Understanding Parkinson’s Disease: Model Based Approach

Venkatesh Atul Bhattaram*,

Ohid Siddiqui¶, Joga Gobburu**- Pharmacometrics, OCP, CDER/FDA

¶- Biometrics, OB, CDER/FDA

Acknowledgements• External

– Clinical• Stanley Fahn MD, Parkinson’s Study Group• Karl Kieburtz MD, NET-PD Steering Committee

– Statistics• David Oakes PhD, University of Rochester• Jordan Elm MS, Medical University of South Carolina

– Programmer• Arthur Watts BS, University of Rochester

Acknowledgements• Internal

– Robert Temple MD, Associate Director for Medical Policy

– Division of Neuropharmacological Drug Products• Russell Katz MD, John Feeney MD, Leonard Kapcala

MD– Office of Biostatistics

• Jim Hung PhD– OCP/DCP-1

• Mehul Mehta PhD, Ramana Uppoor PhD– Pharmacometrics Group, OCP

• The objective of this part of the presentation is to exemplify the application of disease models. Trial design and endpoints will be discussed at a future meeting.

Impetus• Drugs to slow the progression of diseases such as

Parkinson’s, Alzheimer’s are under development.

• Innovative trial designs/endpoints/analyses with model based statistical methodologies being proposed to discern ‘protective drug effect’.

• FDA is asked to comment on the acceptability of these trial designs and pre-specified analyses.– Critical to understand disease/baseline characteristics,

disease progression, placebo/drug effects, and statistical issues (Missing data, etc).

Initial ThoughtsDec 04

Concept DevelopmentJan 05

1st Internal MeetingFeb 05

Data CollectionSep 05

OCP/OB GroupDec 05

2nd Internal MeetingOct 05

Preliminary M/SMarch 05

3rd Internal MeetingApril 20, 06

4th Internal MeetingAug 2nd, 06

CPSCOct 06

DIAJan 07

Clinical/StatSpring 07

ACCP SymposiumSep 05

Single point analysis will not differentiate between protective and symptomatic effects

Unified Parkinson Disease Rating Scale (UPDRS) The UPDRS is a rating tool to follow the longitudinal course of Parkinson's Disease. It is made up of the 1) Mentation, Behavior, and Mood, 2) ADL and 3) Motor sections. These are evaluated by interview. 199 represents the worst (total) disability), 0--no disability.

2Extract Clinical Trial Information*

• BASELINE EFFECT/ MODEL

• PLACEBO MODEL

• DROP-OUT MODEL

• DESIGN

• PATIENT DEMOGRAPHICS

MECHANISM-SYMPTOMS-OUTCOMES

1IDENTIFY KEY QUESTION(S)

Build Disease & Drug Model

TIME

4Plug Sponsor Data,

Play & Decide (Go/No Go, trial design)

• TRIAL DESIGN

• PATIENT SELECTION

• SAMPLE SIZE

• SAMPLING TIMES

• ENDPOINTS, ANALYSIS

3Simulate Scenarios

UPDATE

Modeling CycleModeling Cycle

* Variety of model validation approaches were employed

Key Scientific Questions1. What are the influential demographic factors

influencing the baseline clinical scores and progression?

2. How do we describe the progression of Parkinson’s disease (Linear/Nonlinear)?

3. Why patients drop-out of these trials?

Parkinson’s Disease Database

Data Source #Patients Trial Duration

Trial#1 NDA 400 1yr + 3yr follow-up

Trial#2 External 400 1yr + follow-up

Trial#3 NDA 900 9mo + follow-up

Trial#4 NDA 200 9mo + follow-up

Trial#5 External 300 1.5yr

Patient Population Model

Demographics

• Influence of various demographics such as age, gender, disease duration, smoking, caffeine intake on baseline UPDRS scores were evaluated using regression techniques.

Disease Progression Characteristics

Mean (SD) of Total UPDRS scores for patients with Parkinson’s disease treated with levodopa alone or in combination with selegiline for 5 years and during the one-month washout period

Selegiline

Eur.J.Neurology, 1999, 6: 539-547

Mean (SD) of Total UPDRS scores for patients with Parkinson’s disease treated with levodopa alone or in combination with pramipexole for 4 years

Levodopa, Pramipexole

Arch.Neurology, 2004, 61: 1044-1053

Time, months

Creatine-Minocycline

Neurology, 2006, 66: 664-671

Mean (SD) of Total UPDRS scores for patients treated with placebo, creatine, minocycline for 52 weeks.

