Webinar: Knowledge-based approaches to decreasing clinical attrition rates

May 2018

Speakers: Dr. Richard K. HarrisonGavin ConeyTeresa Fishburne

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• Clarivate Analytics is the global leader in providing trusted insights and analytics to accelerate the pace of innovation

• Delivering intelligence for Life Science professionals in discovery, preclinical, clinical, commercialization and generics

• Powering life science data and trusted content with analytics

• Clarivate Professional Services offer unrivalled data science expertise, evidence-based consulting and independent advice across the pharmaceutical R&D value chain

Xtalks Partner

for this Event

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• Over 30 years of experience in the life sciences industry

• Career has focused on all aspects of pre-clinical drug discovery

• Has held positions of increasing responsibility at Aventis, Merck, DuPont and Wyeth Pharmaceuticals

Dr. Richard K. HarrisonChief Scientific Officer

• Supports decision making by professionals within Life Science organizations who are interested in gaining intelligence relating to: • Clinical Development• Clinical Operations • Competitive Intelligence

• Has worked within informatics for 18 years and within the Life Sciences for the last 8 years

Gavin Coney Head of Clinical Products

• Over 20 years of industry experience in Clinical Operations, Regulatory Affairs and Quality management

• Manages a team of clinical and regulatory professionals that create high-value solutions to address customers’ever-evolving needs for strategic intelligence.

Teresa FishburneHead of Clinical & Regulatory

Professional Services

Clarivate Analytics speakers

Industry Overview

Dr. Richard K Harrison

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What is the current rate of clinical trial attrition?

5% 7%

17%

65%

91%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Animal model tomarket

Phase I tomarket

Phase II tomarket

Phase III tomarket

Submission tomarket

Pro

bab

ility

of

succ

ess

to

mar

ket

• According to the Centers for Medical Research (CMR) the probability of success moving from Phase 1 to market is less than 10% over all therapy areas

Ref: CMR Factbook, 2016

6

What are the main causes of attrition?

• Efficacy is the reason for failure in more than half of Phase II and Phase III trials

• Failure is across all therapy areas with Oncology being the greatest

52%

3%

24%

15%

6%

Reason for failure 2013-2015

Efficacy Operational Safety Strategy Commercial

32%

17%

13%

5%

7%

7%

6%

13%

Percentage failure by therapeutic area

Oncology Central nervous system

Musculoskeletal Infectious disease

Cardiovascular Alimentary

Metabolic Other

Nature Reviews Drug Discovery 15, 817–818 (2016)

7

What are the main causes of attrition?

• According to a study by Astra Zeneca

40% of their projects failed in clinical

trials because no clear link was made

between the target and the disease

• An additional 29% failed

because the compound

chosen did not have the

correct physical properties or

did not reach the target tissue

Nature Reviews Drug Discovery 13419-431 (2014)

8

Clinical failure rates and reasons for failure

• According to the Centers for Medical Research (CMR) in

the year 2015 the probability of success moving from

Phase 1 to market is less than 10% over all therapy

areas

• Efficacy is the reason for failure in more than half of

Phase II and Phase III trials

• According to a study by Astra Zeneca 40% of their

projects failed in clinical trials because no clear link was

made between the target and the disease

• An additional 29% failed because the

compound chosen did not have the correct

physical properties or did not reach the target

tissue

Nature Reviews Drug Discovery 15, 817–818 (2016)

9

Knowledge based approaches to decrease attrition

• Insights into trial endpoint success

• Increased use of biomarkers to decrease attrition

• Correlation to trial success

• Optimum number of biomarkers

• The impact of protocol amendments on trial success

• Impact budget planning,

• Impact on patient enrollment

• Impact on cycle times.

• Incorporating strategies to reduce the number of amendments

• When are amendments required

• Making amendments more effective.

Source: Clarivate Analytics, Cortellis Clinical Trials Intelligence

Insights Into Trial Endpoint Success

Gavin Coney

11

Trial protocol design: rising data / intelligence challenge

Granularity of disease understanding: more datapoints and greater specificity

Increasing data collection (Datapoints collected per patient visit)

Rising volumes of published results Real World Evidence

Increased data volumes from multiple cohorts

Digital Health

0

20

40

60

80

Phase 1 Phase 2 Phase 3

2001 - 2005 2011 - 2015

Cap Gemini, Healthcare survey

Medidata: Analysis of hosted trial data

0

5000

10000

15000

20000

25000

1995 2000 2005 2010 2015 2020

12

Dataset: Granular trial design components indexed for every trial

• Condition

• Phase

• Recruitment Status

• Interventions

• Combination?

• Action

• Class

• Intervention Category

• Trial Design

• Active Control

• Start Date

• Primary Endpoint Completion Date

• Enrolment End Date

• End Date

• Patient Segment

• Biomarker

• Biomarker Type

• Biomarker Role

• Age

• Race

• Inclusion Criteria

• Exclusion Criteria

• Endpoints

• Endpoint Types

• Endpoints Met?

