Modeling Alzheimer's disease progression utilizing clinical trial and ADNI data to predict longitudinal trajectory of CDR‐SB

Abstract

There is strong interest in developing predictive models to better understand individual heterogeneity and disease progression in Alzheimer's disease (AD). We have built upon previous longitudinal AD progression models, using a nonlinear, mixed-effect modeling approach to predict Clinical Dementia Rating Scale -Sum of Boxes (CDR-SB) progression. Data from the Alzheimer's Disease Neuroimaging Initiative (observational study) and placebo arms from four interventional trials (N = 1093) were used for model building. The placebo arms from two additional interventional trials (N = 805) were used for external model validation. In this modeling framework, CDR-SB progression over the disease trajectory timescale was obtained for each participant by estimating disease onset time (DOT). Disease progression following DOT was described by both global progression rate (RATE) and individual progression rate (α). Baseline Mini-Mental State Examination and CDR-SB scores described the interindividual variabilities in DOT and α well. This model successfully predicted outcomes in the external validation datasets, supporting its suitability for prospective prediction and use in design of future trials. By predicting individual participants' disease progression trajectories using baseline characteristics and comparing these against the observed responses to new agents, the model can help assess treatment effects and support decision making for future trials.

Advancing the utilization of real‐world data and real‐world evidence in clinical pharmacology and translational research—Proceedings from the ASCPT 2023 preconference workshop

“…Using the nonlinear mixed‐effect approach, a longitudinal AD progression model was developed to predict CDR‐SB progression 31 . A separate longitudinal AD progression model was also developed to predict ADAS‐Cog11 progression 32 .…”

Clinical Pharmacology Applications of Real‐World Data and Real‐World Evidence in Drug Development and Approval–An Industry Perspective

“…Unlike clinical trials, which provide a snapshot of a patient's journey, RWD with long follow‐up can better capture the natural history of a disease and/or disease progression as well as potential risk factors which may influence disease progression. For instance, researchers developing models to complement drug development efforts for Alzheimer's disease used RWD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database alone or together with placebo data from interventional clinical trials, to predict disease progression 36,37 . By predicting individual trajectories of disease progression in the absence of treatment, the model can reliably assess drug effects for molecules in development and can be leveraged for study design of future clinical trials.…”

“…Use of an RWD patient cohort allowed for a larger sample size, more diverse patient population, and longer follow‐up compared with a clinical trial. Combining clinical trial data with RWD allowed a richer dataset for model building, and conducting external validation with clinical trial data also helped assess predictive performance of the model 37 . RWD may be particularly valuable to characterizing disease progression in rare diseases, where clinical trial size is limited by the small patient pool.…”

“…For instance, researchers developing models to complement drug development efforts for Alzheimer's disease used RWD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database alone or together with placebo data from interventional clinical trials, to predict disease progression. 36,37 By predicting individual trajectories of disease progression in the absence of treatment, the model can reliably assess drug effects for molecules in development and can be leveraged for study design of future clinical trials. Use of an RWD patient cohort allowed for a larger sample size, more diverse patient population, and longer follow-up compared with a clinical trial.…”

“…Combining clinical trial data with RWD allowed a richer dataset for model building, and conducting external validation with clinical trial data also helped assess predictive performance of the model. 37 RWD may be particularly valuable to characterizing disease progression in rare diseases, where clinical trial size is limited by the small patient pool. Boucher et al 38 used RWD from the Transthyretin Amyloidosis Outcomes Survey (THAOS) to investigate the natural history of disease progression as well as treatment effect in patients diagnosed with hereditary transthyretin amyloid polyneuropathy.…”