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

Autor: Jamalian S; Genentech, Inc., South San Francisco, California, USA., Dolton M; Roche Products Australia Pty Ltd., Sydney, New South Wales, Australia., Chanu P; Genentech/Roche, Lyon, France., Ramakrishnan V; Genentech, Inc., South San Francisco, California, USA., Franco Y; Genentech, Inc., South San Francisco, California, USA., Wildsmith K; Genentech, Inc., South San Francisco, California, USA., Manser P; Genentech, Inc., South San Francisco, California, USA., Teng E; Genentech, Inc., South San Francisco, California, USA., Jin JY; Genentech, Inc., South San Francisco, California, USA., Quartino A; Genentech, Inc., South San Francisco, California, USA., Hsu JC; Genentech, Inc., South San Francisco, California, USA.
Jazyk: angličtina
Zdroj: CPT: pharmacometrics & systems pharmacology [CPT Pharmacometrics Syst Pharmacol] 2023 Jul; Vol. 12 (7), pp. 1029-1042. Date of Electronic Publication: 2023 May 02.
DOI: 10.1002/psp4.12974
Abstrakt: 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.
(© 2023 Genentech, Inc. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)
Databáze: MEDLINE
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