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专家评审对于根据国际人用药品注册技术协调会M7指导原则澄清(Q)SAR预测中模糊情况的重要性。

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

作者信息

Foster Robert S, Fowkes Adrian, Cayley Alex, Thresher Andrew, Werner Anne-Laure D, Barber Chris G, Kocks Grace, Tennant Rachael E, Williams Richard V, Kane Steven, Stalford Susanne A

机构信息

Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds, LS11 5PS UK.

出版信息

Genes Environ. 2020 Sep 22;42:27. doi: 10.1186/s41021-020-00166-y. eCollection 2020.

Abstract

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. Dr. Chris Barber presented a selection of the reviews carried out using Derek Nexus and Sarah Nexus provided by Lhasa Limited. On review of these compounds, common situations were recognised and are discussed in this paper along with standardised arguments that may be used for such scenarios in future.

摘要

当使用两个互补的(定量)构效关系(Q)SAR模型时,建议根据人用药品技术要求国际协调理事会(ICH)M7指南,使用计算机模拟预测来评估细菌致突变性。使用两个系统可能会提高预测的灵敏度和准确性,但也增加了审查预测结果的必要性,特别是在结果不一致的情况下。在2019年第六届亚洲环境诱变剂大会(ACEM)和日本环境诱变剂学会第48届年会(JEMS)联合会议期间举行的第四届ICH M7/Q SAR研讨会上,发言者展示了他们对使用会前提供的20种化合物进行专家审查的方法,预计这些化合物会产生模糊的(Q)SAR结果。克里斯·巴伯博士介绍了使用拉萨有限公司提供的Derek Nexus和Sarah Nexus进行审查的情况。在对这些化合物进行审查时,识别出了常见情况,并在本文中进行了讨论,同时还讨论了未来可用于此类情况的标准化论据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1ad/7510098/9723b360be47/41021_2020_166_Fig1_HTML.jpg

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