multifaceted aspects of metadata maximize efficiencies€¦ · -clinical drug metabolism &...

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 (1/5)

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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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• Generating study metadata by selecting CRF templates from the Data Standards Library and adding the trial specific metadata.



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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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• After study build, data and metadata will be: – compared against the Study Metadata Repository – validated against the Data Standards Library

Verification


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


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


Target situation: Multi-tier strategy for improved and sustainable data management


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


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


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


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


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


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

Janssen Research & Development 24

multifaceted aspects of metadata maximize efficiencies€¦ · -clinical drug metabolism &...

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