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The emphasis of the guideline (Section 6) now shifts and focuses on an assessment of the mutagenic potential of impurities identified in the preceding risk assessment. Such an assessment is typically made through the use of in silico SAR systems. The guideline defines the need to apply two (Q)SAR methodologies. One methodology should be expert rule based, and the other methodology should be statistical based; however, the guidance does not define which software packages are preferable; this decision is left to the end user. Importantly, it also highlights the need for an expert evaluation of the results.

The use of two methodologies throws up a number of different permutations; these include not only situations where predictions are conflictory in nature but also out of domain predictions. These arise where the molecule in question, or at least a significant proportion of the molecule, is not recognized by the training set of the in silico tool, and hence it cannot accurately predict its mutagenic potential. In such circumstances, expert evaluation is required to make an overall consensus prediction. Barber et al. [24] examine this in detail. They describe the various scenarios potentially encountered (Figure 2.4), examining how to address conflictory predictions as well as out of domain predictions. Barber et al. provide advice on how to challenge predictions made by both rule based and (Q)SAR systems as well as providing a series of examples that serve to provide effective practical illustration of the key points made within the paper.

Figure 2.4 Decision matrix when evaluating two in silico predictions.

In a related study, Green et al. [25] examined the relative predictive performances of popular commercial in silico systems. Using a data set of some 801 chemicals and pharmaceutical intermediates, they showed the overall accuracy of each of the systems was generally comparable, ranging from 68 to 73%; however, their studies showed significant differences in sensitivity of each system (i.e. how many Ames positive compounds are correctly identified) results varying between 48 and 68%. The studies did not, however, identify any stand out system or specific combination of rule based/(Q)SAR systems. Perhaps the most significant finding of the studies was the number of contradictory predictions observed when two different methodologies were applied, i.e. those where one system predicted positive and the other did not or the statistical models were not able to make a prediction. Over one‐third of all the compounds in this 801 compound data set were seen to give a conflictory prediction. The authors concluded there is clearly a need for expert opinion to be applied to determine the appropriate classification.

Ultimately, the outcome of any such assessment is then classified using the system defined by Mueller et al. [26]. This is shown below in Table 2.2.

Table 2.2 Impurities classification with respect to mutagenic and carcinogenic potential and resulting control actions.

Class	Definition	Proposed action for control (details in Sections 7 and 8)
1	Known mutagenic carcinogens	Control at or below compound‐specific acceptable limit
2	Known mutagens with unknown carcinogenic potential (bacterial mutagenicity positive, no rodent carcinogenicity data)	Control at or below acceptable limits (appropriate TTC)
3	Alerting structure, unrelated to the structure of the DS; no mutagenicity data	Control at or below acceptable limits (appropriate TTC) or conduct bacterial mutagenicity assay; if nonmutagenic = Class 5; if mutagenic = Class 2
4	Alerting structure, same alert in DS or compounds related to the DS (e.g. process intermediates), which have been tested and are nonmutagenic	Treat as nonmutagenic impurity
5	No structural alerts, or alerting structure with sufficient data to demonstrate lack of mutagenicity or carcinogenicity	Treat as nonmutagenic impurity

A particular challenge with respect to Class 4 compounds is defining structural similarity. Mathematical approaches such as Tanimoto scores may be utilized; however, great care is required in their use and similarity cannot simply be defined by a score exceeding a predefined threshold. In all cases it is important to assess the environment, both steric and electronic, in close proximity to the alerting moiety within the impurity in question.

Based on the outcome of the SAR assessment, for those compounds considered a concern, in particular those classified as Class 3, further evaluation in the form of testing may be performed. The earlier scope section of the ICH M7 guideline makes clear that the emphasis is on mutagenic impurities and that the bacterial reverse mutation test (Ames) [10] should be used to follow up any SAR alert.

In addition this section provides an overview of potential in vivo follow‐up tests that can be utilized in order to investigate further a positive bacterial assay. The tests themselves are described in detail in Table 2.3 (based on Note 3 within the guideline).

Table 2.3 Tests to investigate the in vivo relevance of in vitro mutagens (positive bacterial mutagenicity).

Source: Reproduced from ICH M7.

in vivo test	Factors to justify choice of test as fit‐for‐purpose
Transgenic mutation assays	For any bacterial mutagenicity positive. Justify selection of assay tissue/organ
Pig‐a assay (blood)	For directly acting mutagens (bacterial mutagenicity positive without S9a)b
Micronucleus test (blood or bone marrow)	For directly acting mutagens (bacterial mutagenicity positive without S9) and compounds known to be clastogenicb
Rat liver unscheduled DNA synthesis (UDS) test	In particular for bacterial mutagenicity positive with S9 only; responsible liver metabolite known to be generated in test species used to induce bulky adducts
Comet assay	Justification needed (chemical class specific mode of action to form alkaline labile sites or single‐strand breaks as preceding DNA damage that can potentially lead to mutations)Justify selection of assay tissue/organ
Others	With convincing justification

^a S9 – Supernatant fraction obtained from an organ (usually liver) homogenate and contains cytosol and microsomes. The microsomes component of the S9 fraction contains cytochrome P450 isoforms (Phase I metabolism) and other enzyme activities.

^b For indirect acting mutagens (requiring metabolic activation), adequate exposure to metabolite(s) should be demonstrated.

The guideline states that such tests can be used to assess the in vivo relevance of the positive findings of the in vitro bacterial reverse mutation test, suggesting that the results may support the establishment of a compound‐specific limit.