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1
The International Conference on Search, Data and Text
Mining and Visualization
Anne Trincot, April 2019
Competitive Inte...
22
Contents
 Introduction
 BizInt and Vantage Point solutions
 VALEM360
 Conclusion
33
Contents
 Introduction
 BizInt and Vantage Point solutions
 VALEM360
 Conclusion
44
Objectives
Two methods to optimize Data Visualization for Competitive Intelligence:
 Pipeline and Clinical Trials Data...
55
R&D Preclinical Phase I Phase II
Phase III
Registration
Go to
market
Lifecycle
Management
Drug development journey and ...
66
Pipeline Databases
Coverage Since 1988 NA Since 1980 Since 1995
Users Researchers,
Translational
researcher
R&D, BD&L R...
77
Clinical Trial Databases or Registries
Coverage US, World Europe World World
Advantages The Oldest and Most
Well-known
...
88
Contents
 Introduction
 BizInt and Vantage Point solutions
 VALEM360
 Conclusion
99
Data Visualizations offered by our suppliers
1010
Data Visualization with BizInt and Vantage Point
Tools to improve and hone Data Visualization
1111
Pipeline information Clinical trial information
Combination
Template
Reports
Competitive Intelligence
Analysis
BizInt...
1212
Update
Pipeline information Clinical trial information
Combination
Old version
Competitive Intelligence
Analysis
New
...
1313
Summarizing pipeline data
Chart visualization
1414
Summarizing pipeline data
Bullseye Chart Piano Chart
1515
Clinical trial visualizations
1616
Other Data Visualizations
Word Cloud from List Selection Bubble Chart
1717
 Combination of various sources
 Helpful to Analyse Pipeline and Clinical Development of our
Competitors
 Some lim...
1818
Contents
 Introduction
 BizInt and Vantage Point solutions
 VALEM360
 Conclusion
1919
Context
 Pancreatic cancer is a pathology of interest to many R&D and strategic
departments, but it is a new disease...
2020
Our investigations
 Centralize the data from different sources and data preparation
 Associate the elements of trea...
2121
“Cancer , Pancreatic & (Phase II)
OR(Phase III)OR(Phase
IV)OR(Phase III/IV)OR(Phase
II/III)OR(Phase I/II)OR(Pre-
regi...
2222
Treatment Decision Tree for Pancreatic Adeno-
carcinoma: Europe
Line of Therapy or Operation Stage
(Any combination)
...
2323
DATAVIZ #1 : The WHEEL
More details on associated clinical Trials
Filter by
2424
DATAVIZ #2: Dashboard (Drugs2Trials)
2525
DATAVIZ #3 : Dashboard (DrugsByStudyCount)
Color = Number of competitors
Size = Disease count
2626
DATAVIZ #4 : Dashboard (Trials’stageOverTime)
Clinical trials’ tendency in First line
metastatic
27
04/04/2019
Take Aways
Interesting and enriching agile working method
This project has taught us:
 Need to work on stru...
28
04/04/2019
VALEM 360 : Conclusion & Perspectives
 Fast implementation of a data-driven approach
 Provide a competitiv...
29
04/04/2019
Contents
 Introduction
 BizInt and Vantage Point solutions
 VALEM360
 Conclusion
30
04/04/2019
Conclusion
Data Visualizations for the Analysis of our Competitive Intelligence
 Very Useful Tools
 Choose...
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IC-SDV 2019: Competitive Intelligence: how to optimize the analysis of pipeline and clinical trials data with BizInt Smart Charts and VantagePoint - Anne Trincot (Global Medical Affairs (GMA), Servier, France)

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BizInt for data compilation, selection and Chart Vizualisation and VantagePoint for specific graphic data representations can help for competitive intelligence analysis.
· Pipeline and clinical trials data
· Structure, reliability and updating of data
· Need to query and export data from different sources
· Added values of verification and visualization of information.
· Description of BizInt and VantagePoint
· Practical examples of the use of these 2 tools for the realization of competitive intelligence reports

Published in: Healthcare
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IC-SDV 2019: Competitive Intelligence: how to optimize the analysis of pipeline and clinical trials data with BizInt Smart Charts and VantagePoint - Anne Trincot (Global Medical Affairs (GMA), Servier, France)

