Precompetitive Collaborations


Published on

Presentation delivered at Next Generation Pharmaceutical workshop in Miami on October 25, 2010.

1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Precompetitive Collaborations

  1. 1. Precompetitive Collaborations October 26, 2010 1
  2. 2. 2 Precompetitive  Refers to standards, data, or processes that are common across an industry and where the adoption, use, or prosecution of which provides no competitive advantage relative to peers.
  3. 3. 3 Precompetitive Mission Statement  Foster collaborations between pharmaceutical, biotechnology, technology, academic, and government organizations in precompetitive space to develop and promote the use of standards, identify partnerships, and transfer technology in order to drive greater process efficiency and lower costs.
  4. 4. 4 Role Description (CW 2009)  The role consists of three primary elements: – (1) the definition and promotion of industry standards (e.g., data models, APIs, processes, etc.) across the Research and Development and Medical continuum through participation on various non-profit entities (Pistoia Alliance, Inc.) and consortia (Clinical Research Information Exchange); – (2) proactive pursuit of pre/non-competitive collaborative application or technology development opportunities (e.g., industry partners collaborating with a vendor on the development of the next generation life sciences electronic notebook), and – (3) identification and cultivation of opportunities to generate revenue by monetizing our portfolio of products and services (e.g., divestment and/or licensing of Pfizer-developed applications).
  5. 5. 5 R&D: Long, Expensive, and Risky 1614121086420 Years Cost = $1.3B/new drug Target Selection Chemical Selection Clinical Trials Launch Discovery (2-10 years) Pre-clinical Testing Laboratory and animal testing Phase 1 20-80 healthy volunteers - safety and dosage Phase 2 100-300 patient volunteers efficacy & safety Phase 3 3,000-5,000 patient volunteers used to monitor adverse reactions to long-term use FDA Review/ Approval
  6. 6. 6 Productivity is Decreasing 6 Source: Tufts Center for the Study of Drug development, PhRMA
  7. 7. 7 Collaborations/Consortia Funding Opportunities  Critical Path Initiative – FDA March 16, 2006 – _list.pdf
  8. 8. 8 Critical Path Funding Opportunities  Better Evaluation Tools – Biomarkers (Disease, Safety), Pregnancy, Infectious Diseases, Cancer, Neuropsychiatric, Presbyopia, Autoimmune/Inflammatory, Imaging, Disease Models (Animals to Humans)  Streamlining Clinical Trials – Innovative Trial Designs, Patient Responses, Process  Harnessing Bioinformatics  21st Century Manufacturing  Products to Advance Urgent Public Health Needs  Specific At-Risk Populations - Pediatrics
  9. 9. 9 Industry Driver: Externalization DATACROBIOCROCHEMCROPHARMA REGISTER DESIGN ASSAY REPORT DISTRIBUTE SYNTHESIZE PHARMA CHEM BIO DATA PHARMA DISTRIBUTEREGISTER ASSAYSYNTHESIZE REPORTDESIGN Selectively Integrated Model Fully Internal Model Cost pressures, disruptive technologies, and other forces often drive business processes to be externalized.
  10. 10. 10 Emerging Net-centric Pharma Processes PHARMA 1 CRO 2 CRO 1 CRO 3 PHARMA 2 PHARMA 3 CRO 4
  11. 11. 11 Opportunity: Changing Tech Landscape More Robust Technologies  Web 2.0  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
  12. 12. 12 Learn from Other Industries Transportation Geospatial Automotive ClinicalRetail Banking Healthcare
  13. 13. 13 Collaborations in the Research Space  Industry Collaboration Groups – Enlight Biosciences  For-profit, Scientific technology development  – PRISM (Pharmaceutical Information Systems Management ) Forum  Discussion group –stale since 2004  – OMG (Object Management Group/Life Sciences Research)  Open, NFP, Basic specifications  - stale since 2005 – W3C (World Wide Web Consortium)  Open, NFP, Basic specs “to lead the web to its full potential”  – DCMI (Dublin Core Metadata Initiative)  Open, NFP, Develops metadata standards  – PRIME
  14. 14. 14 Pistoia Description and Purpose  The primary purpose of the (Pistoia) Alliance is to streamline non-competitive elements of the life science workflow by the specification of common standards, business terms, relationships and processes  Goals – to allow this framework to encompass/support most pre-competitive work between the organisations – to support life science workflow prior to submission – to work with other Standards organisations
  15. 15. 15 15 Phase III Data -> Questions -> R&D Phases... Phase IIPhase ILead OptLead IDHit IDTarget ID Which Target? Which Compound? Which Disease? What Biomarkers? Which Patient? 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? … CD positioning? Safety Biomarkers? Efficacy Biomarkers? … Personalised Healthcare? What Dose? Combination Therapies? Safety Problem Solving … Genome/Genetic Data Sequence Data Expression Data Genome/Genetic Data Pathway Data Patent Data Pharmacology Data Literature Data Clinical Trial Data ExemplarData (External) Exemplar Sub-Questions Stages& KeyQuestions Structural Data
  16. 16. 16 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
  17. 17. 17 Phase III Current Working Groups Phase IIPhase ILead OptLead IDHit IDTarget ID Which Target? Which Compound? Which Disease? What Biomarkers? Which Patient? Stages& KeyQuestions ELN Query Services Working Groups Emergingand EnablingIdeas Chemical Renderer Interface Domain Model Pistoia Workflow - CRO Chem2.0 and Wiki interfaces RDF and Triples standards Vocabulary Services Disease Knowledge Services
