Targeted (Enrichment) Design

1. Targeted (Enrichment) Design

2. Prospective Co-Development of Drugs and Companion Diagnostics • Develop a completely specified genomic classifier of the patients likely to benefit from a new drug • Pre-clinical, phase II data, archived specimens from previous phase III studies • Establish analytical validated test for the classifier • Use the completely specified classifier to design and analyze a new clinical trial to evaluate effectiveness of the new treatment with a pre-defined analysis plan that preserves the overall type-I error of the study.

3. Guiding Principle • The data used to develop the classifier should be distinct from the data used to test hypotheses about treatment effect in subsets determined by the classifier • Developmental studies can be exploratory • Studies on which treatment effectiveness claims are to be based should be definitive studies that test a treatment hypothesis in a patient population completely pre-specified by the classifier

4. Develop Predictor of Response to New Drug Using phase II data, develop predictor of response to new drug Patient Predicted Responsive Patient Predicted Non-Responsive Off Study New Drug Control

5. Primarily for settings where the classifier is based on a single gene whose protein product is the target of the drug • eg Herceptin

6. Evaluating the Efficiency of Strategy (I) • Simon R and Maitnourim A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research 10:6759-63, 2004; Correction and supplement 12:3229, 2006 • Maitnourim A and Simon R. On the efficiency of targeted clinical trials. Statistics in Medicine 24:329-339, 2005

7. Model for Two Treatments With Binary Response • Molecularly targeted treatment T • Control treatment C • 1- Proportion of test + patients • pc control response probability • response probability for test + patients on T is (pc + 1) • Response probability for test – patients on T is (pc + 0)

8. Untargeted Trial • Compare outcome for treatment group T vs control group C without classifier data • Fisher-Exact test at two-sided level .05 comparing response proportion in control group to response proportion in treatment group • Number of responses in C group of n patients is binomial B(n,pc) • Number of responses in T group is • B(n,(1-)(pc+1)+ (pc+0)) • Determine n patients per treatment group for power 1- • Use Ury & Fleiss approximation Biom 36:347-51,1980.

9. Targeted Trial • Compare outcome for treatment group T vs control group C for Assay positive patients • Fisher-Exact test at two-sided level .05 comparing response proportion in control group to response proportion in treatment group • Number of responses in C group of n patients is binomial B(n,pc) • Number of responses in T group is • B(n,pc+1) • Determine nT patients per treatment group for power 1- • Use Ury & Fleiss approximation Biom 36:347-51,1980.

12. Approximations • Observed response rate ~ N(p,p(1-p)/n) • pe(1-pe) ~ pc(1-pc)

13. Number of Randomized Patients Required • Type I error  • Power 1- for obtaining significance

14. Randomized Ratio(normal approximation) • RandRat = nuntargeted/ntargeted • 1= rx effect in test + patients • 0= rx effect in test - patients •  =proportion of test - patients • If 0=0, RandRat = 1/ (1-) 2 • If 0= 1/2, RandRat = 1/(1- /2)2

15. Screened Ratio • Nuntargeted = nuntargeted • Ntargeted =ntargeted/(1-) • ScreenRat = Nuntargeted/Ntargeted=(1- )RandRat

16. No treatment Benefit for Test - Patientsnstd / ntargeted

17. Treatment Benefit for Test – Pts Half that of Test + Pts nstd / ntargeted

18. Relative efficiency of targeted design depends on • proportion of patients test positive • effectiveness of new drug (compared to control) for test negative patients • When less than half of patients are test positive and the drug has little or no benefit for test negative patients, the targeted design requires dramatically fewer randomized patients • The targeted design may require fewer or more screened patients than the standard design

19. TrastuzumabHerceptin • Metastatic breast cancer • 234 randomized patients per arm • 90% power for 13.5% improvement in 1-year survival over 67% baseline at 2-sided .05 level • If benefit were limited to the 25% test + patients, overall improvement in survival would have been 3.375% • 4025 patients/arm would have been required

20. Comparison of Targeted to Untargeted DesignSimon R,Development and Validation of Biomarker Classifiers for Treatment Selection, JSPI

21. Web Based Software for Comparing Sample Size Requirements • http://brb.nci.nih.gov

27. “Stratification Design”

