Life Science externalisation and collaboration overview and the challenges that Life Science companies face in delivering successful data sharing with their partners in either Open Innovation or pre-competitive workflows

1. Curlew Research 2014
Knowledge management with CROs & partners
Nick Lynch
Curlew Research

2. Curlew Research 2014
Summary
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Challenges to Collaboration and its growth in life science
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Models of Data Exchange with CROs and partners
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Curation – empowering scientists for collaboration
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How R&D Search relies on good meta data
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How Training is part of knowledge management 2

3. 3
AstraZeneca Outsourcing
AZ’s outsourcing bill was about $3 billion per annum
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AstraZeneca is a global, innovation-driven, integrated biopharmaceutical company
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AZ employs over 50,000 people
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44% in Europe, 30% in the Americas, 22% in Asia and 4% in ROW
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Has over 9,000 people in our R&D organisation
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Last year AZ invested $4 billion in R&D
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In 2013, worldwide sales totalled $26 billion
About AstraZeneca

4. About AstraZeneca
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Research within AZ comprises
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6 innovative medicines units
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Oncology
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Infection
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Cardiovascular and Metabolic
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Respiratory and Inflammation
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Asia & Emerging Markets
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Neuroscience (virtual)
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Supported by innovative medicines functions eg.
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Drug Safety and Metabolism
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Discovery sciences
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Principally located on three main sites
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Alderley Park, Cheshire (UK) → Cambridge (UK)
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Mölndal, Gothenburg (Sweden)
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Gatehouse Park, Waltham (US)
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Plus: Shanghai
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Drug Discovery and Early Dv within AstraZeneca

5. Curlew Research 2014
Quick Survey!
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Who is working with CROs & partners?
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Who has multiple CROs/partners?
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Who thinks they will have more partnerships in the future?
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Who has shared labs with partners? 5

6. 6
Life Science Information Landscape
Big Life Science Company
Yesterday
Today
Tomorrow
Yesterday
Today
Tomorrow
Innovation Model
Innovation inside
Searching for Innovation
Heterogeneity of collaborations. Part of the wider ecosystem
IT
Internal apps & data
Struggling with change
Security and Trust
Cloud/Services
Data
Mostly inside
In and Out
Distributed
Portfolio
Internally driven and owned
Partially shared
Shared portfolio
A rapidly evolving ecosystem

7. Why Externalise?
•Increase choice
•Higher quality candidates
Increase project resource
•Dynamically resource projects according to need
Flexibility
•Liberate internal scientists
•Access external ideas
Innovation
•Ensure future agility
Reduced fixed costs 7
Understanding drivers for externalisation is key to measuring success & managing information
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8. Curlew Research 2014
Science information management has come along way.....

9. Curlew Research 2014
Where is your sweetspot?
Spectrum of Engagement
Partners use Pharma Software and data
Pharma use partners Software and data
Share Data via File exchange
Share Data via B2B Services
Each relationship CRO/partner will be at a different capability
Different models work in different situations 9

10. Curlew Research 2014
What Relationship do you have
Full embedding is not always the best option
Getting Started
•Basic building blocks but scaling is hard
•Basic data sharing with CROs via email
•Manual effort to bring data in
•Overall coordination is manual
CRO engaged
•Capable of scaling to wider interactions
•Agreed Data contracts
•Transactional support
CRO integrated
•Efficient knowledge transfer
•Efficient data transfer
•Access to tools where necessary
•True b2b/supply chain relationship
•Scalability/agility
CRO embedded
•Using Pharma systems as if employees
•This could be too coupled together and hence not flexible for either party
•Depends on BPO model
Phase1?
Is this too coupled? 10

11. THE WORKFLOW 11

12. Process & Infrastructure
Compound design from Pharma (Design)
Synthesis/
Make
Screen Compounds/ Test
Data Analysis 12
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IT system to share designs
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Track metrics
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Weekly TC/ reports monitor progress
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Reagent store and database
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ELN to capture synthesis information
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Patent ready format
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Bio-ELN in progress
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Test request system
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Sample storage
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Shipping compounds
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QC of data
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IT upload system
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Process to track failed analysis
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Monitoring performance
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Governance
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Audits and compliance

