Authors
Hengameh Dortaj, Abbas Asoudeh-Fard, Aliakbar Alizadeh, Ahmad Vaez, Ahmad Gholami
Published in
Discover nano. Volume 21. Issue 1. Sep 21, 2026. Epub Sep 21, 2026.
Abstract
Osteoarthritis (OA) is a severe, whole-joint illness that is characterized by the permanent destruction of articular cartilage. The failure of traditional therapies and early tissue engineering efforts is mainly due to their reductionist biological strategies and reliance on bulk, non-biomimetic materials that cannot replicate the complex nanoscale architecture of the native extracellular matrix. This review proposes a closed-loop, multidisciplinary roadmap that integrates systems biology and biomimetic nanomedicine to advance OA diagnosis, cartilage regeneration, and nanomaterial safety-by-design. Systems biology, through the application of high-throughput omics data and computational network modeling, identifies the complex transcriptomic and proteomic disturbances that underlie OA and provides accurate molecular blueprints for intervention. Nanotechnology makes these plans real in the physical world in two different ways. Ultra-sensitive systems, such as microfluidic lab-on-a-chip devices and Surface-Enhanced Raman Scattering (SERS) sensors, enable real-time detection of preclinical nanobiomarkers for diagnostic purposes. Engineered nanostructures, such as biomimetic nanocomposites, topographically optimized electrospun scaffolds, and stimuli-responsive nanocarriers, provide specific biophysical and biochemical signals that can restore chondrogenesis by stopping catabolic inflammation from a therapeutic perspective. Finally, as a translational bridge, we highlight the burgeoning discipline of systems toxicology, which uses computational frameworks to achieve genomic biocompatibility and safety-by-design for new nanomaterials. The combination of predictive computer modeling and nanoscale engineering together provides a complete roadmap from the bench to the patient's bedside for the personalized, curative treatment of osteoarthritis.
PMID:
42766250
Bibliographic data and abstract were imported from PubMed on 22 Sep 2026.
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