The assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals is regulated under the ICH M7 guideline, which was used as a template for the related EMA guideline on assessment and control of mutagenic impurities in veterinary medicinal products, with amendments introduced in order to cover the issues specific to VMPs.
The two guidelines acknowledge that structure-based assessments are useful for predicting bacterial mutagenicity outcomes based upon the established knowledge.
It is specified in the guideline that hazard assessment involves an initial analysis of actual and potential impurities by conducting database and literature searches for carcinogenicity and bacterial mutagenicity data in order to classify them as Class 1, 2, or 5. If data for such a classification are not available, an assessment of Structure-Activity Relationships (SAR) that focuses on bacterial mutagenicity predictions should be performed. This could lead to a classification into Class 3, 4, or 5. It is further specified that a computational toxicology assessment should be performed using (Q)SAR methodologies that predict the outcome of a bacterial mutagenicity assay.
Two (Q)SAR prediction methodologies that complement each other should be applied. One methodology should be expert rule-based and the second methodology should be statistical-based. The absence of mutagenic evidence from the two complementary (Q)SAR methodologies is sufficient to conclude that the impurity is of no mutagenic concern, and no further testing is recommended (Class 5). In the case the impurity possesses a structural alert that is shared (e.g., same structural alert in the same position and chemical environment) with the drug substance or related compounds that have been tested and are non-mutagenic, it can be considered as non-mutagenic (Class 4 ).
If warranted, the outcome of any computer system-based analysis can be reviewed with the use of expert knowledge in order to provide additional supportive evidence on relevance of any positive, negative, conflicting or inconclusive prediction and provide a rationale to support the final conclusion.