In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: An industry survey

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• 作者：Krista L. Dobo^a ; ^{krista.l.dobo@pfizer.com} ; Nigel Greene^a ; Charlotta Fred^b ; Susanne Glowienke^c ; James S. Harvey^d ; Catrin Hasselgren^e ; Robert Jolly^f ; Michelle O. Kenyon^a ; Jennifer B. Munzner^a ; Wolfgang Muster^g ; Robin Neft^h ; M. Vijayaraj Reddyⁱ ; Angela T. White^d ; Sandy Weiner^j
• 关键词：Genotoxic impurities ; In silico ; (Q)SAR ; Mutagenicity
• 刊名：Regulatory Toxicology and Pharmacology
• 出版年：2012
• 期刊代码：57_02732300
• 类别：pha
• 出版时间：April, 2012
• 卷：62
• 期：3
• 页码：449-455
• 文件大小：271 K

摘要

With the increasing emphasis on identification and low level control of potentially genotoxic impurities (GTIs), there has been increased use of structure-based assessments including application of computerized models. To date many publications have focused on the ability of computational models, either individually or in combination, to accurately predict the mutagenic effects of a chemical in the Ames assay. Typically, these investigations take large numbers of compounds and use in silico tools to predict their activity with no human interpretation being made. However, this does not reflect how these assessments are conducted in practice across the pharmaceutical industry. Current guidelines indicate that a structural assessment is sufficient to conclude that an impurity is non-mutagenic. To assess how confident we can be in identifying non-mutagenic structures, eight companies were surveyed for their success rate. The Negative Predictive Value (NPV) of the in silico approaches was 94%. When human interpretation of in silico model predictions was conducted, the NPV increased substantially to 99%. The survey illustrates the importance of expert interpretation of in silico predictions. The survey also suggests the use of multiple computational models is not a significant factor in the success of these approaches with respect to NPV.