DeTox: an In-Silico Alternative to Animal Testing for Predicting Developmental Toxicity Potential.

IF 10.1 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Ricardo Scheufen Tieghi, Marielle Rath, José Teófilo Moreira-Filho, James Wellnitz, Holli-Joi Martin, Kathleen Gates, Helena T Hogberg, Nicole Kleinstreuer, Alexander Tropsha, Eugene N Muratov
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引用次数: 0

Abstract

Background: Medication use among pregnant women is common, yet the safety of these medications for the developing fetus/baby is widely understudied. Quantitative Structure-Activity Relationship (QSAR) models can be used to predict the overall and trimester-specific developmental toxicity potential of chemicals, supporting the development of safer medications for pregnant women and regulatory assessment aligned with the 3Rs (refining, reducing, and replacing) of animal testing.

Objectives: This study aimed to collect and curate a database of compounds classified according to their developmental toxicity potential, use this database to develop and validate QSAR models for predicting prenatal developmental toxicity, and implement models via a user-friendly online platform to support regulatory assessments of drug candidates.

Methods: We compiled and curated data from the FDA and Teratogen Information System (TERIS) databases and validated annotations with rigorous literature searches. The database was leveraged to create QSAR models using machine learning algorithms (RF, SVM, LightGBM) with Bayesian hyperparameter optimization. These models were implemented into a web tool.

Results: We built a binary classification QSAR model for overall pregnancy risk, and separate QSAR models for trimester-specific risk, exhibiting correct classification rates of and 76% (overall), 80% (1st trimester), 95% (2nd trimester), and 95% (3rd trimester). Models showed a sensitivity between 53% and 90%, specificity between 46% and 100%, and coverage of 76% assessed using a five-fold external validation protocol. We established a publicly accessible web portal (https://detox.mml.unc.edu/) for developmental toxicity prediction of both overall and trimester-specific toxicity predictions.

Conclusions: DeTox can be employed to support regulatory assessment of pharmaceutical and cosmetic products aligned with the 3Rs of animal testing and to guide the development of safer drugs for pregnant populations. The curated dataset of developmental toxicants is publicly available, and all models are implemented in a public, user-friendly web tool, DeTox (Developmental Toxicity), at https://detox.mml.unc.edu/. https://doi.org/10.1289/EHP15307.

排毒:预测发育毒性潜能的一种替代动物试验的硅技术。
背景:孕妇使用药物是很常见的,然而这些药物对发育中的胎儿/婴儿的安全性还没有得到充分的研究。定量构效关系(QSAR)模型可用于预测化学物质的整体和妊娠期特异性发育毒性潜力,支持开发更安全的孕妇药物,并根据动物试验的3r(精炼、减少和替代)进行监管评估。目的:本研究旨在收集和整理一个根据其发育毒性潜力分类的化合物数据库,利用该数据库开发和验证用于预测产前发育毒性的QSAR模型,并通过用户友好的在线平台实现模型,以支持候选药物的监管评估。方法:我们从FDA和TERIS数据库中收集和整理数据,并通过严格的文献检索验证注释。该数据库利用机器学习算法(RF、SVM、LightGBM)和贝叶斯超参数优化来创建QSAR模型。这些模型被实现到一个web工具中。结果:我们建立了总体妊娠风险的二元分类QSAR模型,以及针对妊娠特定风险的单独QSAR模型,正确分类率分别为76%(总体)、80%(妊娠早期)、95%(妊娠中期)和95%(妊娠晚期)。模型的敏感性在53%至90%之间,特异性在46%至100%之间,使用五倍外部验证方案评估的覆盖率为76%。我们建立了一个可公开访问的门户网站(https://detox.mml.unc.edu/),用于总体和妊娠期特异性毒性预测的发育毒性预测。结论:DeTox可用于支持符合3r动物试验的药品和化妆品的监管评估,并指导为怀孕人群开发更安全的药物。发育毒性物质的管理数据集是公开的,所有模型都在一个公共的、用户友好的网络工具中实现,DeTox(发育毒性),网址为https://detox.mml.unc.edu/。https://doi.org/10.1289/EHP15307。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental Health Perspectives
Environmental Health Perspectives 环境科学-公共卫生、环境卫生与职业卫生
CiteScore
14.40
自引率
2.90%
发文量
388
审稿时长
6 months
期刊介绍: Environmental Health Perspectives (EHP) is a monthly peer-reviewed journal supported by the National Institute of Environmental Health Sciences, part of the National Institutes of Health under the U.S. Department of Health and Human Services. Its mission is to facilitate discussions on the connections between the environment and human health by publishing top-notch research and news. EHP ranks third in Public, Environmental, and Occupational Health, fourth in Toxicology, and fifth in Environmental Sciences.
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