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From the Clinic to the CFOAdaptive Trials and

Financial Decision-Making

July 10th, 2014

Shaping the Future ofDrug 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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Today’s presenters

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Nitin Patel, Ph.D.Chairman, Founder, and CTOCytel Inc.nitin@cytel.com

Frank S. David, M.D., Ph.D.Managing DirectorPharmagellan LLCfrank@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 onstrategic 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”?

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Defining trade-offs

How can we optimize our costs and benefits?

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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-frontunless 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)

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40%

50%

60%

70%

80%

90%

100%

0.710.740.770.80.830.86

Pro

bab

ility

of

Su

cces

s

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

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40%

50%

60%

70%

80%

90%

100%

0.710.740.770.80.830.86

Pro

bab

ility

of

Su

cces

s

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”

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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

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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 DecisionAgreement withRoyalty 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/Unfavorable

• 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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“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

Arms Arms

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 M36 mo

($10.6 M)

Scenario 1(maximize value)

75% 42 mo $42.3 M31 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

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Summary: Adaptive trials and financial decision-making

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Integrate trial planning with analysis of financial metrics

Transparently agree onstrategic 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