Flash optimization of drug combinations for Acinetobacter baumannii with IDentif.AI-AMR.

Abstract

Antimicrobial resistance (AMR) is an emerging threat to global public health. Specifically, Acinetobacter baumannii (A. baumannii), one of the main pathogens driving the rise of nosocomial infections, is a Gram-negative bacillus that displays intrinsic resistance mechanisms and can also develop resistance by acquiring AMR genes from other bacteria. More importantly, it is resistant to nearly 90% of standard of care (SOC) antimicrobial treatments, resulting in unsatisfactory clinical outcomes and a high infection-associated mortality rate of over 30%. Currently, there is a growing challenge to sustainably develop novel antimicrobials in this ever-expanding arms race against AMR. Therefore, a sustainable workflow that properly manages healthcare resources to ultra-rapidly design optimal drug combinations for effective treatment is needed. In this study, the IDentif.AI-AMR platform was harnessed to pinpoint effective regimens against four A. baumannii clinical isolates from a pool of nine US FDA-approved drugs. Notably, IDentif.AI-pinpointed ampicillin-sulbactam/cefiderocol and cefiderocol/polymyxin B/rifampicin combinations were able to achieve 93.89 ± 5.95% and 92.23 ± 11.89% inhibition against the bacteria, respectively, and they may diversify the reservoir of treatment options for the indication. In addition, polymyxin B in combination with rifampicin exhibited broadly applicable efficacy and strong synergy across all tested clinical isolates, representing a potential treatment strategy for A. baumannii. IDentif.AI-pinpointed combinations may potentially serve as alternative treatment strategies for A. baumannii.