1. Impact of Exploratory Analysis on Drug Approval Joga Gobburu Pharmacometrics Office Clinical Pharmacology, CDER, FDA jogarao.gobburu@fda.hhs.gov

2. 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. 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. 4 Impact of Exploratory Analyses 2000-2004 Bhattaram et al. AAPS Journal. 2005; 7(3): Article 51. DOI: 10.1208/aapsj07035110.1208/aapsj070351 ImpactApprovalLabeling Pivotal54%57% Supportive46%30% No Contribution014% Pivotal: Regulatory decision will not be the same without PM review Supportive: Regulatory decision is supported by PM review

5. 5 Impact Discipline ApprovalLabeling PM Reviewer95%100% DCP Reviewer95%100% DCP TL90%94% Medical Reviewer90% @ DCP=Division of Clinical Pharmacology @=survey pending in 1 case Impact of Exploratory Analyses 2005-2006

6. 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. 7 Equivocal Evidence of Effectiveness Pivotal Studies DB#1 Dbl-blind (DB) Randomized PBO Controlled Dose Titration N=75 P<0.05 1 (withdrawal) DB#2 Dbl-blind (DB) Randomized PBO Controlled Dose Withdrawal N=30 P>0.05 1 Agency at this point can ask for more evidence (one or more studies) OR Investigate further across the clinical trial database whether there is a consistent signal of effectiveness or not 1 change in score at the end of study

8. 8 Equivocal Evidence of Effectiveness Pivotal + Other Studies OL-1 Open label (OL) Withdrawal Dose Titration N=75 OL-2 Open label (OL) Continue old dose N=30 DB#1 Dbl-blind (DB) Randomized PBO Controlled Dose Titration N=75 P<0.05 (withdrawal) DB#2 Dbl-blind (DB) Randomized PBO Controlled Dose Withdrawal N=30 P>0.05

9. 9 Significant Dose-Response Relationship – DB1, OL1 ParameterMean (Confidence Interval) Between-Patient Variability (CI) Slope of dose- response, % per mg 4.3* (3.7, 4.6) 56% (46%, 66%) Within-Patient Variability 26% (23%, 29%) Estimate of dose-response slope is similar for individual and combined analyses. Results from combined shown here. Linear mixed effects model employed * p<0.001

10. 10 Significant and Consistent Drug Effects Across Studies

11. 11 Drug in OL1 beat Placebo in DB1 Cross-over comparison

12. 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. 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 Unlikel y Likely

14. 14 Parkinsons Disease Collaboration between Statistics and Pharmacometrics Dr. Bhattaram and Dr. Siddiqui are the project leads with the following team members: FDA Statistics, Clinical, Policy Makers External Statistician, Disease experts

15. 15 Symptomatic or Protective? Placebo Drug A Drug B

16. 16 Symptomatic or Protective? Placebo Drug A Drug B

17. 17 Discern Symptomatic vs. Protective Effects: Delayed Start Design If drug is protective then patients who received drug longer will have lower scores compared those who receive drug late. Placebo Drug Protective Placebo PhaseActive Phase Key Questions: -Endpoint ? -Analysis ? -Handling missing data?

18. 18 Parkinsons Disease Database DataSource#PatientsTrial Duration Trial#1NDA4001yr + 3yr follow-up Trial#2NIH4001yr + follow-up Trial#3NDA9009mo + follow-up Trial#4NDA2009mo + follow-up Trial#5IND3001.5yr

19. 19 Published Data Mean (SD) of Total UPDRS scores for patients with Parkinsons 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. 20 Fraction Remaining Patients with slower progression remain longer in clinical trials (TEMPO)

21. 21 Value of Collaboration between Pharmacometrician, Statistician Statisticians Contribution –Primary statistical analysis Drop-outs –Trial design –Power calculations Pharmacometricians/Disease Experts Contribution –Biological/Mechanistic Interpretation Disease Progression Drug Effects Drop-outs –Trial design, alternative analysis

22. 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. 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?

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