以色列理工学院生物学院Roy Kishony研究组提出最大限度地减少细菌感染中由治疗引起的抗生素耐药性出现的方法。2022年2月25日出版的《科学》杂志发表了这项成果。

将 1113 种治疗前和治疗后细菌分离株的全基因组测序与对 140,349 例尿路感染和 7365 例伤口感染的机器学习分析相结合，他们发现治疗引起的耐药性的出现可以在个体患者层面进行预测和最小化。耐药性的出现很常见，并且不是由新的耐药性进化驱动的，而是由对处方抗生素具有耐药性的不同菌株的快速再感染驱动的。由于大多数感染是从患者自身的微生物群中播种的，因此可以使用患者过去的感染史来预测这些获得耐药性的复发，并通过机器学习个性化的抗生素建议将其最小化，从而提供一种减少耐药病原体出现和传播的方法。

据介绍，细菌感染的治疗目前的重点是选择与病原体敏感性相匹配的抗生素，而较少关注即使是敏感性匹配的治疗也可能由于对治疗产生耐药性而失败的风险。

附：英文原文

Title: Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections

Author: Mathew Stracy, Olga Snitser, Idan Yelin, Yara Amer, Miriam Parizade, Rachel Katz, Galit Rimler, Tamar Wolf, Esma Herzel, Gideon Koren, Jacob Kuint, Betsy Foxman, Gabriel Chodick, Varda Shalev, Roy Kishony

Issue&Volume: 2022-02-25

Abstract: Treatment of bacterial infections currently focuses on choosing an antibiotic that matches a pathogen’s susceptibility, with less attention paid to the risk that even susceptibility-matched treatments can fail as a result of resistance emerging in response to treatment. Combining whole-genome sequencing of 1113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7365 wound infections, we found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common and driven not by de novo resistance evolution but by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from a patient’s own microbiota, these resistance-gaining recurrences can be predicted using the patient’s past infection history and minimized by machine learning–personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens.

DOI: abg9868

Source: https://www.science.org/doi/10.1126/science.abg9868