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. 2022 Mar 14;8(1):e12223.
doi: 10.1002/trc2.12223. eCollection 2022.

Application of predictive models in boosting power of Alzheimer's disease clinical trials: A post hoc analysis of phase 3 solanezumab trials

Ali Ezzati  1 , Christos Davatzikos  2 , David A Wolk  3   4 , Charles B Hall  5 , Christian Habeck  6 , Richard B Lipton  1   5
Affiliations

Application of predictive models in boosting power of Alzheimer's disease clinical trials: A post hoc analysis of phase 3 solanezumab trials

Ali Ezzati et al. Alzheimers Dement (N Y). .

Abstract

Background: The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and would also respond to the therapeutic intervention.

Objective: To investigate if predictive models can be an effective tool for identifying and excluding people unlikely to show cognitive decline as an enrichment strategy in AD trials.

Method: We used data from the placebo arms of two phase 3, double-blind trials, EXPEDITION and EXPEDITION2. Patients had 18 months of follow-up. Based on the longitudinal data from the placebo arm, we classified participants into two groups: one showed cognitive decline (any negative slope) and the other showed no cognitive decline (slope is zero or positive) on the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-cog). We used baseline data for EXPEDITION to train regression-based classifiers and machine learning classifiers to estimate probability of cognitive decline. Models were applied to EXPEDITION2 data to assess predicted performance in an independent sample. Features used in predictive models included baseline demographics, apolipoprotein E ε4 genotype, neuropsychological scores, functional scores, and volumetric magnetic resonance imaging.

Result: In EXPEDITION, 46.3% of placebo-treated patients showed no cognitive decline and the proportion was similar in EXPEDITION2 (45.6%). Models had high sensitivity and modest specificity in both the training (EXPEDITION) and replication samples (EXPEDITION2) for detecting the stable group. Positive predictive value of models was higher than the base prevalence of cognitive decline, and negative predictive value of models were higher than the base rate of participants who had stable cognition.

Conclusion: Excluding persons with AD unlikely to decline from the active and placebo arms of clinical trials using predictive models may boost the power of AD trials through selective inclusion of participants expected to decline.

Keywords: Alzheimer's disease; anti‐amyloid monoclonal antibody; clinical trials; cognitive decline; machine learning; predictive analytics.

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Conflict of interest statement

Ali Ezzati has served on the advisory board of Eisai. Christos Davatzikos receives research support from the following sources unrelated to this manuscript: NIH: R01NS042645, R01MH112070, R01NS042645, U24CA189523; and medical legal consulting work unrelated to this paper. David Wolk received grants from NIH, Merck, Biogen, and Eli Lilly. All payments have been to the University of Pennsylvania. He has received consulting fees from GE Healthcare and Neuronix and honoraria from MH Life Sciences. He also received Alzheimer's Association support to attend to AAIC in 2018 and 2019. He has served on advisory board of Functional Neuromodulation. He has received PET tracer from Eli Lilly for research (no payments were made). Charlie Hal receives research support from NIH and National Institute of Occupational Safety and Health for work unrelated to this manuscript. He receives payments from Washington University, St. Louis, for serving on External Advisory Committee for National Institute of Aging‐sponsored research, University of Iowa, for serving on Data Safety Monitoring Committee for National Institute of Aging‐sponsored research, from National Institutes of Health for serving on Study Section and Special Emphasis Panels for reviewing grant applications. He is also the unpaid director and secretary, Torah and Nature Foundation. His institution received loan of Thorasys tremoFlo C‐100 airwave oscillometer, Thorasys Thoracic Medical Systems Inc., www.thorasys.com, for a pilot study. The device was returned. Chris Habeck receives research support from NIA/NIH unrelated to this publication. He is an advisor for the Clinical trial “Motor Imagery Intervention” run by Helena Blumen at Albert Einstein College of Medicine, no payments are made. He received honorarium for Alzheimer's Research & Prevention Foundation Invited Presentation. Richard B. Lipton receives research support from the following sources unrelated to this manuscript: NIH: 2PO1 AG003949 (mPI), 5U10 NS077308 (PI), R21 AG056920 (Investigator), 1RF1 AG057531 (Site PI), RF1 AG054548 (Investigator), 1RO1 AG048642 (Investigator), R56 AG057548 (Investigator), U01062370 (Investigator), RO1 AG060933 (Investigator), RO1 AG062622 (Investigator), 1UG3FD006795 (mPI), 1U24NS113847 (Investigator), K23 NS09610 (Mentor), K23AG049466 (Mentor), K23 NS107643 (Mentor). He also receives support from the Migraine Research Foundation and the National Headache Foundation. He serves on the editorial board of Neurology, is a senior advisor to Headache, and associate editor for Cephalalgia. He has reviewed for the NIA and NINDS; holds stock options in eNeura Therapeutics and Biohaven Holdings; serves as consultant, advisory board member, or has received honoraria from: Abbvie (Allergan), American Academy of Neurology, American Headache Society, Amgen, Avanir, Biohaven, Biovision, Boston Scientific, Reddy's (Promius), Electrocore, Eli Lilly, eNeura Therapeutics, Equinox, GlaxoSmithKline, Grifols, Lundbeck (Alder), Merck, Pernix, Pfizer, Supernus, Teva, Trigemina, Vector, Vedanta. He receives royalties from Wolff's Headache seventh and eighth edition, Oxford Press University, 2009, Wiley and Informa. He receives consulting fees from Impel NeuroPharma and Novartis and has stock or options in Control M.

Figures

FIGURE 1
FIGURE 1
Change in Alzheimer's Disease Assessment Scale–Cognitive subscale score at different follow‐up timeframes
FIGURE 2
FIGURE 2
Study design. Data from EXPEDITION trial was used for training and data from EXPEDITION2 trial was used for validation. Participants were classified to two groups based on the longitudinal change in ADAS‐Cog score at 15 months and confirmed at 18 months of follow‐up (see text for details). Models were trained to classify participants of training dataset (left block). Subsequently, the newly developed model was applied to the validation dataset to predict if they will have decline in cognition or will remain cognitively stable in longitudinal follow‐up (right block). ADAS‐Cog, Alzheimer's Disease Assessment Scale–Cognitive subscale; SC, stable cognition; DC, declining cognition
FIGURE 3
FIGURE 3
Percentage of patients with Declining or Stable Cognition based on any change on ADAS‐Cog score or minimal clinically relevant change (MCRC) on Alzheimer's Disease Assessment Scale–Cognitive subscale score
See this image and copyright information in PMC

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