The Pistoia Alliance

1. The Pistoia Alliance pistoiaalliance.org A Construct for Pre-competitive Collaboration and Open Innovation • 2010

2. Agenda • Origins of Pistoia • History • Industry Drivers • Technology Trends • Scope and Operations of Pistoia • Mission, Membership, Governance • Projects and Deliverables • Discussion

3. Industry Driver: Externalization Cost pressures, disruptive technologies, and other forces often drive business processes to be externalized. Fully Internal Model Selectively Integrated Model

4. Emerging Net-centric PharmaProcesses PHARMA 1 CRO 1 CRO 2 PHARMA 2 CRO 3 PHARMA 3 CRO 4

5. External Interaction Standards and Specifications BioIT Alliance How the groups interact to support the wider information supply chain Product and Service Suppliers IMI PRISM SILA Pistoia Alliance W3C HCLS CDisc

6. Opportunity: Changing Tech Landscape More Robust Technologies • Web 2.0 / 3.0 • Services-Oriented Architecture • Software-as-a-Service • Open Source Initiatives More Robust External Content • Publicly available chem and bio sources • Richer literature content • Academic Sources of Tools and Data

7. The Path Forward: Standardize, Simplify, Centralize • Standardize our interfaces and messages • Simplify our cross-industry architectures and support models • Centralize services to reap economies of scale and scope

8. Pistoia Mission • The Mission • Pistoia will standardize and streamline data interchange in life science R&D. • The Method • Precompetitive collaboration between life science, academia, and commercial partners. • The Result • Standardization, Simplification & centralisation will drive down the cost of data exchange, cloud computing, and process outsourcing. • The Benefit • Informatics organizations can streamline commodity services, and focus investment on innovation in R&D.

9. Benefits of Pistoia • R&D Organizations • Optimized investments • e.g. reduction of redundant investments across industry • Increased agility to leverage global R&D • Rapid integration, streamlined data interchange and analysis • Informatics Solution Providers • New markets and business models • Reduced cost-of-entry to markets • Reduction of customized solutions, leading to higher margins

10. Pistoia Membership updated: April 2010

11. Pistoia Summary • Overall: • Not just a standards body • Where possible adopting/promoting existing standards. • Defining, refining & publishing X-Pharma use cases • Funding mechanism for pilot PoCs to promote stds and use case adoption • Influencing the vendor community • Helping develop business models • Ability to support future Informatics Innovation

12. Pistoia Programme Plan Q22010 (Draft) W Workshop Develop In-flight yy Standard Approved Publication P Not started Pistoia Participants (Ticker code) Working Groups Domains-Governance Domains-Direction Pistoia Activity Related External Initiatives Extra funding raised Multiple Participants Working Group – possible Knowledge and Information Services (Ian Dix & Cory Brouwer) SESL M W P Vocab scoping Vocab Phase 2 P Domain Vision Open Pharmacology (OPS) IMI Biology W Sequence Services AZN AZN AZN GSK ROG GSK GSK PFZ UNL M M UNL M NOV NOV PFZ Domain Vision P Chemistry ELN Query Services Phase 2 ELN Query Services P Domain Vision P W P Domain Vision Translational Investigation/Study/Assay (ISA) Infrastructure CaBig EBI Pistoia Board face to face Planning W Advisory board? Pistoia Web site Collaboration Environment Links to external groups Technical Vision P P Technical Governance External Liason BBSRC Comms Strategy 2010 2009 Now 2011

13. Current, Active Pistoia Projects • Semantic Enrichment of Scientific Literature • An open knowledge brokering framework standard which will reduce the costs of integration from disparate sources. • Sequence Services • A standard service to provide access to public, private & commercial data & tools, that will enable scientists to search, store & analyse all their sequence based data in a single web interface. • ELN Query Service • A query service standard applicable for use with data types commonly found in electronic lab notebooks

14. SESL Overview

15. Open Stds Consumer Firewall SESL: Biomedical Knowledge Service Framework Multiple Consumers Knowledge Applications Target Dossier Compound Dossier Disease Dossier Network Viz Service Layer Std Public Vocabularies Common Service Broker Proprietary Service Broker Assertion & Meta Data Mgmt Transform / Translate Business Rules Integrator Content Suppliers Supplier Firewall Db 2 Effort required to fit DBs to service layer Db 4 Corpus 1 Db 3 Corpus 5

