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We coupled patient-derived human liver organoids (HLOs) with high-content imaging and automated machine learning (ML) to detect hepatotoxicity-associated phenotypes by morphological profiling. HLOs from six DILI patients and one healthy donor (n = 7) retained donor-specific baseline phenotypes in hepatocyte maturity, metabolism, and morphology. ML models trained per line on MAD-robustized, well-level-aggregated median features achieved compound-level AUROCs of 0.88 (95% CI 0.86-0.90) to 0.91 (95% CI 0.89-0.93) and balanced accuracies of 0.81-0.84 in distinguishing 54 hepatotoxicants from 20 non-hepatotoxic controls. Averaging predictions across all seven lines at an optimized threshold gave 94.4% sensitivity (95% CI 84.6-98.8%) and 85.0% specificity (95% CI 62.1-96.8%), substantially outperforming HLO cytotoxicity assays and cancer cell line profiling. Lines were moderately-to-highly concordant (Pearson r = 0.66-0.85), and benchmark concentration modeling revealed reproducible inter-line differences in phenotypic sensitivity (median 4.0-fold, several compounds exceeding 20-fold). Exposure anchoring of benchmark concentrations to human-equivalent plasma concentrations, using in vitro mass-balance dosimetry and curated plasma free fractions, placed organoid points of departure within 100-fold of clinical C\n                  <jats:sub>max<\/jats:sub>\n                  for 60 of 66 compounds (23-fold residual offset, 95% CI 12-45), and placed two of the three misclassified negative controls an order of magnitude above clinical C\n                  <jats:sub>max<\/jats:sub>\n                  . Sulindac injury signatures were significantly enhanced by TNF\u03b1 co-treatment, demonstrating capacity to detect pro-inflammatory-mediated DILI. This platform preserves inter-line variability while enabling reproducible, scalable multi-line consensus hazard classification; because compounds were tested over a common nominal concentration range, the readout defines hepatocellular hazard, which must be combined with exposure as an independent input to estimate clinical DILI risk.\n                <\/jats:p>","DOI":"10.64898\/2026.09.29.755355","type":"posted-content","created":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T16:15:14Z","timestamp":1791130514000},"source":"Crossref","is-referenced-by-count":0,"title":["High-Throughput Morphological Profiling of Patient-Derived Human Liver Organoids for Hepatotoxicity Hazard Identification and Compound Prioritization"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7301-8370","authenticated-orcid":false,"given":"Sophia R.","family":"Meyer","sequence":"first","affiliation":[{"name":"Department of Medicinal Chemistry, College of Pharmacy, University of 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University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1666-4106","authenticated-orcid":false,"given":"Andrew M.","family":"Tidball","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Tucker","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josepha","family":"Kaganove","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI 48109, 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