Secukinumab Efficacy in Psoriatic Arthritis: Machine Learning and Meta-analysis of Four Phase 3 Trials.

ABSTRACT

BACKGROUND: Using a machine learning approach, the study investigated if specific baseline characteristics could predict which psoriatic arthritis (PsA) patients may gain additional benefit from a starting dose of secukinumab 300 mg over 150 mg. We also report results from individual patient efficacy meta-analysis (IPEM) in 2049 PsA patients from the FUTURE 2 to 5 studies to evaluate the efficacy of secukinumab 300 mg, 150 mg with and without loading regimen versus placebo at week 16 on achievement of several clinically relevant difficult-to-achieve (higher hurdle) endpoints. METHODS: Machine learning employed Bayesian elastic net to analyze baseline data of 2148 PsA patients investigating 275 predictors. For IPEM, results were presented as difference in response rates versus placebo at week 16. RESULTS: Machine learning showed secukinumab 300 mg has additional benefits in patients who are anti-tumor necrosis factor-naive, treated with 1 prior anti-tumor necrosis factor agent, not receiving methotrexate, with enthesitis at baseline, and with shorter PsA disease duration. For IPEM, at week 16, all secukinumab doses had greater treatment effect (%) versus placebo for higher hurdle endpoints in the overall population and in all subgroups; 300-mg dose had greater treatment effect than 150 mg for all endpoints in overall population and most subgroups. CONCLUSIONS: Machine learning identified predictors for additional benefit of secukinumab 300 mg compared with 150 mg dose. Individual patient efficacy meta-analysis showed that secukinumab 300 mg provided greater improvements compared with 150 mg in higher hurdle efficacy endpoints in patients with active PsA in the overall population and most subgroups with various levels of baseline disease activity and psoriasis.