Optimizing Nutrient Supply in Large Engineered Cartilage Tissue Constructs

描述（由申请人提供）：骨关节炎（OA）是一种使人衰弱的退行性疾病，估计有2700万25岁及以上的美国人患有这种疾病。这种疾病导致双关节的关节层的进行性退化，显著损害软骨作为承重材料的主要功能，导致疼痛和限制日常生活活动。腕关节功能组织工程是一项非常有前途的技术，旨在为磨损的关节层提供生物替代物，作为一种大大扩展治疗这种疾病的有限选择的方式。虽然软骨退化偶尔局限于关节层内的小病灶区域，但当退化扩散到更大的表面积（如超过关节层的25%）时，OA通常会出现症状。不幸的是，大型软骨结构的功能性组织工程受到营养运输和消耗平衡的显著限制。几项研究表明，基质沉积和功能特性的阐述优先发生在构建体的周边附近，其中来自周围培养基的营养供应最丰富，而内部的细胞接收较少的营养并产生较少的基质，具有较差的功能特性。在本申请中，针对为大型工程化软骨构建体提供充足营养的技术挑战，提出了一种工程化解决方案，其通过优化穿过构建体层的全厚度的窄通道的数量和间距，从而重现在早期发育期间由软骨管提供的营养供应。必须优化通道在不同维度构建体中的位置，以平衡相互竞争的需求：增加通道密度将在逻辑上增加总营养供应，使其在整个构建体中更均匀地分布。然而，升高的通道密度可以有效地降低细胞密度并增加合成的基质产物在它们结合到细胞外基质之前损失的途径。这种类型的优化分析，其中竞争的需求必须平衡，是非常适合的工程方法，占主导地位的机制调节组织生长。这项工程技术的发展将通过四个具体目标进行：（1）将现有模型中的溶质扩散/结合/消耗和组织生长方程应用到定制编写的有限元软件中，用于分析组织工程结构。(2)实验表征工程软骨中营养供应和基质生长建模所需的参数。(3)使用这些计算工具和实验数据对大型圆柱形和髌骨形关节层结构中的通道放置进行优化分析。(4)使用理论上最佳（N）和次佳（N/2和2N）通道数量以及无通道对照培养大型构建体;比较基质沉积和功能特性，以检测N是否为最佳值;必要时完善模型。

DESCRIPTION (provided by applicant): Osteoarthritis (OA) is a debilitating degenerative disease that afflicts an estimated 27 million Americans age 25 and older. This disease leads to the progressive degradation of the articular layers of diarthrodial joints, significantly compromising the main function of cartilage as a load bearing material, leading to pain and limiting activities of daily living. Cartilage functional tissue engineering is a highly promising technology that aims to provide a biological replacement to worn articular layers, as a modality that considerably expands the limited options in the treatment of this disease. Though cartilage degeneration is occasionally limited to small focal areas within articular layers, OA generally becomes symptomatic when degradation has spread over much greater surface areas (such as greater than 25 percent of the articular layer). Unfortunately, functional tissue engineering of large cartilage constructs is significantly constrained by the balance of nutrient transport and consumption. Several studies have shown that matrix deposition and elaboration of functional properties preferentially occurs near the periphery of constructs, where nutrient supply from the surrounding culture medium is most abundant, whereas cells in the interior receive less nutrients and produce less matrix, with poorer functional properties. In this application, an engineering solution is proposed for the technical challenge of supplying plentiful nutrients for large engineered cartilage constructs by optimizing the number and spacing of narrow channels through the full thickness of construct layers, thus recapitulating the nutrient supply provided by cartilage canals during early development. The placement of channels in constructs of various dimensions must be optimized to balance competing needs: Increasing the channel density would logically increase the total nutrient supply, spreading it more evenly across the entire construct. However, an elevated channel density may effectively decrease the cell density and increase the pathways for loss of synthesized matrix products before they bind to the extracellular matrix. This type of optimization analysis, where competing needs must be balanced, is very well suited for an engineering approach that accounts for the dominant mechanisms regulating tissue growth. The development of this engineering technology will proceed through four specific aims: (1) Implement solute diffusion/binding/consumption and tissue growth equations from existing models into custom-written finite element software for the analysis of tissue engineered constructs. (2) Experimentally characterize the parameters needed for modeling nutrient supply and matrix growth in engineered cartilage. (3) Use these computational tools and experimental data to perform the optimization analysis for channel placement in large cylindrical and patella-shaped articular layer constructs. (4) Culture large constructs using theoretically optimal (N) and sub-optimal (N/2 and 2N) number of channels, as well as channel-free controls; compare matrix deposition and functional properties to test that N is the optimal value; refine model if necessary.