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: 10697363
• 负责人: WENRUI HAO
• 金额: $ 37.98万
• 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/10697363
• 关键词: 3-Dimensional; Alzheimer' s Disease; Area; Biological; Calibration; Cardiovascular Diseases; Clinical; Clinical Data; Clinical Medicine; Clinical Trials; Computer Models; Computing Methodologies; Data; Data Analyses; Data Analytics; Disease; Disease Progression; Epidemiology; Functional disorder; Genetic; Goals; Healthcare; Image; Individual; Medicine; Modeling; Modernization; Patients; Physiological; Research; Research Project Grants; Science; Signal Pathway; Suggestion; Techniques; Technology; Time; Vision; analytical tool; chronic pancreatitis; clinical biomarkers; computational platform; disorder risk; imaging biomarker; innovation; mathematical model; novel therapeutic intervention; optimal treatments; personalized approach; personalized health care; personalized medicine; personalized predictions; precision medicine; treatment planning; usability; virtual

Abstract 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.

摘要 预测性个性化医疗保健和精准医疗开创了一个新的医学时代，在这个时代，传统的 生理信息和新的临床数据，包括遗传数据、成像数据和保健数据， 在正确的时间为正确的患者提供正确的治疗。医疗保健和临床 随着技术的快速发展，大量的临床数据变得广泛可用。 然而，目前的建模技术缺乏结合病理生理学与成像数据。因此，我们认为， 开发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的结合成为这一新领域的迫切需要。这个项目的总体愿景是 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， 个性化医疗预测和精准医疗。我们的五年目标是发展PDSDM 计算建模平台，并在各种疾病的患者数据上验证该平台。具体来说，我们 将开发一个交互式计算平台，以建立PDSDM模型，并开发一个计算 模块进行模型的自动仿真。然后，我们将开发一个模型校准模块， 临床患者数据到由生理信号通路产生的参数化数学模型 网络，也将纳入成像数据作为空间计算域;此外，最佳 个性化治疗研究将在该计算建模平台上进行， 临床试验这个创新框架将整合数学建模、计算方法、数据 分析和数据驱动的优化技术，以提供个性化的空间计算模型， 每一个人我们将在各种生物医学疾病上验证这一新框架， 疾病，慢性胰腺炎，阿尔茨海默病与现有的临床和生物学数据。拟议 这项研究意义重大，因为它将为个性化疾病进展提供3D预测， 可以更准确地评估个性化的疾病风险。它还将提供一个系统的方法来评估 可用的治疗计划，然后虚拟地为每个人提供最佳的治疗建议。