Clinical Proof-of-Concept (POC)

Ashwin Gollerkeri, M.D.

OutlineOutline Exploratory Drug Development Defining POC Defining POC Improving Probability of Success with Greater

Emphasis on POCp Improving POC Through Better Patient Selection Practical Considerations of Patient Selection

Strategies Summary/conclusion

Exploratory Drug Development- Phase I/IIExploratory Drug Development Phase I/II

P li i lPhase I (Fi t i

Phase II (P f f Ph III R i t tiPre-clinical (First-in-

Human)(Proof-of-Concept)

Phase III Registration

• Evaluate safety/PKE l i f

• Establish POCInform phase 3

Phase 1 Phase II

S f t Effi (

• Inform phase 2

• Early signs of efficacy

• Inform phase 2

• Inform phase 3

Endpoints • Safety• PK

• Efficacy (responserates, time-to-event)

Outcomes • Dose/regimen for Phase 2 and beyond

• Efficacy sufficient for Phase 3/ registration

Design • Dose-escalation • Single-arm• Randomized

Patient population • Unrestricted • Restricted to

indication

Sample size • 30-50 • 75-150

Success Rates from First-in-Man to RegistrationRegistration

Kola & Landis (2004) Nature Rev Drug Disc

Success Rates by Phase of DevelopmentSuccess Rates by Phase of Development

Kola & Landis (2004) Nature Rev Drug Disc

Cost of Drug Development by PhaseCost of Drug Development by Phase

Roy A (2012) Manhattan Institute

Defining POCDefining POC PhRMA recommendation:“POC is the earliest point in drug development processPOC is the earliest point in drug development process at which the weight of evidence suggests that it is “reasonably likely” that the key attributes for success are present and the key causes of failure absent” Varies by person, project, candidate, etc…

Goals by end of PoCGoals by end of PoC

P li i lPhase I (Fi t i

Phase II (P f f Ph III R i t tiPre-clinical (First-in-

Human)(Proof-of-Concept)

Phase III Registration

• Establish POCInform phase 3

We’d like to know

• Inform phase 3

We d like to know Best dose and regimen (safety driven) Indications Administered in combination with what, if anything Patients who will respond

Pfizer Confidential │ 8

Degree of efficacy

POC Essential ElementsPOC Essential Elements Define essential elements of target product profile Determine level of risk tolerance for POC Determine level of risk tolerance for POC Low weight of evidence when consequences of being

wrong are benign and benefits of speed are high High weight of evidence where consequences of wrong High weight of evidence where consequences of wrong

POC are severe Determine which elements of POC are already

“reasonably likely” on basis of prior information Determine which of the remaining elements are not

likely to be significant threats to the programlikely to be significant threats to the program Determine which of the still remaining elements

cannot be practically evaluated or changed

Decision Criteria for POCDecision Criteria for POC Based on outcome Set required effect size (∆) lower reference value Set required effect size (∆), lower reference value

(LRV) below which, drug would not have valueHR TV HR MAV

HR ≤ 0.63 &

0.63 0.80 1.00PFSHazard ratio

HR ≤ 0.63 &Upper bound of 90% CI < 1 &Upper bound of 50% CI ≤ 0.80

GO to phase III NO GO

HR≥ 0.80

Re‐evaluate1-sided significance level of 0.10 and 80% powerGO to phase III NO GO

Examples of Oncology POC TrialsExamples of Oncology POC Trials Design Randomized in same patient population as phase

III/registration Standard-of-care (SOC) +/- Experimental compound Experimental compound vs. SOC

Single- arm trial Compare with historical controls

Endpointsp Same as endpoints which will be used in phase III/Registration

Time-to-event (OS, PFS) PFS typically favored due to shorter trial; need to correlate with yp y ;

registrational endpoint (usually OS)

Sample sizes Typically based on 1-sided α 0.05, 80% poweryp y , p

75‐80 patients per arm of randomized study

Attrition of Last-Stage Drug DevelopmentAttrition of Last Stage Drug Development

Adapted from Arrowsmith 2011

Patient Selection Strategies to Reduce Attrition in Phase 3Attrition in Phase 3

Advantages to using biomarker to identify patients likely to benefitpatients likely to benefit Smaller sample sizes required

More expeditious path to reach Go/No Go to phase 3

Less expensive Avoids treating patients with little opportunity of benefitting

Success of biomarker selection dependent on: Success of biomarker selection dependent on: Prevalence of marker Effect size Clinical performance of diagnostic assay

Patient Selection StrategiesPatient Selection Strategies Prospectively select biomarker positive patients Requires confidence that biomarker is predictive of Requires confidence that biomarker is predictive of

outcome Need for clinical data in biomarker-negative patients Enrich for biomarker positive patients in otherwise all-

comers trial Retrospective analysis of response based on Retrospective analysis of response based on

biomarker Efficacy signal may be diluted by biomarker-negative

patients Adaptive designs to incorporate biomarker

information in Phase 2information in Phase 2

Patient Selection Strategies Incorporation of stratified medicine approach leads to increase in

eNPV versus all-comers

Patient Selection Strategies

• Increased eNPV when patient selection used prospectively (e.g. trastuzumab)

