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