A Computational Approach to the Design of a Bioartificial Pancreas

该项目的主要目标是优化设计可植入的生物人工胰腺，用于治疗1型糖尿病。该设计基于将健康胰腺细胞移植到凝胶介质（琼脂糖凝胶）中，并将含有细胞的介质封装在两层纳米孔半透膜之间，以阻止患者的免疫细胞攻击移植细胞。包膜组织移植是一种消除长期使用免疫抑制剂的新方法，这是移植治疗的主要挑战之一。纳米孔膜被设计用来阻断免疫细胞，同时允许营养物质通过，使移植器官尽可能长时间地存活。UCSF生物设计实验室主任S. Roy博士的团队正在探索一种植入式生物人工胰腺的设计，这种胰腺将被植入患者的手臂，并与动脉和静脉相连，类似于动静脉移植。生物人工胰腺发展的关键挑战是通过提供足够的营养来维持移植细胞的长时间存活，其中氧气是限制因素。计划让研究生和本科生以及高中生（特别是女生）参与本研究的几个方面。该项目的协同方法将提供第一个长期可行的植入式生物人工胰腺，而无需免疫抑制治疗。该项目致力于开发一个多物理场、多尺度的数学和计算模型，以研究用于治疗1型糖尿病的植入式生物人工胰腺的设计。这种可植入的生物人工胰腺将包括一个封装的腔体，里面装有被移植的胰岛细胞，以及一个将腔体连接到患者心血管系统的移植物。该包封室被建模为多层多孔弹性介质，由两层半透膜包封孔弹性凝胶容纳细胞组成。这种封装策略可以防止宿主的免疫细胞攻击移植物。所提出的数学宏观模型捕获了被封装的多层多孔弹性胰岛腔内血清的过滤，以及血流与将血液输送到胰岛腔的动静脉移植物之间的流体结构相互作用。为了研究移植细胞的氧供应，将流-结构相互作用模型与描述管状移植物、孔弹性膜和胰岛腔内氧浓度的三种非线性平流-反应-扩散模型相耦合。提出了一种新的分区松耦合格式来求解这一问题。在微观尺度上，将采用光滑粒子流体动力学求解器研究细孔弹性介质结构对移植细胞供氧的影响。深度神经网络将用于研究参数估计。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The main goal of this project is optimal design of an implantable, bioartificial pancreas for the treatment of Type 1 diabetes. The design is based on transplanting the healthy pancreatic cells into a gel medium (agarose gel), and encapsulating the cell-containing medium between two nanopore semi-permeable membranes to block the patient's immune cells from attacking the transplant. Encapsulated tissue transplantation is a novel approach to eliminating long-term use of immunosuppressants, which is one of the major challenges in transplantation therapy. The nanopore membranes are designed to block the immune cells while allowing passage of nutrients to keep the transplant viable as long as possible. The team around the collaborator Dr. S. Roy, Director of the Biodesign Laboratory at UCSF, is exploring a design of an implantable bioartificial pancreas, which will be implanted in the patient's arm, and connected to an artery and a vein similar to an arterio-venous graft. The key challenge in the development of the bioartificial pancreas is maintaining the survival of transplanted cells for an extended period of time, by providing sufficient access to nutrients, of which oxygen is the limiting factor. Involvement of graduate and undergraduate students, as well as high school students (particularly girls), in several aspects of this research is planned. The synergistic approach to the proposed project will provide a first, long-term viable implantable bioartificial pancreas without the need for immunosuppressive therapy. This project addresses the development of a multi-physics, multi-scale mathematical and computational model to study the design of an implantable, bioartificial pancreas for the treatment of Type 1 diabetes. The implantable, bioartificial pancreas will consist of an encapsulated chamber containing the transplanted pancreatic cells called islets, and a graft connecting the chamber to the patient’s cardiovascular system. The encapsulation chamber is modeled as a multi-layered poroelastic medium consisting of two semi-permeable membranes encapsulating a poroelastic gel holding the cells. The encapsulation strategy prevents the host’s immune cells from attacking the transplant. The proposed mathematical macro-scale model captures filtration of blood serum within the encapsulated multi-layered poroelastic islet chamber, and the fluid-structure interaction between the blood flow and the arterio-venous graft carrying blood to the islet chamber. To study oxygen supply to the transplanted cells, the fluid-structure interaction model is coupled to three nonlinear advection-reaction-diffusion models describing oxygen concentration in the tubular graft, in the poroelastic membrane, and in the islet chamber. A novel partitioned, loosely coupled scheme for the numerical solution of this problem is proposed. At the micro-scale, a Smoothed Particle Hydrodynamics solver will be used to study the influence of the fine poroelastic medium structure on oxygen supply to the transplanted cells. Deep Neural Networks will be used to study parameter estimation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.