Disease progression model describes typical observed well

Disease progression model describes observed distribution well

Disease Progression Characteristics

• A linear model can reasonably describe UPDRS change post 8 weeks.– The models presented here and data from the

early dose-finding of the new compound need to be used to support the design/analysis choices for the registration trials

Missing Data Mechanism

Understanding why patients drop-out of Parkinson’s trials

• Clearly patients who discontinued early had worse symptoms compared to those who stayed.

• Graphical displays were generated to understand the drop-out pattern.– UPDRS scores in patients who discontinued for

example in 0-16 versus 16-32 weeks were compared

• Specific risk factor for drop-outs (Parametric Hazard Models)– Δ UPDRS at last observed visit?

• Relative to baseline or previous visit?– Rate of Δ between first and last observed visit?

Higher scores lead to early treatment discontinuation

Rescuemedication

Time,

Is probability of drop-out related to change in scores from baseline visit?

Duration=20 weeks

Δ = 8 units

Time,

Duration adjusted UPDRS change

Is probability of drop-out related rate of change in scores from previous visit?

Δ = 6 units

2 weeks

Time,

Is probability of drop-out related to slope?

Time,

Slope

Drop-model: ValidationModel systematically deviates from observed

Drop-model: ValidationModel reproduces observed well

Summary of drop-out modeling

• Predominant reason for drop-out worsening of symptoms – Duration adjusted change and rate of change in

UPDRS scores from previous visit are principal determinants of discontinuation

• Validation to ensure the model predicts discontinuation rates well across varied trial designs (fixed vs. titration dosing) is in progress

Statistical Issues in Model Based Analysis and Simulations

Key Statistical Questions• Does a linear disease progression model

reasonably describe change in UPDRS post 8 weeks randomization?

• What are the reasonable trial design and endpoint choices?– What are the false-positive and false-negative rates

of concluding protective effect?

• How do we integrate the clinical pharmacology findings and statistical findings to address regulatory issues?

Longitudinal Analysis• Across various drugs, the mean maximum

symptomatic effect appears to be achieved within 4-8 weeks. Beyond that point, change in UPDRS scores over time was described well using a linear model.

• Model validation was evaluated using standard diagnostics– Predicted versus Observed– Individual Fits

Delayed start design (Alternate Model)

Explored endpoints to discern protective and symptomatic effects

• Placebo Phase– Compare the slope difference between the

placebo and drug groups at an alpha of 5%

• Active Phase– Compare the least square mean difference of

the placebo (now on drug) and drug groups, using repeated measures at an alpha of 5%

DiseaseDrugTrial

Models

Baseline UPDRS model Drop-out model

Trial design

Disease progression model No protective effect – Null model

• Sample Size : 500• Number of Arms: 2• Allocation : 1:1• Trial Duration : 72 weeks• Placebo Phase : 0-26 weeks• Active Phase : 26-72 weeks• Measurements : 0, 4, 8, 16, 20, 26, 32, 42, 52, 58, 72

weeks• Drop-outs : 30% per arm

Clinical trial simulations of a purely symptomatic drug

We considered three dropout scenarios.

(a) Equal dropouts in both drug and placebo groups

(b) Unequal dropouts (Higher in placebo group vs. drug group)

(c) Dropouts due to need for symptomatic treatment and toxicity leading to treatment discontinuation.

Delayed start design (No protective effect - Null Model)

1 Linear Random-effect regression model2 Repeated measures (MMRM) analyses

Dropout Scenario

Placebo Phase (Slope based Comparison-ITT sample) 1

Active Phase (Endpoint LS Means comparison)2

Available cases

LOCF- ITT sample

Dropout not related to drug or disease

5.20 5.00 5.80

Dropout due to lack of effectiveness (equal drop-outs)

5.15 16.35 22.60

Dropout due to lack of effectiveness (unequal drop-outs)

4.95 7.55 11.50

Dropout due to lack of effectiveness and/or toxicity

4.70 12.25 29.15

Dropout due to unobserved outcomes of the trial

6.05 30.15 40.60

Type-I Error rate Under Null (no protective effect) Model

• Placebo phase preserves Type I error rate

Manage and Leverage Knowledge

Knowledge

Placebo & Disease Models

Information

• Demographics • Time course• Drop-out• Drug Effects

Translation to recommendingprimary statistical analysis methodology for disease modifying agents in Parkinson’sdisease.

Questions to the Subcommittee

• Is the overall approach to quantifying various part of the disease models reasonable?

• Is the approach to qualifying the models reasonable?

• What appropriate forum does the committee suggest for sharing these advances with the public?