Personalizing Matrix Assisted Autologous Chondrocyte Implantation

自体软骨细胞移植(ACI)治疗膝关节软骨损伤获得成功 边缘和仅限于年轻、健康和活跃的患者。随着第二代ACI的到来， 作为矩阵辅助ACI(MACI)，一个新的机会出现了。我们假设如果矩阵的设计是 患者特定的(即特定于细胞的组织合成能力)，将可能不仅 提高ACI的长期有效性，但将其适应症扩大到更广泛的患者群体，无论 年龄或健康。因此，这项研究项目的首要目标是使MACI个性化。我们的创新 个性化MACI的方法结合了以下两个高度相互关联的主题：(A)一类新的 高度可调的水凝胶，具有时空控制的降解(使患者匹配的组织 合成能力)、高模能力(以恢复功能)和矩阵保持能力(以 最大限度地减少组织损失)。(B)采用一种通用的计算工具，其基础是一个行之有效的 理论框架，它将分析与患者特定细胞的反应相关的数据，并基于 这些信息，预测相应的水凝胶结构和降解，使组织生长和 在动态载荷环境(如膝盖)中保持机械完整性。为了实现我们的目标 总体研究目标、具体目标如下。我们的目标是确定模型常数，使 个性化水凝胶的设计，首先在没有机械加载的情况下(目标1)，然后在 机械加载(目标2)。我们将通过集成的实验和模拟来实现这一点 运动中结合使用了一种自学习算法。这将导致数据的构建-- 驱动的预测计算模型。一旦开发完成，我们将测试 个性化MCI中的数学模型使用大型动物模型，专门用于治疗软骨病变 猪的膝盖(目标3)。在这项为期五年的研究项目完成后，我们预计将开发出 一种预测计算工具，并建立了一种用于个性化的新型高度可调水凝胶平台 麦西。计算预测工具的普遍性使其能够在未来得到广泛应用 研究其他支架和细胞，包括骨关节炎软骨细胞和干细胞。

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 possible 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 (to 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.