The document discusses advanced feasibility modelling for predicting the success of clinical trials. It describes using data-driven algorithms and semantic analysis of large datasets to answer business questions. New tools are presented for semantically filtering trials by disease and inclusion/exclusion criteria. Case studies analyze trends in new clinical trial sites opened from 2006-2015 for oncology and cardiovascular indications.

1. Advanced Feasibility Modelling
Session: 23rd Feb. Data-Driven Country Feasibility and Site Performance Prediction
Presenter:
David J Cocker
Chief Scientific Officer
MDCPartners
david.cocker.@mdcpartners.be

2. My talk today
Useful visuals
‘Less bling
more info’
Easy to understand
Understanding the
data you see
Data sources
Get to the value fasterDoing more
NewOld
Meta
statistics
Algorithms based
on looking back
Very
Old
Volume

5. Network of event horizons
Planning
v
v
v
v v
v v
v
v v
vv
v v
Commonality of process vs “I’ll do it my way”

6. People think lots
of data sources is good

7. BUSINESS QUESTIONS
Translating silos of data into
quantifiable information
Semantic Transformation
Creating ontologies to rank
and classify medical concepts

8. Introduction
Let’s start by agreeing that feasibility is a confidence
prediction in your downstream research network.

9. Planning
P
1 P
2
P
3
Event horizon

10. Planning
P
1 P
2
P
3
Event horizon

11. A range of robust
algorithms to get to
your data fast
All reports are exportable via URL
and kept active for one month

12. A range of robust
algorithms to get
your data fast

13. New tools to semantically filter
on trials and segment in/exclusion

14. New tools to semantically filter
on trials and segment in/exclusionDiseases
Excl-drug term
indication
Inclusion term
drug

16. BUSINESS QUESTIONS
Epidemiology
Patient group discovery tools and options
After 8 years of development, our technology stack is sufficiently robust to expand to
public and private data assessment. From this private data repository, new dashboards
and insights can be rendered.

17. BUSINESS QUESTIONS
Epidemiology
New tools to semantically filter on trials and segment in/exclusion
600,000 words grouped by
protocol concepts

18. Looking back at past results
Can we learn from our mistakes?

19. Opening of new sites: case study 1
2014 (57) 2015 (128)
• 4 companies (Merck, Lilly, JNJ, Pfizer)
• Period 2006-----2015
• Sites opened in cardiovascular indications
(hypertension, hyperlipidemia, atherosclerosis, diabetes)
• Sites opened in immunological factors in
oncology

20. New investigators

21. Immuno-Oncology
new sites/trial
4.3
New sites/total sites
opened
0.185
Sites/trial
23
In this new therapeutic area, on
average, 4 out of 23 sites are new
sites which were not used by the
company before (18.5%)
1685/5,956
US sites 27%

22. New investigators onco

23. Cardiovascular trial analysis
4 major companies in the space
In these CV therapeutic areas, on average there are 3
new and 22 historic sites per trial. (11.6% new)
Trials 1,549
Average new site openings
Migrating from metabolic to oncology
Representing 24% site load of this therapeutic area
Diabetes
Lipid
lowering
10,000/22,000
US sites 47%

25. To concepts
From text
he illusion of just counting words