Computational approaches to predicting xenobiotic metabolism by the human gut microbiota

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.

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.

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.