Computational approaches to predicting xenobiotic metabolism by the human gut microbiota.

INTRODUCTION: The human gut microbiota significantly influences drug pharmacokinetics and pharmacodynamics, driving interindividual variability in efficacy and toxicity. As experimental characterization of microbiome-mediated metabolism remains resource-intensive, computational prediction has emerged as an auxiliary strategy for comprehensive ADMET profiling.

AREAS COVERED: Based on a structured literature search up to 2026, this review evaluates key computational resources: 10 databases, 6 predictive algorithms, 1 genome-scale metabolic reconstruction platform, and 3 microbiome-metabolome integration models. We analyze their specific strengths, limitations, and integration into drug discovery pipelines and personalized medicine scenarios.

EXPERT OPINION: While current in silico tools robustly predict qualitative metabolic potential and responsible taxa, their application in physiologically based pharmacokinetic modeling is hindered by data limitations. Training datasets exhibit a profound bias toward isolated in vitro screening, alongside a critical lack of quantitative kinetic parameters. Overcoming these bottlenecks requires generating high-quality, physiologically relevant data through collaboration among computational biologists, laboratory researchers, physicians, pharmaceutical companies, and regulatory agencies. This interdisciplinary ecosystem will enable platforms that continuously learn from real-world feedback, bridging the gap between microbiome sequencing, rational drug design, and precision medicine.