Multifidelity and multiscale modeling of the spleen function in sickle cell disease with in vitro, ex vivo and in vivo validations

镰状细胞病脾功能的多保真度和多尺度建模，并进行体外、离体和体内验证

• 批准号: 10469422
• 负责人: Pierre BUFFET
• 金额: $ 65.02万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-19 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10469422
• 关键词: Accounting; Acute; Adhesions; Adhesiveness; Adhesives; Anemia; Biomechanics; Blood Circulation; Blood specimen; Cell Communication; Cell Shape; Cell model; Clinical; Collaborations; Complement; Complex; Complication; Computer Models; Coupling; Data; Decision Making; Devices; Endothelium; Erythrocytes; Expectancy; Fiber; Filtration; Goals; Hematological Disease; Hepatic; Hereditary Disease; Hereditary Spherocytosis; Human; Hypoxia; Immune system; In Vitro; Lead; Learning; Life; Link; Malaria; Measures; Mechanics; Medical; Microfluidic Microchips; Microfluidics; Modeling; Molecular; Morphology; Mutate; Organ; Output; Oxygen; Paper; Paris, France; Pathogenicity; Patients; Perfusion; Phagocytosis; Physicians; Play; Polymers; Process; Prognosis; Proteins; Quality of life; Residual state; Risk; Role; Sampling; Shapes; Sickle Cell; Sickle Cell Anemia; Sickle Hemoglobin; Source; Spleen; Stem cell transplant; Surface; System; Training; Validation; acute chest syndrome; base; biophysical properties; cohort; deep learning; deep neural network; design; ex vivo perfusion; experimental study; feeding; gene therapy; high dimensionality; human subject; improved; in silico; in vivo; learning progression; macrophage; mimetics; molecular dynamics; mouse model; multi-scale modeling; neural network; particle; predictive modeling; prevent; retention rate; senescence; sickling; simulation; spatiotemporal; tool; transplantation therapy; vaso-occlusive crisis

Project Summary The spleen plays a key role in the human immune system but also clears senescent red blood cells (RBC) from the circulation and those altered by acquired or inherited diseases. In patients with sickle cell disease (SCD), the spleen is one of the first targets of pathogenic processes and a potential protector against major complications. Under hypoxic conditions, mutated sickle hemoglobin (HbS) polymerizes to fibers which increase both the stiffness and adhesion of RBC. Splenic filtration of altered RBC prone to sickling (a process that cannot be directly observed in human subjects) contributes to anemia and likely triggers acute splenic sequestration crises (ASSC). On the other hand, it potentially prevents complications associated with intravascular sickling. Self- amplified blockade of vessels with sickled RBCs is indeed a hallmark of vaso-occlusive crises, acute chest syndrome, and acute hepatic crises, that severely impact the life quality and expectancy of patients with SCD. We propose to formulate and validate a new predictive modeling framework for how the spleen filters altered RBC in SCD by synergistically integrating in silico, in vitro, ex vivo and in vivo data using multifidelity-based neural networks (NN). This will deliver predictive models that can continuously learn when new data become available, a paradigm shift in biomedical modeling. We will develop multiscale/multifidelity computational models (and corresponding NN implementations) that link sub-cellular, cellular, and vessel level phenomena spanning across four orders of magnitude in spatio-temporal scales. This scale coupling will be accomplished using a molecular dynamics/dissipative particle dynamics (MD/DPD) framework. We will validate these predictive computational models by data from in vitro and ex vivo experiments, and RBC quantitative features collected in SCD patients. Specifically, we will use three new spleen-on-a-chip microfluidic devices with oxygen control and the unique human spleen perfusion setup of our foreign partner, with the following aims: Aim 1: Develop and validate a splenic inter-endothelial slit filtration model; Aim 2: Develop new models of RBC macrophage adhesion and of phagocytosis in the spleen; Aim 3: Perform Spleen-on-a-Chip experiments and validation; Aim 4: Validate the predictive framework based on RBC samples from patients. Realization of our four Specific Aims will significantly increase our understanding of the complex pathogenic and protective roles of the spleen in SCD. Feeding our new multifidelity neural networks with morphological and functional measures of RBC circulating in SCD patients will lead to models for residual spleen function in SCD, which should help predict the risk of acute splenic sequestration crises, and guide optimal timing for Stem Cell Transplantation or Gene Therapy. The new paradigm in using deep learning tools to integrate data from different sources will be applicable to modeling many other blood diseases.

项目摘要 脾脏在人类免疫系统中起关键作用，但也清除了衰老的红细胞（RBC） 循环和因获得或遗传疾病而改变的循环。在患有镰状细胞疾病（SCD）的患者中 脾脏是致病过程的第一个目标之一，也是潜在的保护因素，以防止重大并发症。 在低氧条件下，突变的镰状血红蛋白（HBS）聚合与纤维相结合，这两者都会增加 RBC的刚度和粘附。脾气暴躁的RBC易于恶心的脾脏过滤（这一过程不可能 在人类受试者中直接观察到）有助于贫血，并可能触发急性脾隔离危机 （ASSC）。另一方面，它有可能阻止与血管内疾病有关的并发症。自己- 用镰状RBCS扩增对船只的封锁确实是血管牙合，急性胸部的标志 综合征和急性肝危机，严重影响SCD患者的生活质量和期望。 我们建议为脾脏过滤器如何改变并验证一个新的预测建模框架 rbc通过基于多质量的基于多因素的体外，体内和体内数据在体外，体外和体内数据中的协同整合。 神经网络（NN）。这将提供预测模型，这些模型可以不断学习新数据何时成为 可用，生物医学建模的范式转移。我们将开发多尺度/多重计算模型 （以及相应的NN实现）将跨细胞，细胞和血管水平现象链接起来 在时空尺度中的四个数量级。此比例耦合将使用 分子动力学/耗散粒子动力学（MD/DPD）框架。我们将验证这些预测性 来自体外和离体实验的数据的计算模型，以及收集的RBC定量特征 SCD患者。具体而言，我们将使用具有氧气控制和 我们外国伴侣的独特人脾脏灌注设置，其目的是：目标1：开发和 验证脾脏间皮层缝隙过滤模型；目标2：开发RBC巨噬细胞粘附的新模型 和脾脏中的吞噬作用； AIM 3：执行片段片的实验和验证；目标4：验证 基于患者的RBC样品的预测框架。 实现我们的四个特定目标将显着提高我们对复杂的致病性和 脾脏在SCD中的保护作用。用形态学和 SCD患者RBC循环的功能测量将导致SCD中残留脾脏功能的模型， 这应该有助于预测急性脾隔离危机的风险，并指导干细胞的最佳时机 移植或基因治疗。使用深度学习工具来整合来自不同的数据的新范式 来源将适用于建模许多其他血液疾病。