%0 Journal Article %J J Alzheimers Dis %D 2017 %T Sembragiline in Moderate Alzheimer's Disease: Results of a Randomized, Double-Blind, Placebo-Controlled Phase II Trial (MAyflOwer RoAD). %A Nave, Stephane %A Doody, Rachelle S %A Boada, Merce %A Grimmer, Timo %A Savola, Juha-Matti %A Delmar, Paul %A Pauly-Evers, Meike %A Nikolcheva, Tania %A Czech, Christian %A Borroni, Edilio %A Ricci, Benedicte %A Dukart, Juergen %A Mannino, Marie %A Carey, Tracie %A Moran, Emma %A Gilaberte, Inma %A Muelhardt, Nicoletta Milani %A Gerlach, Irene %A Santarelli, Luca %A Ostrowitzki, Susanne %A Fontoura, Paulo %X

BACKGROUND: Sembragiline is a potent, selective, long-acting, and reversible MAO-B inhibitor developed as a potential treatment for Alzheimer's disease (AD).

OBJECTIVE: To evaluate the safety, tolerability, and efficacy of sembragiline in patients with moderate AD.

METHODS: In this Phase II study (NCT01677754), 542 patients with moderate dementia (MMSE 13-20) on background acetylcholinesterase inhibitors with/without memantine were randomized (1:1:1) to sembragiline 1 mg, 5 mg, or placebo once daily orally for 52 weeks.

RESULTS: No differences between treated groups and placebo in adverse events or in study completion. The primary endpoint, change from baseline in ADAS-Cog11, was not met. At Week 52, the difference between sembragiline and placebo in ADAS-Cog11 change from baseline was - 0.15 (p = 0.865) and 0.90 (p = 0.312) for 1 and 5 mg groups, respectively. Relative to placebo at Week 52 (but not at prior assessment times), the 1 mg and 5 mg sembragiline groups showed differences in ADCS-ADL of 2.64 (p = 0.051) and 1.89 (p = 0.160), respectively. A treatment effect in neuropsychiatric symptoms (as assessed by the difference between sembragiline and placebo on BEHAVE-AD-FW) was also seen at Week 52 only: - 2.80 (p = 0.014; 1 mg) and - 2.64 (p = 0.019; 5 mg), respectively. A post hoc subgroup analysis revealed greater treatment effects on behavior and functioning in patients with more severe baseline behavioral symptoms (above the median).

CONCLUSIONS: This study showed that sembragiline was well-tolerated in patients with moderate AD. The study missed its primary and secondary endpoints. Post hoc analyses suggested potential effect on neuropsychiatric symptoms and functioning in more behaviorally impaired study population at baseline.

%B J Alzheimers Dis %V 58 %P 1217-1228 %8 2017 %G eng %N 4 %1 http://www.ncbi.nlm.nih.gov/pubmed/28550255?dopt=Abstract %R 10.3233/JAD-161309 %0 Journal Article %J J Alzheimers Dis %D 2016 %T Accurate Prediction of Conversion to Alzheimer's Disease using Imaging, Genetic, and Neuropsychological Biomarkers. %A Dukart, Juergen %A Sambataro, Fabio %A Bertolino, Alessandro %K Aged %K Aged, 80 and over %K Alzheimer Disease %K Apolipoproteins E %K Area Under Curve %K Brain %K Cognitive Dysfunction %K Disease Progression %K Female %K Fluorodeoxyglucose F18 %K Follow-Up Studies %K Humans %K Magnetic Resonance Imaging %K Male %K Middle Aged %K Neuropsychological Tests %K Positron-Emission Tomography %K Prognosis %K Radiopharmaceuticals %K ROC Curve %K Sensitivity and Specificity %X

A variety of imaging, neuropsychological, and genetic biomarkers have been suggested as potential biomarkers for the identification of mild cognitive impairment (MCI) in patients who later develop Alzheimer's disease (AD). Here, we systematically evaluated the most promising combinations of these biomarkers regarding discrimination between stable and converter MCI and reflection of disease staging. Alzheimer's Disease Neuroimaging Initiative data of AD (n = 144), controls (n = 112), stable (n = 265) and converter (n = 177) MCI, for which apolipoprotein E status, neuropsychological evaluation, and structural, glucose, and amyloid imaging were available, were included in this study. Naïve Bayes classifiers were built on AD and controls data for all possible combinations of these biomarkers, with and without stratification by amyloid status. All classifiers were then applied to the MCI cohorts. We obtained an accuracy of 76% for discrimination between converter and stable MCI with glucose positron emission tomography as a single biomarker. This accuracy increased to about 87% when including further imaging modalities and genetic information. We also identified several biomarker combinations as strong predictors of time to conversion. Use of amyloid validated training data resulted in increased sensitivities and decreased specificities for differentiation between stable and converter MCI when amyloid was included as a biomarker but not for other classifier combinations. Our results indicate that fully independent classifiers built only on AD and controls data and combining imaging, genetic, and/or neuropsychological biomarkers can more reliably discriminate between stable and converter MCI than single modality classifiers. Several biomarker combinations are identified as strongly predictive for the time to conversion to AD.

%B J Alzheimers Dis %V 49 %P 1143-59 %8 2016 %G eng %N 4 %1 http://www.ncbi.nlm.nih.gov/pubmed/26599054?dopt=Abstract %R 10.3233/JAD-150570