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

Many human-targeted medications have been found to impact the composition of patients’ gastrointestinal microbiomes, which has been proposed as an unrecognized source of drug side effects, comorbidities, and reduced treatment efficiencies. However, current methods to detect such drug effects on the microbiome largely rely on patient sample analysis or in vitro high-throughput screening - which are both laborious and resource intensive. To accelerate the systematic discovery of drug effects on the microbiome, we designed machine learning (ML) workflows on existing data from high-throughput experiments to predict which drugs can inhibit the growth of 40 commensal microbes in monocultures. We employed these models to predict potential effects of thousands of investigational drugs and found that up to 60% of these drugs are predicted to inhibit the growth of at least one commensal microbe. Using prospective in vitro validations, our workflows enabled us to uncover two non-antibiotic drugs, the recently approved chemotherapeutic Entrectinib and the clinical drug candidate PSI-697, to have previously unknown growth inhibition effects on multiple commensal microbes. Furthermore, we show that resistance to the effects of these drugs is mediated by BamB and TolC, implicating these drugs as potential agents to exacerbate antibiotic resistance. We further validated that Entrectinib significantly reduces microbial richness in a synthetic microbial model community. Taken together, our ML-assisted workflow and future extensions can accelerate the systematic discovery of microbiome-drug interactions, with implications for safer drug development and molecular microbiome engineering.