2026-10-14T23:05:37Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34022183 2026-09-29T09:00:43Z category_4 category_13 category_16 category_19 category_21 category_734 category_931 category_64 portal_87 item_type_3 month_year_09_2026

Computational approaches to predicting xenobiotic metabolism by the human gut microbiota Anton S. Kolodnitsky (17271966) Alexey A. Lagunin (603926) Vladimir V. Poroikov (603927) Biochemistry Genetics Physiology Pharmacology Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer ADMET drug metabolism human gut microbiota in silico machine learning PBPK personalized medicine pharmacomicrobiomics <p>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.</p> <p>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.</p> <p>While current <i>in silico</i> 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 <i>in vitro</i> 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.</p> 2026-09-29T09:00:43Z Dataset Dataset 10.6084/m9.figshare.34022183.v1 https://figshare.com/articles/dataset/Computational_approaches_to_predicting_xenobiotic_metabolism_by_the_human_gut_microbiota/34022183 CC BY 4.0