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- Kappos, Ludwig, et al.
(författare)
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Siponimod versus placebo in secondary progressive multiple sclerosis (EXPAND): a double-blind, randomised, phase 3 study
- 2018
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Ingår i: The Lancet. - 0140-6736 .- 1474-547X. ; 391, s. 1263-1273
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Tidskriftsartikel (refereegranskat)abstract
- © 2018 Elsevier Ltd Background: No treatment has consistently shown efficacy in slowing disability progression in patients with secondary progressive multiple sclerosis (SPMS). We assessed the effect of siponimod, a selective sphingosine 1-phosphate (S1P) receptor 1,5 modulator, on disability progression in patients with SPMS. Methods: This event-driven and exposure-driven, double-blind, phase 3 trial was done at 292 hospital clinics and specialised multiple sclerosis centres in 31 countries. Using interactive response technology to assign numbers linked to treatme nt arms, patients (age 18–60 years) with SPMS and an Expanded Disability Status Scale score of 3·0–6·5 were randomly assigned (2:1) to once daily oral siponimod 2 mg or placebo for up to 3 years or until the occurrence of a prespecified number of confirmed disability progression (CDP) events. The primary endpoint was time to 3-month CDP. Efficacy was assessed for the full analysis set (ie, all randomly assigned and treated patients); safety was assessed for the safety set. This trial is registered with ClinicalTrials.gov, number NCT01665144. Findings: 1651 patients were randomly assigned between Feb 5, 2013, and June 2, 2015 (1105 to the siponimod group, and 546 to the placebo group). One patient did not sign the consent form, and five patients did not receive study drug, all of whom were in the siponimod group. 1645 patients were included in the analyses (1099 in the siponimod group and 546 in the placebo). At baseline, the mean time since first multiple sclerosis symptoms was 16·8 years (SD 8·3), and the mean time since conversion to SPMS was 3·8 years (SD 3·5); 1055 (64%) patients had not relapsed in the previous 2 years, and 918 (56%) of 1651 needed walking assistance. 903 (82%) patients receiving siponimod and 424 (78%) patients receiving placebo completed the study. 288 (26%) of 1096 patients receiving siponimod and 173 (32%) of 545 patients receiving placebo had 3-month CDP (hazard ratio 0·79, 95% CI 0·65–0·95; relative risk reduction 21%; p=0·013). Adverse events occurred in 975 (89%) of 1099 patients receiving siponimod versus 445 (82%) of 546 patients receiving placebo; serious adverse events were reported for 197 (18%) patients in the siponimod group versus 83 (15%) patients in the placebo group. Lymphopenia, increased liver transaminase concentration, bradycardia and bradyarrhythmia at treatment initiation, macular oedema, hypertension, varicella zoster reactivation, and convulsions occurred more frequently with siponimod than with placebo. Initial dose titration mitigated cardiac first-dose effects. Frequencies of infections, malignancies, and fatalities did not differ between groups. Interpretation: Siponimod reduced the risk of disability progression with a safety profile similar to that of other S1P modulators and is likely to be a useful treatment for SPMS. Funding: Novartis Pharma AG.
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| 2. |
- Commowick, Olivier, et al.
(författare)
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Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
- 2018
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning,.), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
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