Establishing best practise in the application of expert review of mutagenicity under ICH M7

Barber, Chris; Amberg, Alexander; Custer, Laura; Dobo, Krista L.; Glowienke, Susanne; Gompel, Jacky Van; Gutsell, Steve; Harvey, Jim; Honma, Masamitsu; Kenyon, Michelle; Kruhlak, Naomi L.; Muster, Wolfgang; Stavitskaya, Lidiya; Teasdale, Andrew; Vessey, Jonathan D.; Wichard, Joerg

doi:10.1016/j.yrtph.2015.07.018

Cited by 75 publications

(37 citation statements)

References 24 publications

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“…Consideration may be given to complement formal analysis by expert review. 34 The focus of this publication was set on the question of whether or not (Q)SAR tools for predicting mutagenic properties are ready for application in pesticide regulation. From the regulatory perspective, prediction of mutagenicity is applicable and reliable when using the strict acceptance criteria set for this exercise.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?

Herrmann

Holzwarth

Rime

et al. 2020

Pest Management Science

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The assessment of human health risks resulting from the presence of metabolites in groundwater and food residues has become an important element in pesticide authorisation. In this context, the evaluation of mutagenicity is of particular interest and a paradigm shift from exposure‐triggered testing to in silico‐based screening has been recommended in the European Food Safety Authority (EFSA) Guidance on the establishment of the residue definition for dietary risk assessment. In addition, it is proposed to apply in silico predictions when experimental mutagenicity testing is not possible due to a lack of sufficient quantities of the pesticide metabolite. This, combined with animal welfare and economic considerations, has led to a situation where an increasing number of in silico studies are submitted to regulatory authorities. Whilst there is extensive experience with in silico predictions for mutagenicity in the chemical and pharmaceutical industry, their suitability in pesticide regulation is still insufficiently considered. Therefore, we herein discuss critical issues that need to be resolved to successfully implement (Quantitative) Structure‐Activity Relationship ((Q)SAR) as an accepted tool in pesticide regulation. For illustration purposes, the results of a pilot study are included. The presented study highlights a need for further improvement regarding the predictivity and applicability domain of (Q)SAR systems for pesticides and their metabolites, but also raises other questions such as model selection, establishment of acceptance criteria, harmonised approaches to the combination of model outputs into overall conclusions, adequate reporting and data sharing. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Section: Conclusion and Perspectivementioning

confidence: 99%

(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?

Herrmann

Holzwarth

Rime

et al. 2020

Pest Management Science

View full text Add to dashboard Cite

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“…22−24 Lhasa Limited has extensive expertise in the development of expert systems, such as Derek Nexus 25 (prediction of toxicity), Meteor Nexus 26 (prediction of metabolism), and Zeneth 27 (prediction of chemical degradation), as well as expertise in the application of expert systems in regulatory contexts. 28 Several authors [5][6][7]9 have previously assessed the utility of the paper-based purge factor approach to predict the purge factor for PMIs in a given drug substance synthesis. These authors applied the approach described by Teasdale et al 1−3 to predict the likely purge factors and then compared these predictions to experimentally measured purge factors.…”

Section: Mirabilismentioning

confidence: 99%

New Semi-Automated Computer-Based System for Assessing the Purge of Mutagenic Impurities

Burns

Ott

Teasdale

et al. 2019

Org. Process Res. Dev.

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The ICH M7 guidance provides a series of flexible control options for the control of (potentially) mutagenic impurities (PMIs) that fully align with key risk-based principles. This includes option 4, which leverages existing process knowledge and/or data to justify control of PMIs without the need for routine analytical release testing during manufacturing. One such technique highlighted uses systematic, semiquantitative calculations to define the degree of "purge" of PMIs within a synthetic route to an active pharmaceutical ingredient (API) based on physicochemical properties of the impurities in question, and the manufacturing process being undertaken. This paper introduces a consortium-led initiative, Mirabilis, which aims to build on the semiquantitative purge approach, and harmonize industry best practices by enabling the calculations to be conducted in a standardized, consistent, and reproducible manner. The development of an expert-derived knowledge base for the prediction of reactivity by enhancing expert opinion using evidence derived from the published literature and experimental data is also discussed. Furthermore, this paper describes the application of Mirabilis software for the processes involved in the synthesis of verubecestat, naloxegol oxalate, and camicinal.

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“…Nevertheless, application of expert knowledge is necessary to support or overturn any (Q)SAR prediction. 10 On the basis of this framework, we firstly work out a likely Phase…”

Section: In Silico Assessment Of the Metabolism And Its Safety Signifmentioning

confidence: 99%

In silico assessment of the metabolism and its safety significance of multitarget propargylamine ASS234

et al. 2018

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Establishing best practise in the application of expert review of mutagenicity under ICH M7

Cited by 75 publications

References 24 publications

(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?

(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?

New Semi-Automated Computer-Based System for Assessing the Purge of Mutagenic Impurities

In silico assessment of the metabolism and its safety significance of multitarget propargylamine ASS234

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