multifaceted aspects of metadata maximize efficiencies€¦ · -clinical drug metabolism &...
TRANSCRIPT
Multifaceted aspects of metadata maximize efficiencies
Patrick Genyn, Senior Director
May 10th, 2012
Drug Development Information Governance 9th Annual SAS Health Care & Life Sciences Executive Conference
Janssen Research & Development
Content
• Hermes Initiative – Next future-proof Clinical Data Management (CDM) practice – P4: Process, People, Platform and Partner
• Drug Development Information Governance – Master Data Management – Master Data Governance
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Hermes Initiative
• Improved predictability & transparent process towards our customers
• Sustainable roles, increased efficiency
• Solid foundation for further improvement
• Learning organization, innovation
• High quality deliverables & increased inspection readiness
• Deliverable-based model
• Globally consistent & well understood processes
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Hermes - process
• Aligned with entire Clinical E2E Process map – Linkage guaranteed to other functions (monitoring, stats and
programming, clinical, …) – Deliverables clearly defined with single responsibilities
• Integrate end-to-end the CDM processes across Therapeutic Areas – Ensure consistency where possible – Accept differences where they make sense
• Integrate end-to-end the CDM processes across all phases – Early Development / phase 1 studies – Exploratory & Confirmatory / phase 2 and 3 studies – Medical Affairs and Post Marketing / phase 4 studies
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Hermes - people
• Organizational principles – Internal focus on customer interaction, oversight & innovation – External focus on optimized end-2-end operations – Minimize the number of roles
• 3 integrated organizational structures with focus on – Therapeutic Area: Interaction with Clinical Team & R&D partners – Delivery: Interaction with external e2e operational partners – Infrastructure: Process, Platform and Partner performance
• Learning organization – Formal class room training – On-the-job training and coaching – Comprehensive competency model
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Hermes - partner
• Partnership – Wikipedia definition: A partnership is an arrangement where
parties agree to cooperate to advance their mutual interests. – Challenge: Are our interests really mutual when a partner provides
data management services?
• Principles – Contract is deliverables based
• Responsibility of the quality of the deliverable is with the partner • Accountability of the quality of the deliverable is with the sponsor
– Scope includes : eCRF build, Database build, Ongoing data cleaning/query resolution and Submission ready data package
– Multiple partners to maintain competition – Niche providers for specialty deliverables
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Hermes – platform (2/5)
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• The Data Standards Library contains : – Standard CRF templates (CDASH) – Metadata definitions (SDTM, Therapeutic area)
used to create study metadata
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Hermes – platform (3/5)
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• Generating study metadata by selecting CRF templates from the Data Standards Library and adding the trial specific metadata.
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Hermes – platform (4/5)
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• Study Metadata Repository is used to measure the consistency of metadata
• Study metadata is sent to external or internal partners for eCRF and database build
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Hermes – platform (5/5)
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• After study build, data and metadata will be: – compared against the Study Metadata Repository – validated against the Data Standards Library
Verification
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Content
• Hermes Initiative – Next future-proof Clinical Data Management (CDM) practice – P4: Process, People, Platform and Partner
• Drug Development Information Governance – Master Data Management – Master Data Governance
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Problem Statement
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Pre-
Clin
ical
Chem
Phar
m
Clin
ical
Proj
ect M
gt
Offi
ce
Regu
lato
ry
Qua
lity
As
sura
nce
Med
ical
Saf
ety
No to little cross domain information governance/transparency siloed strategies
Meta Data Mgmt
Meta Data Mgmt
Meta Data Mgmt
Meta Data Mgmt
Meta Data Mgmt
Meta Data Mgmt
Meta Data Mgmt
HCP
Meta Data Mgmt
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Current situation: close interdependency organization – process – systems - data
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Targeted Future Situation
Master/Meta Data Management /Governance
Link
to C
omm
erci
al &
Su
pply
Cha
in
Link
to D
iscov
ery
Data Quality Management & Oversight
Optimal data exchange & deployment / Process Automation / Compound Data Strategies / Patient Outcomes support …
Pre-
Clin
ical
Chem
OPh
arm
Clin
ical
Proj
ect M
gt
Offi
ce
HCP
Regu
lato
ry
Qua
lity
Assu
ram
nce
Med
ical
Saf
ety
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Target situation: Multi-tier strategy for improved and sustainable data management
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What is Data Governance?
