Expanded Tox21 biological assay panel for the prediction of drug-induced liver injury and cardiotoxicity.

IF 10.1 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Tuan Xu, Masato Ooka, Jinghua Zhao, Srilatha Sakamuru, Deborah K Ngan, Li Zhang, Shu Yang, Jameson Travers, Menghang Xia, Tongan Zhao, Carleen Klumpp-Thomas, Hu Zhu, Mathew D Hall, Stephen Ferguson, Natalie D Shaw, David M Reif, Anton Simeonov, Ruili Huang
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Abstract

Background: Toxicology in the 21st Century (Tox21) assay data provide a valuable resource for the prediction of in vivo toxicity using machine learning models. However, the performances of these models previously developed using the pre-existing Tox21 assay data were less than ideal, likely due to insufficient coverage of the biological response space by the assay targets.

Objectives: This study aimed to assess whether expanding the Tox21 portfolio with new assays that probe under-represented targets/pathways related to unanticipated adverse drug effects could improve the predictive capacity of in vitro assay data for in vivo toxicity such as drug induced liver injury (DILI) and cardiotoxicity (DICT).

Methods: Models were constructed using data from the pre-existing panel of 36 assay targets and the expanded panel of 49 assay targets. A feature selection approach was used to determine the optimal number of assays needed for each model. The models were then applied to predict the potential hepatotoxicity and cardiotoxicity of compounds in the Tox21 10K compound library.

Results: For both DILI and DICT prediction, the best-performing models developed using the expanded assay panel required a smaller number of assays to achieve the same level of performance compared to those based on the pre-existing assays. Models constructed by combining both assay data (pre-existing + expanded) and chemical structure consistently outperformed those constructed based on assay data alone, but showed similar performance to those constructed based on chemical structure. The compounds predicted to have the highest toxic potential were experimentally verified to demonstrate the effectiveness of our models in identifying new potentially toxic compounds.

Discussion: The expansion of the Tox21 assay panel has significantly enhanced the predictive capacity of assay data for predicting DILI and DICT potential. This improvement underscores the importance of a diverse and comprehensive in vitro assay portfolio in advancing safety assessment. https://doi.org/10.1289/EHP16190.

用于预测药物性肝损伤和心脏毒性的扩展Tox21生物检测面板。
背景:21世纪毒理学(Tox21)分析数据为使用机器学习模型预测体内毒性提供了宝贵的资源。然而,先前使用已有的Tox21检测数据开发的这些模型的性能不太理想,可能是由于检测目标的生物反应空间覆盖范围不够。目的:本研究旨在评估扩大Tox21的检测组合,探索与意外药物不良反应相关的未被充分代表的靶点/途径,是否可以提高体外检测数据对体内毒性(如药物性肝损伤(DILI)和心脏毒性(DICT))的预测能力。方法:利用已有的36个检测靶点面板和49个检测靶点扩展面板的数据构建模型。使用特征选择方法来确定每个模型所需的最佳分析次数。然后应用这些模型预测Tox21 10K化合物文库中化合物的潜在肝毒性和心脏毒性。结果:对于DILI和DICT预测,与基于现有分析的模型相比,使用扩展分析小组开发的性能最好的模型需要较少的分析来达到相同的性能水平。结合分析数据(预先存在的+扩展的)和化学结构构建的模型始终优于仅基于分析数据构建的模型,但与基于化学结构构建的模型表现相似。通过实验验证了预测具有最高毒性潜力的化合物,以证明我们的模型在识别新的潜在毒性化合物方面的有效性。讨论:Tox21检测小组的扩展显著增强了检测数据预测DILI和DICT潜力的预测能力。这一改进强调了多样化和全面的体外检测组合在推进安全性评估中的重要性。https://doi.org/10.1289/EHP16190。
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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