# Presentation: 185428

## FDA Experience with End of Phase IIa Meetings: An Attempt to Improve Drug Development Decisions 

**Bob Powell, Pharm.D.**
**Office of Clinical Pharmacology**
**FDA**
- _[powellr@cder.fda.gov](mailto:powellr@cder.fda.gov)_
**(301) 796-1589**

**Acknowledge: Larry Lesko, Don Stanski, Joga Gobburu, Peter Lee, Yaning Wang, Jenny Zheng and many others**

## When to Influence Clinical    Drug Development ?

-  **Flexibility**
** ****Learning**

** ****$ R&D Expense**
** ****$ Revenue**

**Discovery**

**Preclinical**

**Phase 1**

**Phase 2**

**Phase 3**

**NDA**

**Market**

## 50% Clinical Trial Failure Rate:Is it true? What to do?

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

**Account for known failure sources (prior information) in clinical trial design**

## Reasons for Poor Decisions(Definition: an outcome which should/could have been anticipated)

**Conspiracy of optimism**
**Framing the problem too narrowly to bring it inside my own comfort zone**
**Not involving the right people**
**Avoiding uncertainty**
**Ignoring information I do not understand**
**Being attached to ‘sunk costs’ – high spent development costs **
**Ignoring risks**
**Assuming no uncertainty in potential outcomes**
**Making decision alone**

**Hammond, Keeney, Raiffa. **_**Smart Choices**_**  Harvard Business School Press, 1999**

**BIAS**

## Model Based Drug DevelopmentWhat is it?

**Objective: ****improve decision quality by employing drug-disease models & clinical trial simulation **
**Model****: mathematical explanation of relationships thought to explain outcome over time period of interest**
**Drug-Disease Model**** (empiric & mechanistic)**
***Disease model*****: relationship of patient (e.g., gender, age, genotype), biomarker (e.g., biochemical, imaging) relationship to disease morbidity and mortality**
***Drug-Disease model*****: addition of drug (dose, concentration, combination, placebo) and patient (e.g., size, age, adherence, dropout) effects and adverse effects to the disease model**
**Simulation****- Target**
**Clinical trial design- optimal**
**New designs-enrichment, randomized withdraw, adaptive**
**Dosage regimen(s) selection**
**Go/No go- Sponsor &/or FDA**
**Labeling- Sponsor &/or FDA**

## End of Phase 2a Meetings

**Purpose****: ****↓ Late phase clinical trial (2b, 3) unnecessary failure**
**Format****: non-binding scientific interchange. Marketing issues should be in the development plan, not at this meeting. **
**Deliverables****: **
**Perform modeling (relevant phase 1/2a data) & simulation of next trial design employing **
**Mechanistic or empirical drug-disease model**
**Literature estimates for comparative drug effects if relevant**
**Placebo effect (magnitude & time-course)**
**Rates for dropout and compliance. (prior FDA experience)**
**Recommendation on sponsors trial design + alternative including patient selection, dosage regimen,...**
**Code from FDA work, Sponsor can extend work (EOP2, NDA)**
**Answers to other questions from the clinical and clinical pharmacology development plan**
**Time-course****: ~ 6 weeks**
**Key sponsor & FDA participants****: physician, biostatistician, clinical pharmacology (pharmacometrics), project management**

## EOP2a Meeting Process

**Meeting Request Letter & Questions**

_**Final Meeting**_
** ****Focus on Drug-disease modeling & Clinical trial simulations**
** ****30-40 min Presentation**
** ****1 hour dialogue focused on trial design, dosage regimens, patient selection**

_**Simulation Strategy**_
** ****Trial Design Alternatives**
** ****Dosage Regimens**
** ****Sample Size**
** ****Patient Selection**
** ****Sponsor or FDA?**

**Sponsor Questions**

**FDA begin data analysis**

**FDA Receive Briefing Package, Data, Next Trial Design**

**Sponsor Phone Meeting: Explain Process & Data Needed**

**FDA Evaluation & Approval**

**Answer other question in writing before meeting**

**6 week start**

## Roles & Responsibilities

** ****Project manager**
** ****Sponsor communication **
** ****FDA meetings**
** ****Documentation**

** ****Physician**
** ****Primary endpoints**
** ****Disease information**
** ****Trial design**
** ****Draft guidance**

** ****Statistician**
** ****Trial Design**
** ****Prior trial information**
** ****Placebo**
** ****Dropout rates**
** ****Simulation**

**Clinical Pharmacology/ Pharmacometrics**
** ****Drug-disease modeling**
** ****Dosage regimens**
** ****Drug interactions**
** ****Simulations**

