1. Understanding Parkinson’s Disease: Model Based Approach Venkatesh Atul Bhattaram*, Ohid Siddiqui ¶, Joga Gobburu* *- Pharmacometrics, OCP, CDER/FDA ¶- Biometrics, OB, CDER/FDA

2. 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

3. 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

4. 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.

5. 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).

6. Initial Thoughts Dec 04 Concept Development Jan 05 1 st Internal Meeting Feb 05 Data Collection Sep 05 OCP/OB Group Dec 05 2 nd Internal Meeting Oct 05 Preliminary M/S March 05 3 rd Internal Meeting April 20, 06 4 th Internal Meeting Aug 2 nd, 06 CPSC Oct 06 DIA Jan 07 Clinical/Stat Spring 07 ACCP Symposium Sep 05

7. 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.

8. 2 Extract Clinical Trial Information* BASELINE EFFECT/ MODEL PLACEBO MODEL DROP-OUT MODEL DESIGN PATIENT DEMOGRAPHICS MECHANISM-SYMPTOMS-OUTCOMES 1 IDENTIFY KEY QUESTION(S) Build Disease & Drug Model TIME 4 Plug Sponsor Data, Play & Decide (Go/No Go, trial design) TRIAL DESIGN PATIENT SELECTION SAMPLE SIZE SAMPLING TIMES ENDPOINTS, ANALYSIS 3 Simulate Scenarios UPDATE Modeling Cycle * Variety of model validation approaches were employed

9. Key Scientific Questions 1.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?

10. Parkinson’s Disease Database DataSource#PatientsTrial Duration Trial#1NDA4001yr + 3yr follow-up Trial#2External4001yr + follow-up Trial#3NDA9009mo + follow-up Trial#4NDA2009mo + follow-up Trial#5External3001.5yr

11. Patient Population Model

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

13. Disease Progression Characteristics

14. 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

15. 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

16. Creatine-Minocycline Neurology, 2006, 66: 664-671 Mean (SD) of Total UPDRS scores for patients treated with placebo, creatine, minocycline for 52 weeks.

17. Disease progression model describes typical observed well

18. Disease progression model describes observed distribution well

19. 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

20. Missing Data Mechanism

21. 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?

22. Higher scores lead to early treatment discontinuation Rescue medication Time,

23. Is probability of drop-out related to change in scores from baseline visit? Duration=20 weeks Δ = 8 units Time, Duration adjusted UPDRS change

24. Is probability of drop-out related rate of change in scores from previous visit? Δ = 6 units 2 weeks Time,

25. Is probability of drop-out related to slope? Time, Slope

26. Drop-model: Validation Model systematically deviates from observed

27. Drop-model: Validation Model reproduces observed well

28. 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

29. Statistical Issues in Model Based Analysis and Simulations

30. 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?

31. 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

32. Delayed start design (Alternate Model)

33. 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%

34. Disease Drug Trial Models Baseline UPDRS model Drop-out model Trial design Disease progression model No protective effect – Null model

35. 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.

36. Delayed start design (No protective effect - Null Model)

37. 1 Linear Random-effect regression model 2 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 5.205.005.80 Dropout due to lack of effectiveness (equal drop-outs) 5.1516.3522.60 Dropout due to lack of effectiveness (unequal drop- outs) 4.957.5511.50 Dropout due to lack of effectiveness and/or toxicity 4.7012.2529.15 Dropout due to unobserved outcomes of the trial 6.0530.1540.60 Type-I Error rate Under Null (no protective effect) Model Placebo phase preserves Type I error rate

38. Manage and Leverage Knowledge Knowledge Placebo & Disease Models InformationInformation Demographics Time course Drop-out Drug Effects Translation to recommending primary statistical analysis methodology for disease modifying agents in Parkinson’s disease.

39. 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?