16. A Production SESL Service Consumer Side Exemplar Application Disease Dossier License Service Layer Service Layer Service Layer Service Layer Std Public Vocabularies Std Public Vocabularies Std Public Vocabularies Std Public Vocabularies Assertion & Meta Data Mgmt Assertion & Meta Data Mgmt Assertion & Meta Data Mgmt Assertion & Meta Data Mgmt Transform / Translate Transform / Translate Transform / Translate Transform / Translate Business Rules Business Rules Business Rules Business Rules Integrator Integrator Integrator Broker Org #3 Broker Org #4 Broker Org #1 Broker Org #2 Integrator Corpus 9 Corpus 13 Corpus 1 Corpus 5 Db 11 Db 15 Db 3 Db 7 Corpus 12 Corpus 16 Corpus 4 Corpus 8 Db 10 Db 14 Db 2 Db 6 Supplier Side

17. Current SESL Participants

18. Sequence Services

19. The Vision As we Propose Today Public Commercial Etc…

20. Overall Status Y Technical Bus Obj Resources Schedule Cost Y R Y G G <Sequence Services> One-Slide Status as of <May 2010> • Key Accomplishments to date • Defined Project Vision. • Split Vision into achievable phases of delivery. • Defined Phase 1 use cases. • Focus on Non-Functional usecases. • Scoring criteria in final stages of drafting. • 5 Vendor presentations booked during May / June 2010. • Cognizant, British Telecom, ThomsonReuters, Genome Quest, & Constellation Technologies. Project Description As a drive to cuts costs, encourage standards, and provide simplification it is proposed that Pistoia commission a set of secure internet hosted sequence services. These services will ultimately provide access to public, private & commercial data & tools, that will enable scientists to search, store & analyse all their sequence based data in a single web interface. Issues / Risks / Escalation requests • Risk of partners not being willing to engage. (low – risk) • Risk of not being able to find partner(s) who can undertake the work within our estimated budget. (med – risk) • Risk of service performance not reaching acceptable levels (med – risk) Project Phase Budget Summary Working Group • Simon Thornber (GSK) • Cary O’Donnell (AstraZeneca) • Quan Yang (Novartis) • Monica Arenz (Novartis) Moving to Implement

21. ELN Query Services

22. ELN Query Service Vision Exploitation Clients Exploitation Clients ELN Application ELN Application Pistoia Query Services Pistoia Query Services Core Services Core Services Data Services Data Services Data Services Data Services Data Services Data Services Experiments Experiments Analytical Analytical Chemical Structures Chemical Structures

23. Overall Status Y Technical Bus Obj Resources Schedule Cost Y Y Y G G ELN Query Services Workgroup Dashboard Status Report as of May 2010 Project Description To deliver a query service standard applicable for use with data types commonly found in electronic lab notebooks (ELN’s). The initial scope will be against chemistry related ELN’s but the solution should aim to be general enough that it can be applied to other scientific notebook applications. • Key Accomplishments to date • Defined project scope/phases/deliverables • Delivered phase 1 user stories to define problem space. • Engaged group for phase 2, and created RFP to engage external resource. • RFP exercise completed, vendor for phase 2 standard development chosen. • GGA engaged and work commenced. Project Benefits Searching of data stored in ELN’s from different vendors. Lowering the costs of using ELN data with partners and CRO’s. Issues / Risks / Escalation requests • Cost Issue: No budget associated with workstream. Need finance for phase 2, to service RFP, will likely need further finance for phase 3. • Schedule Issue: Team have a day-job that takes precedence to workstream. • Resources issues: Need for architecture input to assist with review of phase 2 output. John Duncan Proposed. • Technical Issue: unlikely until phase 3 Budget Summary Project Phase Working Group • Richard Bolton, GSK, Coordinator • David Drake, AZ, team member • Steve Trudel, Pfizer, team member • John Duncan, Pfizer, team member • Uwe Geissler, Novartis, team member • Carol McNab, BMS, team member • Vendor representatives from Symyx, Edge, Accelrys Work

24. Current Status • There are now 31 members of the group in the Ning website from a mix of pharma (GSK, Pfizer, Novartis, AZ, BMS), and vendors (Symyx, Edge Consulting, Accelrys, CS yet to join). Migrated to Basecamp. • Active Participation at biweekly meetings from GSK/AZ/Pfizer/BMS/Symyx/Edge/Accelrys • Agreed 3 delivery phases • Phase 1 Definition of problem space and creation of users stories. • Complete. User Story Document ‘published’ • Phase 2 Creation of ELN Query services definition. • End to end process run through by team to create a full model for two of the user stories. • GGA chosen to complete work. Funding agreed and approved by operations team. Work started but contract not yet in place. • Phase 3 Creation of POC in partnership with Vendor. • Not yet started. Will likely require vendor partnership, budget and technology decision.

25. www.pistoiaalliance.org If you want to go fast, go alone. If you want to go far, go together.