28. DevelopPredictor of Response to New Rx Predicted Responsive To New Rx Predicted Non-responsive to New Rx New RX Control New RX Control Developmental Strategy (II)

29. Developmental Strategy (II) • Do not use the test to restrict eligibility, but to structure a prospective analysis plan • Having a prospective analysis plan is essential • “Stratifying” (balancing) the randomization is useful to ensure that all randomized patients have tissue available but is not a substitute for a prospective analysis plan • The purpose of the study is to evaluate the new treatment overall and for the pre-defined subsets; not to modify or refine the classifier • The purpose is not to demonstrate that repeating the classifier development process on independent data results in the same classifier

30. R Simon. Using genomics in clinical trial design, Clinical Cancer Research 14:5984-93, 2008 • R Simon. Designs and adaptive analysis plans for pivotal clinical trials of therapeutics and companion diagnostics, Expert Opinion in Medical Diagnostics 2:721-29, 2008

31. Validation of EGFR biomarkers for selection of EGFR-TK inhibitor therapy for previously treated NSCLC patients • PFS endpoint • 90% power to detect 50% PFS improvement in FISH+ • 90% power to detect 30% PFS improvement in FISH− • Evaluate EGFR IHC and mutations as predictive markers • Evaluate the role of RAS mutation as a negative predictive marker Outcome FISH + (~ 30%) Erlotinib 2nd line NSCLC with specimen 1° PFS 2° OS, ORR FISH Testing Pemetrexed 1-2 years minimum additional follow-up FISH − (~ 70%) Erlotinib Pemetrexed 4 years accrual, 1196 patients 957 patients

32. Analysis Plan A • Compare the new drug to the control for classifier positive patients • If p+>0.05 make no claim of effectiveness • If p+ 0.05 claim effectiveness for the classifier positive patients and • Compare new drug to control for classifier negative patients using 0.05 threshold of significance

33. Sample size for Analysis Plan A • 88 events in classifier + patients needed to detect 50% reduction in hazard at 5% two-sided significance level with 90% power • If 25% of patients are positive, then when there are 88 events in positive patients there will be about 264 events in negative patients • 264 events provides 90% power for detecting 33% reduction in hazard at 5% two-sided significance level

34. Analysis Plan B(Limited confidence in test) • Compare the new drug to the control overall for all patients ignoring the classifier. • If poverall 0.03 claim effectiveness for the eligible population as a whole • Otherwise perform a single subset analysis evaluating the new drug in the classifier + patients • If psubset 0.02 claim effectiveness for the classifier + patients.

35. Sample size for Analysis Plan B • To have 90% power for detecting uniform 33% reduction in overall hazard at 3% two-sided level requires 297 events (instead of 263 for similar power at 5% level) • If 25% of patients are positive, then when there are 297 total events there will be approximately 75 events in positive patients • 75 events provides 75% power for detecting 50% reduction in hazard at 2% two-sided significance level • By delaying evaluation in test positive patients, 80% power is achieved with 84 events and 90% power with 109 events

36. This analysis strategy is designed to not penalize sponsors for having developed a classifier • It provides sponsors with an incentive to develop genomic classifiers

37. Analysis Plan C • Test for difference (interaction) between treatment effect in test positive patients and treatment effect in test negative patients • If interaction is significant at level int then compare treatments separately for test positive patients and test negative patients • Otherwise, compare treatments overall

38. Sample Size Planning for Analysis Plan C • 88 events in test + patients needed to detect 50% reduction in hazard at 5% two-sided significance level with 90% power • If 25% of patients are positive, when there are 88 events in positive patients there will be about 264 events in negative patients • 264 events provides 90% power for detecting 33% reduction in hazard at 5% two-sided significance level

39. Simulation Results for Analysis Plan C • Using int=0.10, the interaction test has power 93.7% when there is a 50% reduction in hazard in test positive patients and no treatment effect in test negative patients • A significant interaction and significant treatment effect in test positive patients is obtained in 88% of cases under the above conditions • If the treatment reduces hazard by 33% uniformly, the interaction test is negative and the overall test is significant in 87% of cases

42. Prospective-Retrospective Study