{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T03:42:19Z","timestamp":1792208539369,"version":"4.4.0"},"reference-count":20,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin Pharma and Therapeutics"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:p>\n                    Clinical translation of novel therapies can be hindered by heterogeneity\u2010driven sample size inflation in late\u2010stage trials. In acetaminophen\u2010induced liver injury (APAP DILI), many patients recover spontaneously, diluting investigational drug efficacy signals. We developed a prognostic enrichment tool to identify patients with worsening injury trajectories for more efficient trial designs. Biomarker model discovery and evaluation used serum samples from three UK cohorts: the MAPP2 APAP DILI biobank (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009147), an independent pre\u2010intervention evaluation cohort from the ongoing MAIL trial (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200934), and healthy controls (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200913). We measured 63 biomarkers and evaluated 321,682 combinations using kernel na\u00efve Bayes classification to predict liver injury trajectory (ALT rising vs. falling). Sensitivity analysis using patient\u2010level grouped cross\u2010validation showed combining multiple biomarkers while constraining collinearity was necessary to maximize performance. A four\u2010biomarker model (MCSFR, WBC, Sodium, K18) achieved AUC 0.868 (derivation) and 0.854 (evaluation). When optimized for prognostic certainty, the model yielded a Positive Likelihood Ratio of 14.4, increasing the Positive Predictive Value for worsening injury from a baseline of 29.4% to 85.7%. Time\u2010dependent cost\u2010minimization modeling for a hypothetical phase 3 trial identified an application threshold (sensitivity 80.0%, specificity 91.7%, Number Needed to Screen 3.4) as the global economic optimum, resulting in an illustrative trial cost reduction from $39.0\u2009M to $8.3\u2009M. This proof\u2010of\u2010concept demonstrates multidimensional biomarker models can resolve signal dilution. Distinguishing patients destined for injury progression reduces sample size requirements, which could de\u2010risk novel therapy development.\n                  <\/jats:p>","DOI":"10.1002\/cpt.70320","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T08:55:22Z","timestamp":1778489722000},"page":"1326-1337","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of Acute Liver Injury Trajectory in Patients Following Acetaminophen Overdose: A Multibiomarker Machine Learning Proof\u2010of\u2010Concept Study"],"prefix":"10.1002","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6231-2603","authenticated-orcid":false,"given":"Chris","family":"Humphries","sequence":"first","affiliation":[{"name":"The University of Edinburgh Centre for Cardiovascular Science  Edinburgh UK"},{"name":"The University of Edinburgh Centre for Precision Cell Therapy for the Liver  Edinburgh 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