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Using AD clinical trials databases for
modeling and simulating clinical trials
Lon S. Schneider, MD, Richard E. Kennedy, MD, PhD,
Guogiao Wang, PhD, Gary R. Cutter, PhD
University of Southern California Keck School of Medicine, Los Angeles
University of Alabama at Birmingham, Birmingham
Computational Modeling—Will it Rescue AD Clinical Trials?
Alzforum Webinar
May 13, 2015
Overview
• Background and context – why do this?
• Simulations vs. modeling
• Databases and methods
• Examples of studies
• Summary and limitations
• Funding: R01 AG03756, Synthesis of Longer-Term
Alzheimer Disease Studies
Background and context
• Obstacles to progressing early development to late development
• Imperative to do more trials; to “get a signal” earlier
• ….many drugs and few (no?) validated targets
• Clinical trials often don’t turn out as planned
• We try to improve the next trial by tweaking the last one, e.g.,
inclusion criteria, outcomes, follow-ups, and biomarkers
– We believe, “what happened in the last trial will happen in the next”
– This ignores substantial trial-to-trial variability that has been observed
• Lack of success in ‘disease-modifying’ trials led to recommendations
– Identify subgroups that are more likely to respond
– Post hoc analyses of clinical trials
– Predictors of progression in observational studies
• Modelling and simulations may help to improve future trials by
assessing effects of design changes
Modeling vs. Simulation
Modeling Simulation
Sensitive to assumptions Sensitive to assumptions
Uses data Builds on models based on data
Useful for summarizing data Summarizes complex inter-relationships
between variables
Takes advantage of random variability and
assesses its long-term effects
Summary statistics Resampling methods
Sets up cause and effect equations Assesses effect of random variability in
model; a “black box” approach
Random variability is a nuisance
variable
Random variability is part of the simulation
Looks back in time Looks forward in time
Our general methods and approaches
Kennedy et al, Alzheimer Dement, 2014
Kennedy et al, Alzheimer Dement, 2013
Post hoc analyses of ApoE
Enrichment using biomarkers: ApoE / Aβ42
Increased Minority Participation
• Low minority enrollment in clinical trials may reflect provider/study bias as well as participant bias
– Exclusion of comorbidities common in minorities
– Concerns over dropout and retention
– Increased variability on outcome measures
• We examined this issue across our meta-database
– (Additional support from P30AG031054, UAB RCMAR)
– Meta-analysis of rates of medical comorbidities
– Simulations of outcomes with African American participation ranging from 20% to 80%
Watson et al, Health Affairs, 2014
Kennedy RE et al CTAD, 2014
Stay tuned for upcoming
publication
Trials outcomes based on age
Schneider, Kennedy, et al, Neurology 2015
Adaptive design: sample size re-estimation
Upcoming publications and presentations
http://www.alzheimersanddementia.com/trci
How do model assumptions of treatment
effects affect Alzheimer’s disease clinical trial
simulations?
Wang G, et al. Tuesday, July 21, 2015: 09:30a,
Exhibit Hall D (Poster #4890)
How Does Differential Effect of Treatment in
ApoE4+ Carriers Change Clinical Trial Design
in Alzheimer’s Disease?
Kennedy RE et al Sunday, July 19, 2015:
04:15p - 05:45p , Convention Center, 207
(Oral #5130)
Wang G, Kennedy RE, Cutter GR, Schneider
LS. Effect of sample size re-estimation in
adaptive clinical trials for Alzheimer’s disease
and mild cognitive impairment. Alzheimer’s &
Dementia: Translational Research & Clinical
Interventions (in press)
Kennedy RE, Cutter GR, Wang G, Schneider
LS. Using baseline cognitive severity for
enriching AD clinical trials: How does MMSE
predict rate of change? Alzheimer’s &
Dementia: Translational Research & Clinical
Interventions (in press)
Summary and comments• Subgrouping, targeting, altering trials designs can have unexpected
effects
– Slowly progressive clinical course can require design alterations
• Simulations and modeling advance protocol development to better assess the effectiveness of the design
– Can guide selection of competing designs to increase probability of success
– Use more available data to make informed decisions
– In a trials context provide a quantitative basis for decision-making
– Provide a reasonable way to manage design considerations in clinical trials
– May lessen risk for inadequate P2 trials and subsequent P3 failures
– Likely better than expert opinion, conventional wisdom
• Caveat! Most drugs fail in clinical development because they lack
efficacy (not because of the trial designs)
END
References
• Kennedy RE, Cutter GR, Schneider LS. Effect of APOE genotype status on
targeted clinical trials outcomes and efficiency in dementia and mild
cognitive impairment resulting from Alzheimer's disease. Alzheimers
Dement 2014;10(3):349–359
• Kennedy RE, Cutter GR, Wang G, Schneider LS. Using baseline cognitive
severity for enriching AD clinical trials: How does MMSE predict rate of
change? Alzheimer’s & Dementia: Translational Research and Clinical
Interventions (in press)
• Wang G, Kennedy RE, Cutter GR, Schneider LS. Effect of Sample Size
Re-estimation in Adaptive Clinical Trials for Alzheimer’s disease and Mild
Cognitive Impairment. Alzheimer’s & Dementia: Translational Research
and Clinical Interventions (in press)
• Schneider LS, Kennedy RE, Wang G, Cutter G. Differences in Alzheimer
disease clinical trial outcomes based on age of the participants. Neurology
2015 Feb 13.
• Kennedy RE, Schneider LS, Cutter GR. Biomarker positive and negative
subjects in the ADNI cohort: clinical characterization and implications for
clinical trials. Current Alzheimer Research, 2013; 9(10)
References
• Schneider LS, Kennedy RE, Cutter GR, Alzheimer's Disease Neuroimaging
Initiative. Requiring an amyloid-beta1-42 biomarker for prodromal Alzheimer's
disease or mild cognitive impairment does not lead to more efficient clinical
trials. Alzheimers Dement 2010;6(5):367–77
• Kennedy RE, Cutter GR, Wang G, Schneider LS. Post hoc analyses of
ApoE genotype-defined subgroups in clinical trials.
• Stone DJ, Molony C, Suver C, Schadt EE, Potter WZ. ApoE genotyping as
a progression-rate biomarker in phase II disease-modification trials for
Alzheimer's disease. Pharmacogenomics J 2010;10(3):161-4
• Watson JL, Ryan L, Silverberg N, Cahan V, Bernard MA. Obstacles and
opportunities in Alzheimer's clinical trial recruitment. Health Aff (Millwood)
2014;33(4):574–579
Alzheimer’s & Dementia: Translational Research &
Clinical Interventions (June 1, 2015)
Open access journals to advance diagnosis, assessment,
translational research, clinical interventions
http://www.alzheimersanddementia.com/trci
http://www.alzheimersanddementia.com/dadm