Using Chemical-Induced Gene Expression in Cultured Human Cells to Predict Chemical Toxicity
Abstract
Chemical toxicity is conventionally evaluated in animal models. However, animal models are resource intensive; moreover, they face ethical and scientific challenges because the outcomes obtained by animal testing may not correlate with human responses. To develop an alternative method for assessing chemical toxicity, we investigated the feasibility of using chemical-induced genome-wide expression changes in cultured human cells to predict the potential of a chemical to cause specific organ injuries in humans. We first created signatures of chemical-induced gene expression in a vertebral-cancer of the prostate cell line for ∼15,000 chemicals tested in the US National Institutes of Health Library of Integrated Network-Based Cellular Signatures program. We then used the signatures to create naı̈ve Bayesian prediction models for chemical-induced human liver cholestasis, interstitial nephritis, and long QT syndrome. Detailed cross-validation analyses indicated that the models were robust with respect to false positives and false negatives in the samples we used to train the models and could predict the likelihood that chemicals would cause specific organ injuries. In addition, we performed a literature search for drugs and dietary supplements, not formally categorized as causing organ injuries in humans but predicted by our models to be most likely to do so. We found a high percentage of these compounds associated with case reports of relevant organ injuries, lending support to the idea that in vitro cell-based experiments can be used to predict the toxic potential of chemicals. We believe that this approach, combined with a robust technique to model human exposure to chemicals, may serve as a promising alternative to animal-based chemical toxicity assessment.
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By inducing changes of whole genome expression of cultured human cells of a specific type with a test drug, and analyzing transcriptome changes, toxicity of the drug to the cell type and corresponding organ can be predicted.
“One can induce changes of whole genome expression of cultured human cells of a specific type by adding a test drug to the culture. By analyzing changes in the transcriptome, toxicity of the drug to the cell type, and to the corresponding organ, can be predicted [131].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [131]
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- Authors
- Liu, R., Yu, X., & Wallqvist, A.
- Affiliations
- Telemedicine & Advanced Technology Research Center (US)
- Corresponding author
- Liu, Ruifeng
- Wallqvist, Anders
- Venue
- Chemical Research in Toxicology
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-11-04
- Volume
- 29
- Issue
- 11
- Pages
- 1883-1893
- ISSN
- 0893-228X (ISSN-L)
- 1520-5010
- OpenAlex
- W2535020870
- Cited by
- 8
- References
- 52
- Citations by year
- 2025: 1 · 2022: 1 · 2019: 2 · 2018: 1 · 2017: 3
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Chemical Research in Toxicology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Defense Threat Reduction Agency (CBCall14-CBS-05-2-0007)
- U.S. Army
- Topics
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
- Keywords
- chemical toxicity prediction, cultured human cells, in vitro toxicity testing, alternatives to animal testing, interstitial nephritis, long QT syndrome, organ damage, prostate cancer cell lines, drug safety, dietary supplements
- MeSH terms
- Bayes Theorem, Cells, Cultured, Humans, Models, Theoretical, Gene Expression, Toxicity Tests
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