Drug Metab Dispos. 2025 Oct 13. pii: S0090-9556(25)09492-9. [Epub ahead of print]53(11): 100183
Drug monitoring is an essential component of precision therapeutics, yet existing data bases to support therapeutic monitoring are limited to data curated from the scientific literature or predicted in silico. We used human liver S9 fraction to generate metabolites from 1114 therapeutic drugs spanning diverse drug classes. Metabolites were analyzed by liquid chromatography-high-resolution mass spectrometry, annotated through differential analysis of preincubation and postincubation samples, curated by comparison to predicted metabolites from BioTransformer 3.0, and compiled into a human liver pharmaceutical metabolite resource, named "Pharmaceutical Metabolite Data Base (PharmMet DB)." Liquid chromatography-high-resolution mass spectrometry showed heterogeneity in product generation, with some drugs mostly being converted to predicted metabolites, while others were converted to hundreds of unpredicted products characterized by mass-to-charge ratio and chromatographic retention time. Phase I metabolism was dominant, with 30,752 oxidized drug metabolites. Glucuronidation was dominant for phase II metabolism, with 6311 drug metabolites. Notably, 89% of tested drugs produced at least 1 metabolite that was not predicted on BioTransformer 3.0, and these novel metabolites were most frequently detected for anti-inflammatory, central nervous system and antimicrobial drug classes. PharmMet DB provides experimental metabolite profiles to detect therapeutic drug exposures in human biospecimens without a requirement for prescription history. PharmMet DB usage with human epidemiology will advance pharmacometabolomics to improve understanding of drug efficacy, adverse reactions, and interactions in precision medicine. SIGNIFICANCE STATEMENT: Pharmaceutical Metabolite Data Base is a new data base of therapeutic drug metabolites suitable for use with liquid chromatography-high-resolution mass spectrometry to monitor patient adherence, detect unreported drug use, for example, in clinical trials, and enhance pharmacoexposomics and pharmacogenomics research. The data base was generated by incubation of therapeutic agents with human liver S9 fraction and curated relative to in silico predicted metabolites. Associated metadata for metabolic processes and drug classes enhance utility for clinical use, especially with untargeted metabolomics analyses of human samples.
Keywords: Drug acetylation; Drug glutathionylation; Drug metabolism; Drug oxidation; Drug sulfation; PharmMet DB