Generation of a comprehensive disease progression model across the Alzheimer's disease continuum: Human/Trial design. (7th December 2020)

• Record Type: Journal Article
• Authors: Hanan, Nathan J.; Sivakumaran, Sudhir; Sinha, Vikram; Haeberlein, Samantha Budd; Gold, Michael; Romero, Klaus
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 9
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.043258
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Adequate assessment of trial design optimization is an important factor to inform clinical trial design for complex disorders such as Alzheimer's disease (AD). Critical Path Institute's Critical Path for Alzheimer's Consortium (CPAD) has achieved EMA qualification and FDA Fit‐for‐Purpose endorsement of the first‐ever quantitative drug development tool (the clinical trial simulator for mild‐to‐moderate AD) and is in the process of developing a disease progression model spanning the entire disease continuum, to enable clinical trial design optimization. The objective of this work is to develop a structural model that characterizes disease progression in AD patients using the integrated CPAD database and optimize experimental design using clinical trial simulations. Method: A disease progression model characterizing the time course of clinically relevant measures (ADAS‐Cog, CDR‐SB, other clinical scales and biomarkers), will be developed using an integrated dataset comprised of multiple observational and clinical trial data sources. Covariates including demographics, time from and to diagnosis, genetic status (APOE4), co‐morbidities and medication use will be assessed using non‐linear mixed effects methods. Monte Carlo simulations will be performed to compare the statistical power by sample size in trials with and without enrichment using relevant covariates. Result: Model selection based on the log‐likelihood ratio and goodness‐of‐fit plots will be used to select the model structure that most adequately describes the integrated database. Covariate analyses will be performed to identify variables that constitute relevant predictors of baseline severity and disease progression rates. Trial simulations for a therapeutic effect using standard drug effect models for symptomatic and disease‐modifying effects will be conducted to estimate an optimal sample size when trials are enriched with a clinically relevant covariate. Conclusion: The simulation output from the disease progression model will enable a more efficient design of clinical studies with better informed inclusion criteria and enrichment strategies. The proposed quantitative methodology permits a comprehensive integration of relevant patient characteristics available for decision‐making.