Computational Model Driven Design of Tissue Engineered Vascular Grafts

 描述（由申请人提供）：尽管自体静脉移植物和合成移植物（用于大口径血管）作为血管外科手术中的置换管道取得了普遍成功，但通常缺乏合适的自体组织（特别是在最年轻和最年长的患者中）和持续的总体高失败率仍然是显著的限制，特别是对于小口径置换。因此，迫切需要另一种战略。在过去的几十年里，组织工程血管移植物（TEVG）已经从台式发展到床边，目前正在儿童和成人中进行临床试验。这些进展主要是通过对由不同表面化学、机械性能和几何特征（例如，孔径、纤维直径和孔隙率）。临床前研究必须关注安全性和有效性，这主要意味着足够的缝线保留和爆破压力、抗血栓性以及体内不会形成狭窄和动脉瘤。尽管取得了如此多的成功，但尚未正式尝试优化支架设计，以产生更接近天然的生物力学性能并具有长期生物稳定性。鉴于制造技术的不断进步，支架参数的几乎无限组合现在是可能的。然而，很难想象试错比较能够确定最佳组合。因此，我们提出了一个新的聚合物支架设计的范例-我们将融合无量纲化，参数敏感性和优化的概念，在体内新血管发展的新验证的计算模型与3个经过验证的小鼠模型，以确定和测试一个新的最佳支架设计。为此，我们将寻求一种双层设计，该双层设计由顺应性聚（癸二酸甘油酯）的内部多孔层和外部多孔性较小、较硬的聚（ε-己内酯）鞘组成，前者促进细胞浸润，后者在内层快速降解和被新组织替代期间支撑内层。计算模型将通过一小系列初始实验得到信息和完善，这些实验揭示了体内孔隙率、纤维直径和刚度的极端效应，同时描绘了炎症和机械介导的基质产生的重叠作用。使用无量纲化、参数敏感性、不确定性量化和优化的形式概念，我们将通过数千次模拟来确定最有希望的少数支架设计。这些设计将被制造成新的支架，并在小鼠体内进行长达2年的测试。与第一系列测试结果的比较将揭示预测的设计是否确实明显更好;如果不是，我们可以重新考虑这个过程。我们的目标的成功完成将建立一个新的计算实验范式的支架设计，导致一个大大改善的TEVG，并通过新的实验方法，生物材料和建模作为其他组织工程应用的原型。

 DESCRIPTION (provided by applicant): Despite the general success of autologous vein grafts and synthetic grafts (for large caliber vessels) as replacement conduits in vascular surgery, both the common lack of suitable autologous tissue (especially in the youngest and very oldest of patients) and the continuing overall high failure rates remain as significant limitations, especialy for small caliber replacements. There is, therefore, a pressing need for another strategy. Over the past few decades, tissue engineered vascular grafts (TEVGs) have advanced from benchtop to bed- side, with clinical trials now underway in both children and adults. These advances have arisen primarily via laborious trial-and-error comparisons of different biodegradable polymeric scaffolds defined by different surface chemistries, mechanical properties, and geometric characteristics (e.g., pore sizes, fiber diameters, and porosities). Pre-clinical studies have necessarily focused on safety and efficacy, which has primarily meant sufficient suture retention and burst pressure, thrombo-resistence, and the lack of formation of stenosis and aneurysm in vivo. Notwithstanding these many successes, there has yet to be a formal attempt to optimize scaffold design to yield biomechanical properties closer to native and having long-term biological stability. Given the continued advances in fabrication techniques, an almost limitless combination of scaffold parameters is now possible. It is inconceivable, however, that trial-and-error comparisons can possibly identify an optimal combination. Hence, we suggest a new paradigm for polymeric scaffold design - we will meld concepts of nondimensionalization, parameter sensitivity, and optimization within a novel validated computational model of in vivo neovessel development with 3 proven mouse models to identify and test a new optimal scaffold design. Toward this end, we will seek a bilayered design consisting of an inner porous layer of compliant poly(glycerol sebacate) that encourages cellular infiltration and an outer less porous, stiffer poly(e-caprolactone) sheath that supports the inner layer during its rapid degradation and replacement with neotissue. The computational model will be informed and refined via a small series of initial experiments that reveal in vivo the effects o extremes in porosity, fiber diameter, and stiffness while delineating overlapping roles of inflammatory- and mechano- mediated matrix production. Using formal concepts of nondimensionalization, parameter sensitivity, uncertainty quantification, and optimization, we then will identify via thousands of simulations those few scaffold designs that are most promising. These designs will be fabricated as new scaffolds and tested in vivo in mice for up to 2 years. Comparisons with results from the first series of tests will reveal if the predicted desigs are indeed significantly better; if not, we can iterate the process. Successful completion of our aims will establish a new computational-experimental paradigm for scaffold design, result in a much improved TEVG, and serve as an archetype for other tissue engineering applications via novel experimental methods, biomaterials, & modeling.