Literature DB >> 32015257

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

Alice B Gottlieb1, Philip J Mease2, Bruce Kirkham3, Peter Nash4, Alejandro C Balsa5, Bernard Combe6, Jürgen Rech7, Xuan Zhu8, David James8, Ruvie Martin8, Gregory Ligozio8, Ken Abrams8, Luminita Pricop8.   

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) end points.
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 end points in the overall population and in all subgroups; 300-mg dose had greater treatment effect than 150 mg for all end points 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 end points in patients with active PsA in the overall population and most subgroups with various levels of baseline disease activity and psoriasis.

Entities:  

Year:  2020        PMID: 32015257     DOI: 10.1097/RHU.0000000000001302

Source DB:  PubMed          Journal:  J Clin Rheumatol        ISSN: 1076-1608            Impact factor:   3.517


  4 in total

Review 1.  An introduction to machine learning and analysis of its use in rheumatic diseases.

Authors:  Kathryn M Kingsmore; Christopher E Puglisi; Amrie C Grammer; Peter E Lipsky
Journal:  Nat Rev Rheumatol       Date:  2021-11-02       Impact factor: 20.543

2.  Efficacy and safety of secukinumab in patients with psoriatic arthritis: A meta-analysis of different dosing regimens.

Authors:  Kai-Lin Zhang; Si-Yuan Hou; Dan Wu
Journal:  Clinics (Sao Paulo)       Date:  2021-10-01       Impact factor: 2.365

3.  Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature.

Authors:  Nicola Luigi Bragazzi; Charlie Bridgewood; Abdulla Watad; Giovanni Damiani; Jude Dzevela Kong; Dennis McGonagle
Journal:  Front Immunol       Date:  2022-03-10       Impact factor: 7.561

4.  Short-Term Efficacy and Safety of Secukinumab for Ankylosing Spondylitis: A Systematic Review and Meta-Analysis of RCTs.

Authors:  Yu Zhou; Jinhui Ma; Juncheng Ge; Bailiang Wang; Debo Yue; Weiguo Wang
Journal:  Mediators Inflamm       Date:  2020-10-26       Impact factor: 4.711

  4 in total

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