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(Q)SAR models – important tool of risk assessment to support practical implementation of ICH M7 guideline

  • 29 September 2017
  • (Q)SAR, genotoxic impurities, GTIs, ICH M7, mutagenic impurities, mutagenicity assay, mutagenicity prediction, qsar validation principles, qualification of impurities,

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Lhasa Limited is a not-for-profit organisation who provides companies with software solutions which support informed decision-making on chemical safety, thereby reducing the need for animal testing, improving the lengthy and costly process of developing new drugs and safeguarding human health from the adverse effects of chemicals. Derek Nexus and Sarah Nexus can be used to help meet the ICH M7 guidelines and Mirabilis is a risk assessment tool which allows users to calculate whether a potentially mutagenic impurity will be purged within a synthetic route. It provides an industry standardised approach which is accepted by regulators under the ICH M7 guidelines. For more information, please visit https://www.lhasalimited.org/.


A general concept of qualification of impurities (or degradation products) is described in the ICH Q3A/B guidelines, whereby qualification is defined as the process of acquiring and evaluating data that establishes the biological safety of an individual impurity (or degradation product) or a given impurity (or degradation) profile at the level(s) specified.

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While ICH Q3A/B provide guidance for qualification and control for the majority of the impurities, limited guidance is provided for those impurities that are DNA reactive, i.e. having the potential to induce direct DNA damage through chemical reaction with DNA. The purpose of the ICH M7 guideline is to provide a practical framework that is applicable to the identification, categorization, qualification, and control of these mutagenic impurities to limit potential carcinogenic risk by establishing appropriate levels of them.
This type of mutagenic carcinogen is usually detected in a bacterial reverse mutation (mutagenicity) assay. Other types of genotoxicants that are non-mutagenic typically have threshold mechanisms and usually do not pose carcinogenic risk in humans at the level ordinarily present as impurities.

Therefore to limit a possible human cancer risk associated with the exposure to potentially mutagenic impurities, the bacterial mutagenicity assay is used to assess the mutagenic potential and the need for controls. Structure-based assessments are useful for predicting bacterial mutagenicity outcomes based upon the established knowledge. There are a variety of approaches to conduct this evaluation including a review of the available literature, and/or computational toxicology assessment such as (Q)SAR.

(Q)SAR and SAR, in the context of ICH M7 guideline, refers to the relationship between the molecular (sub) structure of a compound and its mutagenic activity using (Quantitative) Structure-Activity Relationships derived from experimental data.


(Q)SAR technology becomes especially important whenever there are not enough empirical data for hazard identification and risk assessment purposes. (Q)SAR represents a technology aimed at providing estimates of laboratory test results before the tests are conducted. The development and application of non-testing methods is based on the similarity principle, i.e., the hypothesis that similar chemical structures should exhibit similar chemical behaviour.

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According to ICH M7 guideline, a computational toxicology assessment should be performed using (Q)SAR methodologies that predict the outcome of a bacterial mutagenicity assay. This could lead to a classification into Class 3, 4, or 5 of 5 Classes described in the ICH M7 guideline and which constitute Impurities Classification with Respect to Mutagenic and Carcinogenic Potential. 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).



It is important to note, that the introduction of a new technology into formal decision-making processes have triggered some time ago a discussion related to the barriers to acceptance of (Q)SARs by regulatory agencies. It was considered, that a critical element of regulatory acceptance is the creation of a flexible scientific validation process for (Q)SARs which allows individual regulatory agencies to establish the reliability of (Q)SAR estimates. In other words, the acceptance of (Q)SARs as a non-testing alternative source of data in making decisions is based on the reliability and transparency of a specific (Q)SAR model within a specific regulatory context.

To facilitate the consideration of a (Q)SAR model for regulatory purposes, it should be associated with the following information:

  1. a defined endpoint;
  2. an unambiguous algorithm;
  3. a defined domain of applicability;
  4. appropriate measures of goodness-of-fit, robustness and predictivity;
  5. a mechanistic interpretation, if possible.

As acceptance of (Q)SAR grows to fill the need for data, it is anticipated that statistical validity will remain crucial while mechanistic interpretation of the models and explanation of the (Q)SAR results will be required.

The development of a set of agreed principles was considered important, not only to provide regulatory bodies with a scientific basis for making decisions on the acceptability (or otherwise) of data generated by (Q)SARs, but also to promote the mutual acceptance of (Q)SAR models by improving the transparency and consistency of (Q)SAR reporting.

Update [06/04/2021]


predictive analytocs


It should also be noted that ICH M7 foresees the use of in - silico models in the control of mutagenic impurities and defines four potential approaches to the development of a control strategy, where option 4 relies on understanding of process parameters and impact on residual impurity levels (including fate and purge knowledge) with sufficient confidence that the level of the impurity in the drug substance will be below the acceptable limit such that no analytical testing is recommended for this impurity. (i.e., the impurity does not need to be listed on any specification).

The result of an appropriate risk assessment might be shown as an estimated purge factor for clearance of the impurity by the process.


ICH M7 is intended to provide guidance for new drug substances and new drug products during their clinical development and subsequent applications for marketing. It also applies to post approval submissions of marketed products, and to new marketing applications for products with a drug substance that is present in a previously approved product.

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Source: ICH, EMA and OECD websites