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Updated: Sep 26, 2025
11:06
Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
Published on: January 31, 2022
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Quantitative Systems Toxicology and Drug Development: The DILIsym Experience
1Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, Institute for Drug Safety Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. pwatkins@email.unc.edu.
Methods in Molecular Biology (Clifton, N.J.)
|April 19, 2022
Summary
DILIsym®, a Quantitative Systems Toxicology (QST) model, predicts drug-induced liver injury (DILI) by integrating mechanistic data with liver exposure. This approach aids in identifying toxicity risks and accelerates the delivery of safer medications.
Area of Science:
- Toxicology
- Computational Biology
- Pharmacology
Background:
- Drug-induced liver injury (DILI) poses a significant safety concern in drug development.
- Quantitative Systems Toxicology (QST) models offer a mechanistic approach to predict DILI.
- DILIsym® is a QST model developed over a decade through a public-private partnership.
Purpose of the Study:
- To predict the liver safety liability of new drug candidates.
- To integrate quantitative data on parent and metabolite effects on cellular pathways.
- To combine in vitro experimental data with in vivo liver exposure estimates.
Main Methods:
- DILIsym® integrates data on oxidative stress, mitochondrial dysfunction, and bile acid homeostasis.
- Laboratory data from human experimental systems are utilized.
- Estimates of liver exposure are combined with mechanistic data for outcome prediction.
Main Results:
- DILIsym® is frequently used in pharmaceutical industry decision-making.
- Modeling results are included in regulatory communications and NDA submissions.
- The model identifies dominant mechanisms of liver toxicity and aids in risk factor identification.
Conclusions:
- QST modeling, exemplified by DILIsym®, can accelerate the delivery of safer drugs.
- DILIsym® aids in optimizing the interpretation of liver injury biomarkers.
- The model supports patient-specific risk assessment, considering disease states.
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