Prognostic models of drug-induced neutralizing antibody formation in patients with rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis treated with TNF-α blockersockers

Abstract

Aim: This study aimed to construct prognostic mathematical models utilizing multifactorial regression analysis to assess the risk of developing drug-induced neutralizing antibodies in patients with rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis treated with tumor necrosis factor alpha blockers. Materials and methods: Over a four-year period, we enrolled 213 patients in this study and divided them into three groups: the rheumatoid arthritis group (n=121), the ankylosing spondylitis group (n=50), and the psoriatic arthritis group (n=42). The study included also a group of healthy controls consisting of 31 healthy subjects who matched the patient groups in age, sex, body mass index, and conditions typical for rheumatology patients. Prognostic mathematical models based on statistically significant factors determined through univariate correlation and regression analyses incorporated patient medical history and serological and immunological data. Results: The study encompassed all 213 patients and 31 healthy controls. Data analysis was conducted at 12 and 24 months after commencing treatment. During this follow-up, the patients exhibited the highest percentage of antidrug antibodies. At 6 months, 6.57% of patients had confirmed neutralizing antibodies, which increased to 12.69% at 12 months and 17.72% at 24 months. Multivariate logistic regression analysis revealed that factors such as age over 55 years, excess weight, smoking, and absence of methotrexate treatment at a dose less than 7.5 mg per week had the highest predictive value. Conclusions: Investigating clinical and biological markers with predictive value for individual patients’ therapeutic responses is a complex task. This complexity arises from the interplay of at least three distinct parameters: the patient’s disease state, drug bioavailability, and pathophysiological changes within the patient’s body, all of which are influenced by various factors.