Curlew Research Brussels 2014 Electronic Data & Knowledge Management
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Curlew Research 2014
Knowledge management with CROs & partners Nick Lynch Curlew Research
Curlew Research 2014
Summary
● Challenges to Collaboration and its growth in life science
● Models of Data Exchange with CROs and partners
● Curation – empowering scientists for collaboration – How R&D Search relies on good meta data – How Training is part of knowledge
management 2
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AstraZeneca Outsourcing AZ’s outsourcing bill was about $3 billion per annum
• AstraZeneca is a global, innovation-driven, integrated biopharmaceutical company
• AZ employs over 50,000 people • 44% in Europe, 30% in the Americas, 22% in Asia and 4%
in ROW • Has over 9,000 people in our R&D organisation • Last year AZ invested $4 billion in R&D • In 2013, worldwide sales totalled $26 billion
About AstraZeneca
About AstraZeneca • Research within AZ comprises
• 6 innovative medicines units • Oncology • Infection • Cardiovascular and Metabolic • Respiratory and Inflammation • Asia & Emerging Markets • Neuroscience (virtual)
• Supported by innovative medicines functions eg. • Drug Safety and Metabolism • Discovery sciences
• Principally located on three main sites • Alderley Park, Cheshire (UK) → Cambridge (UK) • Mölndal, Gothenburg (Sweden) • Gatehouse Park, Waltham (US) • Plus: Shanghai
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Drug Discovery and Early Dv within AstraZeneca
Curlew Research 2014
Quick Survey!
●Who is working with CROs & partners? ●Who has multiple CROs/partners? ●Who thinks they will have more partnerships in the future? ●Who has shared labs with partners?
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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
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
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Understanding drivers for
externalisation is key to measuring
success & managing
information
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Curlew Research 2014
Science information management has come along way.....
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
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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?
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THE WORKFLOW 11
Process & Infrastructure Compound design from
Pharma (Design)
Synthesis/ Make
Screen Compounds/
Test
Data Analysis
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• IT system to share designs • Track metrics
• Weekly TC/ reports monitor progress
• Reagent store and database
• ELN to capture synthesis information
• Patent ready format • Bio-ELN in progress
• Test request system • Sample storage • Shipping compounds
• QC of data • IT upload system • Process to track failed
analysis
• Monitoring performance • Governance • Audits and compliance
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
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Some Options….
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<data exchange format>
Broker Application or translator
Shared Application (apps, Citrix, Web)
BIORULES SUPPORT ALL USERS 15
Curlew Research 2014 Flickr user sarah0s / Creative Commons Licensed
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Curlew Research 2014
Business Rules – AZ Drivers
●Get visibility of our assets
●Sharing of experience
●Securing information for the longer term
●Reduced Cycle Time by not repeating work oDon't do anything already done by someone else especially if it didn't work
oDo Build on others’ learning
●Decision support oDiscovery has distributed decision making processes oEverybody makes decisions on a daily basis oNeed as much information as possible
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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
●Get the right people oYour best people are always busy oYou don't want anyone that is easily available
●Skills needed oBe able to see the “Big Picture” oKnowledgeable in their business area oGood inter-personal skills oCapable of making decisions
●Get them at the right level of organisation oMust know how the business works day-to-day
● Include all relevant people oFor us this meant representatives from 5 research areas
situated on 8 sites over 4 countries ~ 20 people!! 19
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Please keep to the Path!
Curlew Research 2014
Training is part of knowledge management
Best practice, Minimum Information and auditing • Define Minimum information requirements
• Experiments (minimum spectra needed, use of templates for common transformations)
• Screening data (based on Assay protocol) • Reports (Standard templates)
• Auditing of data • Both internally and externally created • Peer review
• Training • Hands on training • Exams to support learning • Super users on both sides 22
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Curlew Research 2014
Information Value increases with relationships
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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
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Summary • The type of relationship and its length will shape
information sharing approaches • Requires a good partnership between all parties
• Not just about imposing large company ideas/tools on a small agile collaborator or CROs
• Your scientists will put a great deal of effort into collaborating, help them be part of curation • Use common business rules, agree on vocabulary • Data Curation supports good experiments
• Super-user concept, local experts • Work with your software providers for lighter weight
solutions
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Curlew Research 2014 http://thetechnoliterate.wordpress.com/2013/04/24/its-not-just-about-the-technology/
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
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And finally ….Discussion • Would standards support better data sharing? • Would common business rules help? • What technologies enable easier collaboration? • 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.
• How could we capture, store and retrieve data from ad-hoc experiments that is unique in its kind?) 29
Curlew Research 2014
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