A clinical pharmacogenetic model to predict the efficacy of methotrexate monotherapy in recent-onset rheumatoid arthritis

Abstract

Objective: To develop a clinical pharmacogenetic model to predict the efficacy of methotrexate (MTX) in rheumatoid arthritis (RA).

Methods: Two hundred five patients with newly diagnosed RA and active disease were treated with MTX (initiated at a dosage of 7.5 mg/week and increased to 15 mg/week after 4 weeks) and folic acid (1 mg/day). If the Disease Activity Score (DAS) was >2.4 at 3 months, the dosage of MTX was increased up to 25 mg/week. Twenty-four baseline variables possibly influencing disease state and drug response were selected. In addition, 17 polymorphisms in 13 genes related to the MTX mechanism of action, purine and pyrimidine synthesis, were determined. Factors were compared between responders (defined as patients with a DAS < or = 2.4 at 6 months) and nonresponders. In case of differences, a stepwise selection procedure identified the predictors for response. A clinical score was designed by simplifying regression coefficients of the independent variables. Cutoff levels were chosen based on the clinical score, and positive and negative response rates were calculated. An evaluation of the model was performed in a second group of patients.

Results: The model for MTX efficacy consisted of sex, rheumatoid factor and smoking status, the DAS, and 4 polymorphisms in the AMPD1, ATIC, ITPA, and MTHFD1 genes. This prediction model was transformed into a scoring system ranging from 0 to 11.5. Scores of < or = 3.5 had a true positive response rate of 95%. Scores of > or = 6 had a true negative response rate of 86%. Sixty percent of the patients were categorized as either responders or nonresponders, whereas 32% of the patients were categorized using a nongenetic model. Evaluation of the model in 38 additional patients with RA supported the results.

Conclusion: This study established a model for predicting the efficacy of MTX in patients with RA. This pharmacogenetic model may lead to better-tailored initial treatment decisions in patients with RA.