评估可提取物和可浸出物致敏潜力的计算机工作流程

IF 3.1 Q2 TOXICOLOGY
Martyn L. Chilton, Mukesh Patel, Antonio Anax F. de Oliveira
{"title":"评估可提取物和可浸出物致敏潜力的计算机工作流程","authors":"Martyn L. Chilton,&nbsp;Mukesh Patel,&nbsp;Antonio Anax F. de Oliveira","doi":"10.1016/j.comtox.2023.100275","DOIUrl":null,"url":null,"abstract":"<div><p>As part of a wider toxicological risk assessment to ensure patient safety, extractables and leachables (E&amp;Ls) which are observed above the relevant qualification threshold need to be assessed for their sensitisation potential. This study sought to investigate whether <em>in silico</em> toxicity models could be used to predict the sensitisation hazard and potency potential of E&amp;Ls. An extensive dataset of relevant chemicals was collated by combining and standardising two lists of E&amp;Ls previously published by ELSIE and the PQRI, resulting in a dataset of 790 unique materials. Sensitisation data was then located where possible, resulting in 290 chemicals being associated with dermal sensitisation hazard information, 106 chemicals with dermal sensitisation potency information, and 47 chemicals with respiratory sensitisation information. Existing expert knowledge, in the form of structural alerts within Derek Nexus, was able to accurately predict both the dermal and respiratory sensitisation potential of the E&amp;Ls. 75 different statistical models were also built, using several algorithms and descriptors, and trained on the available dermal sensitisation data. A number of these models proved able to accurately predict the sensitisation potential of the E&amp;Ls, which were found to occupy the same chemical space as the training sets. Finally, hybrid approaches combining expert knowledge and statistical models were investigated, including a tiered system where the skin sensitisation alerts in Derek Nexus provided a hazard prediction, followed by a potency prediction resulting from an alert-based k-nearest neighbours model. The inclusion of the Dermal Sensitisation Thresholds as default, worst-case scenario predictions in cases where similar chemicals were lacking ensured that a prediction was provided for every chemical. It is hoped that this novel workflow, which combines expert knowledge, a statistical model and existing toxicity thresholds, will aid toxicologists when assessing the sensitisation potential of E&amp;Ls administered by any route of administration.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"27 ","pages":"Article 100275"},"PeriodicalIF":3.1000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An in silico workflow for assessing the sensitisation potential of extractables and leachables\",\"authors\":\"Martyn L. Chilton,&nbsp;Mukesh Patel,&nbsp;Antonio Anax F. de Oliveira\",\"doi\":\"10.1016/j.comtox.2023.100275\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>As part of a wider toxicological risk assessment to ensure patient safety, extractables and leachables (E&amp;Ls) which are observed above the relevant qualification threshold need to be assessed for their sensitisation potential. This study sought to investigate whether <em>in silico</em> toxicity models could be used to predict the sensitisation hazard and potency potential of E&amp;Ls. An extensive dataset of relevant chemicals was collated by combining and standardising two lists of E&amp;Ls previously published by ELSIE and the PQRI, resulting in a dataset of 790 unique materials. Sensitisation data was then located where possible, resulting in 290 chemicals being associated with dermal sensitisation hazard information, 106 chemicals with dermal sensitisation potency information, and 47 chemicals with respiratory sensitisation information. Existing expert knowledge, in the form of structural alerts within Derek Nexus, was able to accurately predict both the dermal and respiratory sensitisation potential of the E&amp;Ls. 75 different statistical models were also built, using several algorithms and descriptors, and trained on the available dermal sensitisation data. A number of these models proved able to accurately predict the sensitisation potential of the E&amp;Ls, which were found to occupy the same chemical space as the training sets. Finally, hybrid approaches combining expert knowledge and statistical models were investigated, including a tiered system where the skin sensitisation alerts in Derek Nexus provided a hazard prediction, followed by a potency prediction resulting from an alert-based k-nearest neighbours model. The inclusion of the Dermal Sensitisation Thresholds as default, worst-case scenario predictions in cases where similar chemicals were lacking ensured that a prediction was provided for every chemical. It is hoped that this novel workflow, which combines expert knowledge, a statistical model and existing toxicity thresholds, will aid toxicologists when assessing the sensitisation potential of E&amp;Ls administered by any route of administration.</p></div>\",\"PeriodicalId\":37651,\"journal\":{\"name\":\"Computational Toxicology\",\"volume\":\"27 \",\"pages\":\"Article 100275\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2023-08-01\",\"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/S2468111323000166\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TOXICOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111323000166","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

