Thanks, Neanderthals: How our ancient relatives could help find new antibiotics

Short Wave

NPR

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4.7 • 6K Ratings

🗓️ 30 October 2023

⏱️ 13 minutes

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Summary

Antibiotics have changed the world. They've made it possible to treat diseases that used to mean anything from discomfort to death. But no new classes of antibiotics have made it to the market since the 1980s. What if humans' closest, ancient relatives held the answer to antibiotic resistance?

Some scientists want to discover new antibiotics using machine learning ... and some very, very old relatives of humans. Host Aaron Scott talks to César de la Fuente about using computers to discover the first therapeutic molecules in extinct organisms.

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Transcript

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0:00.0	This message comes from an PR sponsor, ServiceNow.
0:03.2	Everyone's talking about AI, but how can it actually help your business?
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0:12.6	Put AI to work.
0:14.2	See how at servicenow.com slash Gen AI.
0:19.6	You're listening to Shortwave from NPR.
0:26.3	Analytics have changed the world.
0:28.2	They've made it possible to treat so many diseases that used to mean anything from discomfort
0:32.8	to miserable death. But there's a problem.
0:36.8	We're facing a silent pandemic where more and more bacteria are becoming resistant to
0:43.4	available antibiotics.
0:44.7	Cesar Delafuente is a professor of bioengineering at the University of Pennsylvania School of Engineering.
0:50.7	Today, over one million people die every single year as a consequence of untrutable infections.
0:57.2	That's projected to worsen and to actually lead to the death of 10 million people by 2050,
1:03.3	unless we do something about it.
1:05.3	No new classes of antibiotics have made it to the market since the 1980s,
1:09.4	just variations on existing antibiotics.
1:12.3	That's in part because finding and testing new drugs is hugely expensive.
1:16.6	But as a post-doc at MIT, Cesar had an idea,
1:20.2	what if they could use machine learning to get a computer to do it?
1:24.3	The first challenge was how to teach a computer to innovate at the molecular level.
1:28.5	And after much thinking with our collaborators,
	...

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