# Presentation: 423189

## 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 Thoughts

**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**

## 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.

## Modeling Cycle

**Extract Clinical Trial Information***

** ****BASELINE EFFECT/ MODEL**
** ****PLACEBO MODEL**
** ****DROP-OUT MODEL**
** ****DESIGN**
** ****PATIENT DEMOGRAPHICS**

**4**

**Plug Sponsor Data,                     Play & Decide****                         (Go/No Go, trial design)**

**UPDATE**

***Modeling Cycle***

*** *****Variety of model validation approaches were employed***

## Key Scientific Questions

- What are the influential demographic factors influencing the baseline clinical scores and progression?
- How do we describe the progression of Parkinson’s disease (Linear/Nonlinear)?
- 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

## Selegiline

**Selegiline**

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

## Levodopa, Pramipexole

**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

**Rescue**
**medication**

- 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?

- Time,

## Is probability of drop-out related to slope?

- Time,

- Slope

## Drop-model: ValidationModel systematically deviates from observed

## Drop-model: ValidationModel 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%

## Disease

**Drug**
**Trial**
**Models**

**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.**** **
**  ****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)

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

**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 | 5.20 | 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**

**I****n****f****o****r****m****a****t****i****o****n**

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

**Translation to recommending**
**primary statistical analysis **
**methodology for disease **
**modifying agents in Parkinson’s**
**disease.**

## 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?**