使用体外方法结合硅学分析,识别 Tox21 10K 化合物库中潜在的皮肤致敏物质。

IF 3.6 Q2 TOXICOLOGY
Frontiers in toxicology Pub Date : 2024-02-28 eCollection Date: 2024-01-01 DOI:10.3389/ftox.2024.1321857
Zhengxi Wei, Tuan Xu, Judy Strickland, Li Zhang, Yuhong Fang, Dingyin Tao, Anton Simeonov, Ruili Huang, Nicole C Kleinstreuer, Menghang Xia
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引用次数: 0

摘要

导言:皮肤过敏会导致过敏性接触性皮炎,是一种关键的毒理学终点,在职业和消费者中的发病率都很高。本研究优化了 OECD 皮肤过敏测试指南中列出的几种体外检测方法,以便在定量高通量筛选(qHTS)平台上使用,并进行了硅学模型预测,以评估 Tox21 10K 化合物库中优先考虑的化合物的皮肤过敏潜力。方法:首先,我们使用 qHTS KeratinoSensTM (KS) 检测方法筛选了整个 Tox21 10K 化合物库,并根据 KS 结果建立了定量结构-活性关系 (QSAR) 模型。根据 qHTS KeratinoSensTM(KS)筛选结果,我们确定了 288 个化合物的优先级,以涵盖广泛的结构化学类型,并在固相萃取-串联质谱(SPE-MS/MS)直接肽反应性测定(DPRA)、IL-8 均相时间分辨荧光(HTRF)测定、THP1 细胞中 CD86 和 CD54 表面表达以及使用 OECD QSAR 工具箱(v4.5)预测硅学致敏潜力等方面对它们进行了测试。结果使用确定的方法解释分层 qHTS 数据集显示了体外方法的有效性和效率。我们选择了结构化学型来展示这个多样化的化学集合,并探索以前未发现的结构对致敏潜力的贡献。讨论:在此,我们提供了一个规模空前的皮肤过敏数据集,以及相关工具和分析,旨在为化学品评估提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Use of in vitro methods combined with in silico analysis to identify potential skin sensitizers in the Tox21 10K compound library.

Introduction: Skin sensitization, which leads to allergic contact dermatitis, is a key toxicological endpoint with high occupational and consumer prevalence. This study optimized several in vitro assays listed in OECD skin sensitization test guidelines for use on a quantitative high-throughput screening (qHTS) platform and performed in silico model predictions to assess the skin sensitization potential of prioritized compounds from the Tox21 10K compound library. Methods: First, we screened the entire Tox21 10K compound library using a qHTS KeratinoSensTM (KS) assay and built a quantitative structure-activity relationship (QSAR) model based on the KS results. From the qHTS KS screening results, we prioritized 288 compounds to cover a wide range of structural chemotypes and tested them in the solid phase extraction-tandem mass spectrometry (SPE-MS/MS) direct peptide reactivity assay (DPRA), IL-8 homogeneous time-resolved fluorescence (HTRF) assay, CD86 and CD54 surface expression in THP1 cells, and predicted in silico sensitization potential using the OECD QSAR Toolbox (v4.5). Results: Interpreting tiered qHTS datasets using a defined approach showed the effectiveness and efficiency of in vitro methods. We selected structural chemotypes to present this diverse chemical collection and to explore previously unidentified structural contributions to sensitization potential. Discussion: Here, we provide a skin sensitization dataset of unprecedented size, along with associated tools, and analysis designed to support chemical assessments.

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来源期刊
CiteScore
3.80
自引率
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