Precompetitive Collaborations

October 26, 2010

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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.

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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.

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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).

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R&D: Long, Expensive, and Risky

1614121086420

Years

Cost = $1.3B/new drug

TargetSelection

ChemicalSelection

ClinicalTrials

Launch

Discovery(2-10 years)

Pre-clinical TestingLaboratory and animal testing

Phase 120-80 healthy volunteers - safety and dosage

Phase 2100-300 patient volunteers efficacy & safety

Phase 33,000-5,000 patient volunteers used to monitor

adverse reactions to long-term use

FDA Review/Approval

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Productivity is Decreasing

6 Source: Tufts Center for the Study of Drug development, PhRMA

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Collaborations/Consortia Funding Opportunities

Critical Path Initiative– FDA March 16, 2006

– http://www.fda.gov/oc/initiatives/criticalpath/reports/opp_list.pdf

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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

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Industry Driver: Externalization

DA

TA

CR

OB

IO C

RO

CH

EM

CR

OP

HA

RM

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REGISTER

DESIGN

ASSAY

REPORT

DISTRIBUTE

SYNTHESIZE

PHARMA

CHEM

BIO

DATA

PH

AR

MA

DISTRIBUTEREGISTER ASSAYSYNTHESIZE REPORTDESIGN

SelectivelyIntegrated

Model

Fully Internal Model

Cost pressures, disruptive technologies, and other forces often drive business processes to be externalized.

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Emerging Net-centric PharmaProcesses

PHARMA1

PHARMA1

CRO2

CRO2

CRO1

CRO1

CRO3

CRO3

PHARMA2

PHARMA2

PHARMA3

PHARMA3

CRO4

CRO4

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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

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Learn from Other Industries

Transportation

Geospatial

Automotive

ClinicalRetail

Banking

Healthcare

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Collaborations in the Research Space

Industry Collaboration Groups– Enlight Biosciences

For-profit, Scientific technology development http://www.enlightbio.com/content/areas-of-interest/

– PRISM (Pharmaceutical Information Systems Management ) Forum Discussion group –stale since 2004 http://www.prismforum.org/charter.htm

– OMG (Object Management Group/Life Sciences Research) Open, NFP, Basic specifications http://www.omg.org/lsr/ - stale since 2005

– W3C (World Wide Web Consortium) Open, NFP, Basic specs “to lead the web to its full potential” http://www.w3.org/

– DCMI (Dublin Core Metadata Initiative) Open, NFP, Develops metadata standards http://dublincore.org/about/

– PRIME

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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

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Phase III

Data -> Questions -> R&D Phases...

Phase IIPhase ILead OptLead ID Hit IDTarget ID

Which Target? Which Compound?Which Disease?

What Biomarkers?Which Patient?

Disease AssociationBioprocess AssocDruggability‘On Target’ Safety RiskValidation ToolsCompetitive PositionVariant Selection…

DMPK Properties?BioAssay DevelopmentActivity-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

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Structural Data

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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

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Phase III

Current Working Groups

Phase IIPhase ILead OptLead ID Hit IDTarget ID

Which Target? Which Compound?Which Disease?

What Biomarkers?Which Patient?S

tag

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K

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ELN Query Services

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Idea

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Chemical Renderer Interface

Domain Model

Pistoia Workflow - CRO

Chem2.0 and Wiki interfaces

RDF and Triples standards

Vocabulary ServicesDisease Knowledge Services

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Current Member Companiesas 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

GGA

• GlaxoSmithKline • Hoffmann-La Roche   • Infosys Technologies Limited   • Knime • Lundbeck   • Merck   • Novartis • Pfizer • Rescentris   • Royal Society of Chemistry (RSC)     • Symyx     • Thomson Reuters   • UPCO

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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…

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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

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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.

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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

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GoalTo 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

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Provider Perspectives

Clinical trials recruitment is often cumbersome and legacy.

• Quality, Safety, ARRA, Clinical Research, Healthcare complexity

Better tools are absolutely needed• 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 medicationsEHRs are rapidly evolving due to many driving forces

• Data mapping issues• 21CFR11 compliance

Impact on Design/Redesign of current/future EHR technology

Impact on Privacy/Confidentiality, IRB approval

Impact on IT staffing for data mining & delivery

Integration with current CTMS

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ConsumerScorecard

Physician

Pay forPerformance

Patient

Medical History

External Data (Labs, Other providers)

Presenting problem

RetrospectiveEvidence

RetrospectiveEvidence

PhysicianMetrics

PhysicianMetrics

Formulary/Individual

Benefit

Robust Decision Support

– Clinical outcome

– Cost effective

– Drug safety

– Epidemiology

– Bio surveillance

Clinical & Claims Data

Data AnalysisData 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

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PACeR - The Public-Private Partnership

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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…

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Thanks