The Evolving Role of Consensus Metrics in Monitoring, Informing and Predicting Drug

Development Performance

Ken Getz, MBA

Director, Sponsored Programs, Associate Professor

CSDD, Tufts University School of Medicine

November 14, 2018

Primary Characteristics of Metrics in the Future

• Integrated from multiple internal and external sources

• Flexible and adaptive

• Predictive and Proactive

• Continuously learning/Iterative

• Highly accessible and transparent

Agenda

• Drug Development Operating Conditions and Challenges

• Macro-Level Trends Driving the Need for Consensus Metrics

• Mapping the Adoption of Consensus Metrics

• Projecting the Future Role of Consensus Metrics

• Focus on Great Science • Rigid Linear and Sequential Processes

• Insular • Compartmentalized

• Centralized Ownership and Risk

• Proprietary Clinical Data

• Participant as Subject

• PI Oriented Clinical Trials

• Revenue and Profit Driven

Today’s ‘Norm’: Drug Development for the Past 75 Years

A Robust Innovation Engine

4,885 5,482

6,476 6,531

8,010 8,617

9,349 10,150

10,752 10,903

2000 2002 2004 2006 2008 2010 2012 2014 2016 2017

Total Active Drugs in Global R&D Pipeline

Pipeline Growth by Therapeutic Area

Source: FDA

2006 2016 10-Year CAGR

Anticancers

2,069 4,176 7.3%

Neurologics 1,519 4,051 10.3%

Anti-infectives 1,258 2,221 5.9%

Metabolic/Endocrine 1,011 1,999 7.1%

Musculoskeletal 787 1,499 6.7%

Cardiovascular 679 950 3.4%

Immunologics 404 869 8.0%

Respiratory 435 859 7.1%

Dermatologics 403 831 7.5%

Proliferation of Small Sponsors

Overall New Trial Starts

Growth

Top 25 BioPharm

Small-Mid

BioPharm

Small-Mid Proportion

of Total

2009-11 2.4% -3.9% 3.43% 84%

2012-14 6.0% -4.1% 7.4% 89%

2015-17 4.2% 0.4% 4.6% 91%

Number of Companies with

Active Drugs in the R&D Pipeline

Non-Top 50 Pharma Company Share of

Active Pipeline

2000 1,043 49%

2005 1,621 53%

2010 2,207 56%

2015 3,286 61%

New Clinical Trials Initiated Globally…

Source: Pharmaprojects; Tufts CSDD

High Annual and Capitalized Costs

$33.9

$54.6

$94.2

$127.4

$142.2

1995 2000 2005 2010 2015

Source: EvaluatePharma; William Blair & Wells Fargo Securities

$ US Billions

$1,044

$2,558

2003 2013

$US Millions (2013 dollars)

• 26% Direct Costs • 18% Time-Based • 56% Cost of Failure

Capitalized Cost to Develop a Successful Drug Annual Global Spending on Pharma R&D

High and Rising Development Risk

Source: Tufts CSDD

Percentage of All Drugs Entering Clinical Testing that FAIL to Receive Approval

78.7% 80.9%

83.6% 88.1%

in the 1980s 1990s 2000s 2010s

26.1%

19.1%

15.1% 14.7% 13.2% 12.8%

11.4% 11.1%

8.4%

6.6% 6.2% 5.1%

Probability of Achieving Regulatory Approval by Disease

Drug Development Cycle Time (Years from IND Filing to NDA Approval)

6.3 6.8 7.2 5.9 6.0 6.1 6.3 6.8 6.7 6.8

2.9 2.6 2

1.4 1.2 1.75 1.6

1.5 1.4 1.6

87-89 90-92 93-95 96-98 99-01 02-04 05-07 08-10 11-13 14-16P

Mean Clinical Time Mean Approval Time

Source: Tufts CSDD

Return on New Drug/BLA Approvals

Mean Peak Sales per Drug/BLA (in $US Millions)

Return on Investment (IRR)

2008 - 2010 $684 8.1%

2011 - 2013 $571 6.7%

2014 - 2016 $427 4.8%

Source: Deloitte

Rare/Orphan Disease Success Rates and Cycle Times

Drugs with Orphan

Indications

Number Approved

Approval Rate

1986 31 5 14%

1996 58 25 43%

2006 142 24 17%

2016 333 39 12%

AVERAGE 16%

7.8

10.7

12.8

All ApprovedDrugs (2000-

2016)

