Optimizing Protocol Planning, Feasibility,

and Site Selection through an Integrated

View of Clinical Trial Operations and Other

Data Sources

Elisa Cascade

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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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

3

Poll #1 Results

Pharmaceutical Company

32%

CRO23%

Other provider to pharma/CROs

25%

Investigator/Site11%

Other9%

4

Which of the following best describes your company/affiliation? (n=44)

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.

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?

Poll #2 Results

24%21%

38%

17%

0%

10%

20%

30%

40%

50%

Use own list ofinvestigators or

existing siterelationships

Use of an internaldatabase with metrics

Use of an internaldatabase with metrics

+ at least one otherexternal data source

(e.g., 3rd partysubscription)

Internal database withmetrics + external

data sources + EMR

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How does your company select sites? (n=29)

Note: n=12 don’t perform this function.

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

8

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, SiteCloudalso 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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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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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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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)

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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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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Poll #4 Results

No, we would not be willing to

share data23%

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)

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?

Poll #5 Results

10%

38%

52%

0%

10%

20%

30%

40%

50%

60%

Projected based on KOLfeedback and previous

experience

Projected based on bottom-up aggregation of investigator

responses

Projected based on resultsfrom simulation models

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How does your company project enrollment? (n=29)

Note: n=10 don’t perform this function.

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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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.

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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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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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 DrugDevGolden 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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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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Thank You!

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