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From the Clinic
to the CFO
Adaptive Trials and
Financial Decision-Making
July 10th, 2014
Shaping the Future of
Drug Development
This is a Solution Provider Webinar brought to you by DIA in cooperation
with Cytel Inc. and Pharmagellan LLC.
The views and opinions expressed in the following PowerPoint slides are
those of the individual presenter and should not be attributed to Drug
Information Association, Inc. (“DIA”), its directors, officers, employees,
volunteers, members, chapters, councils, or Special Interest Area
Communities or affiliates.
These PowerPoint slides are the intellectual property of the individual
presenter and are protected under the copyright laws of the United States of
America and other countries. Used by permission. All rights reserved. Drug
Information Association, DIA and DIA logo are registered trademarks or
trademarks of Drug Information Association Inc. All other trademarks are
the property of their respective owners.
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2. 7/9/2014
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Today’s presenters
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Nitin Patel, Ph.D.
Chairman, Founder, and CTO
Cytel Inc.
nitin@cytel.com
Frank S. David, M.D., Ph.D.
Managing Director
Pharmagellan LLC
frank@pharmagellan.com
The work and ideas presented here today were developed in collaboration with colleagues
from Ernst & Young and with input from pharmaceutical industry R&D teams
Clinical development – the investor’s view
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• Expensive
• Slow
• Risky
• “Locked-up”
Images (clockwise from top): taxrebate.org.uk; socialcapitalmarkets.net; theguardian.com; firstsafetysigns.com
All of these reduce the value of an R&D investment

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Financial choices in clinical development
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We’re facing stiff
competition, so we need to
be fast.
There’s not enough in the
R&D budget – we need to
cut costs.
Our portfolio is too risky –
for this asset, we need to
increase POS.
It’s hard to know if this is
worth the total cost – we
need an early read.
Adaptive trial designs allow one to make trade-offs
How to integrate trial design and financial strategy
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Proposed SolutionsChallenges
Integrate trial planning with
analysis of financial metrics
Transparently agree on
strategic goals
Develop, analyze and refine
“investable” R&D options
• CFO and investors don’t
understand how trial
design impacts financials
• R&D and CFO / investors
don’t align on key variable
(cost, risk, time, value)
• CFO and investors often
view proposed R&D
investment as unattractive

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Two case studies
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Managing risk and cost
Can we make our trial
more “investable”?
1
Defining trade-offs
How can we optimize our
costs and benefits?
2
Linking Adaptive Trials to Financial Decision-Making
Case study #1 – Context
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Managing risk and cost
• Public small-cap biotech
• Pivotal trial for lead asset
• Limited resources
• External investment option?
Situation
Goals
• “Staged” trial investment
• Clear risk/reward profile in
“financial language”
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Base case study design
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* After cycle 1, all subsequent cycles at 70 mg/m2 vosaroxin on days 1 and 4
VALOR study – Sunesis Pharmaceuticals
Double-blind RCT of vosaroxin in relapsed / refractory AML
NCT01191801; figure taken from poster of Ravandi F. et al. (ASCO, 2012): http://meetinglibrary.asco.org/content/99304-114
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Strategic considerations
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Cytel analysis
• Fixed sample size design assuming Hazard Ratio (HR) =
0.71 has 90% power (450 patients accrued over 24 mo.
and 375 events observed with 6 mo. follow-up)
• But, if HR = 0.77, power drops to 70%
- 90% power at that HR would require >1.6x more patients
• Sunesis wanted to avoid incurring high cost up-front
unless assumption of HR = 0.71 turned out to be optimistic
Could adaptive design reduce up-front cost?
Could it also make opportunity attractive to investors?
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POS and efficacy at fixed sample size
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Cytel analysis
Lower-than-expected efficacy yields lower POS
(at same sample size)
1
40%
50%
60%
70%
80%
90%
100%
0.710.740.770.80.830.86
ProbabilityofSuccess
Hazard Ratio
450Sample Size:
Increasing Efficacy
“Buying POS” by increasing sample size
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Cytel analysis
When efficacy is lower than expected,
increasing sample size can boost POS
1
40%
50%
60%
70%
80%
90%
100%
0.710.740.770.80.830.86
ProbabilityofSuccess
Hazard Ratio
450 730Sample Size:
Increasing Efficacy

