# Presentation: 830131

## Topic 3: Using Disease, Placebo, and Drug Prior Knowledge to Improve Decisions 

**Objectives:**
** ****Context for this work- Bob Powell**
** ****Industry perspective- Jaap Mandema**
** ****FDA perspective- Joga Gobburu**
** ****Parkinson’s disease example-                         ****	****Atul Bhattaram & Ohid Siddiqui**

## Advisory Committee Questions

** ****Parkinson’s disease model**
** ****Is the overall approach reasonable to quantifying various parts of the disease model?**
** ****Is the approach reasonable for selecting the data to model?**
** ****Is the approach reasonable for quantifying the model?**
** ****How should this information be communicated publicly?**

## Decisions in Drug Development and at FDA: How combining prior knowledge with quantitative-based decisions can improve productivity & quality

** ****Bob Powell, PharmD**
**Pharmacometrics                       Offices of Clinical Pharmacology & Translational Sciences**
**robert.powell@fda.gov**

## Outline

**Modeling & simulation impact**
**FDA pharmacometrics work**
**Case for extracting & sharing disease, treatment, placebo, baseline, and dropout information**
**FDA**
**Industry**
**Future options**
**Extracting information**
**Sharing information**

## Modeling & Simulation Influences All Lives Today

**Weather forecasting**
**Global warming scenarios**
**Engineering**
**Plant design**
**Product design**
**Airplanes**
**Cars-crash testing**
**Bridges**
**Microprocessors**
**Widgets**
**Traffic flow-roads**
**Homeland Security**
**Disaster preparedness scenarios**
**Plague**

**Military**
**Space**
**Energy**
**Medical**
**Rx patients**
**Surgery**
**Diagnostics (MRI,...)**
**Education**
**Devices (hip, knee,..)**
**Drugs**
**Molecular design/receptor**
**Formulation**
**Manufacturing**
**Marketing**
**Forensic reconstruction**

## The Ultimate ‘Learn-Confirm’ Paradigm

***The Ultimate ‘Learn-Confirm’ Paradigm***

## Slide 7

## Slide 8

## Slide 9

## Modeling & Simulation Why?

**Decrease bias & risk in decisions**
**Overcome complexity** **(simultaneously thinking about many factors influencing outcome)**
**Increase quality **
**Decrease cost**
**Decrease time**

## Modeling & SimulationProcess

**Act**

**Collect Relevant Information**

**Results**

**Organize into Model(s)**

**Simulate Outcomes or Scenarios**

** ****Decision**
** ****Prediction**
** ****Teach **
** ****Design **
** ****Entertain**

**Predictive check**

** ****Complex**
** ****Multiple dimensions**
** ****Raw data best**

**Learning**

** ****↑**** ****Risk**
** ****Expensive**
** ****Important**

## OCP Pharmacometrics Objectives

**Facilitate quantitatively based regulatory decisions focused on efficacy/safety through a dose (exposure)-response lens**
**High quality partnerships**
**FDA (physicians, clinical pharmacology, biostatistics)**
**Externally**
**Companies (pre-competitive)**
**Knowledge generation (disease, placebo, drug, dropouts)**
**Tools/software**
**Academics**
**Knowledge generation**
**Training**
**Balance**
**Opportunistic (NDA reviews, EOP2a meetings)**
**Planned (solving regulatory problems)**

## FDA Pharmacometrics work

**NDAs**
**42 NDAs + case studies (00-04)                           **_**AAPS Journal**_** 7(3): E503-12, 2005**
**31 NDAs + case studies (05-06) submitted **
**Impact survey (clin pharm, physician, ‘metrics)**
**NDA approval decision: ****≥ ****85% Pivotal or Supportive**
**Labeling: 89% Pivotal or Supportive**
**EOP2a meetings. ****Publication in preparation**
**Planned                                                      ****(regulatory question ****→ prior knowledge + modeling & simulation    ****	****→ recommendation)**
**Parkinson’s disease: ∆ disease progression**
**Non-small cell lung cancer: imaging prediction**
**Osteoarthritis: imaging prediction**

## Driver for Clinical Trial M&S                  Declining Success Across Clinical Phases

**Science 309:726, 2005**

## 50% Phase 3 Clinical Trial Failure Rate:Root cause? What to do?

_** **__**Root Cause**_
** ****Ø Efficacy**
** ↑ ****Toxicity**
**  ****Placebo**
**  ****Baseline**
**  ****Dropouts**
**  ****Patient Selection**

## MODEL BASED DRUG DEVELOPMENT

**SIMULATE DOSING REGIMEN**
** ****DOSE**
** ****FREQUENCY**
** ****DISEASE SEVERITY**
** ****DRUG INTERACTIONS**
** ****PEDIATRICS**

