Peptide CIT8, refined by a machine-learning algorithm, killed the antibiotic-resistant bacterium Acinetobacter baumannii in the lab within 15 minutes
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An algorithm helped find a molecule against a dangerous bacterium, but a drug is a long way off: experiments in living organisms are needed.
In a Frontiers in Microbiology paper, researchers at Houston Methodist Hospital and partners described a 13-residue peptide, CIT8, optimized by a machine-learning algorithm. It stayed active (minimum inhibitory concentration of 4 ug/mL) against 15 of 16 tested A. baumannii strains, including clinical isolates resistant to carbapenems; at 20 ug/mL (5 times the minimum inhibitory concentration) a population of about 5x10^7 CFU/mL fell below the detection limit within 15 minutes. Together with meropenem the peptide showed synergy. The work is preclinical and the experiments were done in vitro: the authors write that proteolytic stability, pharmacokinetics and the risk of resistance have not been studied, and that activity dropped in the presence of divalent cations and human serum; experiments in living organisms are needed.
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