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Adaptive design: Interim sample size re-assessment
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End with
Increased
Sample Size
Transparent, pre-specified plan to increase sample size
only if interim analysis was in “promising zone”
1
NCT01191801; figure taken from poster of Ravandi F. et al. (ASCO, 2012): http://meetinglibrary.asco.org/content/99304-114
Performance of adaptive design
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HR = 0.71 HR = 0.77
Early stopping for futility 0.7% 2.9%
Early stopping for efficacy 26% 12%
Power 95% 80%
Average sample size 490 532
1
Analysis of key performance parameters1
Type 1 error controlled using Cui-Hung-Wang method (10,000
simulations in East® software)
1 Actual values of key design variables used in trial are blinded; analysis here uses illustrative values
based on “Combining Design and Execution of Adaptive Trials: AML Case Study”, C. Mehta and S.
Ketchum, DIA Annual Meeting (2011)
http://www.cytel.com/pdfs/Mehta-DIA-VALOR-ACES-2011.pdf
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Strategic impact
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Sunesis press release, March 29, 2012 (http://ir.sunesis.com/phoenix.zhtml?c=194116&p=irol-newsArticle&ID=1678333)
• Staged investment conserved resources for regulatory
filings and launch preparation
• Obtained external investor in transparent, de-risked trial
Interim Result Interim Decision
Agreement with
Royalty Pharma
Efficacy • Stop recruiting patients
• $25M milestone
• 3.6% royalty
Futility • Stop recruiting patients • No payments
“Promising
Zone”
• Increase sample size
• $25M milestone
• 6.75% royalty + warrants
Favorable/Unfav
orable
• Continue recruiting
patients to planned
sample size
• Option to invest $25M for
3.6% royalty upon
unblinding of trial
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Impact on Sunesis’s risk / reward profile
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Sources: Sunesis SEC filings, Leerink Swann equity research reports, Cytel / E&Y analysis (10,000 trial simulations)
• Increase in Power (70% → 80%)
• Lower odds of incurring a loss (41% → 25%)
• Higher expected net revenue over 10y (+$44M)
• For Royalty Pharma: Odds of incurring a loss = 7% and eIRR = 22%
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
(200,000) 0 200,000 400,000 600,000 800,000 1,000,000
Probability >
Net Revenue
10y Net Revenue ($000s)
Sunesis Partnered, Adaptive Design
Sunesis Fixed Design
Hazard Ratio = 0.77
Sample size increased
Interim stop

