Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure
Open accessAbstract
Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire data set was selected that included all 3472 compounds used in TOPKAT's training set. The remaining 3913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R(2) of linear regression between actual and predicted LD(50) values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R(2) ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD(50) for every compound using all five models. The consensus models afforded higher prediction accuracy for the external validation data set with the higher coverage as compared to individual constituent models. The validated consensus LD(50) models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.
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LD50 values of the oral rat LD50 data set are relatively difficult to predict as they have a higher experimental uncertainty.
“It was noted that LD 50 values of this data set are relatively difficult to predict as they have a higher experimental uncertainty. 50”
From Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks (2018) · cites this paper as [50]
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Self-citation
- Authors
- Zhu, H., Martin, T. M., Ye, L., Sedykh, A., Young, D. M., & Tropsha, A.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Tropsha, Alexander
- Venue
- Chemical Research in Toxicology
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-10-21
- Volume
- 22
- Issue
- 12
- Pages
- 1913-1921
- ISSN
- 0893-228X (ISSN-L)
- 1520-5010
- PMC
- PMC2796713
- OpenAlex
- W1977573154
- Cited by
- 287
- References
- 45
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 30 · 2023: 32 · 2022: 26 · 2021: 30 · 2020: 18 · 2019: 20 · 2018: 15 · 2017: 10 · 2016: 13 · 2015: 13 · 2014: 11 · 2013: 4 · 2012: 13
- FWCI
- 8.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Chemical Research in Toxicology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion, public-domain)
- UNC Libraries
- Funders
- U.S. Environmental Protection Agency
- National Institutes of Health (ES005948)
- National Institute of General Medical Sciences (GM076059, R21GM076059)
- National Institute of Environmental Health Sciences (P42ES005948, ES005948)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Environmental Toxicology and Ecotoxicology
- Keywords
- QSAR, acute toxicity, oral exposure, toxicity prediction, TOPKAT, applicability domain, consensus model, chemical space coverage, external validation, rodent toxicity
- MeSH terms
- Administration, Oral, Animals, Lethal Dose 50, Models, Theoretical, Organic Chemicals, Quantitative Structure-Activity Relationship, Toxicity Tests, Acute, Rats
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