Project Details
Description
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.
Status | Active |
---|---|
Effective start/end date | 5/19/20 → 2/28/25 |
Funding
- National Institute of Environmental Health Sciences: $457,521.00
- National Institute of Environmental Health Sciences: $465,692.00
- National Institute of Environmental Health Sciences: $449,316.00
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