may consider a compound as a Class 3 impurity, without taking into account the negative carcinogenicity. The answer to this question tells you that such a compound does not need to be controlled as a mutagenic impurity and that for all matters of control, it can be treated similarly to nonmutagenic impurities. The rationale behind this is because the ultimate concern with mutagenic impurities is their potential to cause cancer, so if it has been shown that the compound is noncarcinogenic, even if it is mutagenic, the primary concern is eliminated and the mutagenic properties are irrelevant.
2.4.4 Section 4 – Considerations for Marketed Products
The one Q&A in this section focuses on the meaning of significant increase in clinical dose of marketed products.
The relevant section in the guideline (4.3 Changes to the Clinical Use of Marketed Products) states the following (with emphasis on the critical phrase): “Changes to the clinical use of marketed products that can warrant a reevaluation of the mutagenic impurity limits include a significant increase in clinical dose, an increase in duration of use (in particular when a mutagenic impurity was controlled above the lifetime acceptable intake for a previous indication that may no longer be appropriate for the longer treatment duration associated with the new indication), or for a change in indication from a serious or life threatening condition where higher acceptable intakes were justified (Section 7.5) to an indication for a less serious condition where the existing impurity acceptable intakes may no longer be appropriate.”
2.4.4.1 Question 4.1
| Question | Answer |
|---|---|
| What does “significant increase in clinical dose” mean in “4.3 Changes to the Clinical Use of the Marketed Products”? | Any increase in dose of the API that would increase any mutagenic impurity to levels above the acceptable limits is considered significant (see tables 2 and 9 and the addendum). In such cases a reevaluation of the mutagenic impurity limits is recommended. |
This essentially means that any increase in the dose warrants a re‐evaluation of the mutagenic impurity limits.
2.4.5 Section 5 – Drug Substance and Drug Product Impurity Assessment
There are no Q&A drafted on this section.
2.4.6 Section 6 – Hazard Assessment Elements
This section has four Q&As, which relate to the following topics:
Recommendations for validation and documentation to provide for in‐house or not commonly used(Q)SAR models.
Expectations for qualification of an (Q)SAR “out of domain” or “non‐coverage” result to assign an impurity to Class 5.
Ames negative impurities with positive clastogenicity study results.
Rationale for follow‐up assays in Note 3.
2.4.6.1 Question 6.1
In Section 6 of the guideline, it says that (emphasis on the critical phrase): “A computational toxicology assessment should be performed using (Q)SAR methodologies that predict the outcome of a bacterial mutagenicity assay (Ref. 6). 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. (Q)SAR models utilizing these prediction methodologies should follow the general validation principles set forth by the Organisation for Economic Co‐operation and Development (OECD).”
| Question | Answer |
|---|---|
| What information and/or documentation should be provided to regulatory agencies to sufficiently demonstrate validation of (Q)SAR models that are developed in‐house or are not commonly used? | Section 6 of ICH M7 states that “(Q)SAR models utilizing these prediction methodologies should follow the general validation principles set forth by the Organization for Economic Co‐operation and Development (OECD)” (OECD Validation 2007). In the context of ICH M7, the OECD Principles of (Q)SAR Validation are:A defined end point – the model should be trained using experimental data generated according to the standard OECD protocol for the in vitro bacterial reverse mutation assay.An unambiguous algorithm – the algorithm used to construct the model should be disclosed. It should be clear whether the model is considered statistical (constructed via machine learning) or expert rule‐based (created from human expert‐derived knowledge).A defined domain of applicability – describe whether a test chemical falls within the model's applicability domain and how it is calculated. It should warn the user when the model does not have enough information to make a reliable prediction on a chemical.Appropriate measures of goodness‐of–fit, robustness, and predictivity – the model should be evaluated and shown to be sufficiently predictive of bacterial reverse mutagenicity. Standard validation techniques that should be used are recall, cross‐validation, and external validation. Evidence that the model has not been over‐fit should also be provided.A mechanistic interpretation – is there adequate information to allow an assessment of mechanistic relevance to be made (e.g. specific descriptors)? For any system developed in‐house or not commonly used, to demonstrate how each model follows these principles and to understand how a (Q)SAR model was developed and validated, submission of the OECD (Q)SAR Model Reporting Format (QMRF) (OECD QRMF 2017) for each model used should accompany each regulatory submission. A harmonized template for the QMRF was developed by the Joint Research Centre (JRC) and EU Member State authorities. This template summarizes and reports key information on (Q)SAR models, including the results of any validation studies, as well as provides supplementary information on applicability of the model to a given chemical. |
Most stakeholders currently use commercial software that have been validated and approved by the regulatory agencies. However, if one were to develop their own in‐house software, then the criteria for validation, as described above, need to be rigorously followed.
2.4.6.2 Question 6.2
Section 6 of the guideline says the following with respect to the interpretation of the (Q)SAR predictions: “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.”
| Question | Answer |
|---|---|
| When an out of domain or noncoverage result is obtained from one of the two (Q)SAR models as described in ICH M7, can the impurity be classified as a Class 5 impurity? | No. Out of domain or noncoverage is not considered equivalent to Class 5. Additional assessment is warranted. Given that the relationship between chemical structure and DNA reactivity is well understood, it is unlikely that a structure with mutagenic potential would be associated with an out of domain result. However, expert review can provide reassurance in assignment of such impurities to Class 5. Expert review may include one or a combination of the following (Amberg et al. 2019):Comparison to structurally similar analogs for which bacterial reverse mutation assay data are available (read‐across approach).Expert review of the chemical structure to determine if there is potential for the chemical to react with DNA.(Q)SAR output from an additional validated model (see Question 6.1) of the same methodology (i.e. expert rule‐based or statistical) that generates a prediction that is within its applicability domain. |
In most cases it is the statistical (Q)SAR prediction that