2. 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
3. Exploratory Drug Development- Phase I/IIExploratory Drug Development Phase I/II
P li i l
Phase 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/PK
E l i f
• Establish POC
Inform 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 (response
rates, 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
4. Success Rates from First-in-Man to
RegistrationRegistration
Kola & Landis (2004) Nature Rev Drug Disc
5. Success Rates by Phase of DevelopmentSuccess Rates by Phase of Development
Kola & Landis (2004) Nature Rev Drug Disc
6. Cost of Drug Development by PhaseCost of Drug Development by Phase
Roy A (2012) Manhattan Institute
7. 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…
8. Goals by end of PoCGoals by end of PoC
P li i l
Phase 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 POC
Inform 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
9. 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
10. 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 value
HR TV HR MAV
HR ≤ 0.63 &
0.63 0.80 1.00
PFS
Hazard 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‐evaluate
1-sided
significance
level of 0.10
and 80%
power
GO to phase III NO GO
11. 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 withyp 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
12. Attrition of Last-Stage Drug DevelopmentAttrition of Last Stage Drug Development
Adapted from Arrowsmith 2011
13. 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
14. 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
15. 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
16. 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
18. 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
19. 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
20. ReferencesReferences
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Cartwright ME, Cohen S, Fleishaker JC, et. al. Proof of concept: a PhRMA position paper with
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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.
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