26. Backup Slides

27. Where We Are

28. Who we Are: Board of Directors Accelrys ChemAxon Symyx CambridgeSoft Infosys Thomson Reuters GSK AZ Novartis Pfizer Lundbeck BMS Roche

29. Pistoia Membership Levels • Core Member ($15,000) • Those organisations wishing to strongly influence the strategy & direction of the Alliance • Have a majority on the Governance & Strategy Board • Pharma, Life Science, Chemicals/Biologics primary business focus • Participating Member ($10,000) • Those wishing to influence the technical outcomes • Access to Governance & Strategy Board member openings • Technical & Standards Team voting • Contributing Member (free) • Technical & Standards Team voting member • Working Group participation • Offer opinions on technical issues Based around the experiences of: http://www.consortiuminfo.org/

30. Inconsistent Semantics degrade Effective Communication C B A The result from our proprietary assay is “B” CRO But we only record numbers in our assays!! PHARMA

31. Greater Challenge: Unknown Semantic Collisions Image/quote from Abdul-Malik Shakir Revealing assumptions is an essential component of effective communication.

32. STANDARDIZE SIMPLIFY CENTRALIZE PISTOIA MEMBERS SUPPLIERS Crossing the Chasm • Pistoia is the BRIDGE to cross the chasm to a more agile pre-competitive environment

33. Effective Collaboration Participation, direction Trust Recognise Value (internal and external) FTE, $$, IP Assets, Ideas, concepts, pragmatism

34. Pre-competitive Space in the Technology Lifecycle Precompetitive Space Opportunities for Standards

35. Learn from Other Industries Transportation Geospatial Banking Clinical Retail Automotive Healthcare

36. Pistoia Standards Process Governance & Operations Pistoia Working Groups Life Science Community Governance & Strategy Board submit Software and Service Providers propose,comment Technical & Standards Teams Pharma/ BioTech/Agro publish Operational Team Not for Profit (e.g. IMI, EBI) coordinate

37. SESL Mock Up Slide 1 SESL Query Gene: abc Relationship: Any Disease: Diabetes Species: Any Constraint: Tissue: Any

38. SESL Mock Up Slide 2 Export to Network View SESL Results Pivot on Assertion Evidence Species Gene R’ship Disease Paper UID:1234 Co-occurs Mus 1 abc1 Diabetes ArrayExpress: XXX Up-Reg Homo 2 abc1 Diabetes Paper UID:1344 Co-occurs Homo 3 abc2 Diabetes Paper UID:1314 4 Co-occurs Mus abc13 Diabetes 5 OMIMI: XXX Mutation Rattus abc7 Diabetes Paper UID:45643 Co-occurs Mus 6 abc1 Diabetes Paper UID:2143 Co-occurs Homo 7 abc1 Diabetes Paper UID:1204 Co-occurs Mus 8 abc1 Diabetes

39. SESL Mock Up Slide 3 Export to Network View SESL Results - Pivot Return Supporting Evidence Species Gene R’ship Disease 3 Co-occurs Mus 1 abc1 Diabetes 1 1 Up-Reg Co-occurs Homo Mus 3 5 abc1 abc13 Diabetes Diabetes 1 Co-occurs Homo 2 abc1 Diabetes 4 1 Mutation Rattus abc7 Diabetes 6 1 Co-occurs Rattus abc2 Diabetes

40. Pistoia Collaborative Working e.g. 3 parties working together Past - Independence Emerging – Open Collaboration X Y X Y More X Y Z overlap Z We have all worked separately on our environments and with partners since we had budget and people Z Agreeing the pre competitive space, allows for collaboration on Standards and Services As Is - Sequence Services Vision - Sequence Services X Y X Y 3rdParty Service Develop Services that allow decommissioning of internal services at lower or equivalent costs. Also allows for future enhancement costs to be shared Companies replicate much of the same functionality and internally host external content to ensure high service levels and privacy Sequences Z Z Sequences

41. Pistoia Domains – focused on business workflows/supply chains Enabling Knowledge and Information Services Vocabulary Visualisation Application Integration Workflow Others Biology Data Services Chemistry Data Services Translational Data Services

42. Genome/Genetic Data Scope of Pistoia Efforts Target ID Hit ID Lead ID Lead Opt Phase I Phase II Phase III Which Disease? Which Target? Which Compound? What Biomarkers? CD positioning? Safety Biomarkers? Efficacy Biomarkers? … Disease Association Bioprocess Assoc Druggability ‘On Target’ Safety Risk Validation Tools Competitive Position Variant Selection … DMPK Properties? BioAssay Development Activity-Dose studies? ‘Off Target’ Safety Risk? Synthesis routes? Competitive Position? … Genome/Genetic Data Sequence Data Expression Data Structural Data Pathway Data Patent Data Pharmacology Data Literature Data 42

43. Background—How it all started • In Pistoia, Italy • Meeting of GSK, AZ, Pfizer and Novartis—identified similar challenges and frustrations in discovery informatics