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：
10940417
负责人：
Hao Zhu
金额：
$ 9万
依托单位：
TULANE UNIVERSITY OF LOUISIANA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-11-16 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10940417
关键词：
Address Animal Experiments 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 Pollutants Evaluation Face Generations Hepatocyte Hepatotoxicity Human In Vitro Industrialization Injury Internet Libraries Liver Luciferases Machine Learning 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 Toxic effect Toxicology Translating Validation Vertebrates adverse outcome candidate validation cell injury chemical safety 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 knowledgebase large datasets liver injury next generation novel post-market 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.

项目概要/摘要评估肝毒性的实验动物和临床测试需要大量资源和周转时间长。利用计算模型直接预测新化合物的毒性是一种方法降低药物开发成本并筛选多种工业化学品的有前景的策略和环境污染物目前缺乏安全评估。然而，目前的计算复杂毒性终点（例如肝毒性）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特性或毒性终点，具有明确的机制，但未能解决复杂的生物活性，例如肝毒性。此举的首要目标建议开发新颖的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地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进行实验验证。我们已经生成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完善。为此，我们的项目将产生新颖的预测通过应用 1) 虚拟细胞应激途径模型来进行机制分析和新化合物的评估； 2）计算预测以填补特定目标的缺失数据在通道内； 3）通过三种互补的生物测定进行体外实验验证； 4) 离体使用能够进行生化转化的汇集原代人肝细胞进行实验验证。这这项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，通过使用新型机器的计算预测获得针对感兴趣的目标化合物的学习方法和体外实验。我们将验证并使用我们的建模工作流程直接评估新化合物的肝毒性并优先考虑候选化合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门户网站传播面向世界各地可访问互联网的公共用户。重要的是，这项研究将为下一步的研究铺平道路。通过结合以下内容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的机制驱动的建模和网络- 基于公开大数据的复杂化学毒性预测框架。这些可交付成果不仅会优先考虑化合物进行安全测试或对公共健康产生重大影响新化学品的开发，同时也揭示了毒性机制。