Comprehensive review evaluates preclinical osteoarthritis models and emerging bioengineered platforms, highlighting rational selection strategies to bridge the translational drug development gap.
Osteoarthritis (OA) is a complex degenerative joint disease driven by mechanical overload, inflammation, metabolic disorders, and aging. Despite substantial advances in basic and translational research, no disease-modifying OA drugs (DMOADs) have been approved for clinical use, largely due to the translational gap between preclinical experimental models and clinical practice. Experimental models are essential tools for investigating OA pathophysiology and evaluating therapeutic strategies, among which animal models play an irreplaceable role in recapitulating the in vivo joint environment. This review comprehensively evaluates current OA experimental models, with a focus on animal systems, including surgically induced structural instability, mechanically induced models, chemical induction approaches, metabolism-related models, spontaneous aging models, and genetically modified strains. We assess each model's advantages, limitations, and appropriate applications, clarifying their ability to reflect specific OA subtypes. Moreover, we summarize recent progress in in vitro platforms, ex vivo tissue explants, and emerging technologies such as organ-on-a-chip and organoid models, which provide complementary insights to animal models. This review aims to establish a rational model selection framework aligned with OA pathological features, thereby bridging the preclinical-clinical translational gap and facilitating the development of effective therapeutics. The translational potential of this article This review systematically evaluates OA experimental models and analyzes their practical utility in translational research of different OA subtypes. It establishes a reasonable framework for model selection and describes how cutting-edge bioengineered platforms complement traditional in vitro and animal studies, providing a reference for improving the predictive reliability of preclinical research and advancing studies on personalized OA therapies.
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舒建雄 et al. (2026) studied this question.
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