Improving Tissue Engineered Vascular Graft Performance via Computational Modeling

通过计算建模提高组织工程血管移植物的性能

基本信息

项目摘要

PROJECT SUMMARY Tissue engineered vascular grafts (TEVGs) have demonstrated potential to revolutionize cardiovascular care, with multiple grafts now in clinical trials in children and adults. Yet, there remains a pressing need to optimize these grafts to improve outcomes and enable wide-spread usage. In this proposal, we build upon a strong foundation of prior findings but introduce an innovative multi-fidelity computational-experimental approach that promises to accelerate greatly the development of improved TEVGs. Although the proposed approach is general with broad applicability, we will focus on one particular application – TEVGs for congenital heart surgery – to refine the approach and illustrate its utility. Specifically, we will use a pre-clinical juvenile ovine model to collect the longitudinal data needed to develop and inform novel multiscale computational models that will be melded to describe the in vivo development of a neovessel from an implanted biodegradable polymeric scaffold. Our approach will be informed by data from three initial, non-optimal designs, then used to identify via formal methods of optimization preferred microstructural scaffold parameters and an overall geometry that optimizes in vivo function. Particularly novel will be our ability to account for normal developmental changes in the lamb vasculature and coupling of cell signaling, growth and remodeling, and 3D hemodynamics in a novel multi-fidelity, multiscale workflow that allows optimization of desired biological and physiological outcomes. To achieve these goals, we propose three Specific Aims: 1) To quantify normal vascular development and performance of three baseline TEVG designs in a lamb model; 2) To develop and employ a novel multiscale fluid-solid-growth (FSG) simulation framework to optimize TEVG design; 3) To validate the model-identified optimal TEVG design in a longitudinal large animal study. Our team is uniquely positioned for success, combining expertise in animal models of congenital heart disease, development of TEVGs and their clinical translation, finite element simulations of cardiovascular hemodynamics and biomechanics, modeling vascular growth and remodeling, and identifying and modeling mechanisms of mechanobiology. Our approach is innovative in that we will 1) meld macro (organ) level simulations of cardiovascular biomechanics with micro level simulations of vascular cell signaling, 2) develop a novel, generally applicable paradigm for model-driven optimization of tissue engineered structures that provides control over outcomes, and 3) facilitate clinical translation of TEVGs with improved performance. Successful completion of this study will be significant in multiple ways – not only will it result in a new (optimal) design of a TEVG for use in the Fontan surgical procedure, performed in children born with single ventricle congenital heart defects, it will also establish a novel computational-experimental paradigm in cardiovascular tissue engineering that promises to accelerate the development of diverse implants.
项目摘要 组织工程的血管移植物(TEVG)表现出可能革新心血管护理的潜力, 现在在儿童和成人的临床试验中有多个移植物。但是,仍然需要优化的迫切需求 这些移植物可改善结果并实现广泛的用法。在此提案中,我们建立在一个强大的基础上 先前发现的基础,但引入了一种创新的多保真计算实验方法 有望大大加快改善的Tevgs的发展。虽然提出的方法是一般的 有了广泛的适用性,我们将专注于一种特定的应用-TEVGS用于先天性心脏外科手术 - 完善方法并说明其效用。具体而言,我们将使用临床前的少年卵巢模型收集 开发和告知新型多尺度计算模型所需的纵向数据,这些模型将被融合 描述来自植入的可生物降解聚合物支架的新固定剂的体内发展。我们的 方法将通过三个初始非最佳设计的数据来告知方法,然后用来通过形式方法识别 优化优先的微结构支架参数和整体几何形状,可在体内优化 功能。特别是新颖的是我们能够考虑羔羊正常发育变化的能力 细胞信号传导,生长和重塑的脉管系统和耦合以及新型多余性中的3D血液动力学 多尺度工作流程允许优化所需的生物学和物理结果。实现这些 目标,我们提出了三个具体目标:1)量化正常的血管发展和三个的性能 基线TEVG在羔羊模型中设计; 2)开发和使用一种新型的多尺度流体固体生长(FSG) 模拟框架以优化TEVG设计; 3)验证模型识别的最佳TEVG设计 纵向大型动物研究。我们的团队是成功的独特位置,结合了动物的专业知识 先天性心脏病的模型,TEVG的发展及其临床翻译,有限元 心血管血流动力学和生物力学的模拟,对血管生长和重塑建模以及 识别和建模机制的机理。我们的方法是创新的,因为我们将1)融合 带有微级模拟的血管细胞模拟心血管生物力学的宏(器官)级别模拟 信号传导,2)开发一种新颖的,通常适用于模型驱动的组织工程的范式 提供控制结果的结构,以及3)促进TEVG的临床翻译,并有所改善 表现。这项研究的成功完成将以多种方式重要 - 不仅会导致 TEVG的新(最佳)设计用于Fontan外科手术,在有单身的孩子中进行 心室先天性心脏缺陷,它还将在 有望加速潜水工具的心血管组织工程。

项目成果

期刊论文数量(0)
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Jay D. Humphrey其他文献

