Optimizing Protocol Planning, Feasibility, and Site Selection through an Integrated View of Clinical Trial Operations and Other Data Sources

1. Optimizing Protocol Planning, Feasibility,
and Site Selection through an Integrated
View of Clinical Trial Operations and Other
Data Sources
Elisa Cascade

2. Session Format
• We will be testing out a new polling system
during this lunch session to solicit feedback from
attendees
• Following the session, results will be available to
participants in the SCOPE presentation slides
• Please be patient with us and the
new technology 
• Our fingers are crossed…
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3. Initial Test of the Polling System
Audience Poll #1:
• Which of the following best describes your
company / affiliation?
1.Pharmaceutical company
2.CRO
3.Other provider to pharmaceutical companies
and/or CROs
4.Investigator / Site Staff
5.Other
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4. Poll #1 Results
Pharmaceutical
Company
32%
CRO
23%
Other provider
to pharma/CROs
25%
Investigator/Site
11%
Other
9%
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Which of the following best describes your company/affiliation? (n=44)

5. Finding the Right Investigative Sites &
Accurately Predicting Enrollment
• Today’s challenge is matching the right
investigator to a particular protocol to avoid non-
enrolling or under enrolling sites
– 11% do not enroll a patient
– 37% fail to meet enrollment targets
• Better matching has the potential to decrease
costs, improve quality, & improve investigator
satisfaction
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Source: Tufts Center for the Study of Drug Development 2013.

6. Poll #2: Is Site Selection Evidence Driven
in Your Company?
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• Variation in how
pharmaceutical companies
and CROs select sites,
sometimes even within the
same organization
• Tendency is to work with the
sites you know
– Especially when the
process is decentralized
1. Use own list of investigators
or existing site relationships
2. Use of an internal database
with metrics
3. Use of an internal database
with metrics + at least one
other external data source
(e.g., 3rd party subscription)
4. Internal database with
metrics + external data
sources + EMR
5. Don’t perform this function
How does your company
select sites?

7. Poll #2 Results
24%
21%
38%
17%
0%
10%
20%
30%
40%
50%
Use own list of
investigators or
existing site
relationships
Use of an internal
database with metrics
Use of an internal
database with metrics
+ at least one other
external data source
(e.g., 3rd party
subscription)
Internal database with
metrics + external
data sources + EMR
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How does your company select sites? (n=29)
Note: n=12 don’t perform this function.

8. Operational Challenges with Relationship
& Evidence Approaches
• Process that relies on previous relationships
– Challenging to share knowledge across
projects/teams
– Organization may lack common tools for
accessing data
• Evidence-driven process
– Requires data sources to be used sequentially or
– May require manual effort to integrate data
across sources
Commercial solutions are available today to address these
challenges
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9. Case Example: DrugDev SiteCloud
• Integrates investigator, site, and protocol data in a
secure hosted system:
– Assigns a universal identifier known as
the DrugDev Golden Number, to match and
master records
– Toolset with an integrated view of information
indexed to the same DrugDev Golden Number
• In addition to helping individual companies, SiteCloud
also powers:
– The Investigator Databank collaboration
– The TransCelerate Investigator Registry
Technologies such as SiteCloud provide the platform and
toolset for evidence-based site selection
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10. Factors Used to Predict Site Performance
• Limited published literature around factors used
to predict site performance
• Potential factors mentioned across publications
include:
– Clinical research focus
– Site experience in the indication
– Available patient population
– Performance on previous studies
– Time to first subject consented
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11. Poll #3: What’s Most Important in Site
Selection?
Audience Poll:
• In your own experience, which of the following
factors do you consider to be most important
when selecting a site for a study?
1.Clinical research focus
2.Site experience in the indication
3.Available patient population
4.Performance on previous studies
5.Time to first subject consented
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12. Poll #3 Results
62%
19%
17%
2%
0%
0% 20% 40% 60% 80% 100%
Available patient population
Site experience in the indication
Performance on previous studies
Clinical research focus
Time to first subject consented
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In your own experience, which of the following factors do you consider to
be most important when selecting a site for a study? (n=42)

13. Alignment of Evidence to Predictive
Factors
• CTMS is the only source for site-level performance and speed
• Historically, CTMS data has been limited to internal company studies,
however, data sharing has emerged as an option for collaborations
(e.g., Investigator Databank)
Factor FDA 1572 Clinical Trials
Registries (e.g.,
clinicaltrials.gov)
Clinical Trial
Management
Systems (CTMS)
EMR/EHR
Research focus   
Site experience   
Performance on
previous studies

