A 3D osteoarthritis model targeting patient populations with high risk genetic polymorphisms

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Abstract Osteoarthritis (OA) is a degenerative joint disease that affects an estimated 30 million adults in the United States and results in an economic burden of over $130 billion per year. Although the burden of OA is immense, current non-surgical treatments are only palliative, and no disease-modifying OA drugs (DMOADs) presently exist to address the problem. This lack of success in identifying DMOADs is frequently attributed to the variable causes of OA initiation and the dearth of human cartilage available for screening potential DMOADs. To increase the likelihood of identifying DMOADs, we propose to study a segmented OA population based on defined genetic predisposition to OA development. To produce a nearly unlimited source of human cartilage for use in DMOAD screening, we will use induced pluripotent stem cells (iPSCs) as a cell source for cartilage tissue engineering. The goal of this project is to create a platform screening technology to identify the therapeutic requirements of OA-associated genetic risk factors. Our approach is to create a three-dimensional in vitro model of OA which utilizes iPSC lines that have been modified to contain defined genetic variations. In Aim 1 we will employ genome editing technology to generate OA-associated single-nucleotide polymorphisms (SNPs) in the genome of hiPSCs. Engineered cartilage formed from these edited cells will be characterized using biochemical and micromechanical assays and then treated with inflammatory cytokines to induce OA-like changes in the cartilage. The resulting in vitro OA model will be validated by measuring matrix degradation, loss of mechanical properties, and production of inflammatory mediators. In Aim 2, our iPSC-based model of OA will be transferred to a 96-well plate format to facilitate the development of an OA drug screening platform. A set of model therapeutics known to inhibit inflammatory degradation will be used to validate the sensitivity of the model and to define high-throughput readouts of OA progression. Finally, two libraries of novel bioactive compounds will be screened for their ability to slow OA-associated degradation in our in vitro model. This proposal will help elucidate the mechanism by which genetic variants result in increased risk for OA and will catalyze the development of tailored OA therapeutics by providing a platform technology for identifying therapeutic effectiveness based on defined risk factors.