Multi-Target Gene Therapy for Osteoarthritis: Dual-Axis Modeling and In Silico Validation

Background Osteoarthritis (OA) remains therapeutically intractable despite advances in regenerative medicine. Single-target interventions—including mesenchymal stromal cells, platelet-rich plasma, and gene therapies—consistently yield transient symptomatic relief without durable structural modification. The recent Phase 2 failure of GLPG1972, a potent ADAMTS-5 inhibitor (ROCCELLA trial, n = 932), exemplifies this persistent challenge. Objective This study proposes a multi-target gene therapy strategy integrating computational structural analysis, network perturbation modeling, and clinical failure analysis to address the dual-axis nature of OA pathogenesis. Methods This computational study integrated structural analysis of therapeutic proteins (IL-1Ra, SOX9, IGF-1) using PyMOL, molecular docking (AutoDock Vina) to evaluate ADAMTS-5 inhibitor binding, Monte Carlo network perturbation analysis (n = 1,000 iterations) quantifying multi-target synergy via an ECM Recovery Score, Reynolds algorithm shRNA design with BLAST off-target validation, and human-canine sequence homology assessment across all therapeutic targets. Results Structural analysis confirms therapeutic transgenes preserve critical functional domains. Network perturbation demonstrates significantly higher ECM Recovery Scores for multi-target intervention (76.2 ± 8.3) versus single-target control (43.6 ± 12.1; p < 0.001). Analysis of GLPG1972 failure reveals an “Exosite Bypass” mechanism supporting complete enzyme elimination via shRNA rather than catalytic inhibition. Validated shRNA sequences for ADAMTS-5 and MMP-13 achieved Reynolds scores of 8–9/9. Human-canine homology analysis (mean 90.5% identity) supports canine models for translational studies. Conclusions Computational analysis, clinical failure mechanisms, and network modeling converge on the necessity of simultaneous multi-axis targeting. The proposed dual-vector AAV system addressing inflammation (IL-1Ra), anabolism (SOX9, IGF-1), and catabolism (ADAMTS-5/MMP-13 shRNA) provides a framework for experimental validation. This work is hypothesis-generating and requires experimental confirmation in cell-based and animal models.