{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T21:16:10Z","timestamp":1785014170275,"version":"3.55.0"},"reference-count":44,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81870015"],"award-info":[{"award-number":["81870015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Br J Clin Pharmacol"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>This study aims to identify predictive plasma protein biomarkers for anti\u2010tuberculosis drug\u2010induced liver injury (ATB\u2010DILI) and develop machine learning models for pre\u2010treatment risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective nested case\u2013control study, proteomic profiling of pre\u2010treatment plasma from 24 patients (12 ATB\u2010DILI, 12 controls) identified differentially expressed proteins, which were validated by ELISA in an independent cohort (35 ATB\u2010DILI, 37 controls). Multiple machine learning algorithms were implemented to develop clinical prediction models and evaluate the prognostic value of the identified protein biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Proteomic analysis of pre\u2010treatment samples from the exploratory cohort identified five significantly differentially expressed proteins: antithrombin III, apolipoprotein D, carboxypeptidase B2, Chromogranin\u2010A, and Retinol\u2010binding protein 4. These proteins are functionally implicated in inflammatory responses, oxidative stress, and drug metabolism pathways. Validation using baseline plasma from an independent cohort confirmed consistent expression patterns for all five proteins (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01), with directional changes matching the discovery phase findings. The random forest model, built on these pre\u2010treatment biomarkers, demonstrated robust predictive performance in the test set (AUC\u2009=\u20090.94, sensitivity\u2009=\u200990.0%, specificity\u2009=\u200990.0%, accuracy\u2009=\u20090.90). Importantly, consensus across multiple machine learning approaches (GBDT, SVM, GBM, etc.) confirmed predictive stability and generalizability of this protein signature (inter\u2010model AUC range: 0.85\u20130.96).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study has successfully identified five pre\u2010treatment plasma protein signature that, when incorporated into machine learning models, may enable the prediction of ATB\u2010DILI risk, offering potential for early intervention in tuberculosis therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/bcp.70446","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T01:50:41Z","timestamp":1768441841000},"page":"1716-1729","source":"Crossref","is-referenced-by-count":2,"title":["Plasma proteomics biomarkers for predicting anti\u2010tuberculosis drug\u2010induced liver injury: A comprehensive assessment"],"prefix":"10.1002","volume":"92","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4132-2925","authenticated-orcid":false,"given":"Ming\u2010Gui","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine West China Hospital, Sichuan University  Chengdu China"},{"name":"State Key Laboratory of Respiratory Health and Multimorbidity  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng\u2010Meng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine West China Hospital, Sichuan University  Chengdu China"},{"name":"Department of Respiratory and Critical Care Medicine Genertec 363 Hospital  Chengdu 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