{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T01:26:06Z","timestamp":1790731566157,"version":"4.1.0"},"reference-count":65,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T00:00:00Z","timestamp":1625270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FDA internal funding","award":["E0767701"],"award-info":[{"award-number":["E0767701"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>An effective approach for assessing a drug\u2019s 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, p = 0.0036). Furthermore, we developed a machine-learning-based predictive model by using daily dose and nitrogen-containing benzene substituent alerts as the top inputs and achieved the predictive performance of area under curve (AUC) of 70%. Additionally, we confirmed the reactivity of the nitrogen-containing benzene substituent aniline and related metabolites using quantum chemistry analysis and explored the underlying mechanisms. These identified structural alerts could be helpful in identifying drug candidates that carry a potential risk of drug-induced ADs to improve their safety profiles.<\/jats:p>","DOI":"10.3390\/ijerph18137139","type":"journal-article","created":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T22:34:36Z","timestamp":1625438076000},"page":"7139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose"],"prefix":"10.3390","volume":"18","author":[{"given":"Yue","family":"Wu","sequence":"first","affiliation":[{"name":"National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieqiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Fu","sequence":"additional","affiliation":[{"name":"National Center for Toxicological Research, Division of Biochemical Toxicology, U.S. Food and Drug Administration, Jefferson, AR 72079, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weida","family":"Tong","sequence":"additional","affiliation":[{"name":"National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8087-3968","authenticated-orcid":false,"given":"Huixiao","family":"Hong","sequence":"additional","affiliation":[{"name":"National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minjun","family":"Chen","sequence":"additional","affiliation":[{"name":"National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.jaut.2009.09.008","article-title":"Recent insights in the epidemiology of autoimmune diseases: Improved prevalence estimates and understanding of clustering of diseases","volume":"33","author":"Cooper","year":"2009","journal-title":"J. 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