Drug development Archives - Phonesnews.com

October 29, 2025

New method could improve manufacturing of gene-therapy drugs

Some of the most expensive drugs currently in use are gene therapies to treat specific diseases, and their high cost limits their availability for those who need them. Part of the reason for the cost is that the manufacturing process yields as much as 90 percent non-active material, and separating out these useless parts is slow, leads to significant losses, and is not well adapted to large-scale production. Separation accounts for almost 70 percent of the total gene therapy manufacturing cost. But now, researchers at MIT’s Department of Chemical Engineering and Center for Biomedical Innovation have found a way to greatly improve that separation process.

The findings are described in the journal ACS Nano, in a paper by MIT Research Scientist Vivekananda Bal, Edward R. Gilliland Professor Richard Braatz, and five others.

“Since 2017, there have been around 10,000 clinical trials of gene therapy drugs,” Bal says.

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October 4, 2025

AI maps how a new antibiotic targets gut bacteria

For patients with inflammatory bowel disease, antibiotics can be a double-edged sword. The broad-spectrum drugs often prescribed for gut flare-ups can kill helpful microbes alongside harmful ones, sometimes worsening symptoms over time. When fighting gut inflammation, you don’t always want to bring a sledgehammer to a knife fight.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and McMaster University have identified a new compound that takes a more targeted approach. The molecule, called enterololin, suppresses a group of bacteria linked to Crohn’s disease flare-ups while leaving the rest of the microbiome largely intact. Using a generative AI model, the team mapped how the compound works, a process that usually takes years but was accelerated here to just months.

“This discovery speaks to a central challenge in antibiotic development,” says Jon Stokes, senior author of a new paper on the work,

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August 29, 2025

MIT researchers develop AI tool to improve flu vaccine strain selection

Every year, global health experts are faced with a high-stakes decision: Which influenza strains should go into the next seasonal vaccine? The choice must be made months in advance, long before flu season even begins, and it can often feel like a race against the clock. If the selected strains match those that circulate, the vaccine will likely be highly effective. But if the prediction is off, protection can drop significantly, leading to (potentially preventable) illness and strain on health care systems.

This challenge became even more familiar to scientists in the years during the Covid-19 pandemic. Think back to the time (and time and time again), when new variants emerged just as vaccines were being rolled out. Influenza behaves like a similar, rowdy cousin, mutating constantly and unpredictably. That makes it hard to stay ahead, and therefore harder to design vaccines that remain protective.

To reduce this uncertainty,

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August 19, 2025

A new model predicts how molecules will dissolve in different solvents

Using machine learning, MIT chemical engineers have created a computational model that can predict how well any given molecule will dissolve in an organic solvent — a key step in the synthesis of nearly any pharmaceutical. This type of prediction could make it much easier to develop new ways to produce drugs and other useful molecules.

The new model, which predicts how much of a solute will dissolve in a particular solvent, should help chemists to choose the right solvent for any given reaction in their synthesis, the researchers say. Common organic solvents include ethanol and acetone, and there are hundreds of others that can also be used in chemical reactions.

“Predicting solubility really is a rate-limiting step in synthetic planning and manufacturing of chemicals, especially drugs, so there’s been a longstanding interest in being able to make better predictions of solubility,” says Lucas Attia, an MIT graduate student and one of the lead authors of the new study.

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August 17, 2025

Using generative AI, researchers design compounds that can kill drug-resistant bacteria

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA).

Using generative AI algorithms, the research team designed more than 36 million possible compounds and computationally screened them for antimicrobial properties. The top candidates they discovered are structurally distinct from any existing antibiotics, and they appear to work by novel mechanisms that disrupt bacterial cell membranes.

This approach allowed the researchers to generate and evaluate theoretical compounds that have never been seen before — a strategy that they now hope to apply to identify and design compounds with activity against other species of bacteria.

“We’re excited about the new possibilities that this project opens up for antibiotics development. Our work shows the power of AI from a drug design standpoint, and enables us to exploit much larger chemical spaces that were previously inaccessible,” 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.

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August 8, 2025

New system dramatically speeds the search for polymer materials

Scientists often seek new materials derived from polymers. Rather than starting a polymer search from scratch, they save time and money by blending existing polymers to achieve desired properties.

But identifying the best blend is a thorny problem. Not only is there a practically limitless number of potential combinations, but polymers interact in complex ways, so the properties of a new blend are challenging to predict.

To accelerate the discovery of new materials, MIT researchers developed a fully autonomous experimental platform that can efficiently identify optimal polymer blends.

The closed-loop workflow uses a powerful algorithm to explore a wide range of potential polymer blends, feeding a selection of combinations to a robotic system that mixes chemicals and tests each blend.

Based on the results, the algorithm decides which experiments to conduct next, continuing the process until the new polymer meets the user’s goals.

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July 18, 2025

MIT launches a “moonshot for menstruation science”

The MIT Health and Life Sciences Collaborative (MIT HEALS) has announced the establishment of the Fairbairn Menstruation Science Fund, supporting a bold, high-impact initiative designed to revolutionize women’s health research.

Established through a gift from Emily and Malcolm Fairbairn, the fund will advance groundbreaking research on the function of the human uterus and its impact on sex-based differences in human immunology that contribute to gynecological disorders such as endometriosis, as well as other chronic systemic inflammatory diseases that disproportionately affect women, such as Lyme disease and lupus. The Fairbairns, based in the San Francisco Bay Area, have committed $10 million, with a call to action for an additional $10 million in matching funds.

“I’m deeply grateful to Emily and Malcolm Fairbairn for their visionary support of menstruation science at MIT. For too long, this area of research has lacked broad scientific investment and visibility, despite its profound impact on the health and lives of over half the population,” says Anantha P.

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June 19, 2025

Supercharged vaccine could offer strong protection with just one dose

Researchers at MIT and the Scripps Research Institute have shown that they can generate a strong immune response to HIV with just one vaccine dose, by adding two powerful adjuvants — materials that help stimulate the immune system.

In a study of mice, the researchers showed that this approach produced a much wider diversity of antibodies against an HIV antigen, compared to the vaccine given on its own or with just one of the adjuvants. The dual-adjuvant vaccine accumulated in the lymph nodes and remained there for up to a month, allowing the immune system to build up a much greater number of antibodies against the HIV protein.

This strategy could lead to the development of vaccines that only need to be given once, for infectious diseases including HIV or SARS-CoV-2, the researchers say.

“This approach is compatible with many protein-based vaccines, so it offers the opportunity to engineer new formulations for these types of vaccines across a wide range of different diseases,

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April 22, 2025

Equipping living cells with logic gates to fight cancer

One of the most exciting developments in cancer treatment is a wave of new cell therapies that train a patient’s immune system to attack cancer cells. Such therapies have saved the lives of patients with certain aggressive cancers and few other options. Most of these therapies work by teaching immune cells to recognize and attack specific proteins on the surface of cancer cells.

Unfortunately, most proteins found on cancer cells aren’t unique to tumors. They’re also often present on healthy cells, making it difficult to target cancer aggressively without triggering dangerous attacks on other tissue. The problem has limited the application of cell therapies to a small subset of cancers.

Now Senti Bio is working to create smarter cell therapies using synthetic biology. The company, which was founded by former MIT faculty member and current MIT Research Associate Tim Lu ’03, MEng ’03, PhD ’08 and Professor James Collins,

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