Development of the Methodology for in Silico Reactivity-Based Purge Predictions: Making Mirabilis Think Like a Chemist

Abstract

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.

Identifying the Risk of Formation of Nitrosamines and Other Potentially Mutagenic Impurities during API Manufacture Using In Silico Risk Assessment

“…The software already incorporates information on the potential formation of new PMIs that originate from existing structures tracked within the purge factor analysis, with 34% of the implemented purging reactions leading to new potentially mutagenic structures. For the new alerts feature, the focus is on nonmutagenic origins for PMI formation, thereby targeting the research to areas where information is currently outside the scope of the software …”

“…Recent developments in the methodology for the automated assignment of reactivity-based purge factors incorporate the functional groups and reagents which the user has indicated are present in a reaction. 20 Common reactive functional groups are identified by substructure matching against a predefined dictionary of relevant structural patterns. Reagents may be identified by synonym matching against a database containing common reagents or can be manually associated with a reaction stage by the user.…”

“…For the new alerts feature, the focus is on nonmutagenic origins for PMI formation, thereby targeting the research to areas where information is currently outside the scope of the software. 20 Alerts consist of a concise expert review of condition combinations which may lead to an impurity type being formed. Investigation of the scientific literature, including textbooks and specific review articles, 3,30 builds a general picture of the functional group interconversions that can lead to the impurity.…”

“…The development of an in silico tool, Mirabilis, to provide a semiautomated, standardized method to predict the purge of a PMI based on the original work of Teasdale et al has previously been reported . More recently, further refinements have enabled an improved understanding of the conditions (reagents, structural features) present within each stage . The identification of risk is something which can be done manually, with a trained chemist likely to identify the key risks utilizing experience and/or existing literature.…”

“…19 More recently, further refinements have enabled an improved understanding of the conditions (reagents, structural features) present within each stage. 20 The identification of risk is something which can be done manually, with a trained chemist likely to identify the key risks utilizing experience and/or existing literature. However, where multiple risks exist (e.g., an obvious and a potential), it is possible to overlook the potential risk in the face of a more immediate known risk.…”