Evaluation of QSAR models for tissue-specific predictive toxicology and risk assessment of military-relevant chemical exposures: A systematic review

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2024-09-13 DOI:10.1016/j.comtox.2024.100329
Jennifer L. Fisher , Kelly T. Williams , Leah J. Schneider , Andrew J. Keebaugh , Carrie L. German , Adam M. Hott , Narender Singh , Rebecca A. Clewell
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Abstract

The use of in silico modeling tools for predictive toxicology has potential to improve force health protection in the military by helping to efficiently evaluate the risk of adverse health effects from operational exposures. Thus, a systematic review was performed to understand if existing quantitative structure–activity relationship (QSAR) models for tissue-specific toxicity were potentially adaptable for use in risk assessments of military-relevant exposures. Within this systematic review, we assessed 563 peer-reviewed publications in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines and Organization for Economic Co-operation and Development (OECD) 2023 quantitative structural-activity relationship Assessment Framework. From these publications, we further evaluated 129 existing models that utilize QSAR and tissue-specific data for predicting toxicity in the following tissues: liver (i.e., hepatotoxicity), heart (i.e., cardiotoxicity), lung (i.e., respiratory toxicity), the central nervous system (neurotoxicity), and kidney (i.e., nephrotoxicity). The methodology, performance, and accessibility of these models and analysis code were thoroughly documented and then assessed to determine advancements and inadequacies for occupational and military application. While ∼ 58 % of the 129 tissue-specific QSAR approaches followed at least 3 OECD guidelines, there were only 8 tissue-specific models that satisfied all screening criteria. The most common criteria not satisfied was mechanistic interpretation of the model (i.e., OECD criteria number five). Furthermore, while the greatest number of publications and models were available for the liver, many of them were for pharmaceutical applications. Moreover, there were limited available models for heart and kidney for any application. In conclusion, our findings underscore the necessity for additional and updated tissue-specific QSAR models to predict various organ-specific targets while addressing military specific needs. Furthermore, increased publication of model workflows or user-friendly applications are crucial to enhancing model accessibility. In this systematic review, we provide an overview of the databases, resources, and future strategies to advance tissue-specific QSAR model development.

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评估用于组织特异性预测毒理学和军事相关化学品暴露风险评估的 QSAR 模型:系统综述
在预测性毒理学中使用硅学建模工具可帮助有效评估作战暴露对健康产生不利影响的风险,从而有可能改善军队的健康保护。因此,我们进行了一项系统综述,以了解现有的组织特异性毒性定量结构-活性关系(QSAR)模型是否可用于军事相关暴露的风险评估。在本次系统性综述中,我们根据《系统性综述和荟萃分析首选报告项目》(PRISMA)2020 指南和经济合作与发展组织(OECD)2023 定量结构-活性关系评估框架,对 563 篇同行评议出版物进行了评估。根据这些出版物,我们进一步评估了 129 个现有模型,这些模型利用 QSAR 和特定组织数据预测以下组织的毒性:肝脏(即肝毒性)、心脏(即心脏毒性)、肺(即呼吸毒性)、中枢神经系统(神经毒性)和肾脏(即肾毒性)。对这些模型和分析代码的方法、性能和可访问性进行了全面记录和评估,以确定在职业和军事应用方面的先进性和不足之处。在 129 种组织特异性 QSAR 方法中,有 58% 的方法至少遵循了 3 项 OECD 准则,但只有 8 种组织特异性模型符合所有筛选标准。最常见的不符合标准是模型的机理解释(即 OECD 第 5 项标准)。此外,虽然有关肝脏的出版物和模型数量最多,但其中许多是用于制药的。此外,用于心脏和肾脏的任何应用模型都很有限。总之,我们的研究结果表明,有必要增加和更新组织特异性 QSAR 模型,以预测各种器官特异性靶标,同时满足军事上的特定需求。此外,增加模型工作流程或用户友好型应用的发布对于提高模型的可及性至关重要。在这篇系统综述中,我们概述了推进组织特异性 QSAR 模型开发的数据库、资源和未来战略。
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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
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