## Case Study: HIV Phase 2a Meeting Key Questions for ‘Drug X’

**Is the target AUC of 950 ng****∙****h/mL ****(based on relationship to viral load suppression at day 11 of Phase IIa trial****) reasonable to select the best dose for Phase III? **
**Is testing BID and QD regimens appropriate?**
**Are 4 weeks adequate to select the dosage regimen for Phase III?**

## RNA Change from BL vs AUC(0-24)

** ****An AUC(0-24) of ~950 ng-h/mL is predicted to achieve a 1.5 log10 decrease in HIV-1 RNA from BL**

## A Mechanistic PK/PD Model

**Mechanistic viral dynamic model offers potential advantages over empirical models:**
**Time course of virus load described**
**Schedules (bid vs. qd) can be differentiated**
**Drug-drug interactions**
**Different design scenarios can be evaluated**
**Adherence **
**Pharmacodynamic interactions**
**Drop out**
**Resistance**

## Proposed Phase IIb Design

- _Cohort 1 (N=50):_
**‘****Drug X’ 1 mg BID + LPV/r 400/100mg BID**
- _Cohort 2 (N=50)_:
**‘****Drug X’ 2 mg BID + LPV/r 400/100mg BID**
- _Cohort 3 (N=50)_:
**‘****Drug X’ 4 mg QD + LPV/r 400/100mg BID**
- _Standard of Care (SOC) (N=25)_:
**COMBIVIR (ZDV/3TC 300/150mg BID) **
**                                      ****+ LPV/r 400/100mg BID**

## Virus Dynamic Model (virus load vs time)

- CD4+ Cells

- Virus

- Latent
- Infected

- Active
- Infected

- +

- d2

- d3

- 

- d1

- c

*a*

*p*

- fAVT

- fLVT

- fA=0.96 and fL=0.03

**PI**

**(N)NRTI**

**(N)NRTI**

- _J Acquir Immun Defic Syndr_ 26:397, 2001

- production rate
-       of target cell
- d1: dying rate of
-       target cell
- c:   dying rate of virus
- :   infection rate
-       constant
- d2: dying rate of
-       active cells
- d3: dying rate of latent
-       cells
*p:   production *rate of
-       virus

## ‘Drug X’ Observed and Model Predicted Mean Virus Load vs Time & Dose  

- 2 mg QD
- 4 mg QD
- 2 mg BID
- 6 mg BID

## Individual Patients Fit for Drug X

**Individual Patients Fit for Drug X**

## Drug-Disease Model ComponentsApplication

**Adherence**
**↑****Dose → GI Adverse Effects → ↓Adherence**
**Dropout rate: biphasic**
**Prior experience from other drugs**
**Drug-drug interaction**
**PK**
**PD**
**Verified model with data from prior drugs...did not share with sponsor**

##  Viral Dynamics During 4 Weeks‘Drug X’ + Kaletra

-  **Viral Dynamics During 4 Weeks****‘Drug X’ + Kaletra**

**HIV RNA (Log10 copies/mL) **

**Time (week)**

** ****Drug X**** 0.5****, 1, 2 mg BID and 4QD **

**Kaletra BID**

**Log****10****(50)**

**2Log Drop**

## Dropout Model: Prior Study Submission

**Dropout Model: Prior Study Submission**

## 20 Simulated trials (2 log drop, 90% Adherence, No Drop-out) suggest 2 BID is most likely the winner

**20 Simulated trials (2 log drop, 90% Adherence, No Drop-out) suggest**** 2 BID is most likely the winner**

**4      8     12     16    20    24    28    32    36     40    44    48**

**Chances of Being the Winner**

**0%**

**10%**

**20%**

**30%**

**40%**

**50%**

**60%**

**70%**

**80%**

**90%**

**100%**

- EQUAL

- 4 QD

- 2 BID

- 1 BID

**Week**

##  HIV Phase 2a Meeting Key FDA Response to Questions for ‘Drug X’

**Is the target AUC of 950 ng****∙****h/mL reasonable? **
**Concentration targeted dose selection is more appropriate to compare schedule (BID vs QD)****.**
**Is testing BID and QD regimens appropriate?**
**BID regimen is preferable**
**0.5 mg BID, instead of 4 mg QD, is worth considering**
**Are 4 weeks adequate to select the dose?**
**Low power to discern dose-viral load response thr’ 96wks. Selection of doses based on toxicity might be possible.**
**No, weeks 12-16 acceptable for preliminary assessment (pick dose for Phase III trial) and week 24 for confirmation based upon prior experience. Continue trial through week 48 for all doses.**
**In addition:**
**Kaletra effect is so strong that it may be difficult to demonstrate ‘Drug X’ dose-response in combination**
**Phase IIb trial was adequately designed to determine dose-response**