SpecialtyDiseases

Rare Diseases

IND Filing to Approval (in years)

Source: Tufts CSDD

Fundamental Challenges

• Intensifying protocol complexity

• Highly fragmented operating processes • Limited coordination & integration • Growing reliance on external service providers • Increasing diversity of point-based solutions

• Outdated, reactive and tactical practices

• Underutilized assets

Trends in Protocol Design Practices Typical Phase III Pivotal Trial (means) 2001 - 2005 2011-2015 Change

Total Number of Endpoints 7 13 86%

Total Number of Eligibility Criteria 31 50 61%

Total Number of Distinct Procedures 22 35 59%

Total Number of Procedures Performed 110 187 70%

Total Number of Planned Volunteer Visits 12 15 25%

Proportion of Data ‘Non-Core’ 18% 32% 78%

Number of Investigative Sites 40 65 63%

Number of Countries 5 10 100%

Number of Patients Randomized 729 597 -18%

Direct Cost Per Procedure per Patient per Visit $728 $978 34%

Total Data Points Collected 494,236 929,203 88%

Number of Data Collection Applications Used 2 6 200%

Source: Tufts CSDD

14.0%

18.3%

19.5%

26.5%

32.3%

49.4%

61.1%

69.7%

70.4%

77.4%

100.0%

Study start-up

Electronic source data capture (eSource)

Electronic Medical Record (EHR/EMR)

Investor Grant Payments

Paper CRF

eCOA / ePRO

Clinical Trial Management System (CTMS)

Electronic Trial Master File (eTMF)

Safety/Pharmacovigilance

Randomization and Trial Supply Management

Electronic Data Capture (EDC)

Clinical Data Management Applications Used Percent of companies using either

proprietary or commercial applications

Source: Tufts CSDD, 2017; N=287 biopharmaceutical companies

Impact on Performance

Source: Tufts CSDD, 2015

Typical Phase III Pivotal Trial (means)

‘High’ vs. ‘Low’ Complexity Protocols

Recruitment

Time from Protocol Ready to FPFV

+12% longer

Time from Protocol Ready to LPLV

+73% longer

Typical Phase III Pivotal Trial (means) 2001-2005 2011-2015

Study Start-Up

Total Cycle Time from Site Identification to FPI 25.6 weeks (CoV .63)

29.1 weeks (CoV .76)

Data Management

Time to Build Study Database 65.7 days (CoV .41)

68.3 days (CoV .48)

Time from Patient Visit to Data Entry 6.9 days (CoV .66)

8.1 days (CoV .89)

LPLV to Data Base Lock 33.4 days (CoV .75)

36.1 days (CoV .93)

Frequency of Amendments and Change Orders

Source: Tufts CSDD, 2015

1.8

2.2 2.3

1.9

Phase I Phase II Phase III Phase IIIb/IV

Mean Number of Amendments per Protocol

Mean Number of Change Orders per Study

1.1

2.5

4.6

2.3

Phase I Phase II Phase III Phase IV

R&D Spending Segments

$38.3 $60.4

$71.6 $68.6 $65.7 $10.4

$24.3

$43.1 $59.7 $78.6

$5.9

$9.5

$12.7

$13.7

$15.3

2000 2005 2010 2015 2020P

Internal Staff and Infrastructure CRO Services Investigative Site Services

Source: EvaluatePharma; CenterWatch; William Blair & Wells Fargo Securities

$ US Billions

Collaboration Disconnects

• Sponsors using an average of three primary outsourcing models – transactional, full and FSP -- simultaneously

– No observed commitment to a single model

• 38% of sponsors believe that their collaborations consistently fail to meet cost and cycle time expectations

• Only 14% of CROs report that they have opportunities to regularly participate in upfront planning and study design