• Results

• Adverse Events

• Sponsor / Collaborator

• Commercially Relevant?

• Site Name

• Contact Name

• City

• State

• Country

• Region

• Enrolment Count

• Enrolment Rate

Trial / Intervention Trial milestones Trial Protocol Insights Patient Enrollment

Source: Cortellis Clinical Trials Intelligence, Clarivate Analytics, April 2018

• Over 300,000 Trials• Focus on drugs, biomarkers and devices• All therapy areas; Global coverage

13

Rising use of biomarkers%

of

Tri

als

Ap

ply

ing

Bio

mar

kers

By

Ro

le

% G

row

th In

Ap

plic

atio

n O

f B

iom

arke

r R

ole

s O

ver

5 y

ear

s

Biomarker Role

Therapeutic Effect Toxic Effect Disease

Source: Biomarker Roles Within Clinical Trials. An Analysis Of Clinical Trials from 2007-2011 and 2012-2016. Clarivate Analytics. Coney, Gavin. Clarivate Analytics, Cortellis Clinical Trials Intelligence. www.clarivate.com

Disease marker: The biomarker indicates if a disease already exists (diagnostic biomarker), or how such a disease may develop in an individual case regardless of the type of treatment (prognostic biomarker).Therapeutic effect marker: The biomarker gives an indication of the probable effect of treatment on the patientToxic effect marker: The biomarker indicates a treatment-related adverse reaction

14

Growth in trial volume by phase

Source: Cortellis Clinical Trials Intelligence, March 2018

All trials within the time period, irrespective of whether the trial endpoints were met

Nu

mb

er o

f T

rial

s

End Year

Phase 2

Phase 3

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Trial endpoint success: Reporting bias

Nu

mb

er o

f T

rial

s

Endpoints Achieved

Endpoints Met

Yes No

• Phase 1 – 4• 4,550 trials with known outcomes with start date 2005 or later• Trial endpoint success determined by explicit statement made by sponsor referencing

the endpoint

Source: Cortellis Clinical Trials Intelligence, March 2018

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Are phase II trials failing quietly? Focus on phase 3 successes

Nu

mb

er o

f T

rial

s

Phase 2 Phase 3 Endpoints Met

Commercially Relevant: Is the primary intervention being studied owned by the trial sponsor?

Source: Cortellis Clinical Trials Intelligence, March 2018

Phase 2 Phase 3Phase / End Year

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Biomarkers and trial endpoint success

Pro

po

rtio

n O

f Tr

ials

Are Biomarkers Used?

Yes No

Endpoints Met

Source: Cortellis Clinical Trials Intelligence, March 2018

Is at least one biomarker applied within the trial design. Could be biomarker for efficacy, toxicity or disease

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Number of biomarkers vs endpoint success by phaseP

has

e

Average number of biomarkers

Endpoints Met

• All trials with known outcome; Segmented By Phase and then trial endpoint status• Mean of Number of Biomarkers within each of the 8 groups

Source: Cortellis Clinical Trials Intelligence, March 2018

Phase 1

Phase 4

Phase 3

Phase 2

1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4

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Segmenting biomarker role and endpoint successN

um

ber

Of

Tria

ls –

End

po

int

No

t M

et

Biomarker Role Combinations

• All trials categorized according to the combination of biomarkers• Ranked by descending number of trials that were reported to have reached their endpoint

(green = yes; red = no)• Yellow bar displays the % of trials in each category that were reported as successful

Source: Clarivate Analytics, Cortellis Clinical Trials Intelligence

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic Effect

Disease

% o

f ca

tego

ry t

hat

did

re

ach

en

dp

oin

t

Nu

mb

er o

f T

rial

s –

End

po

int

Me

t

21

Endpoint success and application of biomarker rolesN

um

ber

of

Tri

als –

End

po

int

No

t M

et

Biomarker Role Combinations

% of Trials = Yes

Source: Clarivate Analytics, Cortellis Clinical Trials Intelligence

Disease=YesNoneAllToxic=Yes

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

Therapeutic Effect

Toxic EffectDisease

% of Trials using this combination that were

successful

Endpoints Not MetEndpoints Met% of endpoints met in category

Endpoints Met

Therapeutic Effect

Toxic Effect

Disease

Rank order now selected as % of trials in category that were reported as reaching their endpoint

Nu

mb

er o

f T

rial

s –

End

po

int

Me

t

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Summary

• A biomarker strategy has been seen to positively impact likelihood of meeting trial endpoint

