New strategies for trial design and biomarker discovery

1. New strategies for trial design and biomarker discovery Emilio Bria U.O.C. di Oncologia Medica d.U. Azienda Ospedaliera Universitaria Integrata Policlinico ‘G.B. Rossi’ – VERONA emilio.bria@ospedaleuniverona.it Roma, 18 Maggio 2012

2. Cancer that cannot be found • Died from bladder cancer in 1978, • NEGATIVE CITOLOGY, haematuria since at least 1967 • having been diagnosed with microinvasive bladder cancer in 1973 • frankly invasive disease in 1976. • P53 mutation • P53 mutation • P53 mutation Hubert Humphrey, US Vice President (1964–1968) Hypothesis: p53mut as biomarker of ‘subclinical’ cancer

3. ‘The Ideal’ Medicine Knolewdge of biological targets Medicine for The Single Individual Medicine for The population

4. May 18st, Why this issue? • ‘New age’ ofmedicaloncology • Biologicaladvances and/or discovery • New availabletechniques • Big amountofknowledgefromourbasic science friends (……those ‘scientists’….) • Manynewdrugsavailable • Withalmostunknownmechanismofaction

5. ‘The Ideal’ Curve Cured Patients

6. How was that? Sign. detrimental No difference ‘Negligible benefit‘ Small benefit

7. Can we ‘perform’ betterwiththesecurves?

8. Yes……….wecould! Biomarkers’ Driven vs Unselection Approach • ThreeRCTs (1987 patients) • Attrition rate 45% (range 20-60%) Interaction: p=0.025 Power 80%, alpha-error 0.05 Heterogeneity: p=0.025

9. New drugs in development for cancer treatment PhRMA report 2009 – The Challenge of Numbers PM Lo Russo et al., Clin Cancer Res 2010

10. The challenge of anticancer drug development Success rates from 1-in-human to registration for ten large pharma companies in the US/Europe [1991–2000]. • Time- and resource-intensive process • >800 million $ to bring a new oncology drug to market!

11. Potential effects of personalised medicine on pharmaceutical industry drug development

12. What issues for clinical trial design? • New methodologies: • More ‘targets’ (more or less) • More patient’ (sub-)groups • Re-assessmentofend-points: • Isresponseadequateforphase II? • Is the phase II fashion a reasonableapproach? • Is the phase III trial alwaysrequired (ofcourse, a provocativequestion)? • If yes, whichkindofRCTs?

13. New ‘Smart’ Drugs:Always ‘Targeted’? • Un-Targeted design • Randomized comparison without measuring the ‘classifier’ • Example: ECOG 4599-Beva • Targeted design • Randomize only test + patients • Example: EURTAC (Erlotinibvs CT – EGFR-M+) Modified - Courtesy of Simon R, 2008

14. Butsomethingismovingon…….. • AssociationbetweenVEGF genotype and median OS aswellasgrade 3 or 4 hypertension, Schneider [ECOG 2100 update], JCO Oct 2008

15. How Trastuzumab Entered the market? Buyse, ASCO 2005

16. What if Trastuzumab were developed per conventional approach in all comers Simulation of Randomized Phase III Trial in which 100% of active patients show a treatment effect Simulation of Randomized Phase III Trial in which 25% of active patients show a treatment effect Modified by Gianni, 2011

17. ........The plot thickens!

18. ‘Average’ Drug Development 1 yr 1-2 yrs 2+ yrs At least 5 yrs with multicenter, cooperative trials Courtesy of Billingham C, 2008

19. The Roleof ‘Early’ phases (I-II) isCRUCIAL ! • After preclinical, in the 1-3 yrs of drug development, you can: • Easily control drug effect • Monitor either biological and clinical action • Identify the ‘REAL’ target (if present!) • When the drug enters phase III, only early stopping can be applied (with all related concerns….)

