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Empirically establishing drug exposure records directly from untargeted metabolomics data

Zhao, H. N.; Kvitne, K. E.; Brungs, C.; Mohan, S.; Charron-Lamoureux, V.; Bittremieux, W.; Tang, R.; Schmid, R.; Lamichhane, S.; El Abiead, Y.; Andalibi, M. S.; Mannochio-Russo, H.; Ambre, M.; Avalon, N. E.; Bryant, M.; Caraballo-Rodriguez, A. M.; Maya, M. C.; Chin, L.; Ellis, R. J.; Franklin, D.; Girod, S.; Gomes, P. W. P.; Hansen, L.; Heaton, R.; Iudicello, J. E.; Jarmusch, A. K.; Khatib, L.; Letendre, S.; Magyari, S.; McDonald, D.; Mohanty, I.; Cumsille, A.; Moore, D. J.; Rajkumar, P.; Ross, D. H.; Sapre, H.; Shahneh, M. R. Z.; Thomas, S. P.; Tribelhorn, C.; Tubb, H. M.; Walker, C.; Wang, C

2024-10-10 bioinformatics
10.1101/2024.10.07.617109 bioRxiv
Show abstract

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N-acyl lipid changes in a cohort with human immunodeficiency virus. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.