Impact of Exploratory Analysis on Drug Approval

Joga Gobburu

Pharmacometrics

Office Clinical Pharmacology, CDER, FDA

jogarao.gobburu@fda.hhs.gov

2

Take Home Message• Exploratory (e.g., pharmacometric) analyses

are often used to make regulatory decisions– Decisions are not entirely driven by the pre-specified

statistical analysis

• Need for change– Integrate strengths of both approaches

• Think “How exploratory analyses can help drug development?”

– Opportunities for collaboration between pharmacometricians and statisticians are abundant

• Think about “How can I facilitate this collaboration?”

3

Pharmacometrics (or Quantitative Experimental Medicine?)

• Science that deals with quantifying disease and pharmacology

• Applications– Benefit/Risk, dose individualization, trial design

• Diverse expertise– Clinical pharmacologists, Pharmacometricians,

Clinicians, Statisticians, Bioengineers

• Tools– Linear/Nonlinear Mixed effects models, Longitudinal

data analysis, Biological models, Stochastic simulations

4

Impact of Exploratory Analyses 2000-2004

Bhattaram et al. AAPS Journal.  2005; 7(3): Article 51. DOI:  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 reviewSupportive: Regulatory decision is supported by PM review

5

Pivotal: Regulatory decision will not be the same without PM reviewSupportive: 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 Exploratory Analyses 2005-2006

6

NDA Case Study• Drug is proposed for a ‘rare’ debilitating,

fatal disease with no approved treatment.• One trial successful and other failed

– Failure likely due to trial execution errors• Potential miscommunication about dose timing

– Primary variable: Change in symptom score

• Key question– Is there adequate evidence for the

effectiveness?

7

Equivocal Evidence of EffectivenessPivotal Studies

DB#1Dbl-blind (DB)Randomized

PBO ControlledDose Titration

N=75P<0.051

(withdrawal)

DB#2Dbl-blind (DB)Randomized

PBO ControlledDose Withdrawal

N=30P>0.051

Agency at this point can ask for moreevidence (one or more studies)

OR

Investigate further across the clinicaltrial database whether there is a consistent signal of effectiveness or not

1change in score at the end of study

8

Equivocal Evidence of EffectivenessPivotal + Other Studies

OL-1Open label (OL)

WithdrawalDose Titration

N=75

OL-2Open label (OL)

Continue ‘old’ doseN=30

DB#1Dbl-blind (DB)Randomized

PBO ControlledDose Titration

N=75P<0.05

(withdrawal)

DB#2Dbl-blind (DB)Randomized

PBO ControlledDose Withdrawal

N=30P>0.05

9

Significant Dose-Response Relationship – DB1, OL1

Parameter Mean (Confidence Interval)

Between-Patient Variability (CI)

Slope of dose-response, % per mg

4.3*(3.7, 4.6)

56%(46%, 66%)

Within-PatientVariability

26% (23%, 29%)

Estimate of dose-response slope is similar for individual and combinedanalyses. Results from combined shown here.

Linear mixed effects model employed

* p<0.001

10

Significant and Consistent Drug Effects Across Studies

11

Drug in OL1 beat Placebo in DB1 Cross-over comparison

12

Value of Exploratory Analysis

• To Patients/FDA– Availability of drug sooner, especially given no

approved treatments (debilitating disease)– Efficient solution to challenging patient enrollment– Fewer review cycles (because of this issue alone)– Ultimately might lead to lower drug costs

• To Sponsor– Alleviated the need for additional trial(s) to

demonstrate effectiveness– Save $$ and time

• Pharmacometrics analyses can and do influence approval decisions!

13

Why did the sponsor not consider making a similar case?

• Unanticipated concern

• Lack of expertise (both technical, strategic)

• Prescriptive behavior on analysis

• Unclear expectations from FDA

Unlikely

Unlikely

LikelyLikely

14

Parkinson’s DiseaseCollaboration between Statistics

and Pharmacometrics

Dr. Bhattaram and Dr. Siddiqui are the project leads with the following team members:

FDAStatistics, Clinical, Policy Makers

ExternalStatistician, Disease experts

15

Symptomatic or Protective?

Placebo

Drug A

Drug B

16

Symptomatic or Protective?

Placebo

Drug A

Drug B

17

Discern Symptomatic vs. Protective Effects: Delayed Start Design

If drug is protective then patients who received drug longer will havelower scores compared those who receive drug late.

Placebo

Drug

DrugProtective

Placebo Phase Active Phase

Key Questions:-Endpoint ?-Analysis ?-Handling missing data?

18

Parkinson’s Disease Database

Data Source #Patients Trial Duration

Trial#1 NDA 400 1yr + 3yr follow-up

Trial#2 NIH 400 1yr + follow-up

Trial#3 NDA 900 9mo + follow-up

Trial#4 NDA 200 9mo + follow-up

Trial#5 IND 300 1.5yr

19

Published DataMean (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

The vertical line represents 2 months

Selegiline ( 5 years)

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

20

Fra

ctio

n R

emai

ning

Patients with slower progression remain longer in clinical trials (TEMPO)

21

Value of Collaboration between Pharmacometrician, Statistician

• Statistician’s Contribution– Primary statistical analysis

• Drop-outs

– Trial design– Power calculations

• Pharmacometrician’s/Disease Expert’s Contribution– Biological/Mechanistic Interpretation

• Disease Progression• Drug Effects• Drop-outs

– Trial design, alternative analysis

22

Value of Exploratory Analyses• Collected a large database of clinical trials

• Extracted patient population, placebo/disease progression, drug effect (not shown) and drop-out information.

• Simulations to answer the key questions mentioned earlier are in progress– Directly useful to advice sponsors

• Conference planning is underway  Disease Models Background: http://www.fda.gov/ohrms/dockets/ac/06/briefing/2006-4248B1-04-FDA-topic%203%20replacement.pdf  

23

Take Home Message• Exploratory (e.g., pharmacometric) analyses

are often used to make regulatory decisions– Decisions are not entirely driven by the pre-specified

statistical analysis

• Need for change– Integrate strengths of both approaches

• Think “How exploratory analyses can help drug development?”

– Opportunities for collaboration between pharmacometricians and statisticians are abundant

• Think about “How can I facilitate this collaboration?”

24