摘要

作为确保患者安全的更广泛毒理学风险评估的一部分,需要评估高于相关资格阈值的可提取物和可浸出物(E&Ls)的致敏潜力。本研究旨在探讨是否可以使用硅毒性模型来预测E&Ls的致敏危害和效力潜力。通过合并和标准化ELSIE和PQRI先前发布的两个E& l列表,整理了一个广泛的相关化学品数据集,形成了790种独特材料的数据集。然后尽可能定位致敏数据,得出290种化学物质与皮肤致敏危害信息相关,106种化学物质与皮肤致敏效力信息相关,47种化学物质与呼吸致敏信息相关。现有的专家知识,以Derek Nexus内部结构警报的形式,能够准确预测E&Ls的皮肤和呼吸致敏潜力。还使用几种算法和描述符建立了75种不同的统计模型,并对可用的皮肤致敏数据进行了训练。许多这样的模型被证明能够准确地预测E& l的敏化电位,它们被发现占据与训练集相同的化学空间。最后,研究了结合专家知识和统计模型的混合方法,包括一个分层系统,其中Derek Nexus的皮肤致敏警报提供了危害预测,然后是基于警报的k近邻模型的效价预测。将皮肤致敏阈值作为默认值,在缺乏类似化学物质的情况下进行最坏情况预测,确保为每种化学物质提供预测。希望这种结合了专家知识、统计模型和现有毒性阈值的新工作流程将有助于毒理学家评估通过任何给药途径给药的E&Ls的致敏潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An in silico workflow for assessing the sensitisation potential of extractables and leachables

An in silico workflow for assessing the sensitisation potential of extractables and leachables

As part of a wider toxicological risk assessment to ensure patient safety, extractables and leachables (E&Ls) which are observed above the relevant qualification threshold need to be assessed for their sensitisation potential. This study sought to investigate whether in silico toxicity models could be used to predict the sensitisation hazard and potency potential of E&Ls. An extensive dataset of relevant chemicals was collated by combining and standardising two lists of E&Ls previously published by ELSIE and the PQRI, resulting in a dataset of 790 unique materials. Sensitisation data was then located where possible, resulting in 290 chemicals being associated with dermal sensitisation hazard information, 106 chemicals with dermal sensitisation potency information, and 47 chemicals with respiratory sensitisation information. Existing expert knowledge, in the form of structural alerts within Derek Nexus, was able to accurately predict both the dermal and respiratory sensitisation potential of the E&Ls. 75 different statistical models were also built, using several algorithms and descriptors, and trained on the available dermal sensitisation data. A number of these models proved able to accurately predict the sensitisation potential of the E&Ls, which were found to occupy the same chemical space as the training sets. Finally, hybrid approaches combining expert knowledge and statistical models were investigated, including a tiered system where the skin sensitisation alerts in Derek Nexus provided a hazard prediction, followed by a potency prediction resulting from an alert-based k-nearest neighbours model. The inclusion of the Dermal Sensitisation Thresholds as default, worst-case scenario predictions in cases where similar chemicals were lacking ensured that a prediction was provided for every chemical. It is hoped that this novel workflow, which combines expert knowledge, a statistical model and existing toxicity thresholds, will aid toxicologists when assessing the sensitisation potential of E&Ls administered by any route of administration.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
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
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信