Alzheimer's disease assessment scale‐cognitive 11‐item progression model in mild‐to‐moderate Alzheimer's disease trials of bapineuzumab

Abstract

IntroductionThe objective of this study was to estimate longitudinal changes in disease progression (measured by Alzheimer's disease assessment scale-cognitive 11-item [ADAS-cog/11] scale) after bapineuzumab treatment and to identify covariates (demographics or baseline characteristics) contributing to the variability in disease progression rate and baseline disease status.MethodsA population-based disease progression model was developed using pooled placebo and bapineuzumab data from two phase-3 studies in APOE ε4 noncarrier and carrier Alzheimer's disease (AD) patients.ResultsA beta regression model with the Richard's function as the structural component best described ADAS-cog/11 disease progression for mild-to-moderate AD population. This analysis confirmed no effect of bapineuzumab exposure on ADAS-cog/11 progression rate, consistent with the lack of clinical efficacy observed in the statistical analysis of ADAS-cog/11 data in both studies. Assessment of covariates affecting baseline severity revealed that men had a 6% lower baseline ADAS-cog/11 score than women; patients who took two AD concomitant medications had a 19% higher (worse) baseline score; APOE ε4 noncarriers had a 5% lower baseline score; and patients who had AD for a longer duration had a higher baseline score. Furthermore, shorter AD duration, younger age, APOE ε4 carrier status, and use of two AD concomitant medications were associated with faster disease progression rates. Patients who had an ADAS-cog/11 score progression rate that was not statistically significantly different from 0 typically took no AD concomitant medications.DiscussionThe beta regression model is a sensible modeling approach to characterize cognitive decline in AD patients. The influence of bapineuzumab exposure on disease progression measured by ADAS-cog/11 was not significant.Trial RegistrationClinicalTrials.gov identifier: NCT00575055 and NCT00574132.

Longitudinal Sensitivity of Alzheimer’s Disease Severity Staging

“…Clinically, understanding the timing of AD progression and prediction of AD stage duration, especially the mild stage, would help tailor treatment plans that parallel the progression, thus improving disease management. [2][3][4] Although methods for predicting progression rates have been studied, 2,5 disease onset and progression is difficult to quantify through studying AD natural history alone, as AD is a complex disease with complex pathogenesis comprised of social, environmental, and genetic factors. One challenge is monitoring change using clinical outcomes.…”

Impact of amyloid-beta changes on cognitive outcomes in Alzheimer’s disease: analysis of clinical trials using a quantitative systems pharmacology model

“…Ranges of parameter values must be able to reproduce clinically observed datasets. Once we set the relation between Aβ units and the standard uptake value ratio (SUVR) of amyloid imaging in the next section, we then simulate the outcome of two clinical experiments: the effect of scopolamine on cognition in MCI subjects with and without Aβ load [ 18 ]; and the effect of APOE genotype on cognitive trajectory in placebo-treated AD patients [ 19 , 32 ].…”

“…Also, the effect of glutamate conductance change on the system outcome is more sensitive to APOE4 load at earlier stages of the pathology, but reverses at more severe AD pathology (i.e., greater effect in APOE4 –/– carriers). There is indeed evidence that APOE4-associated effects play a more important role early in the disease; that is, by determining the age of onset [ 52 ], rather than the progression of the disease [ 19 , 32 , 53 ]. This also suggests that the effect of APOE4 genotype on Aβ clearance and synaptic dysfunction becomes less important as the disease progresses.…”

“…As with any modeling approach, we will constrain the parameters such that the QSP model can reproduce clinical datasets. We will focus on the following clinical datasets: the absolute ADAS-Cog values for minimal cognitive impairment (MCI) subjects with and without Aβ load [ 17 ]; the impact of Aβ on cholinergic dysfunction in MCI patients [ 18 ]; and the effect of differential Aβ clearance associated with the APOE genotype on the cognitive trajectory [ 19 ]. This will also allow us to calibrate the unit of amyloid load in the model using clinical data on the relation between amyloid imaging and cognitive status in MCI patients [ 17 ], and most importantly to derive ranges for the different parameters in the model that are consistent with the observed clinical datasets.…”

Pharmacokinetics and pharmacodynamics of oral mecamylamine – development of a nicotinic acetylcholine receptor antagonist cognitive challenge test using modelling and simulation

“…A learning effect secondary to consecutive testing, measured as a slight improvement in performance after several repetitions during the course of the occasion, was identified for the reaction time of the N-back as has been previously described (Bartels et al, 2010; Collie et al, 2003; Goldberg et al, 2015). This learning effect was successfully incorporated in both models using time-dependent functions (Ito et al, 2010; Samtani et al, 2015). Mecamylamine also induced a reduction of the practice or learning curve resulting of repetition during the reaction time of the 2-back test.…”