结构警报和“六块”毒性的机器学习建模作为动物试验的替代方案

IF 3.1 Q2 TOXICOLOGY
Yaroslav Chushak , Jeffery M. Gearhart , Rebecca A. Clewell
{"title":"结构警报和“六块”毒性的机器学习建模作为动物试验的替代方案","authors":"Yaroslav Chushak ,&nbsp;Jeffery M. Gearhart ,&nbsp;Rebecca A. Clewell","doi":"10.1016/j.comtox.2023.100280","DOIUrl":null,"url":null,"abstract":"<div><p>The “Six Pack” is a set of animal toxicity studies that are widely used by industry and regulatory agencies to evaluate the toxicity of chemicals. It consists of three systemic toxicities (acute oral toxicity, acute inhalation toxicity and acute dermal toxicity) and three specific organ endpoints (eye damage/irritation, skin corrosion/irritation and skin sensitization). In the last two decades there has been a growing effort in the scientific community, as well as in regulatory agencies, to reduce and replace animal tests through implementation of alternative approaches. Computational methods in combination with <em>in vitro</em> measurements are pursued actively as the integrative approach for accurate and reliable assessment of chemical toxicity. Here, we generated structural alerts and developed a set of ten classification models for all six-pack endpoints using different molecular descriptors and machine learning techniques. The coverage of active chemicals by structural alerts was in the range from 24 % for acute inhalation toxicity to 52 % for acute oral toxicity. To establish confidence in model predictions, we used two different approaches to estimate the applicability domain (AD). The first approach was based on similarity distance between the query chemical and chemicals in the training set. In the second approach, the AD was estimated based on distance to model. The prediction accuracy of models evaluated using the validation sets was in the range from 0.67 for acute inhalation toxicity to 0.78 for acute dermal toxicity. The evaluation of models for chemicals within the similarity-based AD showed similar accuracy compared with the whole validation set. On the other hand, improvement of model performance was observed by using the distance to model approach to estimate AD, e.g. when distance to model was set to 0.3 the accuracy of predictions ranged from 0.75 for acute inhalation toxicity to 0.86 for acute oral toxicity. The combination of structural alerts and classification models provide a rapid means to screen a list of compounds for six-pack toxicity and to prioritize chemicals for <em>in vitro</em> toxicity evaluation.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Structural alerts and Machine learning modeling of “Six-pack” toxicity as alternative to animal testing\",\"authors\":\"Yaroslav Chushak ,&nbsp;Jeffery M. Gearhart ,&nbsp;Rebecca A. Clewell\",\"doi\":\"10.1016/j.comtox.2023.100280\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The “Six Pack” is a set of animal toxicity studies that are widely used by industry and regulatory agencies to evaluate the toxicity of chemicals. It consists of three systemic toxicities (acute oral toxicity, acute inhalation toxicity and acute dermal toxicity) and three specific organ endpoints (eye damage/irritation, skin corrosion/irritation and skin sensitization). In the last two decades there has been a growing effort in the scientific community, as well as in regulatory agencies, to reduce and replace animal tests through implementation of alternative approaches. Computational methods in combination with <em>in vitro</em> measurements are pursued actively as the integrative approach for accurate and reliable assessment of chemical toxicity. Here, we generated structural alerts and developed a set of ten classification models for all six-pack endpoints using different molecular descriptors and machine learning techniques. The coverage of active chemicals by structural alerts was in the range from 24 % for acute inhalation toxicity to 52 % for acute oral toxicity. To establish confidence in model predictions, we used two different approaches to estimate the applicability domain (AD). The first approach was based on similarity distance between the query chemical and chemicals in the training set. In the second approach, the AD was estimated based on distance to model. The prediction accuracy of models evaluated using the validation sets was in the range from 0.67 for acute inhalation toxicity to 0.78 for acute dermal toxicity. The evaluation of models for chemicals within the similarity-based AD showed similar accuracy compared with the whole validation set. On the other hand, improvement of model performance was observed by using the distance to model approach to estimate AD, e.g. when distance to model was set to 0.3 the accuracy of predictions ranged from 0.75 for acute inhalation toxicity to 0.86 for acute oral toxicity. The combination of structural alerts and classification models provide a rapid means to screen a list of compounds for six-pack toxicity and to prioritize chemicals for <em>in vitro</em> toxicity evaluation.</p></div>\",\"PeriodicalId\":37651,\"journal\":{\"name\":\"Computational Toxicology\",\"volume\":null,\"pages\":null},\"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/S246811132300021X\",\"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/S246811132300021X","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

