P-3.53An improved workflow to perform in silico mutagenicity assessment of impurities as per ICH M7 guideline
P-3.53An improved workflow to perform in silico mutagenicity assessment of impurities as per ICH M7 guideline
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P-3.53改进的工作流程，根据 ICH M7 指南对杂质进行计算机致突变性评估
DOI：
10.1016/j.toxlet.2014.06.563
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发表时间：
2014
影响因子：
3.5
通讯作者：
A. Sedykh
中科院分区：
文献类型：
--
作者：
R. Saiakhov;S. Chakravarti;A. Sedykh
目的：计算机工具的使用是ICH M7指导原则的中心点之一。然而，在真实的生活中的DNA反应性杂质的计算评估仍然是具有挑战性的。本研究的目的是使用QSAR统计系统和定量读取建模方法开发和应用有效的工作流程，以获得符合ICH M7建议的可靠遗传毒性评估。新的多层方法提供了毒性警报的详细信息，并减少了假阳性，假阴性，同时增加了测试覆盖率。这种计算机模拟方法可成功用于评估新药候选物、杂质和代谢产物的致突变性。方法和数据：CASE Ultra是一个基于QSAR统计的计算机程序，它可以自动从化学数据库中提取结构-活性知识，并将这些知识应用于预测测试化学品的活性。MultiCASE Inc.开发的几种模型。单独和在与FDA药物评价和研究中心的研究合作协议内，以及用于定量预测毒性的跨读建模技术，该技术使用化学品的邻近分布。结合几种模型和跨引擎读取的多层方法用于细菌致突变性评价，导致灵敏度、特异性和覆盖率显著提高。研究结果：使用外部验证集和真实的生活案例研究作为示例，证明了致突变杂质评估的各种情况下有效工作流程导致的预测性能的改善。http：//dx. doi。org/10.1016/j. toxlet。2014.06. 563
Purpose: Use of in silico tools is one of the central points of ICH M7 guideline. However computational assessment of DNA reactive impurities in real life is still challenging. Purpose of this study is to develop and apply an effective workflow using a QSAR statistical system and a quantitative read across modeling methodology to obtain reliable genotoxicity assessments adhering to the ICH M7 recommendations. The novel multi-tier methodology provides the detailed information of toxicity alerts and reduces false positives, false negatives while increasing the test coverage. This in silico approach can be successfully used for assessment of mutagenicity of new drug candidates, impurities and metabolites. Method and data: CASE Ultra is a QSAR statistics based computer program that automatically extracts structure-activity knowledge from chemical databases and applies the knowledge for predicting activity in test chemicals. Several models, developed by MultiCASE Inc. alone and within Research Cooperation Agreement with Center for Drug Evaluation and Research of FDA were utilized, as well as a read across modeling technique for quantitative prediction of toxicity that uses the neighborhood profiles of chemicals. A multi-tier approach in combining several models and read across engine for bacterial mutagenicity evaluation resulted in significant improvements in the sensitivity, specificity and coverage. Results of the study: The improvements in the predictive performance as a result of the effective workflow is demonstrated for various scenarios of mutagenic impurity assessments, using an external validation set and real life case studies as examples. http://dx. doi. org/10.1016/j. toxlet. 2014.06. 563