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Comments from partners
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1
“Sunesis’ use of an adaptive trial design offers us an opportunity to invest
in this promising biopharmaceutical product candidate on terms that are a
win-win for both Sunesis and Royalty Pharma:
Sunesis gains access to a flexible, novel financing structure and we are
able to invest in vosaroxin at a time when we believe its likelihood of
commercial success will be high.”
– Pablo Legorreta, CEO, Royalty Pharma1
“The innovative yet practical design provided multiple favorable scenarios
that allowed us to proceed with our pivotal Valor study …
It is difficult to imagine going forward with traditional methods alone.”
– Steven Ketchum, Sr. VP R&D, Sunesis Pharmaceuticals2
1 Sunesis press release, March 29, 2012 (http://ir.sunesis.com/phoenix.zhtml?c=194116&p=irol-newsArticle&ID=1678333)
2 S. Ketchum, personal communication
Case study #2 – Context
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Defining trade-offs
• Hypothetical Ph2-ready asset
• “Niche” indication
• Perceived low POS and value
• Limited management guidance
Situation
Goals
• Range of options with different
strategic implications
• Basis for discussion between R&D
and senior management
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Base case study design
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Maturation Period Maturation Period
Phase 2 (up to 41 months) Phase 3 (up to 55 months)
End End
Patient
Select.
and
Rand.
SOC
SOC +
DRUG
SOC
SOC +
DRUG
ArmsArms
Patient
Selection and
Randomization
$10.6 M*
68% PoS
$36.8 M*
75% PoS
Analysis
(~9 mos)
Recruitment
(20 months)
Recruitment
(35 months)
105 months • $47.4M cost • 59% POS
Prototypical development plan for niche hematologic oncology asset; inputs based on collaborator insights and industry benchmarks.
Are there faster, lower risk, and/or cheaper options?
What are the trade-offs with expected value?
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Various adaptive designs can meet strategic needs
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Base Case
Scenario 1: Group Sequential w/ 1 Interim Analysis (IA), Ph 2 / 3 hybrid
Scenario 2: Group Sequential w/ 2 IAs, Ph 2 /3 hybrid
IA #1
Planned
Ph 2
End
Planned
Phase 3
End
Ph 3
Start
IA #2 Up to
Required
Events
Up to
Required
Events
IA #1
Quarter
Goal: Maximize Value
Goal: Shorten Time to First Get Out
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Evaluating the options head-to-head
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POS Time eNPV First Get-Out
Base Case 59% 74 mo $5.1 M
36 mo
($10.6 M)
Scenario 1
(maximize value)
75% 42 mo $42.3 M
31 mo
($28.6 M)
Scenario 2
(shorten time to
first get-out)
59% 34 mo $34.9 M
17 mo
($12.2 M)
Cytel / E&Y analysis; implied distribution of HRs was calculated from “base case” POS (from industry assumptions) for use in scenario calculations
Basis for iterative discussion between R&D and
management of trade-offs and implications
2
Summary: Adaptive trials and financial decision-making
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Integrate trial planning with
analysis of financial metrics
Transparently agree on
strategic goals
Develop, analyze and refine
“investable” R&D options
• CFO and investors don’t
understand how trial
design impacts financials
• R&D and CFO / investors
don’t align on key variable
(cost, risk, time, value)
• CFO and investors often
view proposed R&D
investment as unattractive
Proposed SolutionsChallenges

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Backup
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Select references for further information
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On adaptive designs discussed in presentation
• Mehta CR, Pocock SJ. 2011. Adaptive increase in sample size when interim results
are promising: A practical guide with examples. Statistics in Medicine 30:3267-3284.
• Macca J et al. 2006. Adaptive Seamless Phase II/III Designs – Background,
Operational Aspects, and Examples. Drug Information Journal 40: 463-473.
On planning and implementation of adaptive trials
• Gaydos B et al. 2009. Good practices for adaptive clinical trials in pharmaceutical
product development. Drug Information Journal 43: 539-556.
• He W et al. 2012. Practical Considerations and Strategies for Executing Adaptive
Clinical Trials. Drug Information Journal 46:160-174.
Introduction to modeling financial returns from clinical trials
• Patel NR, Ankolekar S. 2007. A Bayesian approach for incorporating economic
factors in sample size design for clinical trials of individual drugs and portfolios of
drugs. Statistics in Medicine 26: 4976-4988.
Forthcoming books with broad coverage of topics discussed in presentation
• He W, Pinheiro J, Kuznetsova OM (ed.) 2014. Practical Considerations for Adaptive
Trial Design and Implementation. Springer (in press).
• Antonijevic Z (ed.) 2014. Optimizing the design and investment strategy of
Pharmaceutical R&D programs and portfolios. Springer (in press).

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Impact on Sunesis’s risk / reward profile (HR = 0.71)
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Sources: Sunesis SEC filings, Leerink Swann equity research reports, Cytel / E&Y analysis (10,000 trial simulations)
• Increase in Power (90% → 95%)
• Lower odds of incurring a loss (10% → 5%)
• Lower expected net revenue over 10y (-$10M)
• For Royalty Pharma: Odds of incurring a loss = 1% and eIRR = 24%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
(200,000) 0 200,000 400,000 600,000 800,000 1,000,000
Probability >
Net Revenue
10 yr Net Revenue ($000s)
Partnered, Adaptive design
Fixed sample size design
Hazard Ratio = 0.71