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Updated: Mar 25, 2026
Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
Published on: January 31, 2022
Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope
Denis Mulliner1, Friedemann Schmidt1, Manuela Stolte1
1R&D DSAR/Preclinical Safety FF, Sanofi-Aventis Deutschland GmbH , Industriepark Hoechst, Building H831, D-65926 Frankfurt am Main, Germany.
Abstract:
Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate in preclinical models, and it can originate from pharmacologically unrelated drug effects, such as pathway interference, metabolism, and drug accumulation. Because liver toxicity still ranks among the top reasons for drug attrition, the reliable prediction of adverse hepatic effects is a substantial challenge in drug discovery and development. To this end, more effort needs to be focused on the development of improved predictive in-vitro and in-silico approaches. Current computational models often lack applicability to novel pharmaceutical candidates, typically due to insufficient coverage of the chemical space of interest, which is either imposed by size or diversity of the training data. Hence, there is an urgent need for better computational models to allow for the identification of safe drug candidates and to support experimental design. In this context, a large data set comprising 3712 compounds with liver related toxicity findings in humans and animals was collected from various sources. The complex pathology was clustered into 21 preclinical and human hepatotoxicity endpoints, which were organized into three levels of detail. Support vector machine models were trained for each endpoint, using optimized descriptor sets from chemometrics software. The optimized global human hepatotoxicity model has high sensitivity (68%) and excellent specificity (95%) in an internal validation set of 221 compounds. Models for preclinical endpoints performed similarly. To allow for reliable prediction of "truly external" novel compounds, all predictions are tagged with confidence parameters. These parameters are derived from a statistical analysis of the predictive probability densities. The whole approach was validated for an external validation set of 269 proprietary compounds. The models are fully integrated into our early safety in-silico workflow.
Insights
Developing predictive computational models for drug-induced liver injury (DILI) is crucial. This study presents a robust in-silico approach using support vector machines trained on a large dataset, achieving high accuracy in predicting hepatotoxicity for novel drug candidates.
Area of Science:
- Pharmacology and Toxicology
- Computational Chemistry
- Drug Discovery
Background:
- Drug-induced liver injury (DILI) is a major cause of drug attrition, difficult to predict with current preclinical models.
- Existing computational models often fail to cover the chemical space of novel drug candidates.
- Improved in-silico methods are urgently needed for early identification of safe drug candidates.
Purpose of the Study:
- To develop and validate advanced in-silico models for predicting drug-induced liver toxicity.
- To enhance the reliability of computational approaches in drug discovery and development.
- To integrate predictive models into early safety workflows for experimental design support.
Main Methods:
- Collected a large dataset of 3712 compounds with human and animal liver toxicity findings.
- Clustered hepatotoxicity into 21 detailed preclinical and human endpoints.
- Trained support vector machine models using optimized descriptors and validated with internal and external datasets.
Main Results:
- The global human hepatotoxicity model demonstrated high sensitivity (68%) and excellent specificity (95%) on an internal validation set.
- Models for preclinical endpoints showed similar performance.
- Predictions were tagged with confidence parameters derived from statistical analysis for external validation.
Conclusions:
- The developed in-silico approach provides a reliable method for predicting drug-induced liver toxicity in novel compounds.
- The models are integrated into an early safety workflow, aiding in the identification of safer drug candidates.
- This approach addresses the limitations of current computational models by improving chemical space coverage and prediction accuracy.
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