Assessment of Efficacy and Safety of Zagotenemab: Results From PERISCOPE-ALZ, a Phase 2 Study in Early Symptomatic Alzheimer Disease.

BACKGROUND AND OBJECTIVES: Zagotenemab (LY3303560), a monoclonal antibody that preferentially targets misfolded, extracellular, aggregated tau, was assessed in the PERISCOPE-ALZ phase 2 study to determine its ability to slow cognitive and functional decline relative to placebo in early symptomatic Alzheimer disease (AD). METHODS: Participants were enrolled across 56 sites in North America and Japan. Key eligibility criteria included age of 60-85 years, Mini-Mental State Examination score of 20-28, and intermediate levels of brain tau on PET imaging. In this double-blind study, participants were equally randomized to 1,400 mg or 5,600 mg of zagotenemab, or placebo (IV infusion every 4 weeks for 100 weeks). The primary outcome was change on the Integrated AD Rating Scale (iADRS) assessed by a Bayesian Disease Progression model. Secondary measures include mixed model repeated measures analysis of additional cognitive and functional endpoints as well as biomarkers of AD pathology. RESULTS: A total of 360 participants (mean age = 75.4 years; female = 52.8%) were randomized, and 218 completed the treatment period. Demographics and baseline characteristics were reasonably balanced among arms. The mean disease progression ratio (proportional decline in the treated vs placebo group) with 95% credible intervals for the iADRS was 1.10 (0.959-1.265) for the zagotenemab low-dose group and 1.05 (0.907-1.209) for the high-dose, where a ratio less than 1 favors the treatment group. Secondary clinical endpoint measures failed to show a drug-placebo difference in favor of zagotenemab. No treatment effect was demonstrated by flortaucipir PET, volumetric MRI, or neurofilament light chain (NfL) analyses. A dose-related increase in plasma phosphorylated tau181 and total tau was demonstrated. Zagotenemab treatment groups reported a higher incidence of adverse events (AEs) (85.1%) compared with the placebo group (74.6%). This difference was not attributable to any specific AE or category of AEs. DISCUSSION: In participants with early symptomatic AD, zagotenemab failed to achieve significant slowing of clinical disease progression compared with placebo. Imaging biomarker and plasma NfL findings did not show evidence of pharmacodynamic activity or disease modification. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov: NCT03518073. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that for patients with early symptomatic AD, zagotenemab does not slow clinical disease progression.

Tau as a diagnostic instrument in clinical trials to predict amyloid in Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) is characterized by the presence of both amyloid and tau pathology. In vivo diagnosis can be made with amyloid and tau positron emission tomography (PET) imaging. Emergent evidence supports that amyloid and tau accumulation are associated and that amyloid accumulation may precede that of tau. This report further investigates the relationship between amyloid and tau to assess whether elevated cortical tau can predict elevated amyloid in participants with early symptomatic AD. METHODS: Florbetapir F18 and flortaucipir F18 uptake were evaluated from baseline PET scans collected in three multi-center studies with cognitively impaired participants, including A05 (N = 306; NCT02016560), TB (N = 310; TRAILBLAZER-ALZ; NCT03367403), and TB2 (N = 1165; TRAILBLAZER-ALZ 2; NCT04437511). Images were assessed using visual and quantitative approaches to establish amyloid (A+) and tau (T+) positivity, as well as a combination method (tauVQ) to establish T+. Associations between global amyloid and tau were evaluated with positive and negative predictive values (PPV, NPV) and likelihood ratios (LR+, LR-). Predictive values within subgroups according to ethnicity, race, cognitive score, age, and sex were also evaluated. The relationship between regional tau (four target and two reference regions were tested) and global amyloid was investigated in A05 participant scans using receiver-operating characteristic (ROC) curves. RESULTS: PPV for amyloid positivity was ≥93% for all three trials using various A+ and T+ definitions, including visual, quantitative, and combination methods. Population characteristics did not have an impact on A+ predictability. Regional analyses (early tau (Eτ) volume of interest (VOI), temporal, parietal, frontal) revealed significant area under the ROC curve in Eτ VOI compared to frontal region, regardless of reference region and consistent among visual and quantitative A+ definitions (p < 0.001). DISCUSSION: These findings suggest that a positive tau PET scan is associated (≥93%) with amyloid positivity in individuals with early symptomatic AD, with the potential benefits of reducing clinical trial and health care expenses, radiation exposure, and participant time. Highlights: Positron emission tomography (PET) evaluates candidates for Alzheimer's disease (AD) research. A positive tau PET scan is associated (≥93%) with amyloid positivity.A positive amyloid PET is not necessarily associated with tau positivity.Tau PET could be the sole diagnostic tool to confirm candidates for AD trials.

Combining tau-PET and fMRI meta-analyses for patient-centered prediction of cognitive decline in Alzheimer's disease.

