Predicting progression of Alzheimer's disease: understanding the variance in progression of AD (Rachelle Doody)

In their pioneering work, Rachelle Doody and colleagues assert that modeling of group disease progression is critical for estimating change in clinical trials of disease-modifying therapies. Despite years of observation in small studies, there is no clear model for how AD progresses or for the sources of variance in progression between patients. According to these authors, multivariate models of disease progression can also identify the factors that contribute to the variance and thereby foster the ability to stratify patients in clinical trials or to predict progression in individual patients. Doody and her group have shown that an estimate of early disease progression, the pre-progression rate, is predictive of the time it will take to develop substantial decline [1].

Doody and her group have also used mixed-effects regression analysis to examine the role of demographic, biological, clinical and psychometric characteristics in predicting progression of AD in a large patient cohort [2]. This work suggests that premorbid IQ (probably a surrogate for cognitive reserve) and the early, intrinsic progression rate are the most predictive variables, yet these are seldom captured as baseline characteristics and are therefore seldom balanced or accounted for in the analysis of clinical trials. The persistence of anti-dementia drug use from the time of symptom onset is also a predictive variable [3], whereas most study protocols simply collect information on concurrent drug use at the time of randomization. Although the pre-progression rate and premorbid IQ could also be used as fixed effects or covariates in analysis of clinical trials data, stratification is preferred in order to ensure that there are equal-sized groups of those above and below median IQ(reflecting cognitive reserve), and equal-sized groups of slow, intermediate and rapidly progressing patients(possibly reflecting biological differences) since treatment response could conceivably differ between these groups.

Although the Doody group has been modeling the progression rate for some time, the question of inter-individual variance in progression rates is a new focus of investigation. The new analysis suggests that the factors contributing most to variance in outcomes - such as the Alzheimer Disease Assessment Scale cognitive (ADAS cog), Clinical Dementia Scale Sum of Boxes (CDR-SB), Instrumental Activities of Daily Life, Physical Self-Maintenance Scale, and Mini Mental State Examination (MMSE) - are the pre-progression rate, the presence or development of parkinsonism, and the presence or development of psychosis. In their multivariable model, which includes time, the American National Adult Reading Test estimate of IQ, the progression rate and the presence or development of parkinsonism or psychosis, one-third to one-half of variance between individuals was explained, depending on which outcome (MMSE, ADAS, CDR-SB, Physical Self-Maintenance Scale, Instrumental Activities of Daily Life) was considered.

Taken together, the studies on progression rates and variance suggest that patients should be stratified based upon IQ and pre-progression rate (as opposed to MMSE), and that studies should capture the development of parkinsonism or psychosis for use as time-dependent covariates in the comparison of treatment groups. Studies should probably also quantify disease-long exposure to anti-dementia drugs, as opposed to simply recording their use during the clinical trial. Although the potential for enriching clinical trials populations through the use of IQ and pre-progression rates also exists, the authors would not recommend this until their effects in treatment trials are better understood.

Creating a new composite score for optimizing responsiveness to decline in early AD and very early AD (Suzanne Hendrix)

Suzanne Hendrix has proposed empirically derived composite outcome scores that optimize the power for measuring clinical disease progression for trials in MCI and pre-MCI populations [4]. The goal of this work is to improve the responsiveness of clinical outcomes to disease progression in early stages of AD, with the expectation that this will give treatments the best chance for showing an effect. The proposed composite outcome scores for MCI utilize items from standard AD instruments, such as the ADAS-cog, but rather than summing all item scores they use an optimization algorithm to select and weigh the items that are declining most in the population of interest, excluding items that are not yet declining in these early AD populations.

Approaches based on principal components factor analysis and different regression techniques have been compared for robustness over multiple study datasets and across enriched (amyloid beta 42-positive, apolipoprotein E4 allele carriers) and non-enriched populations. When ADAS-cog, MMSE and CDR-SB items are considered, similar item combinations are identified as responsive to disease progression despite the diversity of the populations and methodology, indicating that the majority of MCI subjects in clinical studies have a common symptom profile over time. The items that are common across populations and methodologies are delayed word recall and orientation from the ADAS-cog, orientation to time from the MMSE and all six CDR box scores. The partial least-squares model - which is a compromise between an item response theory or principal components approach and an approach that directly optimizes the sensitivity to changes over time - appears to result in the most robust combinations, and excludes redundant items with lower responsiveness from the composite score. This method also estimates the optimal weighting of the items to reflect the relative importance of each item in the population under consideration.

Composite scores can be constructed utilizing cognitive items only, global or functional items only or a combination for optimal responsiveness. This methodology could also be applied to biomarker data in order to identify which biomarkers measure decline that is not redundant with clinical outcomes. These responsive composite clinical outcomes allow smaller and shorter clinical trials in early disease, and will help with validation of biomarkers in these early stages. Population enrichment provides additional improvement over item optimization alone [5], but optimizing within an enriched population does not result in substantially different item combinations, indicating that the methodology successfully identifies the disease-specific decline across homogeneous or heterogeneous MCI patient populations.

The placebo group simulation approach: an alternative to long-term placebo-controlled trials (René Spiegel)

René Spiegel presented his teams' development work on the placebo group simulation approach (PGSA), a novel clinical study design for use in long-term trials with putative disease-course altering drugs for use in AD. The PGSA is intended to circumvent a major limitation of randomized placebo-controlled double-blind clinical trial designs; that is, the need to expose prodromal AD patients at high risk for dementia to extended placebo treatment, which may result in problems with patient recruitment [6], questions about the representativeness of study samples and ethical issues.

The PGSA uses stochastic modeling to forecast predefined endpoints and trajectories of neuropsychological outcomes from the data that are routinely available at the outset of clinical trials: basic demographic, biological and neuropsychological data for all study participants. These model-based, forecasted endpoints and trajectories of the study sample constitute the background - the simulated placebo group - against which potential drug effects can be contrasted.

Development and initial testing of PGSA algorithms for the ADAS-cog and the composite score of a neuropsychological test battery (NP-Batt9) using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) MCI patient cohort [7] were described in Spiegel and colleagues [8]. A new analysis of data from the ADNI 1 AD patient cohort, using the published PGSA algorithm for the NP-Batt9, confirms the high concordance between the model-based forecasted data and the actually observed neuropsychological data (Figure 1) and supports the application of the PGSA algorithm for the neuropsychological test battery used in ADNI 1 over a wide spectrum of preclinical and clinical AD patients [9].

The PGSA algorithm developed from the ADNI 1 MCI database was then adapted to a modified neuropsychological battery and applied to data from amnestic MCI patients available in the National Alzheimer's Coordination Center database (grant number U01 AG016976). The results obtained from this larger and less narrowly defined patient sample confirm that mathematically modeled trajectories of neuropsychological measures show high concordance with the empirically observed outcomes in amnestic MCI patients. Based on these retrospective analyses of publicly available data from large MCI and AD patient samples, Spiegel and colleagues thus conclude that the PGSA algorithms for the ADAS-cog and a NP-Batt allow valid forecasts of the distribution of the trajectories and endpoints of relevant neurocognitive outcomes recorded from amnestic MCI and symptomatic AD patients. Pending further confirmatory studies in international sets of data, prospective studies should be performed, preferably in the context of real-life anti-AD drug development. Given their inherent limitations (focus on quantified indices of efficacy, lack of concomitant controls for potential drug side effects), PGSA-based trials are expected to complement randomized placebo-controlled trials in the later phases of anti-AD drug development; that is, at stages when there is adequate evidence of a drug's efficacy and safety available from preceding placebo-controlled clinical studies.