9%

14%

18%

27%

34%

Protocol Deviations

Number of Change Orders

Site Start-Up Speed

Data Quality

Enrollment Speed

Most Frequent KPIs Measuring Relationship Effectiveness

Source: Tufts CSDD, 2017

2008 2009 2010 2011 2012 2013 2014 2015

Multi-year filers First time filers

The Global Community of FDA-Regulated Investigators

Source: Tufts CSDD

33,920

24,805

29,670

27,604 28,521 28,872

30,069

32,816

3.5

6.5

5.2

7.9

17.5

22.0

0 5 10 15 20 25 30 35 40

Repeat Sites

New Sites

Site ID Site Selection Study Start-up

26.2 weeks

36.4 weeks

Site Identification to Initiation Cycle Times (Weeks)

Investigative Site Performance

Source: Tufts CSDD, 2016 and 2017

Plan to Actual Timelines

Fail to Enroll a Single

Patient 11%

Under Enroll 37%

Meet Enrollment

Targets 39%

Well Exceed

Enrollment Targets

13%

Enrollment Activation

and Achievement Rates Increase in Planned Study Duration to Reach Target

Enrollment

Overall 94%

Cardiovascular 99%

CNS 116%

Endocrine/Metabolic 113%

Oncology 71%

Respiratory 95%

Mapping the Adoption of Consensus Metrics

1980 – 2000s 2000 - 2020 Post - 2020

Insular Comparative, Pre-competitive

Basic, lagging Intermediate, root cause

Reactive Responsive

Static Pre-approved adaptive

Low – limited accessibility Improving accessibility

Current and Projected Sources of Clinical Research Data

Current Projected in 3 Years

Electronic and Paper Case Report Forms 100% 100%

Local and Central Labs 60% 65%

Smart Phones 45% 92%

Electronic Clinical Outcomes Assessments 21% 93%

Electronic Health and Medical Records 20% 67%

eSource 38% 84%

Mobile Health and Wearable Devices 29% 76%

Social Media 6% 27%

Source: Tufts Center for the Study of Drug Development, 2018; N=257 pharmaceutical, biotech and contract research companies

Three Transformational Movements

23

• Multi-stakeholder mobilization and consensus • Relevance, trust and ownership • Feasibility, simplicity and convenience • Transparency and disclosure

• Enlightened Design and Planning • Targeted Site and Patient Identification • Real time adaptation and adjustment • Ongoing line-of-sight • Predictive analytics machine learning

• Continuous assessment and learning from patient response to available and investigational treatments and diagnostics

• Open, secure, integrated collection, storage and access to patient health and medical data

• Sophisticated utilization of structured and unstructured data

Learning Health

Systems

Patient Engagement

Rich Data & Analytics

• Focus on Great Science • Rigid Linear and Sequential Processes

• Focus on Great and Feasible Science • Multi-Directional, Iterative, Adaptive

• Insular • Compartmentalized

• Integrated • Open

• Centralized Ownership and Risk • Shared Ownership and Risk

• Proprietary Clinical Data • Stakeholder Learning from Patient Experience

• Participant as Subject • Participant as Partner

• PI Oriented Clinical Trials • Revenue and Profit Driven

• Patient/Patient Data oriented clinical trials • Health outcomes driven

From ‘Company-’ to ‘Patient-Centric’ Drug Development

Mapping the Adoption of Consensus Metrics

1980 – 2000s 2000 - 2020 Post - 2020

Insular Comparative, Pre-competitive Open

Basic, lagging Intermediate, root cause Advanced, leading

Reactive Responsive Predictive

Static Pre-approved adaptive Continuous, flexible learning

Low – limited accessibility Improving accessibility High Cross-Platform Accessibility

Anticipating Needs and Opportunities

New Models New Skills and Competencies New Infrastructure

• Patient informed and engaged design and execution

• Embedded within large clinical care settings

• Optimized for convenience, open innovation and transparency

• Broader mix of clinical trials by size, scope and design

• Roving, flexible clinical research professionals

• Patient and professional navigators

• Data scientists

• Recognized/certified capabilities and support

• HCP trained/enabled

• Portable, mobile solutions

• Data-oriented vs. process-oriented

• Open, cloud-based

• Unified, integrated data

• Interrogative and predictive analytics; machine learning

• Remote, risk-assessment based analytics

Ken Getz

Founder and Board Chair, CISCRP

Director, Sponsored Programs

Associate Professor

CSDD, Tufts University School of Medicine

617-636-3487, Kenneth.getz@tufts.edu