Feasibility means different things to different people

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

2. Feasibility means different things to different people

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

4. More data

5. Data Volume

6. Multiple versions of the truth

7. Leverage information • Can we leverage these expanding public data sources? • To fix these poor assumptions

8. Working Toward automation Achieve Automation To Development From book Time Spent

9. DRKS JapicCTI REPEC INSCTR RBEC

10. 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

11. 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?

12. Trends show a moving academic and industrial landscape

13. 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

14. 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

15. 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

16. 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

17. 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

18. Feasibility Efficiency = Feasibility Quality= Adding a new component to the feasibility formula

19. 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

20. 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!

21. 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

22. Identifying experienced individuals in organizations Impact factor MeSH Therapy relevance

23. Epidemiology Drugs Treatment use Condition Sponsor Site Trials Investigator

24. 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

25. 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

26. 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.

27. 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

28. Visualization of clinical trial registries Disambiguating a trial registry can render a nice picture Breast Cancer sites Rituximab sites

29. 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

30. 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

31. Navigating complex interdependencies The model is under stress More trust Better communication Commercial relevance Social equity Medical need

32. 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.

33. 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