# Presentation: 828356

## Disease ModelsOverview and Case Studies

**Joga Gobburu**
**Pharmacometrics**
**Office Clinical Pharmacology, **
**Office of Translational Sciences, CDER, FDA**

## Pharmacometrics Survey

**Between 2000-2006, 72 NDAs needed Pharmacometrics Reviews/Analyses**
**For each of the Pharmacometrics Reviews, the ‘customers’ were asked to rate the impact on approval related and labeling decisions:**
**Pivotal: Decision would not have been the same without Pharmacometrics analysis**
**Supportive: Decision was well supported by the Pharmacometrics analysis**
**No Contribution: No need for the Pharmacometrics analysis**

## Impact of Pharmacometrics Analyses 2000-2004

***Bhattaram et al. AAPS Journal*****.  2005; 7(3): Article 51. DOI:  **_[10.1208/aapsj070351](http://dx.doi.org/10.1208/aapsj070351)__ _

| Impact | Approval | Labeling |
| --- | --- | --- |
| Pivotal | 54% | 57% |
| Supportive | 46% | 30% |
| No Contribution | 0 | 14% |

*Pivotal: Regulatory decision will not be the same without PM review**Supportive: Regulatory decision is supported by PM review*

## Pivotal: Regulatory decision will not be the same without PM reviewSupportive: Regulatory decision is supported by PM review

| Impact →
Discipline | Approval | Labeling |
| --- | --- | --- |
| PM Reviewer | 95% | 100% |
| DCP Reviewer | 95% | 100% |
| DCP TL | 90% | 94% |
| Medical Reviewer | 90%@ | 90%@ |

***DCP=Division of Clinical Pharmacology***
***@=survey pending in 1 case***

- Impact of Pharmacometrics Analyses 2005-2006

## NDA#1: Approval of monotherapy oxcarbazepine in pediatrics for treating partial seizures using prior clinical data

- FDA/Sponsor pursued approaches to best
- utilize knowledge from the previous trials to
- assess if monotherapy in pediatrics can
- be approved without new controlled trials

## The sponsor was pursuing an accelerated approval, for drug to prevent a life-threatening disease, based on a biomarker even though clinical endpoint analysis failed in two pivotal trials

- NDA#2: Establishment of biomarker-outcome relationship allowed more efficient future trial design

## NDA#2: Establishment of biomarker-outcome relationship allowed more efficient future trial design

**Ratio of biomarker level to baseline**

**Hazard ratio=10.0 (95% CI 2.5-30.0)**
**p<0.001**

**Relative risk of the disease event**

## NDA#3: Insights into trial failure reasons will lead to more efficient future trials

**Mild Baseline Disease**
**Non-Responders**

**Severe Baseline Disease**
**Responders**

## Females seem to be more sensitive to QT prolongation

**Slope**

**Slope**

**Slope**

**Slope**

## Need/Opportunities for Innovative Quantitative Methods in Drug Development

**Optimal design to show ‘disease modifying’ effects?**

**Good marker(s) of survival benefit in cancer patients?**

**Maximize the change of success of a 2yr obesity trial?**

**Given 85% of depression trials fail, how to improve success?**

**Best dose for a 26wk trial based on 12 wk data?**

**Providing solutions for these issues calls**
**for efficient use of prior knowledge**

## Manage and Leverage Knowledge

**Knowledge**

**Placebo & Disease Models**

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

** ****Biomarker-Endpoint **
** ****Time course**
** ****Drop-out**
** ****Inclusion/Exclusion**
**  ****criteria (Trial)**

** ****Parkinson’s**
** ****Obesity, Diabetes**
** ****Tumor-Survival**
** ****Rheumatologic condition**
-  **HIV**
** ****Epilepsy**
** ****Pain**

***We are referring to such diverse quantitative ***
***approach(es) as ‘Disease Modeling’***

## Core Development Strategy for Testosterone Suppressants

**Disease Model**

**Reporter Gene Assay**

**Preclinical**

**Clinical Trial Simulation**

**Dose optimization in cancer patients**

**Pivotal trial**

** ****|----*2 mo-----|**

***Actual execution time.- it does account for time spent accumulating resources.**

**|----*2 mo-----|**

**|----*2  mo-----|**

**|----*3 mo-----|**

**|---------*12 mo--------------|                   **

**- Early screening of compounds based on IC****50 ****value.**
**- High thr’put method to filter thousands of compounds**
**- Based on prior experience, a few potential entities will be selected for the next phase**

**IC****50**

**PKPD data**

**- In vitro IC****50**** as a guide for preclinical dose selection**
**- Animal models to measure all possible biomarkers e.g. GnRH, LH, T and Drug conc. **

**- Invitro and preclinical data for clinical dose and regimen selection**
**- Clinical development  plan**

**- Pilot study for dose optimization thr’ innovative trial designs**

**PKPD data**

***From Pravin Jadhav, VCU/FDA***

## Obesity

- Obesity trials are large, over 1-2 yrs and fraught with challenges due to high drop-out rate

**Dr. Jenny J Zheng**
**Dr. Wei Qiu**
**Dr. Hae Young Ahn**

## Obesity

**Baseline Body Weight**
**3000 patients**

**Model Qualification**

## Patients with small weight loss drop-out

**0-12 **

**12-24 **

**24-36 **

**36-52 **

**Drop-out patients**

**Remaining patients**

## Obesity: Time Course of Placebo Effect

## Value to Drug Development

- Effective use of prior data for designing future registration trials
- Might lead to alternative dosing considerations
- Titration vs. fixed dose
- Could lead to increased trial success
- Allows of designing useful shorter duration trials for future compounds for screening and initial dose range selection

## Diabetes

- How to reliably select doses for registration trials based on abbreviated dose finding trials
- Need arose from an EOP2A meeting
- Work in progress: No patient population and drop-out models yet.

**Drs. Vaidyanathan, Ahn, Yim, Zheng, Wang, **
**Gobburu, Powell, Sahlroot, Orloff**

## Pivotal Trial Dose Selection: Anti-Diabetic

- Sponsor conducted 12 wk dose ranging trial in diabetics
- Key Regulatory Question
**What is a reasonable dose range and regimen for the pivotal trial(s)?**
- Challenge
**Estimate of effect size on HbA1c at 26 wks not available. Effect size on FPG available.**

## FPG-HbA1c relationship

- HbAlc

- FPG

- Drug Conc.

- Time (Week)

**FPG-HbA1c relationship**
**from historic studies**
**employed to estimate**
**effects on HbA1c of the**
**new compound**

*Jusko et al*

## Biological relationship between FPG-HbA1c bridged information gap

**+**

**=**

**Drug X (Sponsor) **
**in 72 patients**

**Drug X (other)**
**in 28 patients**

**Hybrid dataset**
**in 100 patients**

## Value to Drug Development

- More informed dose/regimen selection
- Could lead to increased trial success
- Quantitative analysis was critical
- Effective use of prior data for predictions
- Supports conduct of useful shorter duration trials for future compounds

## Disease Models: Challenges

**Data Management**
**How to best maintain an efficient database?**
**Analysis**
**How to best conduct meta-analysis?**
**Identify and fill gaps (time-varying biomarkers in survival models)?**
**Inter-disciplinary collaboration**
**Biologists, Pharmacologists, Statisticians, Disease Experts, Quantitative Clinical Pharmacologists, Engineers need to come together to develop these models as a team.**