Thanks, Neanderthals: How our ancient relatives could help find new antibiotics
Oct 30, 2023
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César de la Fuente, a bioengineering professor at the University of Pennsylvania, discusses the potential of our ancient relatives in the fight against antibiotic resistance. He highlights how machine learning is used to uncover new antibiotics from ancient molecules, specifically those from Neanderthals and Denisovans. De la Fuente delves into ethical concerns surrounding de-extinction and shares insights on a promising peptide called Neanderthaline 1. The conversation is a fascinating blend of ancient science and modern medicine, perfect for curious minds.
Machine learning has expedited the process of discovering new antibiotics, with thousands of preclinical candidates identified in hours.
Molecular de-extinction, using DNA data from ancient humans like Neanderthals, has yielded promising peptides for developing new antibiotics.
Deep dives
The Rise of Antibiotic Resistance
Antibiotics have revolutionized healthcare by treating various diseases, but the increasing resistance of bacteria to these drugs poses a silent pandemic. Without new classes of antibiotics since the 1980s, finding and testing new drugs has become expensive. However, researchers have employed machine learning to tackle this issue. By training a computer to execute Darwin's algorithm of evolution, scientists have developed more effective antibiotics. These computer-created antibiotics have shown promise in killing bacteria, even in clinical isolates. The use of artificial intelligence (AI) in antibiotic discovery has expedited the process, allowing thousands of preclinical candidates to be identified in hours, compared to years in traditional methods.
Scientists have adopted the concept of molecular de-extinction to solve present-day problems by bringing back molecules from the past. By accessing DNA data from ancient humans like Neanderthals and Denisovans, researchers developed computational tools fueled by machine learning to mine for potential peptides with anti-infective traits. These tiny proteins from extinct relatives yielded promising results in killing bacteria. Through solid-phase chemical synthesis, peptides were created and tested, demonstrating their efficacy in reducing infections in animal models. This approach provides templates for further optimization and exploration in the quest for new antibiotics.
AI and the Future of Antibiotic Development
The incorporation of Artificial Intelligence (AI) in antibiotic discovery has revolutionized the field, with the ability to discover thousands of preclinical candidates in a matter of hours. Traditional antibiotic discovery can take years to identify a single candidate. AI has expanded the realm of peptidic antibiotics, increasing the known number from around 6,000 to over one million. The rapid progress in this emerging field offers hope for creating life-saving treatments and improving human health. Researchers are optimistic about the potential of AI to accelerate the development of new antibiotics and therapies.
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.