Background Secukinumab demonstrated sustained efficacy in patients with ankylosing spondylitis (AS) through 5 years in pivotal Phase III studies. Here, we present efficacy and safety results (52-week) of secukinumab in patients with AS from the MEASURE 5 study. Methods MEASURE 5 was a 52-week, Phase III, China-centric study. Eligible patients were randomly assigned (2:1) to receive subcutaneous secukinumab 150 mg or placebo weekly for the first five doses and then once every 4 weeks (q4w). All placebo patients switched to secukinumab 150 mg q4w starting at Week 16. Primary endpoint was Assessments of SpondyloArthritis international Society (ASAS) 20 at Week 16. Randomization was stratified by region (China vs . non-China). Results Of 458 patients (secukinumab 150 mg, N = 305; placebo, N = 153) randomized, 327 (71.4%) were from China and 131 (28.6%) were not from China. Of these, 97.7% and 97.4% patients completed Week 16 and 91.1% and 95.3% (placebo-secukinumab) patients completed Week 52 of treatment. The primary endpoint was met; secukinumab significantly improved ASAS20 response at Week 16 vs. placebo (58.4% vs. 36.6%; P < 0.0001); corresponding rate in the Chinese population was 56.0% vs. 38.5% ( P < 0.01). All secondary efficacy endpoints significantly improved with secukinumab 150 mg in the overall population at Week 16; responses were maintained with a trend toward increased efficacy from Week 16 to 52. No new or unexpected safety signals were reported up to Week 52. Conclusions Secukinumab 150 mg demonstrated rapid and significant improvement in signs and symptoms of AS. Secukinumab was well tolerated and the safety profile was consistent with previous reports. Efficacy and safety results were comparable between the overall and Chinese populations. Trial registration ClinicalTrials.gov, NCT02896127; https://clinicaltrials.gov/ct2/show/NCT02896127?term=NCT02896127&draw=2&rank=1 .

In this article, a novel Bayesian framework is proposed for real‐time system identification with calibratable model classes. This self‐calibrating scheme adaptively reconfigures the model classes to achieve reliable real‐time estimation for the system state and model parameters. At each time step, the plausibilities of the model classes are computed and they serve as the cue for calibration. Once calibration is triggered, all model classes will be reconfigured. Thereafter, identification will continue to propagate with the calibrated model classes until the next recalibration. Consequently, the model classes will evolve and their deficiencies can be corrected adaptively. This remarkable feature of the proposed framework stimulates the accessibility of reliable real‐time system identification. Examples are presented to demonstrate the efficacy of the proposed approach using noisy response measurement of linear and nonlinear time‐varying dynamical systems under stationary condition.

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