Zheng Lian, Kunpeng Su, Hui Lu, Changpeng Qu and Xuexiao Ma* Pages 1 - 14 ( 14 )
Background: Osteoarthritis (OA) represents a persistent degenerative joint ailment. As OA advances, profound joint pain coupled with diminished joint function inflicts substantial physical distress and psychological strain on patients. Presently, pharmacological solutions for arthritis remain limited, primarily encompassing analgesics and joint replacement surgical procedures. Hence, non-operative strategies to mitigate osteoarthritis progression have captured significant attention in orthopedic research.
Objective: This study aims to discern a definitive causal linkage between ADAMTS-4/5 and osteoarthritis through Mendelian randomization analysis. Moreover, it seeks to anticipate the therapeutic efficacy of a suite of emergent hydroxyquinolines for osteoarthritis using the Quantitative Structure-Activity Relationship (QSAR) methodology.
Methods: Within this study, genetic variants specific to knee osteoarthritis were procured as exposure variables from a genome-wide association study (GWAS). Genetic variant data for ADAMTS-4/5 served as the endpoint to evaluate the causal nexus employing univariate Mendelian randomization. This analysis underpins the hypothesis that ADAMTS-4/5 presents a promising therapeutic target for osteoarthritis management. The suppressive properties of novel hydroxyquinolines against ADAMTS-4/5 were subsequently examined through conformational analyses, underscoring the potential of these compounds as therapeutic candidates for osteoarthritis.
Results: IVW outcomes from the Mendelian randomization revealed a significant association of KOA (OR: 1.1675, 95% CI: 1.0003-1.3627, P = 0.0495) with ADAMTS-5. However, KOA (OR: 1.0801, 95% CI: 0.9256-1.2604, P = 0.3278) displayed no evident connection with ADAMTS-4. Notably, the instrumental variables manifested neither heterogeneity nor horizontal pleiotropy. In this research endeavor, 16 pharmacological models were formulated via the CoMSIA method within 3D conformational relationship evaluations. A synergistic interplay of hydrophobic, spatial, and hydrogen-bonded receptor domains emerged as the most predictively potent. The cross-validation coefficient q2 for the optimum model stood at 0.716, with a principal component score of 5, a regression coefficient r2 of 0.971, a standard estimation error of 0.351, and an f-value of 156.951. Such metrics intimate the commendable predictive prowess of our devised CoMSIA models.
Conclusion: The research unearthed a robust causal interrelation between ADAMTS-5 and osteoarthritis via Mendelian randomization. Furthermore, a credible drug model targeting ADAMTS-5 was constructed. Collectively, these findings illuminate a path forward in the pursuit of target-specific drugs for osteoarthritis management in subsequent investigations.
Osteoarthritis,3D-QSAR,Mendelian randomization,Causal inference,Genetic variation.