Ping Chiao

The influence of biological and technical factors on quantitative analysis of amyloid PET: Points to consider and recommendations for controlling variability in longitudinal data

In vivo imaging of amyloid burden with positron emission tomography (PET) provides a means for studying the pathophysiology of Alzheimers and related diseases. Measurement of subtle changes in amyloid burden requires quantitative analysis of image data. Reliable quantitative analysis of amyloid PET scans acquired at multiple sites and over time requires rigorous standardization of acquisition protocols, subject management, tracer administration, image quality control, and image processing and analysis methods. We review critical points in the acquisition and analysis of amyloid PET, identify ways in which technical factors can contribute to measurement variability, and suggest methods for mitigating these sources of noise. Improved quantitative accuracy could reduce the sample size necessary to detect intervention effects when amyloid PET is used as a treatment end point and allow more reliable interpretation of change in amyloid burden and its relationship to clinical course.

Impact of Reference/Target Region Selection on Amyloid PET Standard Uptake Value Ratios in the Phase 1b PRIME Study of Aducanumab.

SUV ratios (SUVRs) are commonly used to quantify tracer uptake in amyloid-β PET. Here, we explore the impact of target and reference region-of-interest (ROI) selection on SUVR effect sizes using interventional data from the ongoing phase 1b PRIME study (NCT01677572) of aducanumab (BIIB037) in patients with prodromal or mild Alzheimer disease. Methods: The florbetapir PET SUVR was calculated at baseline (screening) and at weeks 26 and 54 for patients randomized to receive placebo and each of 4 aducanumab doses (1, 3, 6, and 10 mg/kg) using the whole cerebellum, cerebellar gray matter, cerebellar white matter, pons, and subcortical white matter as reference regions. In addition to the prespecified composite cortex target ROI, individual cerebral cortical ROIs were assessed as targets. Results: Of the reference regions used, subcortical white matter, cerebellar white matter, and the pons, alone or in combination, generated the largest effect sizes. The use of the anterior cingulate cortex as a target ROI resulted in larger effect sizes than the use of the composite cortex. SUVR calculations were not affected by correction for brain volume changes over time. Conclusion: Dose- and time-dependent reductions in the amyloid PET SUVR were consistently observed with aducanumab only in cortical regions prone to amyloid plaque deposition, regardless of the reference region used. These data support the hypothesis that florbetapir SUVR responses associated with aducanumab treatment are a result of specific dose- and time-dependent reductions in the amyloid burden in patients with Alzheimer disease.

CLINICAL DEMENTIA RATING-SUM OF BOXES RESPONDER ANALYSIS IN PRIME: A RANDOMIZED PHASE 1B STUDY OF THE ANTI-AMYLOID BETA MONOCLONAL ANTIBODY ADUCANUMAB (BIIB037)

logical and age-related disorders and 3) report and comment on the clinical utility of the biomarker signature. In this analysis, we expand on Lunnon et al. 2012 and address all of these concerns. Methods:We obtained sixteen gene expression data sets, including two additional AD data sets, neurological, autoimmune, diabetes and arterial related disorders from public and in-house sources. All predictive modelling was performed using the R package ‘caret’ with 1000 bootstrap resamples used to evaluate model performance. We re-developed the Random Forest (RF) AD classification model published in Lunnon et al. 2012, using 79 AD-cases and 79 controls for training.We evaluated themodel in two independent AD datasets, generated using two different microarray platforms (Illumina N1⁄4221 and Affymetrix N1⁄439). To assess specificity for AD, the two AD expression sets and all Non-AD data were combined and used as a large test/validation dataset totalling 1839 samples, on which, the AD RF classifier was tested for its ability to predict AD from non-AD. Results: The AD RF classification model achieved a training accuracy of 88%, and a mean test accuracy of 68.88% (95% CI, 62.74-74.51%) within the first independent AD dataset (Illumina) and 59.52%(95% CI, 45.79-74.34%) in the second independent AD dataset (Affymetrix). When evaluating the AD classification model’s ability to discriminate AD samples from non-AD samples, the model achieved a mean accuracy of 59.7% (95% CI, 55.13-64.08) and equated to a PPV of 0.6, NPV of 0.4, positive Clinical Utility Index of 0.39 and a negative Clinical Utility Index of 0.22. Conclusions: In its current form, the AD classification model’s clinical utility is ‘poor’ in AD discovery (confirmation) and a ’poor’ predictor for screening (ruling out) AD. Further investigations and comparisons of signatures in larger AD datasets and other disorders are required.

Randomized, placebo-controlled, phase 1b study of anti-beta-amyloid antibody aducanumab (biib037) in prodromal ad/mild ad dementia: Interim results by patient subgroup

25 WFN15-1092 Dementia 2 Randomized, placebo-controlled, phase 1b study of anti-beta-amyloid antibody aducanumab (biib037) in prodromal ad/mild ad dementia: Interim results by patient subgroup J. Sevigny, P. Chiao, L. Williams, T. Chen, Y. Ling, J. O’Gorman, C. Hock, R. Nitsch, A. Sandrock. Clinical Development Neurodegeneration and Exp Med Clin Dev, Biogen, Cambridge, USA; Clinical Research MA Neurodegeneration and Exp Med Clin Dev, Biogen, Cambridge, USA; Drug Safety & Risk Management, Biogen, Cambridge, USA; Biostatistics Biometrics, Biogen, Cambridge, USA; Clinical Development MA MS Clin Dev Center, Biogen, Cambridge, USA; Biostatistics, Biogen, Cambridge, USA; Division of Psychiatry Research, Neurimmune Holding AG and University of Zurich, Zurich, Switzerland; Biogen, Development Sciences, Cambridge, USA