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Development of the Methodology for in Silico Reactivity-Based Purge Predictions: Making Mirabilis Think Like a Chemist
Organic Process Research & Development ( IF 3.4 ) Pub Date : 2023-03-29 , DOI: 10.1021/acs.oprd.3c00003
James A. McManus 1 , Rocío Lopez-Rodríguez 1 , Natasha S. Murphy 1 , Lara James 1 , Daniel C. O’Connor 1 , Georgina C. Gavins 1 , Michael J. Burns 1
Affiliation  

Synthetic routes to drug substances can result in the introduction of potentially mutagenic impurities (PMIs). The ICH M7 guideline offers a range of control options that assures that the level of this impurity in the drug substance and drug product is below the acceptable limit. Control option 4 leverages the use of predicted purge and outlines the method of utilizing chemical knowledge, literature evidence, and process knowledge to predict the purge of a PMI during drug substance synthesis. If the predicted levels of an impurity in the API are sufficiently lower than the acceptable limit, there will be no requirement for routine analytical testing. Mirabilis is an in silico tool that offers a standardized and conservative approach for the purge calculation of PMIs. The recent developments to methodology for assessing reactivity-based purges within Mirabilis aim to make predictions in a manner more consistent with how a chemist would assess the same situation. The condition-based approach considers the reactants and reagents present and how they may interact, bringing about significant improvements to the specificity and applicability of predictions versus the previous transformation-based approach. Purge predictions for reactivity are now available for 30 impurity types, including N-nitroso compounds and secondary aliphatic amines. Three case studies demonstrate how the new approach provides purge calculations that better align with expert users due to the increased specificity of the predictions.

中文翻译:

基于计算机反应性的清除预测方法的开发:让紫茉莉像化学家一样思考

原料药的合成途径可能会导致引入潜在的致突变杂质 (PMI)。ICH M7 指南提供了一系列控制选项,确保原料药和药品中这种杂质的含量低于可接受的限度。控制选项 4 利用预测清除,并概述了利用化学知识、文献证据和工艺知识来预测原料药合成过程中 PMI 清除的方法。如果 API 中杂质的预测水平充分低于可接受的限度,则不需要进行常规分析测试。Mirabilis 是一种计算机工具,为 PMI 的清除计算提供标准化且保守的方法。评估紫茉莉中基于反应性的净化的方法的最新进展旨在以与化学家评估相同情况的方式更加一致的方式做出预测。基于条件的方法考虑了存在的反应物和试剂以及它们如何相互作用,与之前基于转换的方法相比,显着提高了预测的特异性和适用性。现在可对 30 种杂质类型进行反应性清除预测,包括N-亚硝基化合物和脂肪族仲胺。三个案例研究展示了新方法如何提供因预测特异性增加而更好地与专家用户保持一致的清除计算。
更新日期:2023-03-29
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