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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.

项目总结/文摘