Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning.

Many human-targeted medications have been found to impact patients' gastrointestinal microbiomes, which has been proposed as an unrecognized source of drug side effects, comorbidities, and reduced treatment efficiencies. However, current methods for detecting such effects, such as patient sample analysis or in vitro high-throughput screening, are both labor- and resource-intensive. To accelerate the discovery of drug effects on the microbiome, we developed machine learning models that predict whether a small, drug-like molecule is likely to inhibit the growth of any of 40 representative human gut commensal microbes. We employed these models to virtually screen thousands of investigational drugs, revealing a strong propensity for human-targeted compounds to potentially modulate commensal microbes. Prospective in vitro validations uncovered two nonantibiotic drugs, the recently approved anti-cancer agent entrectinib and the clinical drug candidate PSI-697, to have previously unknown growth inhibition effects on multiple commensal gut microbes. Furthermore, we show that resistance to the effects of these drugs is mediated by known antibiotic resistance mechanisms BamB and TolC. Additionally, entrectinib significantly reduced microbial richness in a synthetic microbial model community. Taken together, our machine learning-assisted workflow and future extensions can triage microbiome-drug interactions to prioritize experimental testing and validation.