BACKGROUND: The placebo response and the underlying disease progression is difficult to differentiate in longitudinal Alzheimer's disease (AD) studies, yet it is crucial to understand for designing clinical trials and interpreting results. OBJECTIVES: The placebo response in ADAS-cog11 from various studies was evaluated against model predictions derived from historical placebo data to demonstrate potential interpretation of study results using a prior understanding of expected disease progression. METHODS: The placebo response component from a previously published disease progression model was used to estimate the longitudinal placebo response, and the disease progression in the placebo group in various case studies were evaluated. In addition, placebo data from the Coalition Against Major Diseases (CAMD) database in mild to moderate AD patients is described. RESULTS: The case studies demonstrated potential different results in disease progression in a placebo group, and the impact on understanding the magnitude of drug effect. Baseline cognitive function is an important covariate of disease progression, therefore, it is important to evaluate the baseline severity and predict disease progression accordingly when comparing trial results. Furthermore, study duration, sample size, and study design may affect the placebo response, all of which have the potential to confound understanding of study results. CONCLUSION: The recent failures in Phase III AD studies are not likely due to insufficient cognitive decline in the control groups. A meta-analytic approach using all available data provides a robust understanding of placebo effect, disease progression, and potential interpretation of treatment effects, and offers a useful tool to aid in both trial design and interpretation.
BACKGROUND: The placebo response and the underlying disease progression is difficult to differentiate in longitudinal Alzheimer's disease (AD) studies, yet it is crucial to understand for designing clinical trials and interpreting results. OBJECTIVES: The placebo response in ADAS-cog11 from various studies was evaluated against model predictions derived from historical placebo data to demonstrate potential interpretation of study results using a prior understanding of expected disease progression. METHODS: The placebo response component from a previously published disease progression model was used to estimate the longitudinal placebo response, and the disease progression in the placebo group in various case studies were evaluated. In addition, placebo data from the Coalition Against Major Diseases (CAMD) database in mild to moderate ADpatients is described. RESULTS: The case studies demonstrated potential different results in disease progression in a placebo group, and the impact on understanding the magnitude of drug effect. Baseline cognitive function is an important covariate of disease progression, therefore, it is important to evaluate the baseline severity and predict disease progression accordingly when comparing trial results. Furthermore, study duration, sample size, and study design may affect the placebo response, all of which have the potential to confound understanding of study results. CONCLUSION: The recent failures in Phase III AD studies are not likely due to insufficient cognitive decline in the control groups. A meta-analytic approach using all available data provides a robust understanding of placebo effect, disease progression, and potential interpretation of treatment effects, and offers a useful tool to aid in both trial design and interpretation.
Authors: Daniela J Conrado; Jane Larkindale; Alexander Berg; Micki Hill; Jackson Burton; Keith R Abrams; Richard T Abresch; Abby Bronson; Douglass Chapman; Michael Crowther; Tina Duong; Heather Gordish-Dressman; Lutz Harnisch; Erik Henricson; Sarah Kim; Craig M McDonald; Stephan Schmidt; Camille Vong; Xiaoxing Wang; Brenda L Wong; Florence Yong; Klaus Romero Journal: J Pharmacokinet Pharmacodyn Date: 2019-05-24 Impact factor: 2.745
Authors: Neta Zach; David L Ennist; Albert A Taylor; Hagit Alon; Alexander Sherman; Robert Kueffner; Jason Walker; Ervin Sinani; Igor Katsovskiy; Merit Cudkowicz; Melanie L Leitner Journal: Neurotherapeutics Date: 2015-04 Impact factor: 7.620
Authors: Mitzi M Gonzales; Sudarshan Krishnamurthy; Valentina Garbarino; Ali S Daeihagh; Gregory J Gillispie; Gagan Deep; Suzanne Craft; Miranda E Orr Journal: Mech Ageing Dev Date: 2021-10-21 Impact factor: 5.498