ArticleInternational journal of environmental research and public health2021
Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Drug-disease interactions in inflammation: mechanisms, clinical Impact, and future directions.Inflammopharmacology · 2026Review
- Developmental neurotoxicity (DNT) QSAR combination prediction model establishment and structural characteristics interpretation.Toxicology research · 2024Article
- SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.Frontiers in chemistry · 2022Article
- Modeling and insights into the structural characteristics of drug-induced autoimmune diseases.Frontiers in immunology · 2022Article
- Bilateral Panuveitis and Exudative Retinal Detachments Associated with Alpelisib.Case reports in oncologyArticle
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Authors and funding
6 authors.
Funding
Abstract
An effective approach for assessing a drug's potential to induce autoimmune diseases (ADs) is needed in drug development. Here, we aim to develop a workflow to examine the association between structural alerts and drugs-induced ADs to improve toxicological prescreening tools. Considering reactive metabolite (RM) formation as a well-documented mechanism for drug-induced ADs, we investigated whether the presence of certain RM-related structural alerts was predictive for the risk of drug-induced AD. We constructed a database containing 171 RM-related structural alerts, generated a dataset of 407 AD- and non-AD-associated drugs, and performed statistical analysis. The nitrogen-containing benzene substituent alerts were found to be significantly associated with the risk of drug-induced ADs (odds ratio = 2.95,
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