A dynamical model of preeclampsia development

先兆子痫发展的动力学模型

• 批准号: 9978462
• 负责人: LESLIE MYATT
• 金额: $ 24.15万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9978462
• 关键词: Accounting; Address; Admission activity; Affect; Aging; Angiogenic Factor; Arthritis; Autoimmune Process; Behavior; Biological Markers; Biological Models; Cardiovascular system; Characteristics; Clinical; Clinical Trials; Combined Modality Therapy; Complex; Computer Simulation; Consensus; Data; Data Set; Databases; Development; Diabetes Mellitus; Differential Equation; Disease; Equation; Etiology; Fetus; Fright; Functional disorder; Goals; Growth; Hematology; Individual; Inflammation; Intervention; Investigation; Measurable; Mediator of activation protein; Metabolic; Metabolism; Methodology; Modeling; Modification; Morbidity - disease rate; Mothers; Neonatal Intensive Care; Nerve Degeneration; Observational Study; Ophthalmology; Outcome; Oxidative Stress; Patients; Pharmacologic Substance; Phenotype; Placenta; Pre-Eclampsia; Pregnancy; Pregnancy Complications; Pregnancy Outcome; Premature Birth; Prevention; Process; Quality Control; Randomized Clinical Trials; Research; Research Personnel; Source; Statistical Models; Stroke; Syndrome; System; Techniques; Time; Treatment Protocols; Vascular System; Woman; angiogenesis; base; body system; cancer type; drug candidate; dynamic system; early onset; experimental study; fetal; genetic makeup; improved; in silico; infant death; innovation; insight; longitudinal dataset; maternal condition; mathematical model; model development; mortality; new therapeutic target; novel; pathophysiology of preeclampsia; personalized medicine; predictive marker; premature; prevent; senescence; sex; simulation; therapeutic target; tool development; trophoblast

A serious complication of pregnancy, preeclampsia, is a major source of maternal and fetal morbidity and mortality. Preeclampsia is a heterogeneous condition variably involving different organ systems. Despite decades of investigation there are no known biomarkers that can predict the different preeclampsia phenotypes and no treatment other than delivering the placenta and baby. There is an urgent need to apply an alternative methodology that would allow identification of predictive biomarkers for different PE phenotypes and corresponding therapeutic targets. Dynamical modeling uses differential equations to describe the behavior of a system and in contrast to statistical modeling allows computation of the outcomes of experiments that are significantly different from the ones used to build and calibrate the model. The model's initial conditions and parameters are determined by patient's phenotype and genetic makeup. By varying them it is possible to simulate a wide variety of patients. A patient's development can be analyzed through numerical solution of the model equations. The impact of up- or down-regulating a potential therapeutic target, various treatment protocols, and combined treatment aimed at multiple therapeutic targets can be simulated directly through corresponding modifications of the equations and parameters. Therefore, dynamical modeling is a perfect tool for development of personalized treatments. It has been successfully applied to research various types of cancer, diabetes, arthritis, stroke, cardiovascular, metabolic, hematologic, autoimmune, neurodegenerative and ophthalmological diseases. However, with one exception, pregnancy complications have not yet been studied via dynamical modeling. We propose to stimulate ongoing research with a dynamical model of the development of preeclampsia. Our long term goals are: 1) to encode the complex interrelationships among various mediators of the preeclampsia pathophysiology in a dynamical model and calibrate it using existing quantitative data; 2) to utilize the model to derive early composite biomarkers for various preeclampsia phenotypes; and 3) to simulate, in silico, large-scale, randomized clinical trials to identify potential therapeutic targets and personalized treatment protocols for women with preeclampsia. The overall objective of this proposal, which is the first step towards the long-term goals, is to formulate a robust dynamical model of preeclampsia pathophysiology in terms of measurable variables and calibrate it with the existing data. We will first create and validate a rich database of existing cross-sectional and longitudinal datasets from prior studies of women with preeclampsia. We will then develop and validate a robust dynamical model explaining the maternal and placental components, the primary processes and dynamics of early onset preeclampsia displaying the range of clinical behaviors and accounting for fetal sex. We will then extend the model to incorporate late onset preeclampsia and potentially other subtypes.

先兆子痫是一种严重的妊娠并发症，是孕产妇和胎儿发病的主要原因 和死亡率。先兆子痫是一种异质性疾病，不同程度地涉及不同的器官系统。尽管经过了数十年的研究，仍然没有已知的生物标志物可以预测不同的先兆子痫 表型，除了分娩胎盘和婴儿之外没有其他治疗。急需申请 一种替代方法，可以识别不同 PE 的预测生物标志物 表型和相应的治疗靶点。 动态建模使用微分方程来描述系统的行为，与统计建模相反，动态建模允许计算与用于构建和校准模型的结果显着不同的实验结果。模型的初始条件和参数由患者的表型和基因组成决定。通过改变它们，可以模拟各种各样的患者。可以通过模型方程的数值求解来分析患者的发育情况。通过对方程和参数进行相应修改，可以直接模拟上调或下调潜在治疗靶点、各种治疗方案以及针对多个治疗靶点的联合治疗的影响。因此，动态建模是开发个性化治疗的完美工具。它已成功应用于研究各种类型的癌症、糖尿病、关节炎、中风、心血管、代谢、血液、自身免疫、神经退行性疾病和眼科疾病。 然而，除了一个例外，妊娠并发症尚未通过动力学模型进行研究。 我们建议通过先兆子痫发展的动态模型来刺激正在进行的研究。我们的长期目标是：1）在动态模型中对先兆子痫病理生理学的各种介质之间复杂的相互关系进行编码，并使用现有的定量数据对其进行校准； 2) 利用该模型推导出各种先兆子痫表型的早期复合生物标志物； 3）通过计算机模拟大规模随机临床试验，以确定先兆子痫女性的潜在治疗目标和个性化治疗方案。 该提案的总体目标是实现长期目标的第一步，是根据可测量变量制定一个稳健的先兆子痫病理生理学动力学模型，并用现有数据对其进行校准。我们将首先创建并验证一个丰富的数据库，其中包含先前对先兆子痫女性的研究的现有横断面和纵向数据集。然后，我们将开发并验证一个强大的动力学模型，解释母体和胎盘的组成部分、早发先兆子痫的主要过程和动力学，显示临床行为的范围并解释胎儿性别。然后，我们将扩展该模型以纳入晚发性先兆子痫和潜在的其他亚型。