An integrated natural disease progression model of nine cognitive and biomarker endpoints in patients with Alzheimer s Disease

Transcription

1 An integrated natural disease progression model of nine cognitive and biomarker endpoints in patients with Alzheimer s Disease Angelica Quartino 1* Dan Polhamus 2*, James Rogers 2, Jin Jin 1 212, Genentech *authors contributed equally to the work presented 1. Genentech Inc. South San Francisco, CA 2. Metrum Research Group, Tariffville, CT

2 Background Alzheimer s Disease a progressive neuro-degenerative disease 2 Mild to moderate AD Progression of biomarker abnormality Max Min Time Jack et al, Lancet Neurology 12:27 (213) CSF biomarkers of disease (amyloid): Aβ 42, Brain amyloid load: amyloid PET imaging CSF biomarkers of disease (neuro-degeneration): t-tau and p-tau Brain atrophy: volumetric MRI ( whole brain volume, hippocampal volume, ventricle size) Cortical activity: FDG-PET Cognitive and functional impairment scales: ADAS-Cog 12-item, CDR-SOB, MMSE Trouble remembering recent events to inability to preform basic tasks and full time care Death within ~ 9 years from diagnosis

3 Objectives 3 To establish a natural disease progression model integrating multiple biomarkers and endpoints in patients with mild Alzheimer s Disease 1. è An enhanced ability to identify and understand disease progression and impact of covariates and drug treatment effect, rather than within endpoints è Ability to simulate realistic multivariate longitudinal data, to allow assessment of studies with co-primary endpoints 1) Polhamus D et al. AAIC 213 CDR-SOB, vmri in MCI

4 Data and Methods Alzheimer s Disease Neuroimaging Initiative Database 4 Natural history non-treatment study in USA/Canada 298 mild Alzheimer s Disease subjects* Baseline MMSE 2-26 Baseline covariates e.g. age, gender Up to 3 years longitudinal changes in: ADAS-Cog 12-item score CDR each of 6 items score volumetric MRI (hippocampal, ventricles) Software: OpenBUGS v MMSE: Mini-Mental State Examination * Data in the analysis was from ADNI database extracted on 22 January 214 :

5 Results Integrated Longitudinal Alzheimer s Disease Model Prognostic Factors Endpoints Age and gender ApoE4 genotype Baseline MMSE CSF Aβ 42, ptau, ttau FDG-PET Amyloid PET Treatment Effect Underlying AD disease progression Cognition ADAScog12 Cognition/Function CDR 6-items scores Biomarkers Hippocampal volume Ventricular volume Underlying Disease Status 6 Underlying disease status Time Time Endpoint Score 8 6 Endpoint raw score Underlying disease status Underlying Disease Status Endpoint Score 8 6 Endpoint raw score Time Time

6 Results Correlation between predicted disease status and observed endpoints ADAS-Cog12 score 1 1 CDR SOB score Observed endpoint score 6 ADAS12 absolute score Hippocampus absolute score Hippocampus Underlying disease volume status Ventricles absolute score Ventricular volume Underlying disease status Underlying disease status Predicted underlying disease status Worse

7 Results The model accurately captures the central trend of observed data 7 Change score from baseline Change from baseline ADAS-Cog12 ADAS12score Hippocampus volume CDR CDRSUM SOB score Ventricular Ventricles volume Year Time (Years) observed data (red dots) and simulated data (black lines) with 9% credible interval (shaded area)

8 Results The model maintains observed correlation between endpoints 8 Hippocampus volume Ventricular volume ADAS-Cog12 score Spearman's rho Spearman s rho CDR SOB score CDRSum ADAS12 Ventricles ADAS-Cog12 score Ventricular volume Observed correlation (red line) Simulated correlation (black distribution)

9 Results Impact of prognostic factors on underlying disease progression rate 9 Slower disease progression Faster disease progression Reference subject ApoE4 negative Baseline MMSE 23 Age 7 Interaction high baseline MMSE- ApoE4 positive High age High baseline MMSE 1 ApoE4 positive Posterior estimated effect of normalized prognostic factors relative to reference subject (with uncertainty) MMSE: Mini-Mental State Examination

10 Results Change in disease progression and endpoints for subpopulations 1 Score Value Underlying disease progression 8 6 Raw score ADAS-Cog12 score ADAS cog progression of the average individual Year Hippocampus progression of the average individu 6 Hippocampus volume CDR SOB score CDR sum progression of the average individual Ventricular volume Year Ventricular progression of the average individu Year Time (Years) Raw score worse Baseline MMSE 2 26 ApoE4 genotype Negative Positive Year Time (Years) Year

11 Application Projected impact of a hypothetical treatment effect across endpoints 11 DM factor Change score Change from baseline ADAS-Cog12 ADAS12 score -1 Change from baseline CDR CDRsum SOB score % reduction ADNI AD progression % Change score -1 Percent change from baseline Years Hippocampus volume Hippocampus - Percent change from baseline Ventricles Years Ventricular volume Years Time (Years) Years Observed unadjusted mean of ADNI data (dots) Model predictions of a hypothetical treatment effect using baseline covariates (lines and shaded area)

12 Summary 12 Longitudinal PKPD model for Alzheimer s Disease Alzheimer s Disease Model Placebo Model Drug Effect Model Model properties Application Greater insights of disease progression, impact of patient covariates and drug treatment effect Comparison of novel-treatment outcomes and placebo response to historical data across endpoints Allows translation of information across endpoints and biomarkers Joint analysis of multiple endpoints (e.g. co-primary endpoints) Ability to assess the sensitivity of the different endpoints in subpopulations Trial design optimization for multiple endpoints Ability to simulate realistic multivariate longitudinal data

13 Acknowledgements Co-authors: Dan, Jim and Jin Colleagues and team members at Genentech Alzheimer s Disease Neuroimaging Initiative