Unlocking the Mechanism of Action: A Cost-Effective Flow Cytometry Approach for Accelerating Antimicrobial Drug Development

Abstract

Antimicrobial resistance is one of the greatest challenges to global health. While the development of new antimicrobials can combat resistance, low profitability is reducing the number of new compounds brought to the market. Elucidating the mechanism of action is crucial for developing new antimicrobials. This process can become expensive as there are no universally applicable pipelines and scientific expertise in different fields is required. One way to determine the mechanism of action is the use of predictive modeling, as antimicrobials can be classified into limited groups.. We demonstrate a cost-effective flow cytometry approach for determining the mechanisms of action of new compounds. Cultures of Actinomyces viscosus and Fusobacterium nucleatum were treated with a range of antimicrobials and measured by flow cytometry. A Gaussian mixture mask was applied over the data to construct a phenotypic fingerprint. The fingerprints were used to train random forest classifiers, and classifiers were used to predict the mechanism of action of cephalothin. Significant statistical differences were found among the 10 different treatment groups. A pairwise comparison between treatment groups showed a statistical difference for 35 out of 45 pairs for Actinomyces viscosus and 32 out of 45 pairs for Fusobacterium nucleatum after 3.5h of treatment. The best performing random forest classifier yielded a Matthews correlation coefficient of 0.92 and the mechanism of action of cephalothin could be successfully predicted. These findings suggest that flow cytometry can be a cheap and fast alternative for determining the mechanism of action of new antimicrobials.