13. Customer (pharma/ biotech)
Partner
Project sharing
Design sharing
Project sharing
Chemistry Synthesis Experiment
D
M
T
A
DMTA: Requesting and Tracking
Design Sharing environment
Capture of Chemical Synthesis and accessible back into Pharma
Screening data (DMPK, Biology)
Project collabo- ration spaces
Example Pre-clinical Workflow
Design, Make, Test, Analyse (DMTA)
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Screening (DMPK, Biology) Request
Screening Data to Pharma

14. Curlew Research 2014
Some Options…. 14
<data exchange format>
Broker Application or translator
Shared Application
(apps, Citrix, Web)

15. BIORULES SUPPORT ALL USERS 15

16. Curlew Research 2014
Flickr user sarah0s / Creative Commons Licensed
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17. Curlew Research 2014
Business Rules – AZ Drivers
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Get visibility of our assets
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Sharing of experience
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Securing information for the longer term
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Reduced Cycle Time by not repeating work
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Don't do anything
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already done by someone else
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especially if it didn't work
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Do
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Build on others’ learning
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Decision support
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Discovery has distributed decision making processes
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Everybody makes decisions on a daily basis
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Need as much information as possible
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18. Curlew Research 2014
Project
lifetime
Time
Information Value
After Project closure
Structured Information Value
Poor meta data Lack of curation process
Good information practice
Clear business rules
Curation process defined
Can we quantify this gap?
When do investments payback?
Data created for specific Project
Reasonable knowledge of
data & decisions
What do decision makers need?
The Customer changes over time, so rules need to adapt
http://www.b-eye-network.com/view/3365?jsessionid=48f7500a16e486668a5b968273f709e2 http://www.b-eye-network.com/blogs/linstedt/archives/2007/01/time_value_of_m.php http://www-128.ibm.com/developerworks/webservices/library/ws-soa-ims2/
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People
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Get the right people
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Your best people are always busy
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You don't want anyone that is easily available
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Skills needed
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Be able to see the “Big Picture”
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Knowledgeable in their business area
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Good inter-personal skills
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Capable of making decisions
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Get them at the right level of organisation
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Must know how the business works day-to-day
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Include all relevant people
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For us this meant representatives from 5 research areas situated on 8 sites over 4 countries ~ 20 people!!
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20. Curlew Research 2014
Please keep to the Path!

21. Curlew Research 2014
Training is part of knowledge management

22. Best practice, Minimum Information and auditing
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Define Minimum information requirements
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Experiments (minimum spectra needed, use of templates for common transformations)
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Screening data (based on Assay protocol)
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Reports (Standard templates)
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Auditing of data
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Both internally and externally created
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Peer review
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Training
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Hands on training
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Exams to support learning
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Super users on both sides 22

23. Curlew Research 2014

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Information Value increases with relationships 24
VALUE
VALUE
VALUE
VALUE

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You can ease the issues here
This is
Manageable with good metadata
What type of data?
Good enterprise search can bring real value 25

26. Summary
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The type of relationship and its length will shape information sharing approaches
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Requires a good partnership between all parties
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Not just about imposing large company ideas/tools on a small agile collaborator or CROs
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Your scientists will put a great deal of effort into collaborating, help them be part of curation
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Use common business rules, agree on vocabulary
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Data Curation supports good experiments
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Super-user concept, local experts
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Work with your software providers for lighter weight solutions 26

27. Curlew Research 2014
http://thetechnoliterate.wordpress.com/2013/04/24/its-not-just-about-the-technology/

28. Curlew Research 2014
Thanks to...
Liz Calder
Eva Lotta Westberg
Janet Nason
Dave Nicholls
Vijay Chhajlani
Steve Peters
Goran Hanson
IBIS and BioELN Teams
Chris Davies
David Drake
Garry Pairaudeau
Kyle Fang
Hong Xuo
Niklas Fjellman
Christine Xia
Barry Jones
28

29. And finally ….Discussion
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Would standards support better data sharing?
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Would common business rules help?
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What technologies enable easier collaboration?
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How can we structure and mange non-repetitive data and make them searchable? (Data generated on a daily basis could with some effort be standardized and structured. These could be documented in databases and entered into tables.
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How could we capture, store and retrieve data from ad- hoc experiments that is unique in its kind?) 29
Curlew Research 2014