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Longitudinal Exposure—Response Modeling of Multiple Indicators of Alzheimer’s Disease Progression

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The Journal of Prevention of Alzheimer's Disease Aims and scope Submit manuscript

Abstract

Background

Progression in Alzheimer’s disease manifests as changes in multiple biomarker, cognitive, and functional endpoints. Disease progression modeling can be used to integrate these multiple measures into a synthesized metric of where a patient lies within the disease spectrum, allowing for a more dynamic measure over the range of the disease.

Objectives

This study aimed to combine modeling techniques from psychometric research (e.g., item response theory) and pharmacometrics (e.g., hierarchical models) to describe the multivariate longitudinal disease progression for patients with mild-to-moderate Alzheimer’s disease. Additionally, we aimed to extend the subsequent model to make it suitable for clinical trial simulation, with the inclusion of covariates, to explain variability in latent progression (i.e., disease progression) and to aid in the assessment of enrichment strategies.

Design

Multiple longitudinal endpoints in the Alzheimer’s Disease Neuroimaging Initiative database were modeled. This model was validated internally using visual predictive checks, and externally by comparing data from the placebo arms of two Phase 2 crenezumab studies, ABBY (NCT01343966) and BLAZE (NCT01397578).

Setting

The Alzheimer’s Disease Neuroimaging Initiative began in 2004: the initial 5-year study (ADNI-1) was extended by 2 years in 2009 by a Grand Opportunities grant (ADNI-GO), and in 2011 and 2016 by further competitive renewals of the ADNI-1 grant (ADNI-2 and ADNI-3, respectively). This work studies natural progression data from patients with confirmed Alzheimer’s disease. The Phase 2 ABBY and BLAZE trials evaluated the safety and efficacy of crenezumab in patients with mild-to-moderate Alzheimer’s disease.

Participants

From the Alzheimer’s Disease Neuroimaging Initiative database, 305 subjects who had a baseline diagnosis of mild-to-moderate Alzheimer’s disease were included in modeling. From the ABBY and BLAZE studies, 158 patients were included from the studies’ placebo arms.

Measurements

Longitudinal cognitive and functional assessments modeled included the Clinical Dementia Rating (both as Sum of Boxes and individual item scores), the Mini-Mental State Examination, the Alzheimer’s Disease Assessment Scale — Cognitive Subscale, the Functional Activities Questionnaire, the Montreal Cognitive Assessment, and the Rey Auditory Verbal Learning Test. Also included were the imaging variable fluorodeoxyglucose-positron emission tomography and the following magnetic resonance imaging volumetrics: entorhinal, fusiform, hippocampal, intra-cranial, mid-temporal, ventricular, and whole brain.

Results

Applying item response theory approaches in this longitudinal setting showed clinical assessments informing a common disease scale in the following order (from early disease to late disease): Rey Auditory Verbal Learning Test, Functional Activities Questionnaire, Montreal Cognitive Assessment, Alzheimer’s Disease Assessment Scale — Cognitive Subscale 12, Clinical Dementia Rating — Sum of Boxes, and Mini-Mental State Examination. The Clinical Dementia Rating communication and home-and-hobbies items were most informative at earlier disease stages, while memory, orientation, and personal care informed the disease status at later stages. A clinical trial simulation model was developed and accurately described within-sample longitudinal distribution of endpoints. Simplifying the model to use only baseline age, MMSE, and APOEε4 status as predictors, out-of-sample mean progression of ADAS-Cog and CDR Sum of Boxes in the ABBY and BLAZE placebo arms was accurately described; however, the variability in these endpoints was underpredicted and suggests possibility for further model refinement when extrapolating from the ADNI sample to trial data. Clinical trial simulations were performed to exemplify use of the model to investigate hypothetical disease modification effects on the multivariate, longitudinal progression on the Alzheimer’s Disease Assessment Scale — Cognitive Subscale and the Clinical Dementia Rating — Sum of Boxes.

Conclusions

The latent variable structure of item response theory can be extended to capture a variety of scales that are common assessments and indicators of disease status in mild-to-moderate Alzheimer’s disease. These models are not intended to support causal inferences, but they do successfully characterize the observed correlation between endpoints over time and result in concise numerical indices of disease status that reflect the totality of evidence from considering the endpoints jointly. As such, the models have utility for a variety of tasks in clinical trial design, including simulation of hypothetical drug effects, interpolation of missing data, and assessment of in-sample information.

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Data availability statement: For up-to-date details on Roche’s Global Policy on the Sharing of Clinical Information and how to request access to related clinical study documents, see here: https://go.roche.com/data_sharing. Requests for the modeling data underlying this publication requires a detailed, hypothesis-driven statistical analysis plan that is collaboratively developed by the requestor and company subject matter experts. Direct such requests to global.patient_level_data_sharing@roche.com for consideration. Anonymized records for individual patients across more than one data source external to Roche can not, and should not, be linked due to a potential increase in risk of patient re-identification. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete list of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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Acknowledgments

We would like to thank all Alzheimer’s Disease Neuroimaging Initiative (ADNI), ABBY, and BLAZE participants, clinicians, and administrators. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and the DOD ADNI (Department of Defense award number W81XWH-12-2-0012). The ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation; Araclon Biotech; Bioclinica, Inc.; Biogen; Bristol Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai, Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development, LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer, Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (https://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California. Crenezumab was discovered and developed in collaboration with AC Immune SA, Lausanne, Switzerland. Editorial support in the development of this manuscript was provided by Chris Ackroyd, MSc, of Health Interactions, funded by F. Hoffmann-La Roche Ltd, Basel, Switzerland.

Funding

Funding: Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and the DOD ADNI (Department of Defense award number W81XWH-12-2-0012). The Metrum Research Group (Tariffville, CT, USA) and Genentech, Inc. (South San Francisco, CA, USA) were involved in the funding of the work, and the analysis and interpretation of the data. The ABBY and BLAZE studies were funded by Genentech, Inc. Editorial support in the preparation of this manuscript was funded by F. Hoffmann-La Roche Ltd, Basel, Switzerland.

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Correspondence to Michael J. Dolton.

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Conflict of interest: DGP and JAR are employees of the Metrum Research group. MJD was an employee of Genentech, Inc., a member of the Roche Group, at the time of this work; he is now an employee of Roche Products Pty Limited, Sydney, Australia, and owns stock or stock options in F. Hoffmann-La Roche Ltd. LAH and JYJ are employees of Genentech and own stock in F. Hoffmann-La Roche Ltd. AQ was an employee of Genentech, Inc. and owned stock in Roche Holding Ltd at the time of this study; she is now an employee of AstraZeneca, Gothenburg, Sweden.

Ethical standards: All human procedures were conducted in accordance with the Declaration of Helsinki and Good Clinical Practice.

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MJD was an employee of Genentech, Inc. at the time of this study

AQ was an employee of Genentech, Inc. at the time of this study

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete list of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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Polhamus, D.G., Dolton, M.J., Rogers, J.A. et al. Longitudinal Exposure—Response Modeling of Multiple Indicators of Alzheimer’s Disease Progression. J Prev Alzheimers Dis 10, 212–222 (2023). https://doi.org/10.14283/jpad.2023.13

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