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. Using datasets primarily donated by pharmaceutical companies, an automated analysis of the frequency these prediction scenarios occur, and the likelihood of the associated arguments assigning the correct resolution, could then be conducted. This article highlights that a relatively small number of common arguments may be used to accurately resolve many prediction scenarios to a single conclusion. The use of a standardised method of argumentation and assessment of evidence for a given impurity is proposed to improve the efficiency and consistency of expert review as part of an ICH M7 submission.

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. Adverse outcome pathways have been advocated as a framework for organising evidence in an integrated approach to testing and assessment, which gives context to data and can aid reaching a conclusion as to the adverse outcome (AO). This approach can be combined with a reasoning methodology to give a prediction for an AO and applied to the factors which need to be considered for the ICH S1B WoE to predict for carcinogenicity. Using this approach to the WoE assessment, consistent, scientifically robust, and transparent calls can be made as to whether conducting an animal carcinogenicity study would add value to a human risk assessment and mitigate the need to run animal studies unnecessarily.

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. However, a combination of the data arising from NAMs is likely to be required to improve upon the current paradigm, and consequently a framework is needed to combine evidence in a meaningful way. Adverse outcome pathways (AOPs) represent an ideal construct on which to organize this evidence. In this work, a data structure outlined previously was used to capture AOPs and evidence relating to carcinogenicity. Knowledge held within the predictive system Derek Nexus was extracted, built upon, and arranged into a coherent network containing 37 AOPs. 60 assays and 351 in silico alerts were then associated with KEs in this network, and it was brought to life by associating data and contextualizing evidence and predictions for over 13,400 compounds. Initial investigations into using the network to view knowledge and reason between evidence in different ways were made. Organizing knowledge and evidence in this way provides a flexible framework on which to carry out more consistent and meaningful carcinogenicity safety assessments in many different contexts.