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Personalizing Matrix Assisted Autologous Chondrocyte Implantation

个性化基质辅助自体软骨细胞植入

基本信息

批准号：
8612678
负责人：
Stephanie J Bryant
金额：
$ 29.33万
依托单位：
UNIVERSITY OF COLORADO
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-12 至 2018-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8612678
关键词：
Age Algorithms Animal Model Autologous Cartilage Cells Chondrocytes Clinical Computer Simulation Data Data Analyses Degenerative polyarthritis Drug Formulations Effectiveness Engineering Environment Enzymes Exhibits Extracellular Matrix Degradation Family suidae General Hospitals Generations Goals Growth Health Hydrogels In Situ In Vitro Knee Lead Learning Lesion Massachusetts Mechanics Mediating Medicine Modeling Nature Osteoarthrosis Deformans Outcome Patients Performance Process Qualifying Reaction Research Research Project Grants Stem cells Structure Sulfhydryl Compounds Testing Time Tissue Engineering Tissues Vision Weight-Bearing state base cartilage cell computerized tools design flexibility functional restoration implantation improved in vivo innovation mathematical model novel patient population polymerization public health relevance research study response scaffold screening simulation spatiotemporal success tool

项目摘要

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.

自体软骨细胞植入（ACI）治疗膝关节软骨损伤已取得成功