Journal of Pharmacokinetics and Pharmacodynamics

, Volume 41, Issue 6, pp 581–598 | Cite as

An updated Alzheimer’s disease progression model: incorporating non-linearity, beta regression, and a third-level random effect in NONMEM

  • Daniela J. Conrado
  • William S. Denney
  • Danny Chen
  • Kaori Ito
Original Paper

Abstract

Our objective was to expand our understanding of the predictors of Alzheimer’s disease (AD) progression to help design a clinical trial on a novel AD medication. We utilized the Coalition Against Major Diseases AD dataset consisting of control-arm data (both placebo and stable background AD medication) from 15 randomized double-blind clinical trials in mild-to-moderate AD patients (4,495 patients; July 2013). Our ADAS-cog longitudinal model incorporates a beta-regression with between-study, -subject, and -residual variability in NONMEM; it suggests that faster AD progression is associated with younger age and higher number of apolipoprotein E type 4 alleles (APOE*4), after accounting for baseline disease severity. APOE*4, in particular, seems to be implicated in the AD pathogenesis. In addition, patients who are already on stable background AD medications appear to have a faster progression relative to those who are not receiving AD medication. The current knowledge does not support a causality relationship between use of background AD medications and higher rate of disease progression, and the correlation is potentially due to confounding covariates. Although causality has not necessarily been demonstrated, this model can inform inclusion criteria and stratification, sample size, and trial duration.

Keywords

Alzheimer’s disease Disease progression Patient-level model-based meta-analysis Coalition Against Major Diseases (CAMD) ADAS-cog NONMEM 

Notes

Acknowledgments

The authors would like to thank Dr. Mahesh Samtani (Johnson and Johnson Pharmaceutical Research and Development) for his technical guidance and invaluable input in conducting the analysis. The authors would also like to acknowledge CAMD database management team to provide the assembled dataset and to respond authors’ queries. All authors are employees of Pfizer Inc.

Supplementary material

10928_2014_9375_MOESM1_ESM.docx (515 kb)
Supplementary material 1 (DOCX 515 kb)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Daniela J. Conrado
    • 1
    • 2
  • William S. Denney
    • 1
  • Danny Chen
    • 1
  • Kaori Ito
    • 2
  1. 1.Pharmatherapeutics Clinical PharmacologyPfizer Inc.CambridgeUSA
  2. 2.Pfizer Global Innovative Pharma BusinessPfizer Inc.GrotonUSA

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