Registration Studies

1. Registration Studies

2. Registration studies – Phase III Objectives • To gain regulatory approval • Robustness of trial and endpionts are paramount • Design of trial can be discussed with regulatory agency • To assess the efficacy of the drug compared to existing ‘gold standard’ • To assess the toxicity of the drug compared to existing ‘standard of care’ • Collect QOL where appropriate • Collect relevant pharmaco-economic data

3. Phase III Studies – Patient selection Regulatory authorities wish the population to be as homogenous as possible . Specific tumour type • Stage, grade, target expression, geographical location increasingly important • Biomarkers may be mandatory • Good performance status • Any exclusion criteria based on population and any toxicities seen in earlier studies • Informed consent • Ability to read and write - QOL

4. Phase III Studies – Study Design • Consider choice of endpoints- PFS, OS (RECIST, BIR) • Consider duration of therapy • Consider design • Parallel • Factorial • Crossover • Consider mode of randomisation - reduces selection bias • Equal/unequal • Stratification

5. Phase III Studies – Study Design • Consider degree of blinding - reduces reporting bias • Open label studies are common • Single • Double • Sample size calculations • "Is my trial large enough to demonstrate a clinically significant difference between the groups if one is truly present?"

6. Sample size calculation • In oncology studies, this calculation determines the number of events, not the number of patients, required • Depends on: • Natural intra-patient variability - less important in oncology • Minimum difference between groups considered important by investigator • Acceptable levels of α and β • Remember effect of interim analyses

7. Overall Survival - time to death from any cause Progression-free survival - time to first observation of disease progression Time to treatment failure- time to first observation of disease progression, death or discontinuation Duration of response – time to first observation of response to progression / death Quality of Life- mainlyfor blinded studies. Endpoints

8. Quality of Life • 2 main instruments used • Functional Assessment of Cancer Therapy- General ( FACT-G) • European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire- C30 ( EORTC- C30) • Both modular set up with generic core questionnaires in combination with disease specific modules • Generic core includes: • Physical functioning and well-being, social functioning, social / family well-being etc

9. Health Outcomes • Instruments commonly used and accepted: • SF-36 • Health utility Index • EQ-5D • Increasing importance due to reimbursement / cost containment

10. Basis for NDA Approval • Demonstration of efficacy with acceptable safety in adequate and well-controlled studies • Ability to generate product labeling that • Defines an appropriate patient population for treatment with the drug • Provides adequate information to enable safe and effective use of the drug

11. Accelerated Approval, Fast Track, Priority Review • Accelerated Approval • serious or life-threatening disease • demonstrable benefit over available therapy. • use of surrogate markers • mandated phase IV trials • Fast Track • life-threatening disease • potential to address unmet medical need • Priority review • drug would be a significant improvement compared to available drugs • Review of filing in 6 months

12. Challenges for Oncology Drug Regulations • New “targeted therapies” • Re-define definitions of diseases (based on mechanism of pathogenesis, genetics) • Greater efficacy in selected population may result in smaller patient populations • Novel surrogates to be validated • Dosing aimed at target rather than MTD • Dose studies, chronic administration

13. The IRESSA story

14. EGFR biomarkers were discovered late in the gefitinib development programme ISEL, INTEREST: Unselected trials in pre-treated setting ISEL Gefitinib registration Japan INTEREST IPASS 2002 2005 2007 2009 EGFR protein expression IPASS: Clinically selected trial in first line setting EGFR gene copy number EGFR mutations

15. Gefitinib Placebo The real challenge was to determine which tumours were really dependent on the EGFR pathway ISEL study: Median follow-up 7 months (range 3-15), 58% deaths Median, months 1-year survival, % Log-rank HR (95% CI), 0.89 (0.77, 1.02); p=0.087Cox analysis, p=0.030 Gefitinib 5.6 27 Placebo 5.1 21 Proportionsurviving 1.0 0.8 0.6 0.4 0.2 0.0 0 2 4 6 8 10 12 14 16 Time (months) 1692 1347 877 485 252 104 31 At risk Thatcher et al 2005

16. The dangers of dilution…….. Probability of success in pivotal trial in the presence of a non-responsive subgroup Assumes responders have 33% effect and non-responders 0% effect, trial sized assuming all patients have a 33% effect

17. ISEL: Rigorously predefined subgroups showed a statistically significant increase in survival Never smoked (n=375) Ever smoked (n=1317) 1.0 HR 0.67; 95% CI 0.49, 0.92; p=0.012 HR 0.92; 95% CI 0.79, 1.06; p=0.242 Gefitinib 0.8 Placebo 0.6 Proportion surviving 0.4 0.2 0.0 0 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 Asian origin (n=342) Non-Asian origin (n=1350) 1.0 HR 0.66; 95% CI 0.48, 0.91; p=0.010 HR 0.92; 95% CI 0.80, 1.07; p=0.294 0.8 0.6 Proportion surviving 0.4 0.2 0.0 0 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 Time (months) Cox regression analysis Thatcher et al 2005

