Assessing the utility of common arguments used in expert review of in silico predictions as part of ICH M7 assessments.

Expert review of two predictions, made by complementary (quantitative) structure-activity relationship models, to an overall conclusion is a key component of using in silico tools to assess the mutagenic potential of impurities as part of the ICH M7 guideline. In lieu of a specified protocol, numerous publications have presented best practise guides, often indicating the occurrence of common prediction scenarios and the evidence required to resolve them. A semi-automated expert review tool has been implemented in Lhasa Limited's Nexus platform following collation of these common arguments and assignment to the associated prediction scenarios made by Derek Nexus and Sarah Nexus.

Risk assessment in the 21st century: where are we heading?

Reliance on animal tests for chemical safety assessment is increasingly being challenged, not only because of ethical reasons, but also because they procrastinate regulatory decisions and because of concerns over the transferability of results to humans. New approach methodologies (NAMs) need to be fit for purpose and new thinking is required to reconsider chemical legislation, validation of NAMs and opportunities to move away from animal tests. This article summarizes the presentations from a symposium at the 2022 Annual Congress of the British Toxicology Society on the topic of the future of chemical risk assessment in the 21st century.

Development of a network of carcinogenicity adverse outcome pathways and its employment as an evidence framework for safety assessment.

The traditional paradigm for safety assessment of chemicals for their carcinogenic potential to humans relies heavily on a battery of well-established genotoxicity tests, usually followed up by long-term, high-dose rodent studies. There are a variety of problems with this approach, not least that the rodent may not always be the best model to predict toxicity in humans. Consequently, new approach methodologies (NAMs) are being developed to replace or enhance predictions coming from the existing assays.

Employing an adverse outcome pathway framework for weight-of-evidence assessment with application to the ICH S1B guidance addendum.

Across industry, there is a paradigm shift occurring for carcinogenicity testing, with the focus moving from long term animal studies to alternative approaches. Based on the explorative work done in recent years, the International Council for Harmonization (ICH) recently published a draft addendum to the S1B guidance, which allows for a weight-of-evidence (WoE) assessment to be conducted based on data gathered throughout the pharmaceutical development process and literature to mitigate some testing in rodents if the body of evidence clearly shows undertaking an animal lifetime study would not add value to the risk assessment. While several alternative approaches already exist, and other new approach methodologies (NAMs) are being explored, all of which can contribute to this WoE, it is important that all the evidence can be combined in a meaningful and consistent way to reach a conclusion.

Beyond adverse outcome pathways: making toxicity predictions from event networks, SAR models, data and knowledge.

Adverse outcome pathways have shown themselves to be useful ways of understanding and expressing knowledge about sequences of events that lead to adverse outcomes (AOs) such as toxicity. In this paper we use the building blocks of adverse outcome pathways-namely key events (KEs) and key event relationships-to construct networks which can be used to make predictions of the likelihood of AOs. The networks of KEs are augmented by data from and knowledge about assays as well as by structure activity relationship predictions linking chemical classes to the observation of KEs.

Management of pharmaceutical ICH M7 (Q)SAR predictions - The impact of model updates.

Pharmaceutical applicants conduct (Q)SAR assessments on identified and theoretical impurities to predict their mutagenic potential. Two complementary models-one rule-based and one statistical-based-are used, followed by expert review. (Q)SAR models are continuously updated to improve predictions, with new versions typically released on a yearly basis.

The importance of expert review to clarify ambiguous situations for (Q)SAR predictions under ICH M7.

The use of in silico predictions for the assessment of bacterial mutagenicity under the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline is recommended when two complementary (quantitative) structure-activity relationship (Q)SAR models are used. Using two systems may increase the sensitivity and accuracy of predictions but also increases the need to review predictions, particularly in situations where results disagree. During the 4th ICH M7/QSAR Workshop held during the Joint Meeting of the 6th Asian Congress on Environmental Mutagens (ACEM) and the 48th Annual Meeting of the Japanese Environmental Mutagen Society (JEMS) 2019, speakers demonstrated their approaches to expert review using 20 compounds provided ahead of the workshop that were expected to yield ambiguous (Q)SAR results.

Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals.

Important considerations for the validation of QSAR models for in vitro mutagenicity.

While high-level performance metrics generated from the validation of quantitative structure-activity relationship (QSAR) systems can provide valuable information on how well these models perform and where they need to be improved, they require appropriate interpretation. There is no universal performance metric which will answer all of the questions a user might ask relating to a model, and therefore, a combination of metrics should usually be considered. Furthermore, results may vary according to the chemical space being used to validate a model, and, in some cases, it may be the validation data which is lacking or ambiguous rather than the prediction being made.

Extrapolation of in vitro structural alerts for mutagenicity to the in vivo endpoint.

As part of the hazard and risk assessment of chemicals in man, it is important to assess the ability of a chemical to induce mutations in vivo. Because of the commonalities in the molecular initiating event, mutagenicity in vitro can correlate well to the in vivo endpoint for certain compound classes; however, the difficulty lies in identifying when this correlation holds true. In silico alerts for in vitro mutagenicity may therefore be used as the basis for alerts for mutagenicity in vivo where an expert assessment is carried out to establish the relevance of the correlation.

Utility of published DNA reactivity alerts.

The identification of impurities with mutagenic potential is required for any potential pharmaceutical. The ICH M7 guidelines state that two complementary in silico toxicity prediction tools may be used to predict the mutagenic potential of pharmaceutical impurities. An expert review of the resulting in silico predictions is required, and numerous publications have been released to guide the expert review process.

Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained.

The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity.

Using in vitro structural alerts for chromosome damage to predict in vivo activity and direct future testing.

While the in vivo genotoxicity of a compound may not always correlate well with its activity in in vitro test systems, for certain compound classes a good overlap may exist between the two endpoints. The difficulty, however, lies in establishing the cases where this relationship holds true and selecting the most appropriate protocol to highlight any potential in vivo hazard. With this in mind, a project was initiated in which existing structural alerts for in vitro chromosome damage in the expert system Derek Nexus were assessed for their relevance to in vivo activity by assessing their predictivity against an in vivo chromosome damage data set.