Imago Mundi, Imago AD, Imago ADNI

Lecanemab in patients with early Alzheimer's disease: detailed results on biomarker, cognitive, and clinical effects from the randomized and open-label extension of the phase 2 proof-of-concept study

BACKGROUND

Lecanemab, a humanized IgG1 monoclonal antibody that targets soluble aggregated Aβ species (protofibrils), has demonstrated robust brain fibrillar amyloid reduction and slowing of clinical decline in early AD. The objective of this analysis is to report results from study 201 blinded period (core), the open-label extension (OLE), and gap period (between core and OLE) supporting the effectiveness of lecanemab.

METHODS

The lecanemab study 201 core was a double-blind, randomized, placebo-controlled study of 856 patients randomized to one of five dose regimens or placebo. An OLE of study 201 was initiated to allow patients to receive open-label lecanemab 10mg/kg biweekly for up to 24 months, with an intervening off-treatment period (gap period) ranging from 9 to 59 months (mean 24 months).

RESULTS

At 12 and 18 months of treatment in the core, lecanemab 10 mg/kg biweekly demonstrated dose-dependent reductions of brain amyloid measured PET and corresponding changes in plasma biomarkers and slowing of cognitive decline. The rates of clinical progression during the gap were similar in lecanemab and placebo subjects, with clinical treatment differences maintained after discontinued dosing over an average of 24 months in the gap period. During the gap, plasma Aβ42/40 ratio and p-tau181 levels began to return towards pre-randomization levels more quickly than amyloid PET. At OLE baseline, treatment differences vs placebo at 18 months in the randomized period were maintained across 3 clinical assessments. In the OLE, lecanemab 10 mg/kg biweekly treatment produced dose-dependent reductions in amyloid PET SUVr, improvements in plasma Aβ42/40 ratio, and reductions in plasma p-tau181.

CONCLUSIONS

Lecanemab treatment resulted in significant reduction in amyloid plaques and a slowing of clinical decline. Data indicate that rapid and pronounced amyloid reduction correlates with clinical benefit and potential disease-modifying effects, as well as the potential to use plasma biomarkers to monitor for lecanemab treatment effects.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01767311 .

Inter-Vendor and Inter-Session Reliability of Diffusion Tensor Imaging: Implications for Multicenter Clinical Imaging Studies

Objective

To evaluate the inter-vendor and inter-session reliability of diffusion tensor imaging (DTI) and relevant parameters.

Materials and Methods

This prospective study included 10 healthy subjects (5 women and 5 men; age range, 25-33 years). Each subject was scanned twice using 3T magnetic resonance scanners from three different vendors at two different sites. A voxel-wise statistical analysis of diffusion data was performed using Tract-Based Spatial Statistics. Fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) values were calculated for each brain voxel using FMRIB's Diffusion Toolbox.

Results

A repeated measures analysis of variance revealed that there were no significant differences in FA values across the vendors or between sessions; however, there were significant differences in MD values between the vendors (p = 0.020). Although there were no significant differences in inter-session MD and inter-session/inter-vendor RD values, a significant group × factor interaction revealed differences in MD and RD values between the 1st and 2nd sessions conducted by the vendors (p = 0.004 and 0.006, respectively).

Conclusion

Although FA values exhibited good inter-vendor and inter-session reliability, MD and RD values did not show consistent results. Researchers using DTI should be aware of these limitations, especially when implementing DTI in multicenter studies.

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.

METHODS

We used standard searches to find publications using ADNI data.

RESULTS

(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.

DISCUSSION

Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.