bioRxivPharmacology & Toxicology
A Human Liver Organoid Screening Platform for DILI Risk Prediction
C. J. Zhang, S. R. Meyer, M. J. O'Meara, S. Huang, M. M. Capeling, D. Ferrer-Torres, C. J. Childs, J. R. Spence, R. J. Fontana, J. Z. Sexton
Abstract
Background and AimsDrug-induced liver injury (DILI), both intrinsic and idiosyncratic, causes frequent morbidity, mortality, clinical trial failures and post-approval withdrawal. This suggests an unmet need for improved in vitro models for DILI risk prediction that can account for diverse host genetics and other clinical factors. In this study, we evaluated the utility of human liver organoids (HLOs) for high-throughput DILI risk prediction and in an organ-on-chip system. MethodsHLOs were derived from 3 separate iPSC lines and benchmarked on two platforms for their ability to model in vitro liver function and identify hepatotoxic compounds using biochemical assays for albumin, ALT, and AST, microscopy-based morphological profiling, and single-cell transcriptomics: 1) HLOs dispersed in 384-well formatted plates and exposed to a library of compounds. 2) HLOs adapted to a liver-on-chip system. ResultsO_LIDispersed HLOs derived from the 3 iPSC lines had similar DILI predictive capacity to intact HLOs in a high-throughput screening format allowing for measurable IC50 values of compound cytotoxicity. Distinct morphological differences were observed in cells treated with drugs exerting differing mechanisms of toxicity. C_LIO_LIOn-chip HLOs significantly increased albumin production, CYP450 expression, and ALT/AST release when treated with known DILI drugs compared to dispersed HLOs and primary human hepatocytes. On-chip HLOs were able to predict the synergistic hepatotoxicity of tenofovir-inarigivir and showed steatosis and mitochondrial perturbation via phenotypic and transcriptomic analysis with exposure to FIAU and acetaminophen, respectively. C_LI ConclusionsThe high throughput and liver-on-chip system exhibit enhanced in vivo-like function and demonstrate the potential utility of these platforms for hepatotoxicity risk assessment. Tenofovir-inarigivr associated hepatotoxicity was observed and correlates with the clinical manifestation of DILI observed in patients. LAY SUMMARYIdiosyncratic (spontaneous, patient-specific) drug-induced liver injury (DILI) is difficult to study due to the lack of liver models that function as human liver tissue and are adaptable for large-scale drug screening. Human liver organoids grown from patient stem cells respond to known DILI-causing drugs in both a high-throughput and on a physiological "chip" culture system. These platforms show promise in their use as predictive model for novel drugs before entering clinical trials.
- organism: human
- assay: scRNA-seq
- research focus: drug response / drug discovery
- approach: experimental assay
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- High-Throughput Morphological Profiling of Patient-Derived Human Liver Organoids for Hepatotoxicity Hazard Identification and Compound Prioritization
Sophia R. Meyer, Max A. Garcia, … Jonathan Z. Sexton·bioRxiv·Oct 4, 2026
- A High-Throughput Microphysiological Liver Chip System to Model Direct and Idiosyncratic Drug-Induced Liver Injury Using Human Liver Organoids
C. J. Zhang, S. R. Meyer, … J. Z. Sexton·bioRxiv·Dec 1, 2023
- Regenerative human liver organoids (HLOs) in a pillar/perfusion plate for hepatotoxicity assays
S. Shrestha, P. Acharya, … M.-Y. Lee·bioRxiv·Mar 25, 2024
- Qualifying a human Liver-Chip for predictive toxicology: Performance assessment and economic implications
L. Ewart, A. Apostolou, … D. Levner·bioRxiv·Dec 14, 2021
- Improved Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data
Srijit Seal, Dominic P. Williams, … Andreas Bender·bioRxiv·Jan 10, 2024
- Multicellular, fluid flow-inclusive hepatic in vitro models using NANOSTACKS™: a human-relevant model for drug response prediction
Abdullah Chandra Sekhar Talari, Raffaello Sbordoni, … Valon Llabjani·bioRxiv·Aug 12, 2024