• That effect is not uniform:-

• Positive Phase 2-4; Negative Phase 1

• Toxic Effect markers strongly associated

• Disease markers not strongly correlated

• Need for the best intelligence to inform protocol design

Impact and Strategies Relating To Trial Amendments

Teresa Fishburne

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Amendments

Source: CMR Global Clinical Performance Metrics Programme. CMR International, a Clarivate Analytics businessSource: Getz, et al Tufts CSDD

0

100

200

300

400

500

600

2 or moreamendments

2 or moreamendments

no or 1 amendment no or 1 amendment

Internal Protocol Review -‘Protocol synopsis developed’ to ‘Protocol approved internally’

520 days

43 days62 days

383 days

Protocol Approved to End of Enrolment‘Protocol approved internally’ to “last subject enrolled’

Med

ian

Du

rati

on

(in

Day

s)

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Budget impact and key facts

❖ $20 Billion annual spend in direct and indirect costs

❖ 45% of all amendments are avoidable

❖ 2/3 Phase III trials > 1 global amendment

❖ 74% of all Phase II trials > 1 global amendment

❖ Rare diseases amendments > non-rare

Source: https://aspe.hhs.gov/report/examination-clinical-trial-costs-and-barriers-drug-developmentSource: CMR Global Clinical Performance Metrics Programme. CMR International, a Clarivate Analytics businessSource: Getz, et al Tufts CSDD

$0

$10,000,000

$20,000,000

$30,000,000

$40,000,000

$50,000,000

$60,000,000

Average Cost of Trial Average Cost of Trial After 1Amendment

Average Cost of Trial After 2Amendments

Phase II

Phase III

$12M $13.2M

$35M

$14.4M

$50M

$20M

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Enrollment and cycle times

Source: CMR Global Clinical Performance Metrics Programme. CMR International, a Clarivate Analytics businessSource: Getz, et al Tufts CSDD

30%

60%

10%

0%

10%

20%

30%

40%

50%

60%

70%

Start-up Enrolment Treatment Duration

Proportion of protocol amendments by timing interval

Data are shown for Phase Ip, Phase II and Phase III studies that completed enrolment (Last Patient Enrolled milestone) between 2011 and 2015 and had one or more protocol amendment. This analysis includes ongoing and terminated studies.

Amendments

Enro

llmen

t

0 1 2 3

Cycle time

Trial Budget

Strategies❖ Biomarkers❖ Inclusion / Exclusion Criteria

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Strategy to reduce amendments – use of biomarkers

❖ Rare disease programs and programs that utilized selection biomarkers had higher success rates at each phase of development vs. the overall dataset.

❖ A three-fold higher likelihood of approval from Phase 1 was calculated for programs that utilized selection biomarkers (25.9%, n=512) vs. programs that did not (8.4%, n=9,012).

❖ Bio article concluded that the enrichment of patient enrollment at the molecular level is a more successful strategy than heterogeneous enrollment.

❖ ~150 biomarkers are included on FDA approved drug labels. Such a strategy is associated with improved approval success and reduced approval timelines

Source: Clarivate Analytics, Cortellis Clinical Trials IntelligenceSource: Hay et al; Clinical development success rates for investigational drugs. Nature Biotechnology (2014)Source: BIO, Biomedtracker (2016)

0%

10%

20%

30%

40%

50%

60%

70%

80%

Ph 1 - Ph 2 Ph 2 - Ph 3 Ph 3 -NDA/BLA

Ph 1-Approval

WithoutBiomarkers

With SelectionBiomarkers

Probability of Success With and Without Biomarkers

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Strategy to reduce amendments – Inclusion/exclusion criteria

• Build off approved/launched competing drugs

with like MOA/indications

• Build off internal Phase 1/2

• Literature search

• New or novel criteria

• Incorporating Biomarkers:

• By roles (Therapeutic effect, disease marker, toxic

effect)

• By type (genomic, proteomic, physiological,

biochemical, cellular, structural,

anthropormorphic)

• Biomarkers to address responders vs non-

responders

28Source: Clarivate Analytics, Cortellis Clinical Trials Intelligence

• Newly emerging medical knowledge

• Regular violations of entry criteria

• Low recruitment rates

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Overall strategy to reduce amendments – Key points

Protocol Review Biomarkers Inclusion / Exclusion Criteria

• 19 additional days of internal review= 0 or 1 amendments

• Longer internal review time = shorter trials

• Incorporate more internal review approaches

• Bundle non-urgent amendments, where applicable

• programs that utilized selection biomarkers had higher success rates at each phase of development vs. the overall dataset.

• enrollment at the molecular level is a more successful strategy than heterogeneous enrollment.

• ~150 biomarkers are included on FDA approved drug labels.

• Using internal data to build criteria

• Build off

approved/launched

competing drugs

with like

MOA/indications

• Assess feasibility / appropriateness

• Use of predictive biomarkers to identify responders vs non-responders

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Questions

Cortellis images, CTI, CCI

To learn more go to: Clarivate.com/Cortellis