  1. 1. 1 The International Conference on Search, Data and Text Mining and Visualization Anne Trincot, April 2019 Competitive Intelligence: How to Optimize Pipeline and Clinical Trials Data Analysis?
  2. 2. 22 Contents  Introduction  BizInt and Vantage Point solutions  VALEM360  Conclusion
  3. 3. 33 Contents  Introduction  BizInt and Vantage Point solutions  VALEM360  Conclusion
  4. 4. 44 Objectives Two methods to optimize Data Visualization for Competitive Intelligence:  Pipeline and Clinical Trials Data Analysis with BizInt and VantagePoint Solutions  Global Data Visualizations for a particular Disease: VALEM360 In- house development
  5. 5. 55 R&D Preclinical Phase I Phase II Phase III Registration Go to market Lifecycle Management Drug development journey and data sources
  6. 6. 66 Pipeline Databases Coverage Since 1988 NA Since 1980 Since 1995 Users Researchers, Translational researcher R&D, BD&L R&D, marketing, strategy R&D, regulatory, marketing, strategy Advantages Patent information Deals Patents Link with TrialTrove Regulatory Information Disadvantage Warning – active status Updates No information from public research Updates Dataviz basic yes yes yes
  7. 7. 77 Clinical Trial Databases or Registries Coverage US, World Europe World World Advantages The Oldest and Most Well-known Well-known in Europe Data from Emergent countries More exhaustive Link with Pharmaprojects Tagged data Disadvantage Not exhaustive Not exhaustive Old Website Not exhaustive Not free of charge Dataviz no no no yes
  8. 8. 88 Contents  Introduction  BizInt and Vantage Point solutions  VALEM360  Conclusion
  9. 9. 99 Data Visualizations offered by our suppliers
  10. 10. 1010 Data Visualization with BizInt and Vantage Point Tools to improve and hone Data Visualization
  11. 11. 1111 Pipeline information Clinical trial information Combination Template Reports Competitive Intelligence Analysis BizInt Combination Template Reports Vt Vt BizInt Cleaning Verification
  12. 12. 1212 Update Pipeline information Clinical trial information Combination Old version Competitive Intelligence Analysis New Data Combination Old version Vt Vt New Data
  13. 13. 1313 Summarizing pipeline data Chart visualization
  14. 14. 1414 Summarizing pipeline data Bullseye Chart Piano Chart
  15. 15. 1515 Clinical trial visualizations
  16. 16. 1616 Other Data Visualizations Word Cloud from List Selection Bubble Chart
  17. 17. 1717  Combination of various sources  Helpful to Analyse Pipeline and Clinical Development of our Competitors  Some limitations:  Compatibility restriction (for example OncologyPipeline)  Need expertise and time  Not usable for Big Data
  18. 18. 1818 Contents  Introduction  BizInt and Vantage Point solutions  VALEM360  Conclusion
  19. 19. 1919 Context  Pancreatic cancer is a pathology of interest to many R&D and strategic departments, but it is a new disease within the company  The objective is to compile competitive and environmental data on pancreatic cancer in order to obtain a 360° view of the information  The project was conducted together with the Digital Factory - IT Department
  20. 20. 2020 Our investigations  Centralize the data from different sources and data preparation  Associate the elements of treatment decision tree to each clinical trial, their investigated drugs and study centers  Visualize data via different interactive tools
  21. 21. 2121 “Cancer , Pancreatic & (Phase II) OR(Phase III)OR(Phase IV)OR(Phase III/IV)OR(Phase II/III)OR(Phase I/II)OR(Pre- registration)OR(Registered) OR(Launched)OR(Marketed)” Open Trials Completed Trials (completion data > 2016) Planned Trials (start data > 2016) 250 Distinct Drugs 1311 clinical trials Database of 1311 of clinical trials & 250 drug details Associate the elements of treatment decision tree to each clinical trial Web-App Solution Visualization Tools Drug’s dictionary coding Sources Requests Results Combination Database Visualization pipeline Clinical trials
  22. 22. 2222 Treatment Decision Tree for Pancreatic Adeno- carcinoma: Europe Line of Therapy or Operation Stage (Any combination) Treatment Tree Position Adjuvant/ Neoadjuvant 0,I,II,III Resectable First line III Unresectable First line IV First Line Metastatic Second line or greater/Refractory/Relapsed III, IV Second Line First line Maintenance/Consolidation III Unresectable First line Maintenance/Consolidation IV First Line Metastatic Second line or greater/Refractory/Relapsed Any stage Second Line First line II,III Unresectable First line Any stage First Line Metastatic Line of therapy N/A IV First Line Metastatic Line of therapy N/A III Unresectable Associate the elements of treatment decision tree to each clinical trial
  23. 23. 2323 DATAVIZ #1 : The WHEEL More details on associated clinical Trials Filter by
  24. 24. 2424 DATAVIZ #2: Dashboard (Drugs2Trials)
  25. 25. 2525 DATAVIZ #3 : Dashboard (DrugsByStudyCount) Color = Number of competitors Size = Disease count
  26. 26. 2626 DATAVIZ #4 : Dashboard (Trials’stageOverTime) Clinical trials’ tendency in First line metastatic
  27. 27. 27 04/04/2019 Take Aways Interesting and enriching agile working method This project has taught us:  Need to work on structured data  Structured data extracted from paid databases such as Pharmaprojects or Trialtrove are of poor quality: content, coverage, update, metadata  Need to cross-check the information, to "clean" the data  Importance of setting up dynamic dataviz to help us analyze the information
  28. 28. 28 04/04/2019 VALEM 360 : Conclusion & Perspectives  Fast implementation of a data-driven approach  Provide a competitive landscape database on pancreatic cancer  Different interactive data visualization tools  Improve semi-automatic to fully automatic (daily – weekly update) data driven approaches. API solutions  Improve used methodology based on business feedback  Apply the same method to different pathologies
  29. 29. 29 04/04/2019 Contents  Introduction  BizInt and Vantage Point solutions  VALEM360  Conclusion
  30. 30. 30 04/04/2019 Conclusion Data Visualizations for the Analysis of our Competitive Intelligence  Very Useful Tools  Choose Relevant Representations  Need Expertises in Topics, Information Sources and Data Structures.

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