Academic Journal
Beyond Molecular Structures: Investigating Demographic Factors in Drug-Induced Cardiotoxicity Prediction Models.
| Τίτλος: | Beyond Molecular Structures: Investigating Demographic Factors in Drug-Induced Cardiotoxicity Prediction Models. |
|---|---|
| Συγγραφείς: | Iwan M; Department of Biomedical Engineering, Eindhoven University of Technology, Institute for Complex Molecular Systems (ICMS), P.O. Box 513, Eindhoven 5600 MB, The Netherlands.; Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milan 20156, Italy., Roncaglioni A; Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, Milan 20156, Italy., Grisoni F; Department of Biomedical Engineering, Eindhoven University of Technology, Institute for Complex Molecular Systems (ICMS), P.O. Box 513, Eindhoven 5600 MB, The Netherlands. |
| Πηγή: | Journal of chemical information and modeling [J Chem Inf Model] 2026 Jun 22; Vol. 66 (12), pp. 6962-6971. Date of Electronic Publication: 2026 Jun 02. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Washington, D.C. : American Chemical Society, c2005- |
| Ιατρικοί όροι (MeSH): | Cardiotoxicity*/etiology , Demography*, Humans ; Predictive Learning Models ; Pharmacovigilance ; Machine Learning |
| Περίληψη: | Predicting drug-induced cardiotoxicity remains one of the most important challenges in drug safety, contributing to a substantial share of clinical trial failures and postmarket withdrawals. While clinical evidence shows differences in adverse responses across sex, age, and body mass, incorporating demographic factors into in silico prediction models remains challenging. We developed CARBIDE (CARdiotoxicity Based on Integrated Demographic Evidence), a collection of 27 dataset variants derived from the FAERS pharmacovigilance database, to systematically evaluate whether meaningful structure-demographic interactions could be learned from spontaneous reporting data. Through systematic evaluation of different FAERS filtering criteria, cardiotoxicity definitions, and statistical methods, together with comprehensive ablation studies, we found that machine learning models failed to extract useful structure-demographic relationships. The models either learned population-level statistics or relied solely on structural information, with demographic features derived using our approach providing little additional predictive value. While these findings reveal fundamental limitations in using pharmacovigilance data for demographic-aware toxicity prediction, CARBIDE's systematic evaluation provides important insights for the field, helping guide future efforts toward more effective approaches in personalized cardiotoxicity prediction. |
| Entry Date(s): | Date Created: 20260602 Date Completed: 20260622 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13292213 |
| DOI: | 10.1021/acs.jcim.6c00418 |
| PMID: | 42227710 |
| Βάση Δεδομένων: | MEDLINE |
καταχωρήστε σχόλιο πρώτοι!