© 2015 Evidera. All Rights Reserved.

An ACE for Alzheimer’s Disease: state of the art modeling

J Jaime Caro MDCM FRCPC FACPChief Scientist

IPECAD 2015Boston

Why a new model?

Need a framework that can simulate disease modification and early intervention

– Include interactions across multiple components of the physiology– Incorporate all relevant biomarkers and their connections to disease progression

Additional benefits:– Available now, hence no wait while model is built– Easily customizable to accommodate new information, different assumptions– Transparent and easy to use– Actively being validated and published in peer-reviewed journals.

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DICE

Aspects that persist over time

Can have levels, which can change over time

Many conditions can be present at once

Interested in time spent at a given level (value)

Aspects that happen at a point in time

Characterized by a risk of happening (or sequence)

Many can happen, at any time

Interested in number that happen (and when)

Conditions Events

Discrete Integrator 3

The AD ACE

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Death Institutional care

Profiles

Treatment Adverse events

Screening(early diagnosis)

Cognition

Behavior

Function

DependenceBiomarkers

Disease progression

Context

NPIFAQGDS

DS

Event Condition Both

Baseline ageSex

RaceEducationEthnicityApoE4

Baseline DxMarital statusFamily history

Medical history/drugs

Hippocampus MRIFDG-PETPIB-PET

CSF Aβ42Whole brain MRIVentricles MRI

18F-AV-45 amyloid imagingCSF p-tau

DADECogFAQNTB

MMSEADAS-cogCDR-SBMoCARALVTECogFAQNTB

Data Sources

Alzheimer's disease Neuroimaging Initiative (ADNI)– Longitudinal, multicenter, non-randomized, non-treatment, natural history study – Tracks progression of AD over the course of different disease states using

biomarkers and cognition scales

Assessment of Health Economics in Alzheimer's Disease (AHEAD) model equations

– Developed from individual patient data (published)

Literature– Costs (default US) Gustavsson et al

o Keyed to disease stage (MMSE)

– Utility includes patient (DADE) and caregiver utility (AHEAD)– Mortality (AHEAD based CERAD data)

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Equations (Linear mixed models)

Expected value of some appropriate function of a measure of interest

Intercept (patient-specific)

Appropriate function of time that reflects observed

pattern

Baseline predictors

Linking Variables: Time-dependent values of other

related measures at time s < t

Measure History: Past values of response measure, possibly transformed, at time s < t.

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Formulation of Response and Predictors

0 tk-2 tktk-1Time

tk−2 tk−1

tk−2

tk

tk−1

Predict change in measure between successive visits

Expected value analysed as change from previous visit:

Prior values and other related measures

considered as predictors

Rates of change also considered:

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Inter-Dependencies

T-Tau

Hip. Vol.

FDG

A-Beta

MMSE

ADAS-Cog

CDRSB

NPI

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Patient Profiles

Population defined by a set of profiles

For the AD ACE, each profile is a record drawn directly from ADNI baseline patient data

– Imputation of missing data using population average values– More sophisticated imputation under consideration– External/other data can be incorporated

Patient filtering tool incorporated– Select characteristic(s) to filter on and specify criteria

Disease stages not a predictor of progression -- included to allow – screening– treatment decisions– categorization of trial endpoints.

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Clinical Trial Simulation

Add-on capability to the AD ACE– Cornerstone of this is simulated patient traces– Patients may be simulated beginning with normal cognition, prodromal AD, or AD

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Conclusion

Challenges Addressed with AD ACE?

Modeling of MCI/Prodromal ADInclude disease‐modifying therapiesConsider full heterogeneityComprehensive domains (not just cognition)Full interactions between domainsFlexible to adopt new information and definitions

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Back-ups

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Final MMSE Equation

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Predictor Variable Estimate 95% LB 95% UBIntercept 4.5808 2.4247 6.7369Time since prev. visit (months) - TSPV 0.8814 0.5016 1.2612Age (years) 0.007586 0.001033 0.01414At least 1 copy of the ApoE4 allele (yes vs. no) 0.03918 -0.05704 0.1354Prior value of FDG -0.09916 -0.2537 0.05542TSPV*Prior FDG 0.08252 0.5016 1.2612Prior rate of change from prev. visit in FDG -1.4685 2.4247 6.7369Prior value of CDRSB -0.01131 -0.09183 0.06921TSPV*Prior CDRSB -0.03722 -0.05138 -0.02306Prior rate of change from prev. visit in CDRSB 1.9046 0.8565 2.9528TSPV*Prior rate of change from previous visit in CDRSB -0.3075 -0.4870 -0.1281

Prior value of ADAS13 -0.02411 -0.04215 -0.00608TSPV*Prior value of ADAS13 -0.01289 -0.01622 -0.00955Prior rate of change from prev. visit in ADAS13 -0.2814 -0.4922 -0.07069TSPV*Prior rate of change from prev. visit in ADAS13 0.08004 0.04389 0.1162Prior value of MMSE -0.1462 -0.2075 -0.08491TSPV*Prior value of MMSE -0.04457 -0.05558 -0.03356Prior rate of change from prev. visit in MMSE 3.4055 2.9291 3.8820TSPV*Prior rate of change from prev. visit in MMSE -0.4942 -0.5753 -0.4131

Fit of MMSE Equation – Observed vs. Predicted by Disease Stage