{
  "abstract": "Drug-induced liver injury (DILI) remains a major challenge in drug development because existing in vitro models poorly predict clinical hepatotoxicity. 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 Cmax 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 Cmax. Sulindac injury signatures were significantly enhanced by TNF 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, USA; Department of Internal Medicine, Gastroenterology and"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "name": "S. R. Meyer"
    },
    {
      "name": "M. A. Garcia"
    },
    {
      "name": "N. Zhao"
    },
    {
      "name": "S. F. Boisvert"
    },
    {
      "name": "M. McConnachie"
    },
    {
      "name": "C. J. Zhang"
    },
    {
      "name": "B. S. Halligan"
    },
    {
      "name": "A. M. Tidball"
    },
    {
      "name": "A. Tucker"
    },
    {
      "name": "J. Kaganove"
    },
    {
      "name": "J. W. Wotring"
    },
    {
      "name": "J. A. Yates"
    },
    {
      "name": "D. Flores"
    },
    {
      "name": "R. J. Fontana"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Z. Sexton"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bmyp2acxs4225.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.29.755355",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.29.755355v1.full",
  "id": "bmyp2acxs4225",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.29.755355v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "pharmacology and toxicology"
    ],
    [
      "openalex",
      "3004|Drug-Induced Hepatotoxicity and Protection"
    ]
  ],
  "openalex": "W7219862693",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.29.755355v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bmyp2acxs4225.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:57Z",
      "native_id": "10.64898/2026.09.29.755355",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.29.755355v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "biorxiv:pharmacology and toxicology",
      "subfield": "pharmacology-toxicology"
    }
  ],
  "title": "High-Throughput Morphological Profiling of Patient-Derived Human Liver Organoids for Hepatotoxicity Hazard Identification and Compound Prioritization",
  "title_text": "High-Throughput Morphological Profiling of Patient-Derived Human Liver Organoids for Hepatotoxicity Hazard Identification and Compound Prioritization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:57Z",
  "url": "https://research.draconitic.com/paper/bmyp2acxs4225",
  "venue": "bioRxiv",
  "versions": []
}