Personalizing Matrix Assisted Autologous Chondrocyte Implantation

描述（由申请人提供）：自体软骨细胞植入（ACI）治疗膝关节软骨损伤的成功率很小，而且仅限于年轻、健康和活跃的患者。随着第二代ACI，即矩阵辅助ACI （MACI）的出现，新的机会出现了。我们假设，如果基质的设计是患者特异性的（即，特定于细胞的组织合成能力），不仅可以提高ACI的长期有效性，而且可以将其适应症扩大到更广泛的患者群体，无论年龄或健康状况如何。因此，本研究项目的首要目标是个性化MACI。我们个性化MACI的创新方法结合了以下两个高度相互关联的主题：(a)一种新型高度可调的水凝胶，具有降解的时空控制（使患者匹配的组织合成能力），高模量能力（恢复功能）和基质保留能力（最大限度地减少组织损失）。(b)引入基于完善理论框架的通用计算工具，该工具将分析与患者特异性细胞反应相关的数据，并基于此信息预测相应的水凝胶结构和降解，从而使组织能够在动态加载环境中生长和维持机械完整性（例如在膝关节中）。为了实现我们的总体研究目标，具体目标如下：我们的目标是确定能够设计个性化水凝胶的模型常数，首先是在没有机械载荷的情况下（目标1），然后是在有机械载荷的情况下（目标2）。我们将通过综合实验和模拟活动结合使用自学习算法来实现这一目标。这将导致数据驱动的预测计算模型的构建。一旦开发成功，我们将使用大型动物模型来测试数学模型在个性化MACI中的预测能力，特别是治疗猪膝盖的软骨病变（目的3）。完成后

DESCRIPTION (provided by applicant): Success of Autologous Chondrocyte Implantation (ACI) for treating damaged cartilage in the knee has been marginal and limited to young, healthy, and active patients. With the advent of second generation ACI referred to as Matrix-Assisted ACI (MACI), a new opportunity arises. We hypothesize that if the design of the matrix is patient-specific (i.e., specific to the tissue synthesis capabilities of the cell), it will be pssible to not only improve the effectiveness of ACI long-term, but expand its indication to a wider patient population regardless of age or health. Thus, the overarching goal of this research project is to personalize MACI. Our innovative approach to personalizing MACI combines the following two highly interconnected themes: (a) A new class of highly tunable hydrogels with spatiotemporal control over degradation (to enable patient-matched tissue synthesis capabilities), high moduli capabilities (to restore function), and matrix-retention capabilities (t minimize tissue loss). (b) The introduction of a universal computational tool based on a well-established theoretical framework, which will analyze data related to the response of a patient-specific cell and, based on this information, predict the corresponding hydrogel structure and degradation that enables tissue growth and sustained mechanical integrity in a dynamic loading environment (such as that in the knee). To accomplish our overall research goals, the specific aims are as follows. We aim to determine model constants that enable the design of personalized hydrogels, first in the absence of mechanical loading (Aim 1) then in the presence of mechanical loading (Aim 2). We will accomplish this through an integrated experimental and simulation campaign combined with the use of a self-learning algorithm. This will lead to the construction of the data- driven predictive computational model. Once developed, we will test the predictive capability of the mathematical model in personalized MACI using a large animal model, specifically to treat a chondral lesion in the knee of a swine (Aim 3). At the completion of this five year research project, we expect to have developed a predictive computational tool and established a novel and highly tunable hydrogel platform for personalizing MACI. The universal nature of the computational predictive tool enables it to be broadly applied in future research to other scaffolds and cells, including osteoarthritic chondrocytes and stem cells.