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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要/摘要实验性动物和临床测试评估肝毒性需要广泛的资源和漫长的周转时间。利用计算模型直接预测新化合物的毒性是降低药物开发成本并筛选大量工业化学品的有希望的策略目前缺乏安全评估的环境污染物。但是，当前的计算复杂毒性终点的模型，例如肝毒性，对筛选新化合物不可靠并面临许多挑战。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属性或毒性终点，清晰机制，但无法解决诸如肝毒性之类的复杂生物活性。这是这个的主要目标提案是开发新型机制驱动的虚拟不良结果途径（VAOP）模型以高通量方式快速准确评估肝毒性，由此产生的VAOP模型将使用体外和离体测试的补充对实验验证。我们已经产生了基于抗氧化剂响应元件（AS）途径的初步VAOP模型使用体外测试的初始验证和完善。为此，我们的项目将产生新颖的预测肝毒性模型通过应用1）虚拟细胞应力途径模型用于机理分析和评估新化合物； 2）计算预测以填写特定目标的丢失数据在路径内； 3）三个互补生物测定的体外实验验证； 4）体内实验性验证，具有能够生化转化的汇集的原代人肝细胞。这这项研究的科学方法是开发一个通用的建模工作流，可以利用所有优势可用的短期测试信息，使用新机器从两个计算预测中获得学习方法和体外实验，用于感兴趣的目标化合物。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，并优先考虑综合原代人肝细胞的验证。由此产生的工作流将通过Web门户进行分散对于世界各地的公共用户，可以访问Internet。重要的是，这项研究将为下一个研究铺平道路通过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机制驱动的建模和Web- 基于基于公共访问的大数据基于复杂化学毒性的预测框架。这些可交付成果将不仅通过确定安全测试的化合物或新的化学发展，但也揭示了毒性机制。