ArticleDrug metabolism and disposition: the biological fate of chemicals2025
Pharmaceutical Metabolite Data Base, PharmMet DB: Reference data base for drug metabolites generated by human liver S9 fraction.
Article in Drug metabolism and disposition: the biological fate of chemicals, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Mixtures Biotransformation: Multilayer Molecular Networking of Kratom Liver Metabolites.Journal of natural products · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.