  18. 18. 18 Current Member Companies as of January 2010  Accelrys  AstraZeneca  BioXPR  Boehringer Ingelheim  Bristol-Myers Squibb  Cambridge Crystallographic Data Centre (CCDC)  CambridgeSoft  ChemAxon  ChemITment  Collaborative Drug Discovery (CDD)  DeltaSoft  Edge Consultancy • GlaxoSmithKline • Hoffmann-La Roche • Infosys Technologies Limited • Knime • Lundbeck • Merck • Novartis • Pfizer • Rescentris • Royal Society of Chemistry (RSC) • Symyx • Thomson Reuters • UPCO
  19. 19. 19
  20. 20. 20 Summary of the Work  Model End Points – Permeability (RRCK) – Human Liver Microsomal Stability (HLM) – Pg-p substrate Efflux (MDR) – Molecular Properties such as LogD – DDI CYP 450 Cocktail models (4) – Herg/Dofetilide – Solubility – BBB – ALT – others…
  21. 21. 21 1. Spend only 20% on descriptors and algorithms? 2. Selectively share your models with collaborators and control access? 3. Have someone else host the models / predictions? What if you could… Copyright © 2009 All Rights Reserved Collaborative Drug Discovery Inside company Collaborators Current investments >$1M/yr >$10-100’s M/yr
  22. 22. 22 Collaborations in the Clinical Space  Clinical Data Interchange Standards Consortium (CDISC) Production Standards: – The Study Data Tabulation Model (SDTM) for the regulatory submission of Case Report Tabulations, including the Standard for the Exchange of Nonclinical Data (SEND). – The Analysis Data Model (ADaM) for the regulatory submission of analysis datasets. – The Operational Data Model (ODM) for the transfer of case report form data. – The Laboratory Model (LAB) for the transfer of clinical laboratory data, including pharmacogenomics. – The Biomedical Integrated Research Domain Group (BRIDG) model. – The Case Report Tabulation – Data Definition Specification (define.xml). – The Terminology standard containing terminology that supports all CDISC standards. – The Glossary standard providing common meanings for terms used within clinical research.  Those standards being developed are: – The Protocol Representation Group developing machine-readable medical research protocol standards including the Trial Design model shared with SDTM. – The Clinical Data Acquisition Standards Harmonisation (CDASH) developing data acquisition standards.
  23. 23. 23 Partnership to Advance Clinical electronic Research (PACeR)  A Partnership between leading pharmaceutical companies, health technology vendors, New York-based academic medical centers, standards organizations, and regulators collaborating to build an advanced clinical research capability enabled by the re-purposing of electronic clinical care data
  24. 24. 24 Goal To accelerate the availability to patients of innovative medicines by improving capabilities to conduct clinical research Major Objectives  More rapidly, accurately, and efficiently identify and enroll patients appropriate for clinical trials  Assess gaps between current clinical research capabilities (current state), and those required to meet project goals (ideal state)  Identify regulatory and legal issues, implications for business models, and data and systems necessary to close gaps  Develop a practical, implementable plan for closing the gaps, addressing the requirements of all stakeholders While the initial phase of the work is a collaborative feasibility study, the long- term goal is to build a sustainable capability and business that delivers a superior outcome for patients Project Goal & Objectives
  25. 25. 25 Provider Perspectives Clinical trials recruitment is often cumbersome and legacy. Better tools are absolutely needed EHRs are rapidly evolving due to many driving forces • Quality, Safety, ARRA, Clinical Research, Healthcare complexity Impact on Design/Redesign of current/future EHR technology • Capture of discrete coded condition and medication data is essential • Alerts woven into EHR to prompt provider at point of care • Reuse of EHR data through CDW/EDW technology • Not uniformly implemented • Differing lexicons/ontologies describing conditions and medications Impact on Privacy/Confidentiality, IRB approval Impact on IT staffing for data mining & delivery Integration with current CTMS • Data mapping issues • 21CFR11 compliance
  26. 26. 26 Consumer Scorecard Physician Pay for Performance Patient Medical History External Data (Labs, Other providers) Presenting problem Retrospective Evidence Physician Metrics Formulary/ Individual Benefit Robust Decision Support – Clinical outcome – Cost effective – Drug safety – Epidemiology – Bio surveillance Clinical & Claims Data Data Analysis Protocol Modeling & Assessment, Site Selection, Patient Recruitment PHRs Consumers, healthcare providers, policy makers and payers are leveraging HIT, particularly Electronic Health Records (eHRs) and Health Information Exchanges (HIEs), to analyze health data, contain healthcare costs, and improve quality of clinical care. Clinical Research is well positioned to take advantage of the HIT Pipeline
  27. 27. 27 PACeR - The Public-Private Partnership
  28. 28. 28
  29. 29. 29 Discussion Questions  What are the barriers to precompetitive collaborations in research, development, commercial, medical, etc. arenas?  What are the factors that are stimulating precompetitive collaborations?  What is the “tipping point” and how far away is it?  More…
  30. 30. 30 Thanks