Mechanism-Driven Virtual Adverse Outcome Pathway Modeling for Hepatotoxicity

机制驱动的肝毒性虚拟不良结果途径建模

• 批准号: 10166848
• 负责人: Hao Zhu
• 金额: $ 45.75万
• 依托单位: RUTGERS THE STATE UNIV OF NJ CAMDEN
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-19 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10166848
• 关键词: Address; Animal Model; Animal Testing; Antioxidants; Big Data; Biochemical; Biological; Biological Assay; Biological Markers; Cellular Stress; Chemical Injury; Chemical Structure; Chemicals; Clinical; Complement; Complex; Computer Models; Computer software; Computers; Cryopreservation; Custom; Data; Data Pooling; Data Set; Data Sources; Databases; Development; Drug Costs; Ensure; Environment; Environmental Pollution; Evaluation; Face; Generations; Hepatocyte; Hepatotoxicity; Human; In Vitro; Industrialization; Injury; Internet; Libraries; Liver; Luciferases; Machine Learning; Marketing; Methodology; Methods; Mining; Modeling; Nutraceutical; Online Systems; Pathway interactions; Pharmaceutical Preparations; Pharmacologic Substance; Population; Process; Property; Proteomics; PubChem; Public Health; Quantitative Structure-Activity Relationship; Research; Research Personnel; Resources; Response Elements; Risk; Safety; Signal Transduction; Source; Statutes and Laws; System; Test Result; Testing; Time; Toxic effect; Toxicology; Translating; Validation; Vertebrates; adverse outcome; base; candidate validation; cell injury; combat; computational toxicology; computer framework; computerized tools; cost; data mining; deep neural network; design; developmental toxicity; drug development; endoplasmic reticulum stress; experimental study; hepatocellular injury; improved; in vitro Assay; in vitro testing; in vivo; interest; knowledge base; large datasets; liver injury; next generation; novel; pre-clinical; predictive modeling; reproductive toxicity; research clinical testing; safety assessment; safety testing; screening; search engine; tool; toxicant; transcriptomics; virtual; web portal

PROJECT SUMMARY/ABSTRACT Experimental animal and clinical testing to evaluate hepatotoxicity demands extensive resources and long turnaround times. Utilization of computational models to directly predict the toxicity of new compounds is a promising strategy to reduce the cost of drug development and to screen the multitude of industrial chemicals and environmental contaminants currently lacking safety assessments. However, the current computational models for complex toxicity endpoints, such as hepatotoxicity, are not reliable for screening new compounds and face numerous challenges. Our recent studies have shown that traditional Quantitative Structure-Activity Relationship modeling is applicable for relatively simple properties or toxicity endpoints with a clear mechanism, but fails to address complex bioactivities such as hepatotoxicity. The primary objective of this proposal is to develop novel mechanism-driven Virtual Adverse Outcome Pathway (vAOP) models for the fast and accurate assessment of hepatotoxicity in a high-throughput manner The resulting vAOP models will be experimentally validated using a complement of in vitro and ex vivo testing. We have generated a preliminary vAOP model based on the antioxidant response element (ARE) pathway that has undergone initial validation and refinement using in vitro testing. To this end, our project will generate novel predictive models for hepatotoxicity by applying 1) a virtual cellular stress pathway model to mechanism profiling and assessment of new compounds; 2) computational predictions to fill in the missing data for specific targets within the pathway; 3) in vitro experimental validation with three complementary bioassays; and 4) ex vivo experimental validation with pooled primary human hepatocytes capable of biochemical transformation. The scientific approach of this study is to develop a universal modeling workflow that can take advantage of all available short-term testing information, obtained from both computational predictions using novel machine learning approaches and in vitro experiments, for target compounds of interest. We will validate and use our modeling workflow to directly evaluate the hepatotoxicity of new compounds and prioritize candidates for validation in pooled primary human hepatocytes. The resulting workflow will be disseminated via a web portal for public users around the world with internet access. Importantly, this study will pave the way for the next generation of chemical toxicity assessment by reconstructing the modeling process through a combination of big data, computational modeling, and low cost in vitro experiments. To the best of our knowledge, the implementation of this project will lead to the first publicly available mechanisms-driven modeling and web- based prediction framework for complex chemical toxicity based on publicly-accessible big data. These deliverables will have a significant public health impact by not only prioritizing compounds for safety testing or new chemical development, but also revealing toxicity mechanisms.

项目总结/摘要 评估肝毒性的实验动物和临床试验需要大量资源， 周转时间长。利用计算模型直接预测新化合物的毒性是一种新的方法。 这是一项很有前途的战略，可以降低药物开发成本，并筛选大量的工业化学品。 以及目前缺乏安全评估的环境污染物。然而，目前的计算 复杂毒性终点（如肝毒性）的模型对于筛选新化合物并不可靠 并面临诸多挑战。我们最近的研究表明，传统的定量结构-活性 关系建模适用于相对简单的性质或毒性终点， 机制，但未能解决复杂的生物活性，如肝毒性。这项工作的主要目的是 一项提案是为研究开发新的机制驱动的虚拟不良结果途径（vAOP）模型。 以高通量的方式快速准确地评估肝毒性。 使用体外和离体测试的补充进行实验验证。我们创造了一个 基于抗氧化反应元件（ARE）途径的初步vAOP模型， 使用体外测试进行初步验证和改进。为此，我们的项目将产生新的预测 通过应用1）虚拟细胞应激途径模型进行机制分析， 新化合物的评估; 2）计算预测，以填补特定目标的缺失数据 3）用三种互补生物测定法进行体外实验验证;以及4）离体 用能够生化转化的合并原代人肝细胞进行实验验证。的 本研究的科学方法是开发一个通用的建模工作流程，可以利用所有 可用的短期测试信息，从使用新机器的计算预测中获得 学习方法和体外实验，为目标化合物的利益。我们将验证并使用我们的 建模工作流程，以直接评估新化合物的肝毒性，并优先考虑候选药物， 在合并的原代人肝细胞中进行验证。由此产生的工作流程将通过一个门户网站传播 为世界各地的公众用户提供互联网接入。重要的是，这项研究将为下一项研究铺平道路。 通过以下组合重建建模过程来生成化学毒性评估： 大数据、计算建模和低成本的体外实验。据我们所知， 该项目的实施将导致第一个公开可用的机制驱动的建模和网络， 基于公众可访问的大数据的复杂化学品毒性预测框架。这些 可交付成果将产生重大的公共卫生影响，不仅优先考虑安全测试的化合物， 新的化学发展，但也揭示了毒性机制。