Impact of Exploratory Analysis on Drug Approval

1. 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 Pivotal: Regulatory decision will not be the same without PM reviewSupportive: Regulatory decision is supported by PM review Bhattaram et al. AAPS Journal.  2005; 7(3): Article 51. DOI:  10.1208/aapsj070351

5. Pivotal: Regulatory decision will not be the same without PM reviewSupportive: Regulatory decision is supported by PM review Impact of Exploratory Analyses 2005-2006 DCP=Division of Clinical Pharmacology @=survey pending in 1 case

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#1 Dbl-blind (DB) Randomized PBO Controlled Dose Titration N=75 P<0.051 (withdrawal) 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 DB#2 Dbl-blind (DB) Randomized PBO Controlled Dose Withdrawal N=30 P>0.051 1change in score at the end of study

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

9. Significant Dose-Response Relationship – DB1, OL1 * p<0.001 Linear mixed effects model employed Estimate of dose-response slope is similar for individual and combined analyses. Results from combined shown here.

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? Unlikely Unlikely • Unanticipated concern • Lack of expertise (both technical, strategic) • Prescriptive behavior on analysis • Unclear expectations from FDA Likely Likely

14. Parkinson’s DiseaseCollaboration 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. Symptomatic or Protective? Placebo Drug A Drug B

16. Symptomatic or Protective? Placebo Drug A Drug B

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

18. Parkinson’s Disease Database

19. Selegiline ( 5 years) Published Data Mean (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 Eur.J.Neurology, 1999, 6: 539-547 The vertical line represents 2 months

20. Patients with slower progression remain longer in clinical trials (TEMPO) Fraction Remaining

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?”