Personalizing Matrix Assisted Autologous Chondrocyte Implantation

自体软骨细胞移植（ACI）治疗膝关节软骨损伤的成功， 边缘性，仅限于年轻、健康和活跃的患者。随着第二代ACI的出现， 作为矩阵辅助ACI（MACI），新的机会出现了。我们假设，如果矩阵的设计是 患者特异性（即，特定于细胞的组织合成能力），将有可能不仅 提高ACI的长期有效性，但将其适应症扩展至更广泛的患者人群， 年龄或健康。因此，本研究项目的总体目标是个性化MACI。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： 具有对降解的时空控制的高度可调的水凝胶（以使患者匹配的组织 合成能力）、高模量能力（恢复功能）和基质保持能力（ 最小化组织损失）。(b)介绍一种通用的计算工具， 理论框架，将分析与患者特异性细胞的反应相关的数据，并基于 该信息预测相应的水凝胶结构和降解，使组织生长， 在动态载荷环境中（如膝关节）保持机械完整性。完成我们 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，首先在不存在机械负载的情况下（目标1），然后在存在机械负载的情况下， 机械负载（目标2）。我们将通过综合实验和模拟来实现这一点 运动结合使用一个自学习算法。这将导致数据的构建- 驱动的预测计算模型。一旦开发出来，我们将测试 使用大型动物模型在个性化MACI中建立数学模型，特别是治疗 猪的膝盖（目标3）。在这个为期五年的研究项目完成后，我们预计将开发出 预测计算工具，并建立了一个新的和高度可调的水凝胶平台， MACI计算预测工具的通用性使其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.