Susanne A. Stalford, Alex N. Cayley, Adrian Fowkes, Antonio Anax F. de Oliveira, Ioannis Xanthis, Christopher G. Barber
{"title":"Structuring expert review using AOPs: Enabling robust weight-of-evidence assessments for carcinogenicity under ICH S1B(R1)","authors":"Susanne A. Stalford, Alex N. Cayley, Adrian Fowkes, Antonio Anax F. de Oliveira, Ioannis Xanthis, Christopher G. Barber","doi":"10.1016/j.comtox.2024.100320","DOIUrl":null,"url":null,"abstract":"<div><p>There is widespread acceptance that non-animal studies can be used to assess chemical safety in humans. These New Approach Methodologies (NAMs) typically integrate data from multiple sources including <em>in silico</em> and <em>in vitro</em> models. Regulatory guidelines are being updated to recognise that these scientific advances are allowing animal studies to be replaced without compromising human safety. One such regulation, ICH S1B(R1), was updated in 2022 to include the provision for a weight-of-evidence assessment for carcinogenicity, using six factors to determine if there was sufficient evidence to waive the need to run a rat carcinogenicity assay. The volume of data and evidence, however, can be hard to organise and interpret into a cohesive evaluation. To aid such assessments, software has been developed that combines adverse outcome pathways (AOPs) and reasoning, to organise and contextualise knowledge, and provide an outcome based on the data available. Using this framework, a workflow has been developed to assess the initial outcome and structure expert review to investigate the factors, and potential biological mechanisms which could contribute to a compound’s carcinogenic potential (or lack thereof). The framework was used to structure expert review of three examples of differing activity and levels of supporting evidence. This highlighted where AOPs supported expert review by showing 1) the value in using AOPs to analyse data, 2) the importance of expert review to strengthen confidence in outcomes, and 3) how this approach can accurately predict experimental results. Therefore, using this approach to assess evidence for ICH S1B(R1) will give transparent, scientifically robust, and reproducible calls, and thus reduce the need for rat carcinogenicity studies.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111324000227","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
There is widespread acceptance that non-animal studies can be used to assess chemical safety in humans. These New Approach Methodologies (NAMs) typically integrate data from multiple sources including in silico and in vitro models. Regulatory guidelines are being updated to recognise that these scientific advances are allowing animal studies to be replaced without compromising human safety. One such regulation, ICH S1B(R1), was updated in 2022 to include the provision for a weight-of-evidence assessment for carcinogenicity, using six factors to determine if there was sufficient evidence to waive the need to run a rat carcinogenicity assay. The volume of data and evidence, however, can be hard to organise and interpret into a cohesive evaluation. To aid such assessments, software has been developed that combines adverse outcome pathways (AOPs) and reasoning, to organise and contextualise knowledge, and provide an outcome based on the data available. Using this framework, a workflow has been developed to assess the initial outcome and structure expert review to investigate the factors, and potential biological mechanisms which could contribute to a compound’s carcinogenic potential (or lack thereof). The framework was used to structure expert review of three examples of differing activity and levels of supporting evidence. This highlighted where AOPs supported expert review by showing 1) the value in using AOPs to analyse data, 2) the importance of expert review to strengthen confidence in outcomes, and 3) how this approach can accurately predict experimental results. Therefore, using this approach to assess evidence for ICH S1B(R1) will give transparent, scientifically robust, and reproducible calls, and thus reduce the need for rat carcinogenicity studies.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs