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      Strategies to reduce sample sizes in Alzheimer’s disease primary and secondary prevention trials using longitudinal amyloid PET imaging

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          Abstract

          Background

          Detecting subtle-to-moderate biomarker changes such as those in amyloid PET imaging becomes increasingly relevant in the context of primary and secondary prevention of Alzheimer’s disease (AD). This work aimed to determine if and when distribution volume ratio (DVR; derived from dynamic imaging) and regional quantitative values could improve statistical power in AD prevention trials.

          Methods

          Baseline and annualized % change in [ 11C]PIB SUVR and DVR were computed for a global (cortical) and regional (early) composite from scans of 237 cognitively unimpaired subjects from the OASIS-3 database ( www.oasis-brains.org). Bland-Altman and correlation analyses were used to assess the relationship between SUVR and DVR. General linear models and linear mixed effects models were used to determine effects of age, sex, and APOE-ε4 carriership on baseline and longitudinal amyloid burden. Finally, differences in statistical power of SUVR and DVR (cortical or early composite) were assessed considering three anti-amyloid trial scenarios: secondary prevention trials including subjects with (1) intermediate-to-high (Centiloid > 20.1), or (2) intermediate (20.1 < Centiloid ≤ 49.4) amyloid burden, and (3) a primary prevention trial focusing on subjects with low amyloid burden (Centiloid ≤ 20.1). Trial scenarios were set to detect 20% reduction in accumulation rates across the whole population and in APOE-ε4 carriers only.

          Results

          Although highly correlated to DVR ( ρ = .96), cortical SUVR overestimated DVR cross-sectionally and in annual % change. In secondary prevention trials, DVR required 143 subjects per arm, compared with 176 for SUVR. Both restricting inclusion to individuals with intermediate amyloid burden levels or to APOE-ε4 carriers alone further reduced sample sizes. For primary prevention, SUVR required less subjects per arm ( n = 855) compared with DVR ( n = 1508) and the early composite also provided considerable sample size reductions ( n = 855 to n = 509 for SUVR, n = 1508 to n = 734 for DVR).

          Conclusion

          Sample sizes in AD secondary prevention trials can be reduced by the acquisition of dynamic PET scans and/or by restricting inclusion to subjects with intermediate amyloid burden or to APOE-ε4 carriers only. Using a targeted early composite only leads to reductions of sample size requirements in primary prevention trials. These findings support strategies to enable smaller Proof-of-Concept Phase II clinical trials to better streamline drug development.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13195-021-00819-2.

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          Most cited references50

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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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            The antibody aducanumab reduces Aβ plaques in Alzheimer's disease.

            Alzheimer's disease (AD) is characterized by deposition of amyloid-β (Aβ) plaques and neurofibrillary tangles in the brain, accompanied by synaptic dysfunction and neurodegeneration. Antibody-based immunotherapy against Aβ to trigger its clearance or mitigate its neurotoxicity has so far been unsuccessful. Here we report the generation of aducanumab, a human monoclonal antibody that selectively targets aggregated Aβ. In a transgenic mouse model of AD, aducanumab is shown to enter the brain, bind parenchymal Aβ, and reduce soluble and insoluble Aβ in a dose-dependent manner. In patients with prodromal or mild AD, one year of monthly intravenous infusions of aducanumab reduces brain Aβ in a dose- and time-dependent manner. This is accompanied by a slowing of clinical decline measured by Clinical Dementia Rating-Sum of Boxes and Mini Mental State Examination scores. The main safety and tolerability findings are amyloid-related imaging abnormalities. These results justify further development of aducanumab for the treatment of AD. Should the slowing of clinical decline be confirmed in ongoing phase 3 clinical trials, it would provide compelling support for the amyloid hypothesis.
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              National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease.

              A consensus panel from the United States and Europe was convened recently to update and revise the 1997 consensus guidelines for the neuropathologic evaluation of Alzheimer's disease (AD) and other diseases of brain that are common in the elderly. The new guidelines recognize the pre-clinical stage of AD, enhance the assessment of AD to include amyloid accumulation as well as neurofibrillary change and neuritic plaques, establish protocols for the neuropathologic assessment of Lewy body disease, vascular brain injury, hippocampal sclerosis, and TDP-43 inclusions, and recommend standard approaches for the workup of cases and their clinico-pathologic correlation. Copyright © 2012 The Alzheimer's Association. All rights reserved.
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                Author and article information

                Contributors
                i.lopesalves@amsterdamumc.nl
                jdgispert@barcelonabeta.org
                Journal
                Alzheimers Res Ther
                Alzheimers Res Ther
                Alzheimer's Research & Therapy
                BioMed Central (London )
                1758-9193
                19 April 2021
                19 April 2021
                2021
                : 13
                : 82
                Affiliations
                [1 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, ; Amsterdam, The Netherlands
                [2 ]Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
                [3 ]GRID grid.411142.3, ISNI 0000 0004 1767 8811, IMIM (Hospital del Mar Medical Research Institute), ; Barcelona, Spain
                [4 ]GRID grid.7692.a, ISNI 0000000090126352, Imaging Division, Department of Radiology, , University Medical Center Utrecht, ; Utrecht, The Netherlands
                [5 ]GRID grid.11478.3b, Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, ; Barcelona, Spain
                [6 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Clinical Genetics, , Erasmus MC University Medical Center Rotterdam, ; Rotterdam, The Netherlands
                [7 ]GRID grid.5612.0, ISNI 0000 0001 2172 2676, Universitat Pompeu Fabra, ; Barcelona, Spain
                [8 ]GRID grid.83440.3b, ISNI 0000000121901201, Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, , UCL, ; London, UK
                [9 ]GRID grid.83440.3b, ISNI 0000000121901201, Dementia Research Centre, UCL Queen Square Institute of Neurology, ; London, UK
                [10 ]GRID grid.168010.e, ISNI 0000000419368956, Department of Neurology and Neurological Sciences, , Stanford University, ; Stanford, CA USA
                [11 ]GRID grid.4514.4, ISNI 0000 0001 0930 2361, Clinical Memory Research Unit, Faculty of Medicine, , Lund University, ; Lund, Sweden
                [12 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Psychiatry, , University of California, ; San Francisco, CA USA
                [13 ]GRID grid.413448.e, ISNI 0000 0000 9314 1427, Centro de Investigación Biomédica en Red Bioingeniería, , Biomateriales y Nanomedicina, ; Madrid, Spain
                Author information
                https://orcid.org/0000-0002-1230-8139
                https://orcid.org/0000-0001-7769-8329
                https://orcid.org/0000-0002-5210-9230
                https://orcid.org/0000-0003-2122-740X
                https://orcid.org/0000-0003-1237-2891
                https://orcid.org/0000-0003-3543-3706
                https://orcid.org/0000-0002-6155-0642
                Article
                819
                10.1186/s13195-021-00819-2
                8056524
                33875021
                e5f7f93c-cbaf-421b-ad73-7851313e6c29
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 17 November 2020
                : 26 March 2021
                Funding
                Funded by: Innovative Medicines Initiative ()
                Award ID: 115952
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Neurology
                pet imaging,amyloid,alzheimer’s disease,prevention,sample size,clinical trial
                Neurology
                pet imaging, amyloid, alzheimer’s disease, prevention, sample size, clinical trial

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