The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.

中文翻译：

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过渡到ICH M7（Q）SAR分析中的复合细菌致突变性模型。

国际协调理事会（ICH）M7（R1）指南描述了互补（定量）结构-活性关系（（Q）SAR）模型的使用，以评估新药和非专利药中药物杂质的诱变潜力。从历史上看，CASE Ultra和Leadscope软件平台使用两种不同的基于统计的模型来预测GC（鸟嘌呤-胞嘧啶）和AT（腺嘌呤-胸腺嘧啶）位点的突变，以全面评估细菌诱变。在本研究中，开发了涵盖多种突变类型的复合细菌诱变模型。这些新模型的化学物质数量（n = 9,254和n = 13,514）是对应的非复合模型的两倍以上，并且显示出更好的毒性载体覆盖范围。此外，通过减少需要审查的模型输出数量，使用单个复合细菌诱变性模型可简化ICH M7（Q）SAR工作流程中的杂质分析。一组代表专利药物化学空间的388种药物杂质的外部验证结果表明，性能统计数据的敏感性范围为66-82％，阴性预测率为91-95％和覆盖率为96％。这项工作代表了这些（Q）SAR模型及其在ICH M7（R1）下的使用的重大增强，通过在评估药物杂质的细菌诱变潜力时具有更高的预测准确性，适用性和效率，提高了患者的安全性。一组代表专利药物化学空间的388种药物杂质的外部验证结果表明，性能统计数据的敏感性范围为66-82％，阴性预测率为91-95％和覆盖率为96％。这项工作代表了这些（Q）SAR模型及其在ICH M7（R1）下的使用的重大增强，通过在评估药物杂质的细菌诱变潜力时具有更高的预测准确性，适用性和效率，提高了患者的安全性。一组代表专利药物化学空间的388种药物杂质的外部验证结果表明，性能统计数据的敏感性范围为66-82％，阴性预测率为91-95％和覆盖率为96％。这项工作代表了这些（Q）SAR模型及其在ICH M7（R1）下的使用的重大增强，通过在评估药物杂质的细菌诱变潜力时具有更高的预测准确性，适用性和效率，提高了患者的安全性。