18. IPASS: Phase III study of gefitinib versus doublet chemotherapy in first line NSCLC Gefitinib250 mg/day 1:1 randomization Carboplatin AUC 5 or 6 and Paclitaxel 200mg/m2 3 wkly Endpoints • Patients • Adenocarcinoma histology • Never smokers or light ex-smokers* • PS 0-2 • Provision of tumour sample for biomarker analysis strongly encouraged • Primary • Progression free survival (non-inferiority) • Secondary • Objective response rate • Quality of life • Disease related symptoms • Overall survival • Safety and tolerability • Exploratory • Biomarkers • EGFR mutation • EGFR gene copy number • EGFR protein expression • 1217 patients from East Asian countries *Never smokers:<100 cigarettes in lifetime; light ex-smokers: stopped 15 years agoand smoked 10 pack yrs Carboplatin/paclitaxel was offered to gefitinib patients upon progression PS, performance status; EGFR, epidermal growth factor receptor

19. IPASS: Superior PFS and ORR with gefitinib vs doublet chemotherapy; PFS effect not constant over time Probabilityof PFS Carboplatin / paclitaxel 1.0 Gefitinib N Events 609 453 (74.4%) 608 497 (81.7%) 0.8 HR (95% CI) = 0.741 (0.651, 0.845) p<0.0001 0.6 5.874%48%7% Median PFS (months)4 months progression-free6 months progression-free12 months progression-free 5.761%48%25% 0.4 Primary objective exceeded: Gefitinib demonstrated superiority relative to carboplatin / paclitaxel in terms of PFS 0.2 0.0 0 4 8 12 16 20 24 Months At risk : Gefitinib 609 363 76 24 5 0 212 Carboplatin / paclitaxel 608 412 118 22 3 1 0 Objective response rate 43% vs 32% p=0.0001 Primary Cox analysis and logistic regression with covariates; ITT population HR <1 implies a lower risk of progression on gefitinib

20. IPASS: Gefitinib has a more favourable tolerability profile than doublet chemotherapy Most common AEs (10% on either treatment) with >3% difference between treatments Gefitinib (N=607) Carboplatin/paclitaxel (N=589) #Absolute neutrophil count, white blood cell count, or haemoglobin worsened from baseline to CTC grade 3/4; gefitinib N=599, carboplatin / paclitaxel N=577 *Grouped term (sum of several preferred terms)

21. IPASS: EGFR mutation is a strong predictor for differential PFS benefit between gefitinib and doublet chemotherapy Gefitinib EGFR M+ (n=132)Gefitinib EGFR M- (n=91)Carboplatin / paclitaxel EGFR M+ (n=129) Carboplatin / paclitaxel EGFR M- (n=85) Probabilityof PFS 1.0 EGFR M+HR=0.48, 95% CI 0.36, 0.64 p<0.0001 EGFR M- HR=2.85, 95% CI 2.05, 3.98 p<0.0001 0.8 Treatment by subgroup interaction test, p<0.0001 0.6 0.4 0.2 0.0 0 4 8 12 16 20 24 Time from randomisation (months) M+, mutation positive; M-, mutation negative

22. Mutation status causes conformational change and increased activation WT EGFR Mutant EGFR Ligand Extracellular domain Trans-membrane domain ATP Tyrosine kinase domain Tyrosine phosphorylation Ras-Raf-MAPK Proliferation Pi3K-AKT Survival EGFR internalisation Degradation/recycling EGFR signals longer at the cell membrane

23. IPASS: Superior quality of life and symptom improvement rates for gefitinib in EGFR mutation positive patients p<0.0001 p=0.0003 p<0.0001 % patientswith sustained clinically relevant improvement p-values from logistic regression with covariates. Post-hoc analysis, EFQ populationClinically relevant improvement pre-defined as 6-point improvement for FACT-L and TOI;2-point improvement for LCS, maintained for at least 21 days. EFQ, evaluable for quality of life; FACT-L, Functional Assessment of Cancer Therapy-Lung; TOI, Trial Outcome Index; LCS, Lung Cancer Subscale

24. Conclusions – phase III trial considerations • Patient selection • Activity in specific subsets • Study design • Experimental (& control!) arm – dose, duration of treatment • Frequency of evaluations • Study endpoints • Survival vs PFS? • Use of biomarkers? • Qol • Health economics • Post phase III plans • Early access • Phase IIIb/IV

25. Further Reading • Thatcher N et al, Gefitinib plus best supportive care in previously treated patients with refractory non-small-cell lung cancer: results from a randomisd, placebo controlled, multicentre study (Iressa Survival Evaluation in lung Cancer). Lancet 2005;366:1527-1537. • Chang A et al, Gefitinig (IRESSA) in patients of Asian origin with refractory advanced non-small cell lung cancer: subset analysis from the ISEL study. J Thorac Oncol 2006;1:847-855. • Kelloff GJ et al, New science-based endpoints to accelerate oncology drug development. Eu J Can 2005;41:491-501 • Booth CM et al, Reflections on Medical Oncology: 25 years of clinical trials – where have we come & where are we going. J Clin Oncol 2008;26(1):6-8 • Gutierrez ME et al, Next generation oncology drug development: opportunities & challenges. Nat Rev Clin Oncol 2009;6:259-265 • Nathan DG. The Cancer Treatment Revolution, Wiley & Sons, Inc., 2007 • Mukherjee S. The emperor of all maladies. A biography of cancer. Fourth Estate, 2010

26. Randomization

27. Clinical Trial Design • Stratification: Categorizing subjects into subgroups by specific characteristics • Enables researchers to look into separate subgroups to see whether differences exist

28. Stratification

29. FACT-G

30. Interim analyses • 1 analysis p=0.05 • 2 analyses p=0.083 • 3 analyses p=0.107 • … • 10 analyses p=0.193 • Designs exist to reflect this - extreme p values used for initial analyses to avoid loss of power