评估高通量含硫醇荧光探针筛选反应性的效用:Tox21文库的案例研究

IF 3.1 Q2 TOXICOLOGY
Grace Patlewicz , Katie Paul-Friedman , Keith Houck , Li Zhang , Ruili Huang , Menghang Xia , Jason Brown , Steven O. Simmons
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引用次数: 0

摘要

Tox21项目中生物活性的高通量筛选(HTS)测定旨在评估一系列不同的生物靶标和途径,但解释这些数据的一个重要障碍是缺乏旨在识别非特异性反应性化学物质的高通量筛查(HTS)检测。这是一个重要方面,可以优先考虑在特定测定中测试的化学品,根据其反应性识别混杂的化学品,以及解决皮肤致敏等危险,这些危险不一定是由受体介导的效应引起的,而是通过非特异性机制起作用的。在此,使用基于荧光的HTS测定法来筛选Tox21 10K化学文库中的7872种独特化学物质,该测定法允许鉴定硫醇反应性化合物。将活性化学物质与使用编码亲电信息的结构警报的分析结果进行比较。开发了基于化学指纹的随机森林分类模型来预测测定结果,并通过10倍分层交叉验证(CV)进行了评估。验证集的平均CV平衡准确度为0.648。所开发的模型有望作为一种工具,仅根据化学结构特征筛选未经测试的化学品的潜在亲电反应性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library

High-throughput screening (HTS) assays for bioactivity in the Tox21 program aim to evaluate an array of different biological targets and pathways, but a significant barrier to interpretation of these data is the lack of high-throughput screening (HTS) assays intended to identify non-specific reactive chemicals. This is an important aspect for prioritising chemicals to test in specific assays, identifying promiscuous chemicals based on their reactivity, as well as addressing hazards such as skin sensitisation which are not necessarily initiated by a receptor-mediated effect but act through a non-specific mechanism. Herein, a fluorescence-based HTS assay that allows the identification of thiol-reactive compounds was used to screen 7,872 unique chemicals in the Tox21 10 K chemical library. Active chemicals were compared with profiling outcomes using structural alerts encoding electrophilic information. Random Forest classification models based on chemical fingerprints were developed to predict assay outcomes and evaluated through 10-fold stratified cross validation (CV). The mean CV Balanced Accuracy of the validation set was 0.648. The model developed shows promise as a tool to screen untested chemicals for their potential electrophilic reactivity based solely on chemical structural features.

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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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