## EOP2a Meeting Metrics

**Completed**** 5 over past year, 3 in progress **
**Therapeutic area: problem****:**
**HIV: new mechanism, dosing**
**Prostate Ca: Formulation/dosing**
**Type 2 Diabetes: Genotype, dosing **
**Anticonvulsant: New mechanism**
**VMS (hot flashes): New mechanism, dosing**
**Pain: receptor specificity/adr’s, dosing**
**Weight-loss: new mechanism, dosing**
**Workload****: 5-7 person-months/project**
**Sponsor evaluation**** (post-meeting):        4.1-4.3 (1=worthless, 5=pivotal)**

## Sponsor’s Comments on the Experience (Abstracted from their Senior R&D Meeting slides)

**FDA’s Clinical Pharmacologists are very serious about leveraging Clin Pharm to:**
**Aid selection of dosage regimens for Phases 2b & 3**
**Reduce attrition in Phase 3**
**Design better Phase 3 studies**
**FDA is inviting sponsors to participate for certain drugs**
**Drugs with reliable, quantitative measures of response and reasonable pk-pd models**
**Projects in early Phase 2**
**Particular interest in novel compounds**
**FDA’s preparation was very extensive**
**Analyzed our exposure-response data**
**Applied pk-pd models (based on literature)**
**Had very detailed feedback**
**Our preparation must be extensive**
**Need high caliber Clin Pharm expertise**
**Be ready to submit datasets & programs from all pk-pd studies - be ready for urgent queries**
**Work with our scientific team to prepare thoroughly for the meeting**

## IND/NDA Data Review & AnalysisFDA Clinical Pharmacology Work Plan

**Data Warehouse**** (PKS)**

**Data Visualization & Data Set Creation****              (I-Review)**

**Clinical Trial Simulation**** (TS2)**

**End of Phase 2a Recommendation**** (2b/3 trial design)**

***IND/NDA Data Review & Analysis******FDA Clinical Pharmacology Work Plan***

##  UNITED NATIONS

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**Japanese**

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**PORTUGESE**

**SPANISH**

**ENGLISH**

** ****UNITED NATIONS**
** ****WORLD BANK**
** ****INSEAD**
** ****NOVARTIS**

**ENGLISH**

## FDA

**GENZYME**

**ROCHE**

**MEDIMMUNE**

**J&J**

**IDEC**

**Barr**

**MYLAN**

**Daiichi**

**GSK**

**MERCK**

**Berlex**

**Biogen**

**Ferring**

**AstraZeneca**

**Elan**

**Sanofi**

**ALLERGAN**

**NovoNordisk**

**AMGEN**

**Millenium**

**Eli Lilly**

**ABBOTT**

**PFIZER**

**FDA**

***CDISC***

**Clinical Data Interchange Standards Consortium www.cdisc.org**

## Drug-disease models at FDA

**Primary sources****: literature, scientists, prior NDA’s**
**Types**
**Mechanistic**
**Empirical**
**Diseases**** over past year**
**HIV**
**Diabetes Mellitus**
**Parkinson’s Disease**
**Vasomotor Symptoms (Hot Flashes)**
**SLE-renal flare**
**Prostate Cancer- chemical castration**
**Kidney transplant rejection**
**In Development**
**Osteoporosis**
**Non-small cell lung cancer**
**Considering**
**How to share models & some data on public website. Public dialogue on growing models**

## Diabetes

***Diabetes***

## Modeling Results for FPG & HBA1C Drug X in 1,000 patients

- HbA1c

- FPG

## ‘Save a Disease’ Prototype

**Objective: Quantitative library ****→ ↑**** trial outcome**
**3 levels: clinical trial data, derived quantitative information, drug-disease models**
**Derived quantitative information (mean, variability, time-course) ****Create Standards**
** ****Disease, clinical trial, patient description**
** ****Biomarker/1****° endpoint(s)**
** ****Placebo**
** ****Dropout rate**
** ****Drug (biomarker, 1° endpoint(s), adverse effects)**
** ****Covariates **
**2 diseases chosen for prototype**
**Process: Prototype → small to larger groups (FDA>Sponsors>Academics) Test value, set standards, fund**

## In Summary

**End of Phase 2a Meetings pilot started**
** ****Program evaluation & progression request within 2 months**
** ****Draft guidance publication**
** ****Literature publication(s)**
**Model based drug development**
**‘****Save a Disease’ prototype**
**Send an academic friend to FDA to create a disease model (funding available)**
**Software system for Clinical Pharmacology**
**CDISC standard needs to accelerate. This is a rate limiting step!**
** ****Your comments & recommendations?**