Linking physiology to toxicity using DILIsym®, a mechanistic mathematical model of drug‐induced liver injury
Abstract
The drug development industry faces multiple challenges in the realization of safe effective drugs. Computational modeling approaches can be used to support these efforts. One approach, mechanistic modeling, is new to the realm of drug safety. It holds the promise of not only predicting toxicity for novel compounds, but also illuminating the mechanistic underpinnings of toxicity. To increase the scientific community's familiarity with mechanistic modeling in drug safety, this article seeks to provide perspective on the type of data used, how they are used and where they are lacking. Examples are derived from the development of DILIsym® software, a mechanistic model of drug‐induced liver injury (DILI). DILIsym® simulates the mechanistic interactions and events from compound administration through the progression of liver injury and regeneration. Modeling mitochondrial toxicity illustrates the type and use of in vitro data to represent biological interactions, as well as insights on key differences between in vitro and in vivo conditions. Modeling bile acid toxicity illustrates a case in which the over‐arching mechanism is well accepted, but many mechanistic details are lacking. Modeling was used to identify measurements predicted to strongly impact toxicity. Finally, modeling innate immune responses illustrates the importance of time‐series data, particularly in the presence of positive and negative feedback loops, as well as the need for data from different animal species for better translation. These concepts are germane to most mechanistic models, although the details will vary. The use of mechanistic models is expected to improve the rational design of new drugs. Copyright © 2013 John Wiley & Sons, Ltd.
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Consortiums have been established to develop more comprehensive tissue- and disease-focused models, such as DILIsym (drug-induced liver injury), Certara's QSP Immunogenicity Consortium, and the Critical Path for Alzheimer's Disease.
“Consortiums have also been established to develop more comprehensive tissue‐ and disease‐focused models, such as DILIsym27 (drug‐induced liver injury), Certara's QSP Immunogenicity Consortium, or the Critical Path for Alzheimer's Disease; hence, these may serve in specific cases.”
From Reproducibility of Quantitative Systems Pharmacology Models: Current Challenges and Future Opportunities (2019) · cites this paper as [27]
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- Authors
- Shoda, L. K. M., Woodhead, J. L., Siler, S. Q., Watkins, P. B., & Howell, B. A.
- Affiliations
- The Hamner Institutes for Health Sciences (US)
- Corresponding author
- Howell, Brett A.
- Venue
- Biopharmaceutics & Drug Disposition
- Publisher
- Wiley
- Published
- 2013-11-25
- Volume
- 35
- Issue
- 1
- Pages
- 33-49
- ISSN
- 0142-2782 (ISSN-L)
- 1099-081X
- OpenAlex
- W1609658954
- Cited by
- 75
- References
- 121
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 4 · 2023: 4 · 2022: 5 · 2021: 3 · 2020: 6 · 2019: 10 · 2018: 5 · 2017: 9 · 2016: 11 · 2015: 4 · 2014: 6
- FWCI
- 1.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biopharmaceutics & Drug Disposition (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- drug-induced liver injury, drug safety, mitochondrial toxicity, bile acid toxicity, innate immune response, liver regeneration, in vitro to in vivo translation, time series, positive and negative feedback loops, species differences, toxicity prediction
- MeSH terms
- Animals, Bile Acids and Salts, Humans, Immunity, Innate, Mitochondria, Models, Biological, Software, Chemical and Drug Induced Liver Injury
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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