Integrating Computational and Experimental Models to Predict Toxicity of the Pancreas

整合计算和实验模型来预测胰腺的毒性

• 批准号: 10576042
• 负责人: Uduak Zenas George
• 金额: $ 18.24万
• 依托单位: SAN DIEGO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10576042
• 关键词: Biological; Chemical Structure; Chemicals; Childhood diabetes; Complex; Computer Analysis; Computer Models; Cross-Sectional Studies; Data Set; Development; Diabetes Mellitus; Diet; Disease; Embryo; Environmental Pollutants; Environmental Science; Etiology; Experimental Models; Exposure to; Gene Expression; Gene Targeting; Goals; Habits; Health; Heterogeneity; Impairment; Incidence; Individual; Insulin-Dependent Diabetes Mellitus; Life Cycle Stages; Life Style; Mathematics; Metabolic Diseases; Methodology; Methods; Modeling; Molecular; Morphology; Non-Insulin-Dependent Diabetes Mellitus; Organ; Organogenesis; Outcome; PPAR alpha; Pancreas; Pathogenesis; Pathologic; Pathway interactions; Peroxisome Proliferator-Activated Receptors; Prevalence; Process; Quantitative Structure-Activity Relationship; Receptor Signaling; Research; Resolution; SEARCH for Diabetes in Youth; Science; Signal Transduction; Structure; Techniques; Testing; Tissues; Toxic effect; Toxicology; Translations; Uncertainty; Unhealthy Diet; United States; Youth; Zebrafish; adverse outcome; antagonist; biological systems; chemical property; computational toxicology; cytotoxicity; developmental disease; developmental toxicity; developmental toxicology; diabetic; diet and exercise; dietary; high dimensionality; multi-scale modeling; network models; novel; pancreas development; pharmacologic; physical science; predictive modeling; response; small molecule; spatiotemporal; transcriptomics; translational impact

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.

项目摘要 CDC青年糖尿病研究中心发现，I型糖尿病（T1 D）发病率增加了1.8%， 2002年至2012年间，II型糖尿病（T2 D）每年增加4.8%。环境 青年糖尿病的决定因素（TEDDY）研究将T1 D和T2 D的巨大负担归因于 环境污染物。由于糖尿病和代谢性疾病（尤其是 在青年中），需要发育胰腺毒性的计算模型。了解如何 化学结构、基因表达和靶组织细胞毒性等多种因素综合影响 胰腺健康至关重要。然而，在多个尺度上对多个因素进行综合分析， 由于固有的复杂性、高维性、不确定性和异质性，多层 网络已经成为网络科学中的一种新方法，它结合了多个网络，称为 “层”，成为一个数学对象。多层网络能够代表多个因素， 尺度进行严格的计算分析它们的相互作用。因此，揭示了新的关系， 多尺度的关键因素。本研究的总体目标是通过以下方式创建多层网络模型： 我们可以预测胰腺发育毒性的程度和机制， 斑马鱼（Danio rerio）模型中的结构。目标1将建立定量结构-活性关系 （QSAR）模型，以预测药理学和毒理学暴露导致的毒性机制， 发育中的胰腺目标1的目标是利用多层网络和拓扑聚类模型， 基于化学结构预测暴露与胰腺发育毒性之间的关系。 目标2将利用多尺度建模，使用分子， 胰腺发育毒性的结构和病理标准。目标2的目标是描述 暴露可能破坏胰腺发育和早期糖尿病发病机制的过程。我们 将开发一个严格的预测模型，可用于更好地通知先验测试和预期 结果的小分子在胰腺发育性疾病的背景下，我们将构建一个 框架连接过氧化物酶体增殖物激活受体（过氧化物酶体增殖物激活受体）调节（药理学和 毒理学）与胰腺发育和早期功能异常有关。