the research, added that the “initial data are promising but the Zika RNA vaccine approach described needs further testing to prove efficacy.”</p> <p>Another major area of focus for the researchers is cancer vaccines. Many scientists are working on vaccines that could program a patient’s immune system to attack tumor cells, but in order to do that, they need to know what the vaccine should target. The new MIT strategy could allow scientists to quickly generate personalized RNA vaccines based on the genetic sequence of an individual patient’s tumor cells.</p> <p>The research was funded by the National Institutes of Health, a Fujifilm/MediVector grant, the Lustgarten Foundation, a Koch Institute and Dana-Farber/Harvard Center Center Bridge Project award, the Department of Defense Office of Congressionally Directed Medical Research’s Joint Warfighter Medical Research Program, and the Cancer Center Support Grant from the National Cancer Institute.</p> MIT researchers have devised a new vaccine candidate for the Zika virus. “It functions almost like a synthetic virus, except it’s not pathogenic and it doesn’t spread,” says postdoc Omar Khan. Image: Jose-Luis Olivares/MITResearch, Cancer, Disease, Microbes, Vaccines, Chemical engineering, Koch Institute, Drug development, RNA, Medicine, Whitehead Institute, School of Engineering