Physicochemical and In Silico ADME Profiling of Gut Microbiota-Derived Metabolites: Bridging Computational Prediction and Experimental Validation

The gut microbiota generates hundreds of structurally distinct bioactive metabolites whose physicochemical properties govern how they behave once they reach the host. Descriptors such as lipophilicity (logP/logD), polar surface area, molecular weight, and hydrogen-bond donor and acceptor counts determine whether a metabolite crosses the intestinal barrier by passive diffusion, whether it depends instead on uptake transporters (MCT1/SLC16A1 and SMCT1/SLC5A8 for short-chain fatty acids), and whether efflux pumps and first-pass metabolism in the gut wall and liver erode its systemic exposure. These properties are the mechanistic basis of ADME (absorption, distribution, metabolism, excretion) behavior, and they carry direct preclinical relevance: a metabolite with striking in vitro bioactivity is pharmacologically irrelevant if its phys-chem profile prevents it from reaching an effective concentration at its target, or it may matter for the opposite reason, as an undocumented CYP or transporter modulator with drug interaction potential. In gut–host axis research (gut–brain, gut–liver, gut–skin), phys-chem and ADME profiling is what separates plausible mediators from metabolites that are merely detectable in stool, and it helps explain why microbiota composition, which shapes both metabolite production and intestinal transporter expression, drives such wide variability in exposure between individuals.Despite this, systematic characterization of gut microbiota-derived metabolites (GMDMs) is largely absent from a literature focused on bioactivity and therapeutic targets rather than disposition, and curated datasets and validated predictive models specific to this compound class are lacking. This Research Topic aims to close that gap by consolidating molecular docking, QSAR-derived property predictions, and network pharmacology into an integrated picture of how GMDMs interact with the drug metabolism and transport machinery.Given the size and diversity of the GMDM chemical space, computational methods offer a scalable first-pass filter: they need only chemical structures, which matters because many GMDMs are available in trace amounts, are unstable, or occur as mixtures, and they flag poor absorption, efflux liability, and metabolic instability early. Drug-likeness and ADME filtering is increasingly used as a step before docking and pathway analysis, and it is what narrows a metabolomics-scale list to the few candidates worth resource-intensive testing. Prediction alone, however, is insufficient as proof of mechanism: definitive claims about transporter substrates and inhibitors, or about CYP and UGT metabolism, still rest on validated assays, and current models generalize poorly to the novel scaffolds common in microbiome chemistry. This Research Topic therefore treats computational and experimental work as sequential stages of one pipeline, and welcomes computational studies, experimental validation studies, and integrated papers that pair both.This Research Topic welcomes original research articles, reviews, and methods papers. Topics of interest include:&#8226 Physicochemical profiling and classification of the GMDM chemical space (solubility, ionization state, lipophilicity, stability), establishing structure–property relationships that inform downstream ADME modeling&#8226 Molecular docking and molecular dynamics simulation of GMDMs against drug-metabolizing enzymes (CYPs, UGTs, SULTs) and membrane transporters (P-gp, BCRP, OATPs)&#8226 QSAR modeling of ADME-relevant endpoints (permeability, metabolic stability, transporter interaction potential) across GMDM chemical classes&#8226 Network pharmacology and multi-target analyses of GMDM–enzyme/transporter interaction networks&#8226 In silico screening of GMDM libraries using ADME filters and pharmacokinetic prediction tools&#8226 Computational prediction of gut-epithelial permeability and blood–brain barrier penetration for neuroactive GMDMs&#8226 In vitro transporter and permeability assays (Caco-2/MDCK, vesicular transport, recombinant enzymes, liver microsomes) providing experimental benchmarks for computational predictions&#8226 Reviews of in silico methods applied to endogenous metabolite ADME characterization Topic editors: Dr. Adarsh Kumar Shukla, Ashwani Kumar, Abhishek Dutt Tripathi, Oindrilla Mukherjee Source: https://www.frontiersin.org/research-topics/84962/physicochemical-and-in-silico-adme-profiling-of-gut-microbiota-derived-metabolites-bridging-computational-prediction-and-experimental-validation