Leveraging machine learning to unravel drug-microbiome interactions in the gut

The human gut microbiome (HGM) is a complex, highly diverse microbial ecosystem with extensive enzymatic and metabolic capacity. It plays a pivotal role in the biotransformation of various drugs, thereby modulating their efficacy and pharmacokinetics. Due to significant inter-individual variability in gut microbiota composition, these metabolic activities can result in differential therapeutic responses to the same drug across individuals. Conversely, drugs can also affect the diversity and composition of the HGM, impacting host health. Despite its importance, systematically studying drug-microbiome interactions (DMI) presents substantial experimental challenges. In recent years, machine learning has emerged as a powerful tool in biological and health sciences, offering advanced capabilities in pattern recognition and the discovery of hidden relationships. This progress opens new avenues for DMI research. This review provides an overview of recent advances in HGM-mediated drug biotransformation and drug-induced modification of the HGM, with a particular focus on the application of machine learning techniques in these areas. We aim to elucidate the critical role of machine learning in the systematic exploration and mechanistic understanding of DMI, highlighting its importance as a key research direction for the future.