A dynamical model of preeclampsia development

妊娠的严重并发症先兆子痫是母婴发病率的主要来源。 和死亡率。子痫前期是一种异质性疾病，累及不同的器官系统。尽管进行了几十年的研究，但还没有已知的生物标志物可以预测不同的先兆子痫 表型和除了分娩胎盘和婴儿外没有其他治疗。有迫切的需要申请 允许识别不同PE的预测性生物标记物的替代方法 表型和相应的治疗靶点。动态建模使用微分方程式来描述系统的行为，与统计建模相比，动态建模允许计算与用于建立和校准模型的实验结果显著不同的结果。模型的初始条件和参数由患者的表型和基因构成决定。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解来分析患者的发展情况。通过对方程和参数的相应修改，可以直接模拟上调或下调潜在的治疗靶点、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理想工具。它已成功应用于各种类型的癌症、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的研究。 然而，除了一个例外，妊娠并发症还没有通过动态建模进行研究。 我们建议用先兆子痫发展的动力学模型来刺激正在进行的研究。我们的长期目标是：1)在动力学模型中编码子痫前期病理生理学的各种介质之间的复杂相互关系，并使用现有的定量数据对其进行校准；2)利用该模型得出各种子痫前期表型的早期复合生物标志物；以及3)在电子计算机中模拟大规模的随机临床试验，以确定针对子痫前期妇女的潜在治疗靶点和个性化治疗方案。这项建议的总体目标是根据可测量的变量建立一个强健的子痫前期病理生理学模型，并用现有数据进行校准，这是迈向长期目标的第一步。我们将首先创建和验证现有的横断面和纵向数据集的丰富数据库，这些数据集来自先前对先兆子痫妇女的研究。然后，我们将开发并验证一个强大的动力学模型，解释母体和胎盘的成分，早发性先兆子痫的主要过程和动力学，显示临床行为的范围，并考虑胎儿性别。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。

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.