**IMPACT OPPORTUNITIES- MODEL & SIMULATE KEY DECISIONS**

**COMPANY**** ****→ TRIAL DESIGN (2, 3), GO/NO GO, LABELING, FORMULATION, COMBO’S, PEDS**

**FDA**** ****→ TRIAL DESIGN (2, 3, 4), NDA APPROVAL (BENEFIT/RISK, DOSING REGIMEN), LABELING, APPROVAL CRITERIA (GUIDANCE REVISION), FORMULATION, COMBOS, QT STUDIES, PEDIATRIC WRITTEN REQUESTS**

**CLINICAL TRIAL INFO**

-  **BASELINE**
**  ****PLACEBO EFFECT**
**  ****DROP-OUT RATE**
**  ****ADHERENCE**

***MODEL BASED DRUG DEVELOPMENT***

## 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**

**1, 2, 3**: **PUBLIC LIBRARY**

***Modeling Cycle***

## Duodenal Ulcer Healing Rate in Active (Cimetidine or Ranitidine) vs Placebo  (n=83 studies)

**Moerman, D. E. et. al. Ann Intern Med 2002;136:471-476**

***Duodenal Ulcer Healing Rate in Active (Cimetidine or Ranitidine) vs Placebo*****  (n=83 studies)**

**Good luck**

**Bad luck**

## Placebo Response in Depression

**JAMA 287: 1840-7, 2002**

**↑**** ****trial failure risk**

**↑**** ****false positive risk**

## Parkinson’s disease patients Rx with Levodopa + Selegiline or Placebo for 5 years 

**Eur J Neurol 6: 539, 1999**

## Parkinson’s disease patients Rx with Levodopa + Selegiline or Placebo for 5 years

***Parkinson’s disease patients Rx with Levodopa + Selegiline or Placebo for 5 years***

***Key Questions***
** ****Entry criteria & baseline effect**
** ****Detect disease progression change**
** ****Dropouts**

**Eur J Neurol 6: 539, 1999**

## Alzheimer’s Disease Natural History & Drug Response (Holford)

- Idebenone 90 mg/day
- Idebenone 270 mg/day
- Donepezil
- Eptastigmine
- Tacrine
- Tacrine + estrogen
- Predicted natural history ‘92
- Predicted tacrine response

- ♦
- ◊
- ■
- ∆
- ●
- ○

**Ann Rev P’col Tox’col 41:625, 2001**

## AZT Response Relationships in Early HIV

**Clin Pharmacol Ther 54:556, 1993**

***AZT Response Relationships in Early HIV***
***(Blaschke & Sheiner)***

##  Software Plan: Now & Near Future (acquire, assure, analyze, save)                         

- Warehouse
- PKS

- FDA
- Database
- (eg, EDR,
- CDISC)
- or analysis
- datasets

- SAS, S+

- NONMEM

- WinNonlin

- FDA Data
- Viewing
- Software
- (e.g.,
- i-review,
- WebSDM)

**CDISC**
**Validation**

**CDISC **
**Connector**;
**DMerge**

**QBR**
**Report**

-  Data sets
-  NDA models
-  Disease models

- FDA does not endorse any product

## Future Options

**Extracting information & problem solving**
**FDA & NIH are clinical trial data ****gold mines**
**Disease, placebo, drug, dropout, baseline information **
**Benefits**
**Development strategy & clinical trial design**
**Endpoint & biomarker evaluation**
**Unanticipated benefits**
**Beneficiaries: Industry, FDA, Academics, Public (waste less patient risk & money & time on failed trials**
**Dedicate teams to targeted questions**
**MD’s, biostatistics, epidemiologists, clinical pharmacologists**
**Manage deliverables like PDUFA time**
**Learn efficiency**
**Great FDA new product & career development opportunity**
**Sharing information**

## Sharing Knowledge to Improve Clinical Drug 

***Development & Regulatory Decisions:***
**Data/models of Diseases, Drugs, Placebo, Baseline and Dropouts**
**January 24-25, 2007**
**Washington Marriott Hotel**
**1221 22nd Street NW**
**Washington, DC 20037**

**Objectives:**
** ****Show prior examples for the advantages of sharing information**
** ****Present examples demonstrating the application of sharing information in Parkinson’s Disease, Diabetes, Depression & Cancer to help make decisions**
** ****Consider how information can be shared in a library-type mechanism**
** ****Consider future actions to progress these ideas.**

## Recommendations

**Pre-competitive knowledge sharing          ****Better define it & develop mechanism for systematic sharing (work expectation)**
**Increase investment                                       ****allow physicians, statisticians & quantitative pharmacologists mine & share prior knowledge & problem solve**
**Develop & implement tools**
**CDISC**
**Mining**
**Modeling**
**Simulation**
**What if**