A pathophysiology driven spatial dynamic modeling framework for personalized prediction and precision medicine

用于个性化预测和精准医疗的病理生理学驱动的空间动态建模框架

• 批准号: 10797133
• 负责人: WENRUI HAO
• 金额: $ 12.48万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-08 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10797133
• 关键词: 3-Dimensional; Alzheimer' s Disease; Area; Biological; Biological Markers; Calibration; Cardiovascular Diseases; Clinical; Clinical Data; Clinical Medicine; Clinical Trials; Complex; Computer Models; Computing Methodologies; Data; Data Analyses; Data Analytics; Data Set; Death Rate; Diffusion; Disease; Disease Progression; Elements; Epidemiology; Equation; Functional disorder; Genetic; Goals; Healthcare; Image; Individual; Libraries; Literature; Magnetic Resonance Imaging; Measurement; Medicine; Methods; Modeling; Modernization; Output; Patients; Physiological; Production; Research; Research Project Grants; Science; Signal Pathway; Signal Transduction; Speed; Suggestion; Techniques; Technology; Testing; Time; Tissues; Vision; analytical tool; chronic pancreatitis; clinical biomarkers; complex data; computational platform; cytokine; disorder risk; imaging biomarker; improved; individual patient; innovation; mathematical model; mechanical properties; novel therapeutic intervention; optimal treatments; personalized approach; personalized health care; personalized medicine; personalized predictions; precision medicine; process optimization; simulation; treatment planning; usability; virtual

Summary Predictive personalized healthcare and precision medicine enable a new era of medicine, in which traditional physiological information and new clinical data including genetic data, imaging data, and healthcare data come together to match the right patient with the right treatment at the right time. With healthcare and clinical technology rapidly grow, the vast and varied amounts of clinical data become widely accessible and usable. However, current modeling techniques lack of combining pathophysiology with imaging data. Therefore, developing an innovative data analytical tool that combines personalized clinical data with the existing sciences of epidemiology and clinical medicine becomes an urgent need in this new area. The overall vision of this research project is to develop a pathophysiology driven spatial dynamic modeling (PDSDM) approach for personalized healthcare prediction and precision medicine. Our five-year goals are to develop the PDSDM computational modeling platform and validate this platform on patient data for various diseases. In specific, we will develop an interactive computational platform to build the PDSDM model and develop a computational module to simulate the model automatically. Then we will develop a model calibration module by employing the clinical patient data to parameterize mathematical models arising from physiological signaling pathway networks and will also incorporate the imaging data as the spatial computational domain; moreover, optimal personalized treatment studies will be performed on this computational modeling platform for current available clinical trials. This innovative framework will integrate mathematical modeling, computational methods, data analysis, and data-driven optimization techniques to provide a personalized spatial computational model for each individual. We will validate this new framework on various biomedical diseases such as cardiovascular disease, chronic pancreatitis, and Alzheimer’s disease with existing clinical and biological data. The proposed research is significant because it will provide the 3D prediction for personalized disease progression which would evaluate personalized disease risk more accurately. It will also provide a systematic way to assess the available treatment plans virtually then to provide an optimal treatment suggestion for each individual.

概括 预测性个性化的医疗保健和精密医学实现了一个新的医学时代，其中传统的医学时代 物理信息和新的临床数据，包括遗传数据，成像数据和医疗保健数据 将正确的患者与正确的治疗相匹配。使用医疗保健和临床 技术迅速增长，大量和多样的临床数据变得广泛访问和可用。 但是，当前的建模技术缺乏将病理生理学与成像数据相结合的。所以， 开发一种创新的数据分析工具，该工具将个性化临床数据与现有科学相结合 在这个新领域，流行病学和临床医学成为迫切需要。总体愿景 研究项目是为了开发病理生理驱动的空间动态建模（PDSDM）方法 个性化医疗保健预测和精确医学。我们的五年目标是开发PDSDM 计算建模平台并验证该平台的各种疾病的患者数据。具体而言，我们 将开发一个交互式计算平台来构建PDSDM模型并开发计算 模块自动模拟模型。然后，我们将通过使用 临床患者数据，以参数化由物理信号通路引起的数学模型 网络，还将将成像数据纳入空间计算域；而且，最佳 个性化治疗研究将在此计算建模平台上进行当前可用的 临床试验。这个创新的框架将集成数学建模，计算方法，数据 分析和数据驱动的优化技术，为提供个性化的空间计算模型 每个人。我们将在各种生物医学疾病（例如心血管）上验证这个新框架 现有的临床和生物学数据，疾病，慢性胰腺炎和阿尔茨海默氏病。提议 研究很重要，因为它将为个性化疾病进展提供3D预测 将更准确地评估个性化疾病风险。它还将提供一种系统的方法来评估 实际上，可用的治疗计划为每个人提供最佳的治疗建议。