Development of integrative models for early liver toxicity assessment

早期肝毒性评估综合模型的开发

• 批准号: 9017336
• 负责人: Tao Wang
• 金额: $ 8.1万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9017336
• 关键词: Acute; Address; Adverse effects; Algorithms; Animals; Area; Benchmarking; Biological; Biological Assay; Chemicals; Communities; Complex; Computer Simulation; Data; Data Set; Databases; Descriptor; Development; Dose; Dreams; Ensure; Ethics; Food; Future; Gene Expression; Genomics; Goals; Gold; Health; Hepatotoxicity; Human; In Vitro; Informatics; International; Liver; Machine Learning; Marketing; Mining; Modeling; National Human Genome Research Institute; National Institute of Environmental Health Sciences; Nature; North Carolina; Outcome; Pathway Analysis; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Poison; Process; Productivity; Quality Control; Quantitative Structure-Activity Relationship; Reporting; Research; Resources; Risk; Rodent; Scientist; Shapes; Statistical Methods; Statistical Models; Testing; Time; Toxic effect; Toxicity Tests; Toxicogenetics; Toxicology; Translational Research; United States Environmental Protection Agency; United States Food and Drug Administration; Universities; Variant; Water; base; computerized tools; consumer product; cost; cost effective; cytotoxicity; data modeling; drug development; drug market; drug withdrawal; environmental chemical; experience; high throughput screening; improved; in vitro Assay; in vivo; interest; knowledge base; liver injury; model building; model development; novel; novel strategies; post-market; preclinical study; predictive modeling; programs; response; screening; success; tool; toxicant; validation studies

 DESCRIPTION (provided by applicant): Computational toxicology has become a critical area of research due to the burgeoning need to evaluate thousands of pharmaceutical and environmental chemicals with unknown toxicity profiles, the high demand in time and resources by current experimental toxicity testing, and the growing ethical concerns over animal use in toxicity studies. Despite tremendous efforts, little success has been attained thus far in the development of predictive computational models for toxicity, primarily due to the complexity of toxicity mechanisms as well as the lack of high-quality experimental data for model development. A critical challenge in toxicity testing of chemicals is that toxicity effects are doe-dependent: the true toxic hits may show no toxicity at all at low dose level. Therefore, traditiona high-throughput screening (HTS) that test chemicals only at a single concentration is not suitable for toxicity screening. On the contrary, the recently developed quantitative high-throughput screening (qHTS) platforms can evaluate each chemical across a broad range of concentrations, and is gaining ever-increasing popularity as a tool for in vitro toxicity profiling The concentration-response information generated by qHTS are expected to provide more accurate and comprehensive information of the toxicity effects of chemicals, offering promising data that can be mined to estimate in vivo toxicities of chemicals. However, our previous studies showed that if processed inappropriately, such concentration-response information contribute little to improve the toxicity prediction. This is especially true when multiple types of qHTS data are used together. Therefore, in this study, we will extend our previous approaches to develop novel statistical and computational tools that can curate, preprocess, and normalize the concentration-response information from multiple different qHTS databases. Traditionally, toxicity models are based on either the chemical data (such as the quantitative structure- activity relationship analysis), or the in vitro toxicity profiling data (such as the in vitro-in vivo extrapolations). Our previous experiences suggested that integrating biological descriptors such as the in vitro cytotoxicity profiles or the short-term toxigenomic data, with chemical structural features is able to predict rodent acute liver toxicity with reasonable accuracy. Therefore, the second part of this proposal will be devoted to develop novel computational models for hepatotoxicity prediction by integrating qHTS toxicity profiles and chemical structural information In Aim 1, we will curate, preprocess, and normalize collected public liver toxicity datasets. In ths study, we will model toxicity effects using multiple large public datasets such as HTS and qHTS bioassay data (Tox21[1] and ToxCast[2]), hepatotoxicity side effect reports on marketed failed drugs[3], the Liver Toxicity Knowledge Base Benchmark Dataset (LTKB-BD[4]), etc. Statistical methods for cross-study validation and quality control will be applied to the collected datasets to ensure computational compatibility and to select the appropriate datasets for analysis. In Aim 2, we will develop predictive models for chemicals' liver toxicity based on an integrative modeling workflow that will make use of both structural and in vitro toxicity profiles of a chemical. Our previous studies [5] showed that models using both in vitro toxicity profiles and chemical structural data have better accuracy for rodent acute liver toxicity than models using either data type alone. Here, we will develop a novel modeling workflow that start with defining the functional clusters of chemicals via curated qHTS toxicity profiles, and is followed by developing computational models to correlate chemical and biological data with overall toxicity risks in humans. The predictive models will be validated using independent datasets with over 800 compounds. In Aim 3, we propose to prioritize the qHTS profiling assays used in the model for future toxicity testing. We will evaluate all the in vitro assays as biological descriptors from thee perspectives, including descriptor importance in the integrative toxicity model, correlation with i vivo DILI outcomes, and level of information content estimated by a novel approach based on network analysis.

