Sylvie Retout (1), Isabelle Delor (2), Ronald Gieschke (1), Philippe Jacqmin (2), Jean-Eric Charoin (1) for Alzheimer’s disease Neuroimaging Initiative.
(1) F. Hoffmann-La-Roche Ltd, Basel, Switzerland, (2) SGS-Exprimo, Mechelen, Belgium
Objectives: Population Alzheimer’s disease (AD) progression models have been developed using the Alzheimer’s Disease Assessment Scale – cognitive subscale (ADAS-cog) scores to describe the dementia stage of the disease [1-2]. However, those models cannot be used in the context of drug development projects focusing on earlier stages of the disease such as with prodromal patients, and for which endpoint is assessed by CDR-SOB scores. Our objectives were then to support the development of an AD progression model based on CDR-SOB scores and to demonstrate, by simulation, the usefulness of such a model for clinical trial optimisation.
Methods: An AD progression population model was developed [3] using the CDR-SOB scores from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database [4]. That model enables the estimation of a disease onset time and a disease trajectory for each patient. The model also allows distinguishing fast and slow progressing sub-populations according to, the functional assessment questionnaire (FAQ), the normalized hippocampus volume and the CDR-SOB score at study entry. We used that model in a simulation mode to explore its potential impact in terms of quantitative understanding design elements (inclusion, trial duration, etc) of a respective clinical trial.
Results: The AD model enables clinical trial design optimization, by 1- understanding the impact of inclusion criteria/disease severity on treatment effect and required trial length; 2- simulating the time course of the placebo and treatment trial arms under different scenarios (e.g. alternative sample size, trial durations and measurement times) in order to determine how and when enough effect size should be achieved for differentiation. Furthermore, a robust analysis of the data can be performed at the end of a study, by implementing and quantifying a possible drug effect (e.g. time to maximal effect, effects that increase or decrease over time) on one or more of the parameters of the natural history disease progression model [3].
Conclusions: The use of this novel AD disease progression model is a powerful tool in the context of drug development to optimize clinical trial designs and therefore to maximize the likelihood to bring new medicine to Alzheimer’s patients.
References:
[1] Ito, K. et al. Alzheimers Dement. (2010) 6: 39-53
[2] Samtani, M.N. et al. J. Clin. Pharmacol. (2012) 52: 629-644
[3] Delor, I. et al. Abstr PAGE 22 (2013)
[4] http://www.loni.ucla.edu/ida%20/login.jsp?project=ADNI
Reference: PAGE 22 (2013) Abstr 2767 [www.page-meeting.org/?abstract=2767]
Poster: CNS