Today, the U.S. Department of Health and Human Services (HHS) through the Advanced Research Projects Agency for Health (ARPA-H) announced funding for the Transforming Antibiotic R&D with Generative AI to stop Emerging Threats (TARGET) project, which will use AI to speed the discovery and development of new classes of antibiotics. This program is another action to support the United States’ longstanding commitment to combating antimicrobial resistance (AMR), from groundbreaking innovation to international collaboration. The U.S. is a global leader in the fight against AMR and has a demonstrated track record of progress in protecting people, animals, and the environment from the threat of AMR domestically and globally.
“Antibiotic resistance is a real and urgent threat affecting millions of people. We need to prevent infections and conserve the antibiotics we have. We also urgently need new drugs to treat these increasingly resistant infections. This project will use AI to speed this needed innovation and help ensure we have the medicines we need to keep people alive,” said Secretary Xavier Becerra. Learn more
When I first became a doctor, I cared for an older man whom I’ll call Ted. He was so sick with pneumonia that he was struggling to breathe. His primary-care physician had prescribed one antibiotic after another, but his symptoms had only worsened; by the time I saw him in the hospital, he had a high fever and was coughing up blood. His lungs seemed to be infected with methicillin-resistant Staphylococcus aureus (MRSA), a bacterium so hardy that few drugs can kill it. I placed an oxygen tube in his nostrils, and one of my colleagues inserted an I.V. into his arm. We decided to give him vancomycin, a last line of defense against otherwise untreatable infections.
Ted recovered with astonishing speed. When I stopped by the next morning, he smiled and removed the oxygen tube, letting it dangle near his neck like a pendant. Then he pointed to the I.V. pole near his bed, where a clear liquid was dripping from a bag and into his veins.
“Where did that stuff come from?” Ted asked.
“The pharmacy,” I said.
“No, I mean, where did it come from?”
At the time, I could barely pronounce the names of medications, let alone hold forth on their provenance. “I’ll have to get back to you,” I told Ted. He was discharged before I could. But, in the years that followed, I often thought about his question. Every day, I administer medicines whose origins are a mystery to me. I occasionally meet a patient for whom I have no effective treatment to offer, and Ted’s inquiry starts to seem existential. Where do drugs come from, and how can we get more of them? Learn more
Researchers now employ artificial intelligence (AI) models based on deep learning to make functional predictions about big datasets. While the concepts behind these networks are well established, their inner workings are often invisible to the user. The emerging area of explainable AI (xAI) provides model interpretation techniques that empower life science researchers to uncover the underlying basis on which AI models make such predictions.
In this month’s episode, Deanna MacNeil from The Scientist spoke with Jim Collins from the Massachusetts Institute of Technology to learn how researchers are using explainable AI and artificial neural networks to gain mechanistic insights for large scale antibiotic discovery. Learn more
Since the 1970s, modern antibiotic discovery has been experiencing a lull. Now the World Health Organization has declared the antimicrobial resistance crisis as one of the top 10 global public health threats.
When an infection is treated repeatedly, clinicians run the risk of bacteria becoming resistant to the antibiotics. But why would an infection return after proper antibiotic treatment? One well-documented possibility is that the bacteria are becoming metabolically inert, escaping detection of traditional antibiotics that only respond to metabolic activity. When the danger has passed, the bacteria return to life and the infection reappears.
“Resistance is happening more over time, and recurring infections are due to this dormancy,” says Jackie Valeri, a former MIT-Takeda Fellow (centered within the MIT Abdul Latif Jameel Clinic for Machine Learning in Health) who recently earned her PhD in biological engineering from the Collins Lab. Valeri is the first author of a new paper published in this month’s print issue of Cell Chemical Biology that demonstrates how machine learning could help screen compounds that are lethal to dormant bacteria. Learn more
Jim Collins is one of the leading biomedical engineers in the world. He’s been elected to all 3 National Academies (Engineering, Science, and Medicine) and is one of the founders of the field of synthetic biology. In this conversation, we reviewed the seminal discoveries that he and his colleagues are making at the Antibiotics-AI Project at MIT. Learn more
Casey is taking his newsletter Platformer off Substack, as criticism over the company’s handling of pro-Nazi content grows. Then, The Wall Street Journal spoke with witnesses who said that Elon Musk had used LSD, cocaine, ecstasy and psychedelic mushrooms, worrying some directors and board members of his companies. And finally, how researchers found a new class of antibiotics with the help of an artificial intelligence algorithm used to win the board game Go.
Today’s guests: Kirsten Grind, enterprise reporter for The Wall Street Journal
Felix Wong, postdoctoral fellow at M.I.T. and co-founder of Integrated Biosciences
Additional Reading:
Why Platformer is leaving Substack.
Elon Musk has reportedly used illegal drugs, worrying leaders at Tesla and SpaceX.
Researchers have discovered a new class of antibiotics using A.I. Learn more
Why it matters: the world is in urgent need of new ideas and inspiration to address chronic and infectious diseases, climate change, energy demands and other complex and consequential problems.
Here are some of the biggest discoveries and advances of 2023... AI-assisted discovery: The push to use AI for science notched some advances in 2023 — including solving a famous math problem, finding a new class of antibiotics and predicting the structure of nearly 400,000 possible new materials, which are needed for next-generation batteries, solar cells and computing. Learn more
Using a type of artificial intelligence known as deep learning, MIT researchers have discovered a class of compounds that can kill a drug-resistant bacterium that causes more than 10,000 deaths in the United States every year. Learn more
Antibiotic resistance is among the biggest global threats to human health. It was directly responsible for an estimated 1.27 million deaths in 2019 and contributed to nearly five million more. The problem only got worse during the COVID pandemic. And no new classes of antibiotics have been developed for decades. Learn more
I’ll wager that the event of 2023 that will change our lives the most in coming years is not the sighting of a Chinese spy balloon, the failure of Silicon Valley Bank, the fall of Kevin McCarthy’s speakership or any of the other eruptions that transfixed us this year.
More likely, the event that’s judged most transformative will be some scientific or technological advance that only a handful of people know about right now — because that’s how things almost always go. The first time the word “transistor” appeared in print was in an article in The New York Times in 1948, on Page 46, following a report on two new radio shows, “Mr. Tutt” and “Our Miss Brooks.” I think we can agree that the transistor has had more impact on our daily lives in the 75 years since than either of those bits of entertainment. Learn more