The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host–microbiome metabolic interactions.

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs, yet comprehensive systems-level approaches to interrogate drug-microbiome interactions are lacking. Here, we present a computational resource of human microbial genome-scale reconstructions, deemed AGORA2, which accounts for 7,206 strains, includes microbial drug degradation and biotransformation, and was extensively curated based on comparative genomics and literature searches. AGORA2 serves as a knowledge base for the human microbiome and as a metabolic modelling resource. We demonstrate the latter by mechanistically modelling microbial drug metabolism capabilities in single strains and pairwise models. Moreover, we predict the individual-specific drug conversion potential in a cohort of 616 colorectal cancer patients and controls. This analysis reveals that some drug activation capabilities are present in only a subset of individuals, moreover, drug conversion potential correlate with clinical parameters. Thus, AGORA2 paves the way towards personalised, predictive analysis of host-drug-microbiome interactions.