A Computational Framework to Predict and Understand in situ Heart Valve Tissue Engineering
  • DOI:
    10.1080/24748706.2021.1900703
  • 发表时间:
    2021-06-01
  • 期刊:
  • 影响因子:
  • 作者:
    Elmer Middendorp;Marcos Latorre;Jason M. Szafron;Frank P.T. Baaijens;Jay D. Humphrey;Sandra Loerakker
  • 通讯作者:
    Sandra Loerakker
ブレインサイエンス・レビュー2004
脑科学评论 2004
  • DOI:
  • 发表时间:
    2004
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Daisuke Mori;Guido David;Jay D. Humphrey;James E. Moore Jr.;Miho Terunuma;平田 雅人
  • 通讯作者:
    平田 雅人
Altered mechanical behavior and properties of the human anterior lens capsule after cataract surgery.
白内障手术后人类晶状体前囊的机械行为和特性发生改变。
  • DOI:
    10.1016/j.exer.2009.06.001
  • 发表时间:
    2009
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    R. Pedrigi;J. Dziezyc;Jay D. Humphrey
  • 通讯作者:
    Jay D. Humphrey
Journal of Mechanics of Materials and Structures SPONTANEOUS UNWINDING OF A LABILE DOMAIN IN A COLLAGEN TRIPLE HELIX
材料与结构力学杂志 胶原三螺旋中不稳定域的自发展开
  • DOI:
  • 发表时间:
    2007
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Krishnakumar M. Ravikumar;Jay D. Humphrey;Wonmuk Hwang
  • 通讯作者:
    Wonmuk Hwang
FSGe: A fast and strongly-coupled 3D fluid-solid-growth interaction method
FSGe:一种快速、强耦合的 3D 流固生长相互作用方法
  • DOI:
    10.48550/arxiv.2404.13523
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Martin R. Pfaller;Marcos Latorre;Erica L. Schwarz;F. Gerosa;Jason M. Szafron;Jay D. Humphrey;Alison L. Marsden
  • 通讯作者:
    Alison L. Marsden

Jay D. Humphrey的其他文献

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{{ truncateString('Jay D. Humphrey', 18)}}的其他基金

Computational model-driven design to mitigate vein graft failure after coronary artery bypass
计算模型驱动的设计可减轻冠状动脉搭桥术后静脉移植失败的风险
  • 批准号:
    10683327
  • 财政年份:
    2022
  • 资助金额:
    $ 69.22万
  • 项目类别:
Computational model-driven design to mitigate vein graft failure after coronary artery bypass
计算模型驱动设计减轻冠状动脉搭桥术后静脉移植失败
  • 批准号:
    10539814
  • 财政年份:
    2022
  • 资助金额:
    $ 69.22万
  • 项目类别:
Modeling Multiscale Immuno-Mechanics in Aortic Disease
主动脉疾病的多尺度免疫力学建模
  • 批准号:
    10532786
  • 财政年份:
    2022
  • 资助金额:
    $ 69.22万
  • 项目类别:
Modeling Multiscale Immuno-Mechanics in Aortic Disease
主动脉疾病的多尺度免疫力学建模
  • 批准号:
    10352581
  • 财政年份:
    2022
  • 资助金额:
    $ 69.22万
  • 项目类别:
Multiscale Modeling of Aortic Homeostasis
主动脉稳态的多尺度建模
  • 批准号:
    10471254
  • 财政年份:
    2021
  • 资助金额:
    $ 69.22万
  • 项目类别:
Multiscale Modeling of Aortic Homeostasis
主动脉稳态的多尺度建模
  • 批准号:
    10189114
  • 财政年份:
    2021
  • 资助金额:
    $ 69.22万
  • 项目类别:
Smooth Muscle Cell Proliferation and Degradative Phenotype in Thoracic Aorta Aneurysm and Dissection
胸主动脉瘤和夹层中的平滑肌细胞增殖和降解表型
  • 批准号:
    10184861
  • 财政年份:
    2020
  • 资助金额:
    $ 69.22万
  • 项目类别:
Smooth Muscle Cell Proliferation and Degradative Phenotype in Thoracic Aorta Aneurysm and Dissection
胸主动脉瘤和夹层中的平滑肌细胞增殖和降解表型
  • 批准号:
    10376852
  • 财政年份:
    2019
  • 资助金额:
    $ 69.22万
  • 项目类别:
Smooth Muscle Cell Proliferation and Degradative Phenotype in Thoracic Aorta Aneurysm and Dissection
胸主动脉瘤和夹层中的平滑肌细胞增殖和降解表型
  • 批准号:
    10573756
  • 财政年份:
    2019
  • 资助金额:
    $ 69.22万
  • 项目类别:
Smooth Muscle Cell Proliferation and Degradative Phenotype in Thoracic Aorta Aneurysm and Dissection
胸主动脉瘤和夹层中的平滑肌细胞增殖和降解表型
  • 批准号:
    10132382
  • 财政年份:
    2019
  • 资助金额:
    $ 69.22万
  • 项目类别:

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