Date Published: March 21, 2011
Publisher: BioMed Central
Author(s): René Spiegel, Manfred Berres, André R Miserez, Andreas U Monsch.
Novel compounds with potential to attenuate or stop the progression of Alzheimer’s disease (AD) from its presymptomatic stage to dementia are being tested in man. The study design commonly used is the long-term randomized, placebo-controlled trial (RPCT), meaning that many patients will receive placebo for 18 months or longer. It is ethically problematic to expose presymptomatic AD patients, who by definition are at risk of developing dementia, to prolonged placebo treatment. As an alternative to long-term RPCTs we propose a novel clinical study design, termed the placebo group simulation approach (PGSA), using mathematical models to forecast outcomes of presymptomatic AD patients from their own baseline data. Forecasted outcomes are compared with outcomes observed on candidate drugs, thus replacing a concomitant placebo group.
First models were constructed using mild cognitive impairment (MCI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. One outcome is the Alzheimer Disease Assessment Scale – cognitive subscale (ADAScog) score after 24 months, predicted in a linear regression model; the other is the trajectory over 36 months of a composite neuropsychological test score (Neuro-Psychological Battery (NP-Batt)), using a mixed model. Demographics and clinical, biological and neuropsychological baseline values were tested as potential predictors in both models.
ADAScog scores after 24 months are predicted from gender, obesity, Functional Assessment Questionnaire (FAQ) and baseline scores of Mini-Mental State Examination, ADAScog and NP-Batt with an R2 of 0.63 and a residual standard deviation of 0.67, allowing reasonably precise estimates of sample means. The model of the NP-Batt trajectory has random intercepts and slopes and fixed effects for body mass index, time, apolipoprotein E4, age, FAQ, baseline scores of ADAScog and NP-Batt, and four interaction terms. Estimates of the residual standard deviation range from 0.3 to 0.5 on a standard normal scale. If novel drug candidates are expected to diminish the negative slope of scores with time, a change of 0.04 per year could be detected in samples of 400 with a power of about 80%.
First PGSA models derived from ADNI MCI data allow prediction of cognitive endpoints and trajectories that correspond well with real observed values. Corroboration of these models with data from other observational studies is ongoing. It is suggested that the PGSA may complement RPCT designs in forthcoming long-term drug studies with presymptomatic AD individuals.
A number of compounds with potential to attenuate the progression of Alzheimer’s disease (AD) from a presymptomatic stage to dementia – that is, drugs intended for secondary prevention of dementia due to AD – are currently undergoing testing in man [1,2]. The study design routinely applied in advanced stages of clinical development (late phase 2, phase 3) of central nervous system active compounds is that of the randomized, placebo-controlled clinical trial (RPCT), a procedure implying that a high proportion of patients, up to 50% of the total sample, will receive inactive drug throughout. Given that meaningful study of experimental treatment intended for secondary prevention of dementia due to AD will take 18 months or more for each individual, it is problematic, from an ethical standpoint, to expose patients with mild cognitive impairment (MCI) and similar conditions, who by definition run a high risk of developing dementia, to prolonged exposure to placebo . In addition, the external validity (representativity) of long-term RPCTs may be questioned, as many potential trial participants will decline inclusion in a study that intentionally exposes them to the risk of prolonged inactive treatment.
Preliminary analysis with the number of apolipoprotein E4 alleles placed into three categories (0, 1, 2) showed that two alleles approximately duplicated the effect of one allele. We therefore put the number of E4 alleles as a numerical predictor in our models.
The main goal of the current study is to develop mathematical models of typical disease trajectories of AD, from its presymptomatic to symptomatic stages – that is, to develop algorithms for use to quantitatively compare patients undergoing experimental treatment for secondary prevention of dementia due to AD with their own anticipated untreated disease course. We used the data for 397 MCI subjects from the ADNI database as available in October 2009. The examples presented here concern a univariate (endpoint-related) approach – that is, an algorithm that predicts the MCI subject group’s performance scores on the ADAScog 24 months after their baseline examination – and a multivariate (trajectory-related) approach – that is, an algorithm that forecasts the decline of performance during 36 months, from baseline to the last examination after 3 years – on the composite score of a neuropsychological battery as described previously (NP-Batt) . Both outcomes, a cognitive performance score after 24 months and the trajectory of cognitive change over 36 months, could be of use in studies with experimental drugs for secondary prevention of dementia due to AD. A total of 11 demographic, neuropsychological and biological measures established at baseline, plus their interactions, were included as potential predictors in the univariate and multivariate analyses.
First predictive univariate (endpoint-related) and multivariate (trajectory related) models based on anamnestic, clinical, biological and neuropsychological data from the ADNI database show high correspondence of predicted and real observed values. Corroboration of these models with data from other studies is ongoing. It is hoped that the PGSA, which comprises comparisons between real, observed data of patients on experimental treatment with their own, model-based forecasted trajectories, will be considered for late phase 2 or phase 3 long-term trials with drugs intended for secondary prevention of dementia due to AD.
RS, MB, ARM and AUM have applied for an international patent covering the PGSA. The authors declare they have no other competing interests.
Aβ42: amyloid β42; AD: Alzheimer’s disease; ADAScog: Alzheimer Disease Assessment Scale – cognitive subscale; ADNI: Alzheimer Disease Neuroimaging Initiative; FAQ: Functional Assessment Questionnaire; MCI: mild cognitive impairment; MMSE: Mini-Mental Status Examination; NP-Batt: Neuro-Psychological Battery; PGSA: placebo group simulation approach; RPCT: randomized placebo-controlled trial; SD: standard deviation; T-tau: total tau protein.
RS is the originator of the principle of the PGSA and wrote major parts of the manuscript. MB developed the mathematical models underlying the PGSA. ARM and AUM made important intellectual contributions to the development of the PGSA and provided relevant input to the manuscript.