Information for Computational Toxicologists
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We were recently involved in a cross-industry project to determine whether (Q)SAR models were fit-for-purpose for classification and labelling. To test this hypothesis, a series of companies across different industrial sectors each compiled a data set of chemicals with experimental acute rat oral data. These chemicals were run against the first version of the Leadscope […]
The ICH M7 guideline1 presents the types of methodologies that are relevant to assess mutagenic impurities. The guideline goes further than simply mentioning that QSAR methods and analogs should be considered prior to conducting an experimental study, and it details two complementary methodologies (statistical and expert rule-based), and how the results should be considered to […]
For toxicological tests, a false negative (i.e., predicting a chemical is negative when it is in fact positive) is one type of error. It is often desirable to minimize the number of false negatives to decrease the risk of missing a toxic chemical. Computational toxicology is a rapid high-throughput test of multiple chemicals, and like […]