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 预测性个性化医疗保健和精准医疗开启了医学的新时代，其中传统的 生理信息和新的临床数据，包括遗传数据、影像数据和医疗保健数据来了 共同在正确的时间为正确的患者提供正确的治疗，并提供医疗保健和临床服务。 技术迅速发展，大量不同的临床数据变得可以广泛访问和使用。 然而，当前的建模技术缺乏将病理生理学与成像数据相结合。 开发一种创新的数据分析工具，将个性化临床数据与现有科学相结合 流行病学和临床医学成为这一新领域的迫切需求。 研究项目是开发一种病理生理学驱动的空间动态建模（PDSDM）方法 我们的五年目标是开发 PDSDM。 计算建模平台并根据各种疾病的患者数据验证该平台。 将开发一个交互式计算平台来构建PDSDM模型并开发一个计算 然后我们将利用该模块来自动模拟模型。 临床患者数据参数化生理信号通路产生的数学模型 网络，并将成像数据纳入空间计算域；此外， 将在该计算建模平台上针对当前可用的治疗进行个性化治疗研究 这一创新框架将整合数学模型、计算方法和数据。 分析和数据驱动的优化技术，为 我们将针对心血管等各种生物医学疾病验证这一新框架。 疾病、慢性胰腺炎和阿尔茨海默病的现有临床和生物学数据。 这项研究意义重大，因为它将提供个性化疾病进展的 3D 预测， 它将更准确地评估个性化疾病风险。 可用的治疗计划几乎可以为每个人提供最佳的治疗建议。