“六包”是一套动物毒性研究,被工业和监管机构广泛用于评估化学品的毒性。它包括三种全身毒性(急性口服毒性、急性吸入毒性和急性皮肤毒性)和三个特定的器官终点(眼睛损伤/刺激、皮肤腐蚀/刺激和皮肤致敏)。在过去二十年中,科学界以及管理机构越来越努力通过实施替代方法来减少和取代动物试验。计算方法与体外测量相结合被积极追求作为准确和可靠的化学毒性评估的综合方法。在这里,我们生成了结构警报,并使用不同的分子描述符和机器学习技术为所有六个包装端点开发了一组十个分类模型。结构性警报对活性化学品的覆盖范围从急性吸入毒性的24%到急性口服毒性的52%不等。为了建立模型预测的可信度,我们使用了两种不同的方法来估计适用域(AD)。第一种方法是基于查询化学物质与训练集中化学物质之间的相似距离。在第二种方法中,根据与模型的距离估计AD。使用验证集评估的模型的预测精度在急性吸入毒性的0.67到急性皮肤毒性的0.78之间。与整个验证集相比,基于相似性的AD内化学品模型的评估显示出相似的准确性。另一方面,通过使用模型距离方法来估计AD,可以观察到模型性能的改善,例如,当与模型的距离设置为0.3时,预测的准确性范围从急性吸入毒性的0.75到急性口服毒性的0.86。结构警报和分类模型的结合提供了一种快速的方法来筛选化合物的六包毒性列表,并优先考虑化学物质的体外毒性评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural alerts and Machine learning modeling of “Six-pack” toxicity as alternative to animal testing

The “Six Pack” is a set of animal toxicity studies that are widely used by industry and regulatory agencies to evaluate the toxicity of chemicals. It consists of three systemic toxicities (acute oral toxicity, acute inhalation toxicity and acute dermal toxicity) and three specific organ endpoints (eye damage/irritation, skin corrosion/irritation and skin sensitization). In the last two decades there has been a growing effort in the scientific community, as well as in regulatory agencies, to reduce and replace animal tests through implementation of alternative approaches. Computational methods in combination with in vitro measurements are pursued actively as the integrative approach for accurate and reliable assessment of chemical toxicity. Here, we generated structural alerts and developed a set of ten classification models for all six-pack endpoints using different molecular descriptors and machine learning techniques. The coverage of active chemicals by structural alerts was in the range from 24 % for acute inhalation toxicity to 52 % for acute oral toxicity. To establish confidence in model predictions, we used two different approaches to estimate the applicability domain (AD). The first approach was based on similarity distance between the query chemical and chemicals in the training set. In the second approach, the AD was estimated based on distance to model. The prediction accuracy of models evaluated using the validation sets was in the range from 0.67 for acute inhalation toxicity to 0.78 for acute dermal toxicity. The evaluation of models for chemicals within the similarity-based AD showed similar accuracy compared with the whole validation set. On the other hand, improvement of model performance was observed by using the distance to model approach to estimate AD, e.g. when distance to model was set to 0.3 the accuracy of predictions ranged from 0.75 for acute inhalation toxicity to 0.86 for acute oral toxicity. The combination of structural alerts and classification models provide a rapid means to screen a list of compounds for six-pack toxicity and to prioritize chemicals for in vitro toxicity evaluation.

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