Personalizing Matrix Assisted Autologous Chondrocyte Implantation

描述(由申请人提供)：自体软骨细胞移植(ACI)治疗膝关节受损软骨的成功是微乎其微的，仅限于年轻、健康和活跃的患者。随着被称为矩阵辅助ACI(MACI)的第二代ACI的出现，一个新的机会出现了。我们假设，如果矩阵的设计是针对患者的(即特定于细胞的组织合成能力)，那么不仅有可能改善ACI的长期有效性，而且无论年龄或健康状况如何，它的适应症都有可能扩大到更广泛的患者群体。因此，这项研究项目的首要目标是使MACI个性化。我们个性化MCI的创新方法结合了以下两个高度相关的主题：(A)一种新的高度可调的水凝胶，具有时空控制降解(以实现患者匹配的组织合成能力)、高模数能力(以恢复功能)和基质保持能力(使组织损失最小化)。(B)采用基于成熟理论框架的通用计算工具，该工具将分析与患者特定细胞反应有关的数据，并根据这些信息预测相应的水凝胶结构和降解，从而使组织在动态负荷环境(如膝盖)中生长和保持机械完整性。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，首先在没有机械加载的情况下(目标1)，然后在存在机械加载的情况下(目标2)。我们将通过一个综合的实验和模拟活动，并结合使用一个自我学习算法来实现这一点。这将导致构建数据驱动的预测计算模型。一旦开发出来，我们将使用大型动物模型，特别是治疗猪膝盖软骨病变的大型动物模型，测试个性化MACI中数学模型的预测能力(目标3)。在完成时 在这项为期五年的研究项目中，我们希望开发出一种预测计算工具，并建立一个新的、高度可调的水凝胶平台，用于个性化MCI。计算预测工具的普遍性质使其能够在未来的研究中广泛应用于其他支架和细胞，包括骨关节炎软骨细胞和干细胞。

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.