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.

Public Health Relevance

This research aims to develop a personalized approach to Matrix Assisted Autologous Chondrocyte Implantation by developing i) a new hydrogel platform with high modulus, spatiotemporal control over degradation and matrix retaining capabilities and ii) a computational tool that can predict the best hydrogel formulation that is specific to the patient. !

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Research Project (R01)
Project #
1R01AR065441-01
Application #
8612678
Study Section
Musculoskeletal Tissue Engineering Study Section (MTE)
Program Officer
Wang, Fei
Project Start
2013-09-12
Project End
2018-08-31
Budget Start
2013-09-12
Budget End
2014-08-31
Support Year
1
Fiscal Year
2013
Total Cost
$293,307
Indirect Cost
$78,875
Name
University of Colorado at Boulder
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
007431505
City
Boulder
State
CO
Country
United States
Zip Code
80309
Aisenbrey, Elizabeth A; Bryant, Stephanie J (2018) A MMP7-sensitive photoclickable biomimetic hydrogel for MSC encapsulation towards engineering human cartilage. J Biomed Mater Res A 106:2344-2355
Pascual-Garrido, Cecilia; Rodriguez-Fontan, Francisco; Aisenbrey, Elizabeth A et al. (2018) Current and novel injectable hydrogels to treat focal chondral lesions: Properties and applicability. J Orthop Res 36:64-75
Shaw, Nichole; Erickson, Christopher; Bryant, Stephanie J et al. (2018) Regenerative Medicine Approaches for the Treatment of Pediatric Physeal Injuries. Tissue Eng Part B Rev 24:85-97
Bryant, Stephanie J; Vernerey, Franck J (2018) Programmable Hydrogels for Cell Encapsulation and Neo-Tissue Growth to Enable Personalized Tissue Engineering. Adv Healthc Mater 7:
Schneider, Margaret C; Chu, Stanley; Sridhar, Shankar Lalitha et al. (2017) Local Heterogeneities Improve Matrix Connectivity in Degradable and Photoclickable Poly(ethylene glycol) Hydrogels for Applications in Tissue Engineering. ACS Biomater Sci Eng 3:2480-2492
Brighenti, Roberto; Vernerey, Franck J (2017) A simple statistical approach to model the time-dependent response of polymers with reversible cross-links. Compos B Eng 115:257-265
Schneider, Margaret C; Barnes, Christopher A; Bryant, Stephanie J (2017) Characterization of the chondrocyte secretome in photoclickable poly(ethylene glycol) hydrogels. Biotechnol Bioeng 114:2096-2108
Lalitha Sridhar, Shankar; Schneider, Margaret C; Chu, Stanley et al. (2017) Heterogeneity is key to hydrogel-based cartilage tissue regeneration. Soft Matter 13:4841-4855
Shen, Tong; Vernerey, Franck (2017) Phoretic motion of soft vesicles and droplets: an XFEM/particle-based numerical solution. Comput Mech 60:143-161
Akalp, Umut; Schnatwinkel, Carsten; Stoykovich, Mark P et al. (2017) Structural Modeling of Mechanosensitivity in Non-Muscle Cells: Multiscale Approach to Understand Cell Sensing. ACS Biomater Sci Eng 3:2934-2942

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