20. ‘Early’ phases (II): Limitations 2 1 Courtesy of Billingham C, 2008

21. Studies that met the criteria for appropriate citation of prior data were significantly less likelyto reject the null (33%) than those cited that did not meet the criteria (85%) P = 0.006 1

22. Predictors of success at multivariate analysis: • Positive Phase II results (p=.027) • Pharma company-sponsored (p=.014) • Short interval between Phase II and III publication (p<.001) • Multi-institutional trials (p=.016)

23. Use of PFS/TTP As Primary End Point [%] in RCTs (BC, CRC, NSCLC) Major Journals [1975-2009]

24. Targeted Agents – ’MYTHS’ • Conversely to Classical cytotoxics, Targeted agents selectively hit a specific molecule/enzyme • their functional/clinical effects are directly related to the level of target inhibition • Targeted agents are ‘cytostatic’ in nature: • they will slow down growth, but seldom shrink pre-existing tumor masses Modified - Courtesy of Milella M, ESMO 2008

25. ‘Targeted’ agents, particularly ATP-competitive kinase inhibitors, frequently hit multiple targets Modified - Courtesy of Milella M, ESMO 2008

26. Targeted Agents – ’MYTHS’ • Conversely to Classical cytotoxics, Targeted agents selectively hit a specific molecule/enzyme • their functional/clinical effects are directly related to the level of target inhibition • Targeted agents are ‘cytostatic’ in nature: • they will slow down growth, but seldom shrink pre-existing tumor masses Modified - Courtesy of Milella M, ESMO 2008

27. TRUE, but see PFS & OS!

29. Stupid and Smart Cancers Adapted from G. Sledge, ASCO 2011

30. 2 Courtesy of Billingham C, 2008 Ndr: #1 issue is still there!

31. Medical Oncology Clinical Research Scenario • The phase II randomizedshould: • So far: • Test EXPERIMENTALdrugs/combos, and pick the winnerforfurtherphase III • Be aimedtosafety and activity (i.e. responserates) • DO NOT USEsurvivalend-points • NEVER compare treatment arms • Fromnow on (withtargetedagents): • ?????????????????????

32. What is less dangerous? (…to obtain more accurate results from early studies with targeted agents) SINGLE-ARM Phase II Response as end-point Uncontrolled MULTIPLE-ARM Phase II Random Survival as end-point Controlled

33. Targeted Agents – Phase II • Uncontrolled Design (‘Classical’ Phase II) • High efficiency in identifyingnon-activedrugs (high NPV) • Low efficiency in selecting the best challengersforphase III (low PPV) Modified - Courtesy of Perrone F, AIOM Conf. 2008

34. Targeted Agents – Phase II • Controlled Design (‘Comparative’ Phase II Randomized) • Increase PPV • Shouldbe (mustbe) conductedwith‘relaxed’ statisticalcriteria (i.e. alfa one-sided = 0.20) • MUSTbefollowed (if positive) by a classicalphase III withtraditionalrules Modified - Courtesy of Perrone F, AIOM Conf. 2008

35. Moving to Phase III design • Many cancer treatments benefit only a small proportion of the patients to which they are administered • By targeting treatment to the right patients • Treated patients benefit • Treatment more cost-effective for society • More informative and successful clinical trials Modified - Courtesy of Simon R, 2008

36. Attrition rates in biomarker analysis: the IPASS study

37. Attrition rates in biomarker analysis: the IPASS study HIGH LOW

38. Do neverforget the prognosticeffect! • EGFR mutation status appears to be a good prognostic factor, but a weak predictive factor for survival (G. Clark)

39. Does ‘control’ actually work?

40. Do we all still trust ‘retrospective’ data interpretation for clinical practice?

41. A ‘Virtusstat in medio’ Compromise

42. AR=761/1861 (41%)!!

43. Conducting a phase III trial in the traditional way with broad eligibility • May result in a false negative trial • Unless a sufficiently large proportion of the patients have tumors driven by the targeted pathway • May result in a positive trial • With overall results driven by a subset of patients • Resulting in subsequent treatment of many patients who do not benefit • May provide conflicting results by subgroup analysis mis-interpretation Modified - Courtesy of Simon R, 2008

44. Example: if you test 10 subgroups, your chance is: ~40% ~9% ~2%

46. Biomarker Research Strategy

47. How to deal with the target… • 'Randomize all'design • Biomarker-Stratified Design • 'Targeted'design • Enrichment Design • 'Strategy'('customized') design • Biomarker-Strategy Design • Combo

48. ‘Randomize-All’ Design

49. 1 Courtesy of Billingham C, 2008