Certolizumab Pegol Treatment in Axial Spondyloarthritis Mitigates Fat Lesion Development: 4-Year Post-Hoc MRI Results from a Phase 3 Study

Certolizumab pegol in the treatment of axial spondyloarthritis

Axial spondyloarthritis is a chronic, immune-mediated systemic inflammatory disease encompassing ankylosing spondylitis and nonradiographic axial spondyloarthritis. TNF inhibitors are the preferred second line therapy for patients with active axial spondyloarthritis. Certolizumab pegol is a TNF inhibitor approved for treatment of both. Three large phase III trials (RAPID-axSpA, C-axSpAnd and C-OPTIMISE) and one large phase IV trial (CIMAX) establish its clinical efficacy in treatment of active disease and maintenance of remission for both diseases. Real world evidence demonstrates clinical efficacy and benefits including reduced bone loss, reduced risk of uveitis, safety in pregnancy and lactation and index drug survival of 10 years. It is generally well tolerated, though can be associated with increased risk of serious infections.

Osteoimmunology of Spondyloarthritis

The mechanisms underlying the development of bone damage in the context of spondyloarthritis (SpA) are not completely understood. To date, a considerable amount of evidence indicates that several developmental pathways are crucially involved in osteoimmunology. The present review explores the biological mechanisms underlying the relationship between inflammatory dysregulation, structural progression, and osteoporosis in this diverse family of conditions. We summarize the current knowledge of bone biology and balance and the foundations of bone regulation, including bone morphogenetic protein, the Wnt pathway, and Hedgehog signaling, as well as the role of cytokines in the development of bone damage in SpA. Other areas surveyed include the pathobiology of bone damage and systemic bone loss (osteoporosis) in SpA and the effects of pharmacological treatment on focal bone damage. Lastly, we present data relative to a survey of bone metabolic assessment in SpA from Italian bone specialist rheumatology centers. The results confirm that most of the attention to bone health is given to postmenopausal subjects and that the aspect of metabolic bone health may still be underrepresented. In our opinion, it may be the time for a call to action to increase the interest in and focus on the diagnosis and management of SpA.

Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting

BACKGROUND

Predicting radiographic progression in axial spondyloarthritis (axSpA) remains limited because of the complex interaction between multiple associated factors and individual variability in real-world settings. Hence, we tested the feasibility of artificial neural network (ANN) models to predict radiographic progression in axSpA.

METHODS

In total, 555 patients with axSpA were split into training and testing datasets at a 3:1 ratio. A generalized linear model (GLM) and ANN models were fitted based on the baseline clinical characteristics and treatment-dependent variables for the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS) of the radiographs at follow-up time points. The mSASSS prediction was evaluated, and explainable machine learning methods were used to provide insights into the model outcome or prediction.

RESULTS

The R² values of the fitted models were in the range of 0.90-0.95 and ANN with an input of mSASSS as the number of each score performed better (root mean squared error (RMSE) = 2.83) than GLM or input of mSASSS as a total score (RMSE = 2.99-3.57). The ANN also effectively captured complex interactions among variables and their contributions to the transition of mSASSS over time in the fitted models. Structural changes constituting the mSASSS scoring systems were the most important contributing factors, and no detectable structural abnormalities at baseline were the most significant factors suppressing mSASSS change.

CONCLUSIONS

Clinical and radiographic data-driven ANN allows precise mSASSS prediction in real-world settings. Correct evaluation and prediction of spinal structural changes could be beneficial for monitoring patients with axSpA and developing a treatment plan.