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<p>Antimicrobial resistance (AMR) is an emerging threat to global public health. Specifically, <i>Acinetobacter baumannii</i> (<i>A. baumannii</i>), 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 <i>A. baumannii</i> 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 <i>A. baumannii</i>. IDentif.AI-pinpointed combinations may potentially serve as alternative treatment strategies for <i>A. baumannii</i>.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月21日

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  • Kui You,
  • Nurhidayah Binte Mohamed Yazid,
  • Li Ming Chong,
  • Lissa Hooi,
  • Peter Wang,
  • Isaiah Zhuang,
  • Stephen Chua,
  • Ethan Lim,
  • Alrick Zi Xin Kok,
  • Kalisvar Marimuthu,
  • Shawn Vasoo,
  • Oon Tek Ng,
  • Conrad E. Z. Chan,
  • Edward Kai-Hua Chow,
  • Dean Ho

摘要

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.