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Smart Nanodelivery Systems for Immunometabolic Modulation in Osteoarthritis
Qirui Zhao1,2, Zhewen Mi1,2, Shuya Liu1,2
1Department of Orthopedics The First Affiliated Hospital of Henan University Henan University Kaifeng Kaifeng China.
Abstract:
Osteoarthritis (OA) is a chronic degenerative joint disease characterised primarily by immunometabolic disorders within the synovium-cartilage axis. Conventional therapies are limited by poor drug accumulation and nonspecific distribution at lesion sites, resulting in suboptimal and short-lived efficacy. In recent years, smart responsive nanodelivery systems (SRNSs) have demonstrated considerable potential for OA treatment. This review systematically summarises the major responsive mechanisms of SRNSs-such as pH, reactive oxygen species, enzymes and temperature-and their corresponding targeting strategies, including hyaluronic acid (HA)-cluster of differentiation 44, arginine-glycine-aspartic acid-collagen II and immune ligand recognition. The dual modulatory roles of SRNSs in the synovium-cartilage axis are highlighted. By analysing validation evidence from representative material systems-such as zeolitic imidazolate framework-8, poly(lactic-co-glycolic acid) and liposomes-in animal models, we delineate the synergistic mechanisms of SRNSs in inflammation suppression, metabolic remodelling and tissue regeneration. In the discussion section, we further explore key challenges for SRNSs, including biosafety concerns, lesion heterogeneity, manufacturing processes and regulatory standards. Potential strategies-such as biomimetic membrane camouflage, multi-omics-based stratification, artificial intelligence (AI) simulation and virtual clinical trials-are also proposed. Additionally, by comparing SRNSs with gene therapy, cell-penetrating peptides and exosome-based delivery, this review suggests that future OA therapies may evolve toward hybrid platforms integrating materials, biological systems and gene-based interventions. Looking ahead, smart systems endowed with self-feedback, self-evolution and visualisation capabilities are expected to move OA treatment toward a new era of personalised, adaptive and multidimensional precision interventions.
Insights
Smart responsive nanodelivery systems offer improved osteoarthritis treatment by targeting the synovium-cartilage axis. These systems show potential for inflammation suppression, metabolic remodelling, and tissue regeneration, paving the way for personalized therapies.
Area of Science:
- Biomaterials Science
- Nanotechnology
- Immunometabolism
Background:
- Osteoarthritis (OA) is a degenerative joint disease marked by immunometabolic dysfunction in the synovium-cartilage axis.
- Current OA therapies face challenges with drug delivery, leading to limited efficacy and short-term benefits.
Purpose of the Study:
- To systematically review smart responsive nanodelivery systems (SRNSs) for OA treatment.
- To highlight the mechanisms, targeting strategies, and therapeutic potential of SRNSs in OA management.
Main Methods:
- Review of SRNS responsive mechanisms (pH, ROS, enzymes, temperature) and targeting strategies (HA-CD44, RGD-Col II, immune ligands).
- Analysis of material systems (ZIF-8, PLGA, liposomes) in animal models to assess synergistic effects.
- Comparison of SRNSs with other therapeutic modalities like gene therapy and exosomes.
Main Results:
- SRNSs demonstrate dual modulatory roles in the synovium-cartilage axis.
- Evidence from animal models shows SRNSs synergistically suppress inflammation, promote metabolic remodelling, and aid tissue regeneration.
- SRNSs offer advantages over conventional therapies due to targeted accumulation and distribution.
Conclusions:
- SRNSs represent a promising platform for OA treatment, addressing limitations of conventional therapies.
- Future OA therapies may involve hybrid platforms integrating materials, biological systems, and gene-based interventions for personalized, adaptive treatment.
- Advancements in AI, biomimetic camouflage, and virtual trials can overcome challenges like biosafety and heterogeneity.
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