Speed 
Available patients 
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14. Poll #4: To Share or Not to Share?
1. No, we would not be
willing to share data
2. Yes, we would be willing
to share data, but only at
the aggregate/de-
identified level
3. Yes, we would be willing
to share data at the
investigator and
aggregate level
• Individual company attitudes
towards sharing differ based
on whether investigators and
data are seen as a:
– Competitive advantage or
– Shared resource
• Options for sharing:
– Aggregate level (de-
identified, consent not
required): supports country
selection and enrollment
planning
– Investigator level (requires
consent): informs site
selection
Would your company be willing to
share data to view others data?
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15. Poll #4 Results
No, we would
not be willing to
share data
23%
Yes, we would
be willing to
share data, but
only at the
aggregate/de-
identified level
31%
Yes, we would
be willing to
share data at the
investigator and
aggregate level
46%
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Would your company be willing to share data to view others data? (n=39)

16. Poll #5: Use of Evidence & Sharing to
Predict Enrollment?
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• Variation also observed in
how enrollment projections
are prepared
– Study-level projections
based on KOL feedback
and previous studies
– Study-level feedback
based on bottom-up
investigator feasibility
responses
– Sophisticated study-level
simulation models
1. Projected based on KOL
feedback and previous
experience
2. Projected based on
bottom-up aggregation of
investigator responses
3. Projected based on
results from simulation
models
4. Don’t perform this
function
How does your company
project enrollment?

17. Poll #5 Results
10%
38%
52%
0%
10%
20%
30%
40%
50%
60%
Projected based on KOL
feedback and previous
experience
Projected based on bottom-
up aggregation of investigator
responses
Projected based on results
from simulation models
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How does your company project enrollment? (n=29)
Note: n=10 don’t perform this function.

18. Poll #6: Accuracy of Enrollment
Projections?
Audience Poll:
• How accurate is your initial enrollment
projection?
1. Extremely accurate
2. Somewhat accurate
3. Somewhat inaccurate
4. Extremely inaccurate
5. Don’t perform this function
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19. Poll #6 Results
16% 53% 28% 3%
0% 20% 40% 60% 80% 100%
Response
Extremely accurate Somewhat accurate
Somewhat inaccurate Extremely inaccurate
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How accurate is your initial enrollment projection? (n=32)
Note: n=8 don’t perform this function.

20. Moving Towards More Realistic
Projections
• Despite best efforts, we often hear reports of
dissatisfaction with initial projections
– Quality of data inputs?
• Mean (study average) vs. median (50% of sites)?
– Lack of historical comparator studies?
– Other, non-quantifiable factors?
• Use of an integrated, evidence based approach
to study planning, feasibility, and investigator
selection should help narrow the projection gap
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21. Potential Benefits of Using an Integrated,
Evidence-based Approach
• Improved country selection
• More realistic recruitment projections
• Less time spent prioritizing/selecting investigators
• Reduced rescue sites potentially needed
• Decreased costs and time associated with start-up
of rescue sites
• Fewer non-performing and under-performing sites
• Decreased IT time and costs of investigator and site
data mastering
• Potential for tracking of investigators and sites
across multiple systems (e.g., payments,
investigator portals)
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22. Moving Towards a Return on Investment
(ROI) Calculation (1)
• While the integrated, evidence based approach is
appealing, most companies require an ROI prior to
approving spend
• DrugDev contracted with an external group to
develop an ROI model for our SiteCloud platform
– Use of a universal identifier known as the DrugDev
Golden Number, to match and master records
– Toolset with an integrated view of information
indexed to the same DrugDev Number
– Enablement of data sharing across companies
• The model is populated with:
– Company specific data on time/costs
– % benefit based on customer interviews
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23. Moving Towards a Return on Investment
(ROI) Calculation (2)
• Model is rolling out to customers now, but early
feedback suggests it is not possible to generate ROI
for the “average” company, due to variation in:
– Current processes
– Cleanliness of CTMS data
– Number of data sources
– Toolset currently available
– Personnel type and costs
– Previous quality initiatives
– Participation in data sharing
• We would welcome the opportunity to share the
variation in ROI resulting from an integrated,
evidence based approach in a future SCOPE forum
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24. Thank You!
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