• Data Governance is an organizational structure that creates and enforces policies & procedures for the business use and management of data across the development organizations
• Business Goals for Data Governance – Compliance with internal and external regulations for data usage
and reduce risk exposure relative to data and its use – Business value generated from our data and information assets
• Technical Goals for Data Governance – Establish and enforce standards for data – Improve data quality; remediate its inconsistencies; share data;
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Governance Structure and DDIG Policy
• Terms and Data Definitions
• Data Ownership
• Data Processes
• Quality Requirements
• Business Rules
• Applicable industry Standards
• Applicable Regulations
• Monitoring and Metrics
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Executive Stakeholders
Governance Office
Domain and Functional Experts
Data Owners
Data Users
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What is Master Data in DDIG?
The consistent and uniform set of identifiers and extended attributes that describe the core entities in drug development and are used across multiple business processes or communities, specifically
• Data relevant to 2 or more business communities
• Data critical to the drug development process (e.g. from a compliance perspective)
• Data created once and reused many times
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The MDM Hub Process
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Contribute from
Multiple Sources
Master an Authoritative
View
Distribute to Multiple
Functions
… …
Contributing Data Source
MDM Hub
Adopting Data Store
Validation
QC
Integration
Trustworthy Relevant Timely
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Data Quality Framework - Requirements
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•Unique identification of an instance Uniqueness
•Required, expected and permissible attributes. Completeness
•The true value (in real life) of the data Accuracy
•Formatting requirements •Standard and regulatory requirements Compliance
•Not conflicting with any other data inc. timeliness •Complete from referential integrity perspective Consistency
•All above quality criteria met through the entire lifecycle (create, update, use, distribute and retire) Integrity
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Data Quality Framework - Procedures
06.03.2010
•Data is analyzed to find errors, inconsistencies, data redundancy and incomplete information Data Profiling
•Data Matching and Merging removing duplicates •Data Cleansing (missing data, inconsistent data, formatting …) •Data Enriching from third party sources
Data Management
•Validate from Contributing Data Source to MDM Hub •QC-ing Adopting Data Store against MDM Hub Data Integration
•Data quality issue reporting, analyzing, resolving and tracking •Data quality Issue severity, risk and priority management Issue Remediation
•Baseline, target and improvements from baseline •Compliance to quality requirements and business rules Data Monitoring
Selective
Permanent
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Current scope
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Therapeutic Areas
Market Intelligence
Product Portfolio Planning
Portfolio Risk Management
Product Portfolio Management
Epidemiology
Diagnostics
Marketed Product Support
Biomarkers Research/
Biosignature Research
Genomics
Target Product Profile Definition
Project Portfolio Management
Project Resources
Planning and Management
Monitor and Update Resource
Plan
Functional Initiation & Planning
Functional Maintenance &
Control
Change Control
Senior Management
Support
Product Development
API Small Mol
API Large Mol
Preformulation
Formulation Development
Excipients Development
Packaging Development
Pilot Plant & New Product
Introduction
Analytical Development
Portfolio & Capacity
Management
Strategic Operations
Pre-clinical and clinical Supply -
Planning
Pre-clinical and clinical Supply -
Fulfillment
Non Clinical Development
Toxicology
Laboratory Animal Medicine
BioAnalysis (BA)
Non-Clinical Drug Metabolism &
Pharmacokinetics (DMPK)
Cardiovascular Safety
Clinical Development
Clinical Pharmacology
CDP Management
Study Design Strategies
Trial Planning & Budgeting
Investigator Relationship Management
Conduct and Monitor Trials
Manage Outcomes Research
Data Management, Analysis and
Reporting
Filing & Archiving
Medical Safety
Product Safety Planning and Management
Case Management - Adverse Event Management
Aggregated Reporting
Signal Detection
Regulatory
Regulatory Intelligence
Regulatory Submissions
Dossier Planning
Regulatory Strategy
Regulatory Submission
Management
Global Product License
Management
Labelling Information
Quality and Compliance
Gathering Global Compliance
Requirements
Healthcare Compliance
Manage Policies
Operational Procedures
Monitoring & Control
Training
Risk Management
Partnering
Discovery
Manufacturing
Commercial
Legal
External Partners Management
Co Development Partners
Authorities
Marketing Intelligence
External Provider
Compound Drug Development Program
Clinical Activity Pre-Clinical Activity
Clinical Research Site Drug Development Partner
Reference data & terminology Subject identification