 描述（由申请人提供）：计算毒理学已成为一个关键的研究领域，因为需要评估数千种具有未知毒性特征的药物和环境化学品，当前实验毒性测试对时间和资源的高需求，以及对毒性研究中动物使用的日益增长的伦理问题。尽管付出了巨大的努力，但迄今为止，在开发毒性预测计算模型方面几乎没有取得成功，这主要是由于毒性机制的复杂性以及缺乏用于模型开发的高质量实验数据。 化学品毒性测试的一个关键挑战是毒性效应是剂量依赖性的：在低剂量水平下，真正的毒性可能根本没有毒性。因此，传统的高通量筛选（HTS）仅在单一浓度下测试化学品不适合于毒性筛选。相反，最近开发的定量高通量筛选（qHTS）平台可以在广泛的浓度范围内评估每种化学品，并且作为体外毒性分析的工具越来越受欢迎。qHTS产生的浓度-响应信息有望提供更准确和全面的化学品毒性效应信息，提供了有希望的数据，可以挖掘，以估计体内毒性的化学品。然而，我们以前的研究表明，如果处理不当，这样的浓度-反应信息有助于改善毒性预测。当多种类型的qHTS数据 一起使用。因此，在这项研究中，我们将扩展我们以前的方法，开发新的统计和计算工具，可以从多个不同的qHTS数据库管理，预处理和规范化的浓度响应信息。 传统上，毒性模型是基于化学数据（如定量结构-活性）或基于化学数据（如定量结构-活性）。 关系分析）或体外毒性分析数据（如体外-体内外推）。我们以前的经验表明，将生物学描述符（如体外细胞毒性特征或短期的基因组数据）与化学结构特征相结合，能够以合理的准确度预测啮齿动物的急性肝毒性。因此，本提案的第二部分将致力于通过整合qHTS毒性特征和化学结构信息来开发用于肝毒性预测的新型计算模型。在目标1中，我们将对收集的公共肝毒性数据集进行管理、预处理和标准化。在本研究中，我们将使用多个大型公共数据集（如HTS和qHTS生物测定数据（Tox 21 [1]和ToxCast[2]）、已上市失败药物的肝毒性副作用报告[3]、肝脏毒性知识库基准数据集（LTKB-BD[4]）等）对毒性效应进行建模。交叉研究验证和质量控制的统计方法将应用于收集的数据集， 确保计算兼容性，并选择适当的数据集进行分析。在目标2中，我们将基于综合建模工作流程开发化学品肝毒性的预测模型，该工作流程将利用化学品的结构和体外毒性特征。我们之前的研究[5]表明，使用体外毒性特征和化学结构数据的模型比单独使用任一数据类型的模型对啮齿动物急性肝毒性的准确性更好。在这里，我们将开发一种新的建模工作流程，首先通过策划的qHTS毒性特征定义化学品的功能簇，然后开发计算模型，将化学和生物学数据与人类的总体毒性风险相关联。预测模型将使用包含800多种化合物的独立数据集进行验证。在目标3中，我们建议优先考虑模型中使用的qHTS分析试验，用于未来的毒性试验。我们将从三个角度评估所有体外试验作为生物学描述符，包括综合毒性模型中的描述符重要性，与体内DILI结果的相关性，以及通过基于网络分析的新方法估计的信息含量水平。