# Integrating Computational and Experimental Models to Predict Toxicity of the Pancreas

> **NIH NIH R21** · SAN DIEGO STATE UNIVERSITY · 2023 · $182,446

## Abstract

PROJECT SUMMARY
The CDC SEARCH for Diabetes in Youth study found that Type I Diabetes (T1D) incidence increased by 1.8%
each year between 2002-2012, and Type II Diabetes (T2D) increased by 4.8%. The Environmental
Determinants of Diabetes in the Young (TEDDY) study has attributed a substantial burden of T1D and T2D to
environmental contaminants. Due to the increasing prevalence of diabetes and metabolic diseases (especially
among youth), computational models for developmental pancreatic toxicity are needed. Understanding how
multiple factors such as chemical structure, gene expression and target tissue cytotoxicity integrate and impact
pancreatic health is vital. However, an integrated analysis of multiple factors at multiple scales poses great
challenges due to the inherent complexity, high-dimensionality, uncertainty, and heterogeneity. Multilayer
networks have emerged as a novel methodology in network science that combines multiple networks, called
“layers”, into one mathematical object. Multilayer networks are able to represent multiple factors across multi-
scales for a rigorous computational analysis of their interactions. Thereby, uncovering novel relations between
key factors on a multi-scale. The overarching goal of this research is to create multilayer network models by
which we can predict the magnitude and mechanisms of pancreatic developmental toxicity based on chemical
structure in a zebrafish (Danio rerio) model. Aim 1 will build a Quantitative Structure-Activity Relationship
(QSAR) model to predict mechanisms of toxicity resulting from pharmacological and toxicological exposures in
the developing pancreas. The goal of Aim 1 is to utilize a multilayer network and topological clustering model to
predict the relationship between exposures and pancreatic developmental toxicity based on chemical structure.
Aim 2 will utilize multi-scale modeling to create an Adverse Outcome Pathway (AOP) using molecular,
structural, and pathological criteria for pancreatic developmental toxicity. The goal of Aim 2 is to characterize
the processes by which exposures may disrupt pancreas development and early diabetic pathogenesis. We
will develop a rigorous predictive model that can be used to better inform a priori testing and expected
outcomes of small molecules in the context of pancreatic developmental diseases, and we will construct a
framework to connect peroxisome proliferator-activated receptor (PPAR) modulation (pharmacological &
toxicological) with aberrant pancreatic development and early function.

## Key facts

- **NIH application ID:** 10576042
- **Project number:** 1R21DK134931-01
- **Recipient organization:** SAN DIEGO STATE UNIVERSITY
- **Principal Investigator:** Uduak Zenas George
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $182,446
- **Award type:** 1
- **Project period:** 2023-02-15 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10576042

## Citation

> US National Institutes of Health, RePORTER application 10576042, Integrating Computational and Experimental Models to Predict Toxicity of the Pancreas (1R21DK134931-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10576042. Licensed CC0.

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