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Data Mining to Make Global Feasibility Assessment More Reliable David J. Cocker, Senior Partner MDCPartners , Belgium. Feasibility means different things to different people. This presentation. Evolving clinical trial landscape information newly available via the internet

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slide1

Data Mining to Make Global Feasibility Assessment More ReliableDavid J. Cocker, Senior Partner MDCPartners, Belgium

this presentation
This presentation
  • Evolving clinical trial landscape
  • information newly available via the internet
  • public data sources to enhance feasibility reliability.

Data Mining Disclosure

leverage information
Leverage information
  • Can we leverage these expanding public data sources?
  • To fix these poor assumptions
working toward automation
Working Toward automation

Achieve

Automation

To

Development

From book

Time Spent

slide9

DRKS

JapicCTI

REPEC

INSCTR

RBEC

evolution of trial registry and publication ratio
Evolution of trial registry and publication ratio

An avalanche of new information will descend upon us

Slope publication count going forward

Number trials

Pubs Ratio

Normalized publication count

Artifact of retrospective trial registration

Pubs Registered

Trial start

Five year lag

feasibility on feasibility
Feasibility on Feasibility

However, with a relatively sophisticated industry approach to knowledge management, metrics and analysis…

Why do we get this so wrong, so often?

classic problem but there is a classic solution
Classic problem but there is a classic solution

Delay

Opportunity cost

Problem 1

%

Invest in in-depth feasibility

Problem 2

Over-run

Y1

Y2

The cost of a focus group to discuss likes and dislikes of a study proposal is less than 4,000 EUR. To set up one site is between $50,000 and $80,000.

Planned

Expenditure

Recruitment

Recruitment result

Throwing more money at feasibility. Will it improve reliability?

%

Time

b ad assumptions still plague pharma
Bad assumptions still plague Pharma

A study in diffuse large B cell lymphoma subjects who recently completed R-CHOP therapy.

Internal Clinical team assumptions

Meta-analysis outcomes

76 sites to recruit 750 patients

4 subjects per site

Scanned 750 trials, 60,000 patient mass

Need 188 sites to recruit 750 patients

10 subjects per site

The simplest meta-analysis of a trial registry would have mitigated this poor initial assumption.

Company added another 67 sites

Two year delay

applying meta analysis to classic questions
Applying meta-analysis to classic questions

Protocol

Number required

Patients with

the disease

Where do they live?

Country selection

Logistic Implications

Access

Selection criteria

Selection of site

Sites in area which may be suitable

Go

Experience

Equipment

the environmental trial conveyor belt
The Environmental Trial Conveyor Belt

Equipment

Logistic Implications

The practice

Experience

New

Studies

Feasibility

Regulatory

Publication

pre-emption

Retention

Drug

Supply

My trial is rolling

Monitoring the clinical trial environment

We cannot escape a rolling feasibility process

Rolling feasibility

hard points
Hard points
  • Number of eligible patients expected to recruit
  • Concurrent trial workload, particularly at recruitment stage
  • Previous experience in similar clinical studies
  • Recruitment & retention in prior clinical trials
  • Site personnel study experience and training
  • Trial-required facilities such as laboratories and pharmacies
slide18

Feasibility Efficiency =

Feasibility Quality=

Adding a new component to the feasibility formula

slide19

In-house predictive modeling tools

Predictive modeling and decision support tools

Internal KPI

Global trial activity

Predictions

Best

Guess

Enrolment history

History

Meta-

Evidence

Start-up dynamics

Disclosure

Country performance

Estimations

Academic literature

Private historical data

Global transparency

Survey data solicited from

potential sites

what s out on the net and what s to come
What’s out on the net and what’s to come?
  • Regulatory push, societal expectation
    • Sunshine Act and payments to healthcare professionals
    • Clinical trial registries and result synopses
    • Journal editors requiring registration
    • Institutional review committees and procedures

Conclusion

More disclosure, more transparency, more to come!

data relationship and semantics
Data Relationship and Semantics

Chaos

World demographics

Clinical trial

Registry

FDA, EU

Ad hoc

Web

Information

Conference

seminar

Hospital

Directory

+

Published

Investigator

Medline

Commercial

Web portal

Pharmaceutical

company

Semantics System

Order

It’s not just about clinical research disclosure. It’s about the reality of internet information linking up.

Male

Female

slide23

Epidemiology

Drugs

Treatment use

Condition

Sponsor

Site

Trials

Investigator

slide24

Key data elements of the

The power of semantic web disambiguation

A better view of the environment without the emotion

Condition

Drugs

Treatment use

Investigator

Trials

Sponsor

Site

slide25

Subject enrollment target 700

Population Pool (210,000,000)

Population pool availability

Incidence (189,000)

Female (189,000)

Age (167,456)

ScreeningFailure (16746)

Subject Travelling Distance(134 Km)

An age of information mobility may mean patient mobility

Site load for area 770/ 55 sites

Breast Cancer Phase ll

classify system to research questions
Classify system to research questions

Sponsor

Investigator

Trials

Who

When

Condition

Drugs

Treatment use

What

Where

Site

Information that is on the move, stays on the move. Monitor and re-visit often.

slide27

Trial Count (score)

Let the robot do the legwork, and then debate the assumptions.

Investigator (score)

Trial Count (score)

Number of investigators - 220

Regional population – 3,500,000

Berlin

Investigator (score)

Investigator (score)

Essen as a region

Number of investigators - 96

Regional population – 7,500,000

visualization of clinical trial registries
Visualization of clinical trial registries

Disambiguating a trial registry can render a nice picture

Breast Cancer sites

Rituximab sites

slide29

Competitive catchment zone

50

20

Subject travel assumption

50km

50km

Antwerp

Gent

20km

65km

55km

60km

Brussels

Leuven

Trial experience

50km

Sponsor spread

Drug experience

slide30

Can you answer Questions

Trial experience in years

Estimated enrollment histogram

Average patients per site

Site location

Organization score based on internet footprint

Traffic light system to indicate site availability

United Kingdom

Germany

Belgium

France

Absolute number of patients per site accounting for incidence, catchment radius and screening failure

Ranking data, even if qualitative, allows a better basis for discussion than a crystal ball.

Competing sites in catchment area based on site criteria

navigating complex interdependencies
Navigating complex interdependencies

The model is under stress

More trust

Better communication

Commercial

relevance

Social equity

Medical need

conclusions
Conclusions
  • An automated and rolling corporate engagement in site evaluation and ranking.
  • Mash-up and visualize all available data.
  • Exploit expanding disclosure data as a tangible return on investment for your participation.
  • Validate your historic data with dynamic data.
  • Confirm assumptions through more targeted sampling based on internet meta-analysis.
  • Expand cross industry KPIs.
slide33

Thank you

David J. Cocker

Senior Partner

Product Specialist Clinical Business Intelligence Systems

MDCPartners cvba

Vluchtenburgstraat 5 2630 Aartselaar– Belgium

Office +32 (0) 3 870 97 50

Direct +32 (0) 3 870 97 72

Fax +32(0) 3 870 97 51

www.mdcpartners.be

Product www.ta-scan.com