The control of genotoxic impurities (GTIs) is a crucial activity that is performed for any new chemical entity intended for clinical use. A key element of this is the quality risk assessment. This article seeks to examine the primary components of such a strategy, focusing specifically on the effective use of in silico assessment tools to augment this process, in particular the calculation of theoretical purge factors based on the physicochemical properties of a specific GTI and its interrelationship to the process.

Regulatory requests that marketing authorization holders for chemically synthesized active substances risk assess their medicines for the potential presence of N-nitrosamines have led to a renewed interest in amine nitrosation. We have used published mechanistic and kinetic studies of amine nitrosation to assess the risk that traces of nitrite in the water used during active pharmaceutical ingredient (API) manufacturing could give rise to significant levels of N-nitrosamines. We conclude that the levels of nitrite typically found in water used for API manufacture are very low (<0.01 mg/L) and will not give rise to significant levels of N-nitrosamines through reaction with basic secondary amines (pK a > 9.5) in the majority of cases. The use of less basic amines, elevated processing temperatures, or low pH conditions in combination with elevated levels of nitrite have the potential to generate levels of N-nitrosamines that could lead to significant quantities being present in the isolated API if the downstream processing does not provide an adequate purge. The kinetic models described may be used to risk assess specific situations or processes. For example, the addition of traces of dimethylamine to a nitrosation reaction is predicted to lead to the rapid, quantitative formation of N-nitroso dimethylamine. Simple tertiary alkylamines can nitrosate via a dealkylative process, which is significantly slower than secondary amine nitrosation. Therefore, they do not represent a risk of N-nitrosamine formation under conditions where there is no significant risk of secondary amine nitrosation.

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The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

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The ICH M7 guideline describes a consistent approach to identify, categorize, and control DNA reactive, mutagenic, impurities in pharmaceutical products to limit the potential carcinogenic risk related to such impurities. This paper outlines a series of principles and procedures to consider when generating (Q)SAR assessments aligned with the ICH M7 guideline to be included in a regulatory submission. In the absence of adequate experimental data, the results from two complementary (Q)SAR methodologies may be combined to support an initial hazard classification. This may be followed by an assessment of additional information that serves as the basis for an expert review to support or refute the predictions. This paper elucidates scenarios where additional expert knowledge may be beneficial, what such an expert review may contain, and how the results and accompanying considerations may be documented. Furthermore, the use of these principles and procedures to yield a consistent and robust (Q)SAR-based argument to support impurity qualification for regulatory purposes is described in this manuscript.