![]() Very few successful QSAR models have been reported for predicting in vivo toxicity endpoints that are applicable to the diverse compounds of environmental interest ( 5, 7, 8). Most of these studies included a relatively small number of congeneric compounds and as a result, they had limited applicability for compounds outside of the modeling set. There are several shortcomings of earlier toxicity QSAR models that should be pointed out. The summary of several models reported in earlier publications on acute rodent toxicity are given in Table 1. Many Quantitative Structure Activity Relationship (QSAR) models have been developed for different toxicity endpoints to address this challenge ( 3– 6). Significant savings could be achieved if accurate predictions of potential toxicity could be used to prioritize compound selection for experimental testing, especially for testing in vivo. ![]() The current strategies and guidelines for toxicity testing were described in a recent review ( 1).Īlthough the experimental protocols for toxicity testing have been developed for many years and the cost of compound testing has been reduced significantly, computational chemical toxicology continues to be a viable approach to reduce both the amount of effort and the cost of experimental toxicity assessment ( 2). The test battery varies slightly for pharmaceutical compounds, industrial compounds, and pesticides. Salmonella typhimurium mutation test), one mammalian cell gene mutation assay (e.g., mouse lymphoma cell mutation test) and one in vivo micronucleus test. This test includes one bacterial reverse mutation assay (e.g. ![]() Food and Drug Administration (FDA) and other regulatory agencies. Environmental Protection Administration (EPA), U. For example, a so called “Standard Battery for Genotoxicity Test” was established by the International Conference on Harmonization, U. To address this need, standard experimental protocols have been established by chemical industry, pharmaceutical companies, and government agencies to test chemicals for their toxic potential. It is important to evaluate the toxicity of all commercial chemicals, especially the High Production Volume (HPV) 1 compounds as well as drugs or drug candidates, since these compounds could directly affect human health. The validated consensus LD 50 models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.Ĭhemical toxicity can be associated with many hazardous biological effects such as gene damage, carcinogenicity, or induction of lethal rodent or human diseases. The consensus models afforded higher prediction accuracy for the external validation dataset with the higher coverage as compared to individual constituent models. Ultimately, several consensus models were developed by averaging the predicted LD 50 for every compound using all 5 models. 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. 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. QSAR models of five different types were developed for the modeling set. The remaining 3,913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. 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 dataset was selected that included all 3,472 compounds used in the TOPKAT’s training set. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. In this study, a comprehensive dataset of 7,385 compounds with their most conservative lethal dose (LD 50) values has been compiled. Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints.
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