โ Scribed by Luis G. Valerio Jr.; Kirk B. Arvidson; Emily Busta; Barbara L. Minnier; Naomi L. Kruhlak; R. Daniel Benz
No coin nor oath required. For personal study only.
Computational toxicology employing quantitative structureโactivity relationship (QSAR) modeling is an evidenceโbased predictive method being evaluated by regulatory agencies for risk assessment and scientific decision support for toxicological endpoints of interest such as rodent carcinogenicity. Computational toxicology is being tested for its usefulness to support the safety assessment of drugโrelated substances (e.g. active pharmaceutical ingredients, metabolites, impurities), indirect food additives, and other applied uses of value for protecting public health including safety assessment of environmental chemicals. The specific use of QSAR as a chemoinformatic tool for estimating the rodent carcinogenic potential of phytochemicals present in botanicals, herbs, and natural dietary sources is investigated here by an external validation study, which is the most stringent scientific method of measuring predictive performance. The external validation statistics for predicting rodent carcinogenicity of 43 phytochemicals, using two computational software programs evaluated at the FDA, are discussed. One software program showed very good performance for predicting nonโcarcinogens (high specificity), but both exhibited poor performance in predicting carcinogens (sensitivity), which is consistent with the design of the models. When predictions were considered in combination with each other rather than based on any one software, the performance for sensitivity was enhanced, However, Chiโsquare values indicated that the overall predictive performance decreases when using the two computational programs with this particular data set. This study suggests that complementary multiple computational toxicology software need to be carefully selected to improve global QSAR predictions for this complex toxicological endpoint.
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