BACKGROUND: Tau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures. METHODS: We included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer's disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer's disease-spectrum=46/65). All participants underwent baseline 18F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R2) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline. RESULTS: In both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort. CONCLUSION: Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer's disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.

Magnetic resonance imaging measures of brain volumes across the EXPEDITION trials in mild and moderate Alzheimer's disease dementia.

Introduction: Solanezumab is a monoclonal antibody that preferentially binds soluble amyloid beta and promotes its clearance from the brain. The aim of this post hoc analysis was to assess the effect of low-dose solanezumab (400 mg) on global brain volume measures in patients with mild or moderate Alzheimer's disease (AD) dementia quantified using volumetric magnetic resonance imaging (vMRI) data from the EXPEDITION clinical trial program. Methods: Patients with mild or moderate AD (EXPEDITION and EXPEDITION2) and mild AD (EXPEDITION3), were treated with either placebo or solanezumab (400 mg) every 4 weeks (Q4W) for 76 weeks. vMRI scans were acquired at baseline and at 80 weeks from 427 MRI facilities using a standardized imaging protocol. Whole brain volume (WBV) and ventricle volume (VV) changes were estimated at 80 weeks using either boundary shift integral (EXPEDITION and EXPEDITION2) or tensor-based morphometry (EXPEDITION3). Results: The pooled cohort used for this study consisted of participants with vMRI at baseline and week 80 across the three trials. Analyzed patient subgroups comprised full patient cohort (N = 2933), apolipoprotein E (APOE) ε4+ carriers (N = 1835), and patients with mild (N = 2497) or moderate AD dementia (N = 428). No significant effect (all P-values ≥.05) of treatment was observed in the pooled sample, individual trials, or subgroups of patients with mild or moderate AD or APOE ε4 carriers, in either WBV or VV change. Discussion: Analysis of patients with mild or moderate AD dementia from baseline to 80 weeks using vMRI measures of WBV and VV changes suggested that low-dose solanezumab was not linked to changes in volumes at 80 weeks. Analysis of the pooled cohort did not demonstrate an effect on brain volumes with treatment. Evaluation of a higher dose of solanezumab in the preclinical stage of AD is currently being undertaken.

A difference degree test for comparing brain networks.

Recently, there has been a proliferation of methods investigating functional connectivity as a biomarker for mental disorders. Typical approaches include massive univariate testing at each edge or comparisons of network metrics to identify differing topological features. Limitations of these methods include low statistical power due to the large number of comparisons and difficulty attributing overall differences in networks to local variation. We propose a method to capture the difference degree, which is the number of edges incident to each region in the difference network. Our difference degree test (DDT) is a two-step procedure for identifying brain regions incident to a significant number of differentially weighted edges (DWEs). First, we select a data-adaptive threshold which identifies the DWEs followed by a statistical test for the number of DWEs incident to each brain region. We achieve this by generating an appropriate set of null networks which are matched on the first and second moments of the observed difference network using the Hirschberger-Qi-Steuer algorithm. This formulation permits separation of the network's true topology from the nuisance topology induced by the correlation measure that alters interregional connectivity in ways unrelated to brain function. In simulations, the proposed approach outperforms competing methods in detecting differentially connected regions of interest. Application of DDT to a major depressive disorder dataset leads to the identification of brain regions in the default mode network commonly implicated in this ruminative disorder.

Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge.

Recently, there has been increased interest in fusing multimodal imaging to better understand brain organization by integrating information on both brain structure and function. In particular, incorporating anatomical knowledge leads to desirable outcomes such as increased accuracy in brain network estimates and greater reproducibility of topological features across scanning sessions. Despite the clear advantages, major challenges persist in integrative analyses including an incomplete understanding of the structure-function relationship and inaccuracies in mapping anatomical structures due to inherent deficiencies in existing imaging technology. This calls for the development of advanced network modeling tools that appropriately incorporate anatomical structure in constructing brain functional networks. We propose a hierarchical Bayesian Gaussian graphical modeling approach which models the brain functional networks via sparse precision matrices whose degree of edge specific shrinkage is a random variable that is modeled using both anatomical structure and an independent baseline component. The proposed approach adaptively shrinks functional connections and flexibly identifies functional connections supported by structural connectivity knowledge. This enables robust brain network estimation even in the presence of misspecified anatomical knowledge, while accommodating heterogeneity in the structure-function relationship. We implement the approach via an efficient optimization algorithm which yields maximum a posteriori estimates. Extensive numerical studies involving multiple functional network structures reveal the clear advantages of the proposed approach over competing methods in accurately estimating brain functional connectivity, even when the anatomical knowledge is misspecified up to a certain degree. An application of the approach to data from the Philadelphia Neurodevelopmental Cohort (PNC) study reveals gender based connectivity differences across multiple age groups, and higher reproducibility in the estimation of network metrics compared to alternative methods.