• Negative effects of eNPV when patient selection used retrospectively (e.g. panitumumab)

Trusheim et. al. 2011

Evolution of Non-Small Cell Lung Cancer TreatmentTreatment

Martin Reck , et. al., The Lancet, Volume 382, Issue 9893, 2013, 709 - 719

Reck et. al. 2013

Clinical Experience in 1st L NSCLC: All-comers vs. patient selectioncomers vs. patient selection

Patient Efficacy

Response Progression-free Overall survival: moPopulation Treatment Sample Size Rate: % survival: mo Overall survival: mo

Paclitaxel-cisplatin

1599 23-25 (v. 12)

4.3-4.9 (v. 2.7)

9.3-10.0 (v. 7.4)

Gemcitabine- 2522 26 5.2 9.0

All-comers cisplatin2522 (v. 10) (v. 3.7) (v. 7.6)

Gefitinib(EGFR-inhibitor) +

31,09341,03751,059 No improvement v. chemotherapy

chemotherapy 61,172

EGFR-mutated

Gefitinib(EGFR-inhibitor) v.

7261 71 (v. 47)

9.5 (v. 6.3)

21.6 (v. 21.9)

(15%NSCLC)

chemotherapy

Erlotinib v. chemotherapy

8174 65 (v. 16)

10.4(v. 5.2)

22.9(v. 19.5)

ALK- Crizotinib 65 7 7rearranged (5% NSCLC)

(ALK-inhibitor) 9347 65 (v. 20)

7.7 (v. 3.0) Not available

1Taxol® package insert; 2Gemzar® package insert 3Giaccone et. al. (2004);  4Herbst et. al. (2004);  5Herbst et. al. (2005); 6Getzemeier et. al. (2007); 7Iressa® package insert; 8Tarceva® package insert

Practical considerationsPractical considerations Need for sufficient patient samples CLIA-certified assays needed for patient selection CLIA-certified assays needed for patient selection Establishment of cut-offs for biomarker expression Prevalence of biomarker-positive population Prevalence of biomarker positive population Evidence for correlation between biomarker

expression and outcomep Seemless decision points if adaptive trial design

used Prevent extended slowing or stopping of enrollment

during study

Summary and ConclusionsSummary and Conclusions High cost of attrition in phase 3 setting is shifting

emphasis to better design of phase 2 trialsemphasis to better design of phase 2 trials Establishing appropriate POC in exploratory

development critical for improving probability of success in phase 3

Recent shift in oncology drug development toward f ti ti t l ti i tt t tfocus on prospective patient selection in attempts to address poor overall success rates

Adaptive study designs being considered to Adaptive study designs being considered to efficiently use biomarker data

Practical considerations need to be addressed to ensure success

ReferencesReferences Arrowsmith J. Phase II failures: 2008-2010. Nat Rev Drug Disc (2011);10:1 Cartwright ME, Cohen S, Fleishaker JC, et. al. Proof of concept: a PhRMA position paper with

recommendations for best practice. Nature (2010);87;278DiM i JA H RW G b ki HG Th i f i ti ti t f d DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econ (2003);22:151

Gatzemeier U, Pluzanska A, Szczesna A, et. al. Phase III study of erlotinib in combination with cisplatin and gemcitabine in advanced non-small-cell lung cancer: the Tarceva lung cancer investigation trial. J Clin Oncol (2007);25;1545G G S C G f Giaccone G, Herbst RS, Manegold C, et. al. Gefitinib in combination with gemcitabine and cisplatin in advanced non-small-cell lung cancer: a phase III trial-INTACT 1. J Clin Oncol(2004);22:777

Herbst RS, Giaccone G, Schiller JH, et. al. Gefitinib in combination with paclitaxel and carboplatin in advanced non-small cell lung cancer: a phase III trial- INTACT 2. J Clin Oncol (2004);22:785

Herbst RS, Prager D, Hermann R, et. al. TRIBUTE: a phase III trial of erlotinib hydrochloride (OSI-774) combined with carboplatin and paclitaxel chemotherapy in advanced non-small-cell lung cancer. J Clin Oncol (2005);23:5892

Kola I and Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Disc (2004);3:711

Reck M, Heigner DF, Mak T, et. al. Management of non-small-cell lung cancer: recent developments. Lancet (2013);382:709

Roy SA. Stifling new cures: The true cost of lengthy clinical drug trials. Project FDA (2012). Manhattan Institute for Policy Research. http://www.manhattan-institute.org/html/fda_05.htm

Trusheim MR Burgess B Hu SX et al Quantifying factors for the success of stratified medicine Trusheim MR, Burgess B, Hu SX, et.al. Quantifying factors for the success of stratified medicine. Nat Rev Drug Disc (2011);10:817