24th February 2020 Artificial intelligence creates new antibiotic Using a machine-learning algorithm, researchers at the Massachusetts Institute of Technology (MIT) have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world’s most problematic disease-causing bacteria – including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models. The computer model, which can trawl through 100 million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," explains James Collins, a Professor in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. "Our approach revealed this amazing molecule, which is arguably one of the more powerful antibiotics that has been discovered." "The machine learning model can explore, in silico, large chemical spaces that can be prohibitively expensive for traditional experimental approaches," explains Regina Barzilay, Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). Barzilay and Collins are senior authors of the study, which appears in the journal Cell.
Over the past few decades, very few new antibiotics have been developed by researchers – and most of those newly approved antibiotics are slightly different variants of existing drugs. Current methods for screening new antibiotics are often prohibitively costly, require a significant time investment, and are usually limited to a narrow spectrum of chemical diversity. "We're facing a growing crisis around antibiotic resistance, and this situation is being generated by both an increasing number of pathogens becoming resistant to existing antibiotics, and an anaemic pipeline in the biotech and pharmaceutical industries for new antibiotics," Collins says. The idea of using predictive computer models for "in silico" screening is not new – but until now, these models were not sufficiently accurate to transform drug discovery. Previously, molecules were represented as vectors reflecting the presence or absence of certain chemical groups. However, neural networks today can learn these representations automatically, mapping molecules into continuous vectors which are subsequently used to predict their properties. In this case, the MIT researchers designed their model to look for chemical features that make molecules effective at killing E. coli. To do so, they trained the model on 2,500 molecules, including about 1,700 FDA-approved drugs and 800 natural products with diverse structures and a wide range of bioactivities.
Once the model was trained, the researchers tested it on the Broad Institute's Drug Repurposing Hub, a library of 6,000 compounds. The AI then narrowed down the candidates to one molecule that was predicted to have strong antibacterial activity and featured a chemical structure different from any existing antibiotics. Using a separate machine-learning model, the researchers also showed that this molecule would likely have low toxicity to human cells. This molecule was named "halicin" by the MIT researchers, after the fictional artificial intelligence system from 2001: A Space Odyssey. They tested it against dozens of bacterial strains isolated from patients and grown in lab dishes, and found that it was able to kill many that are currently resistant to treatment – including Clostridium difficile, Acinetobacter baumannii and Mycobacterium tuberculosis. The drug worked against every species they tested, with the exception of Pseudomonas aeruginosa – a difficult-to-treat lung pathogen. To test halicin's effectiveness in living animals, the researchers used it to treat mice infected with A. baumannii, a bacterium that has infected many U.S. soldiers stationed in Iraq and Afghanistan. The strain that they used is resistant to all known antibiotics, but application of a halicin-containing ointment completely cleared the infections within 24 hours. Preliminary studies suggest that halicin kills bacteria by disrupting their ability to maintain an electrochemical gradient across their cell membranes. This gradient is necessary, among other functions, to produce ATP (molecules that cells use to store energy), so if the gradient breaks down, then the cells die. This type of killing mechanism could be difficult for bacteria to develop resistance to, the researchers say. "When you're dealing with a molecule that likely associates with membrane components, a cell can't necessarily acquire a single mutation or a couple of mutations to change the chemistry of the outer membrane. Mutations like that tend to be far more complex to acquire evolutionarily," says paper co-author Jonathan Stokes, a postdoc at MIT and the Broad Institute of MIT and Harvard. In this study, the researchers found that E. coli did not develop any resistance to halicin during a 30-day treatment period. In contrast, the bacteria started to develop resistance to the existing antibiotic ciprofloxacin within one to three days, and after 30 days, the bacteria were 200 times more resistant to ciprofloxacin than they were at the beginning of the experiment. The researchers now plan to pursue further studies of halicin, working with a pharmaceutical company or non-profit organisation, in the hope of developing it for use in humans.
After identifying halicin, the researchers also used their model to screen more than 100 million molecules selected from the ZINC15 database, an online collection of about 1.5 billion chemical compounds. This screen, which took only three days, identified 23 candidates that were structurally dissimilar from existing antibiotics and predicted to be nontoxic to human cells. In laboratory tests against five species of bacteria, the researchers found that eight of the molecules showed antibacterial activity, and two were particularly powerful. The researchers now plan to test these molecules further, and to screen more of the ZINC15's huge database. The researchers also plan to use their model to design new antibiotics and to optimise existing molecules. For example, they could train the model to add features that would make a particular antibiotic target only certain bacteria, preventing it from killing beneficial bacteria in a patient's digestive tract. "This groundbreaking work signifies a paradigm shift in antibiotic discovery and indeed in drug discovery more generally," said Roy Kishony, a professor of biology and computer science at Technion (the Israel Institute of Technology), who was not involved in the study. "Beyond in silica screens, this approach will allow using deep learning at all stages of antibiotic development – from discovery, to improved efficacy and toxicity through drug modifications and medicinal chemistry."
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