the nycomed outsourcing model – data standardisation experience after three years
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The Nycomed outsourcing model – Data standardisation experience after three years. B. Traub - Data Integration Standards PhUSE 2011. Contents. Integrating clinical trial data Working with CROs Current operating model in Data Integration Standards Conclusion and experience. Legacy Nycomed. - PowerPoint PPT PresentationTRANSCRIPT
The Nycomed outsourcing model – Data standardisation experience after three years
B. Traub - Data Integration StandardsPhUSE 2011
Contents
1. Integrating clinical trial data
2. Working with CROs
3. Current operating model in Data Integration Standards
4. Conclusion and experience
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Legacy Nycomed
• Focus area: Core expertise in gastroenterology, inflammatory, respiratory and pain
• Partnering was an essential part of the business model
• The pipeline was built from own research and through co-developments with partners.
• Effective from October 1st 2011, Nycomed is part of the Takeda group
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Disclaimer
The shared content of this presentation is that of legacy Nycomed.
It does not reflect the R&D or CRO experience of legacy Takeda.
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Contents
1. Integrating clinical trial data
2. Working with CROs
3. Current operating model in Data Integration Standards
4. Conclusion and experience
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The data asset of Nycomed
• Clinical Trial data are an essential asset of Nycomed.
• Providing integrated data collected from Clinical Trials requires data
conversion and standardisation processes
• So far CDISC CDASH, SDTM and ADaM standards were adopted
• The data are stored and integrated in a Clinical Data Warehouse
(compliant to Nycomed QMS and GCP / ICH E6)
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Data Integration team
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Nycomed Data Standards– CDISC SDTM and ADaM
– Code lists
– Processes / rules
Data Exchange– Definition of standards and
management of data exchange
– Support data submissionsto regulatory authorities
CRO collaboration Planning of data standardisation
– Definition of deliverables and processes
– QC agreement and oversight
Data Integration
Standards team– Data Integration Manager (DIM)
– SAS programmer
– Data Warehouse Manager
Data Warehouse– Data integrity, comparability
– Controlled access to data
– Data security and compliance
Our role in the Clinical Trial Team (CTT)
Data Science members / responsibilities:
•Data Manager: Data cleaning process and database set-up (to a large proportion)
•Biostatistician: Study specific statistical reporting and generation of analysis data bases
•Data Integration Manager: Data standardisation tasks
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CTTLeader
LOC
Trial support
Data Science
CROs Medical Science
Drug Safety
Regulatory
Trial data flow
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Internal share
Clinical Database
AnalysisData
ORACLE Life Science Data Hub
SDTMData EntryData Cleaning
MappingDerivationrestructuring
Nycomed
TLGTLG SAS program
EDMS
Import / stagingCDASH
Clinical Data warehouse FACT
Contents
1. Integrating clinical trial data
2. Working with CROs
3. Current operating model in Data Integration Standards
4. Conclusion and experience
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Outsourcing at legacy Nycomed
• Basis• High level outsourcing of non-strategic activities
• lean R&D and CRO oversight model with strategic partnerships
• The CRO is accountable to• Provide deliverables according to agreement
• Work according to GCP and CSV principles
• Nycomed is accountable for • Compliance to regulatory requirements
• Oversight of the CRO to ensure quality in regards to task conducted for Nycomed
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Outsourcing at legacy Nycomed cont.
Most of Data Science tasks are completely contracted out .
That requires:
• Strong partnership with CRO• Building expertise on both sides (CDISC, requirements of regulatory
authorities, …)• Nycomed to specify user requirements and to define clear sponsor
standards • Clear QC concept including definition of quality levels to be met
Since Nycomed is accountable for the compliance of the deliverables in regards to GCP and CSV, the CRO processes must fit to the according requirements
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Understanding CRO processes
Basic assumption at legacy Nycomed•The sponsor has the ultimate accountability for all outsourced processes •The CRO works according to his processes and rules
The sponsor needs to know how the CROs worksThe sponsor must ensure that all processes are covered by SOPs The sponsor must ensure that the CRO SOPs do not contradict own
standards
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SAS programming
Creation of SDTM and ADaM datasets is basically a SAS programming task!
What does that mean?
• Nycomed adopted the V-model for computerised systems validation (CSV)
• Since a SAS program is a small Computer System we require that• CSV principles will be applied and thus• software development lifecycle documentation (SDLC) will be created
for the validation of SAS programs
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SAS programming: SDTM / ADaM datasets
SpecificationaCRF mappingSDTMADaM
SpecificationaCRF mappingSDTMADaM
QualificationModel complianceDerivation rules……
QualificationModel complianceDerivation rules……
SAS programming
SAS programming
CROGCP compliant SAS environmentCRO SOP controlled
CROGCP compliant SAS environmentCRO SOP controlled
Review bySponsor
CRO responsibility,no interventionby Sponsor
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Balancing CRO efficiency and Nycomed oversight
CRO efficiency versus Nycomed oversight
• Rely on the CROs expertise
• Have the CRO work according to his processes
• Avoid micromanagement
• Supervise CRO (what they do and how they do it)
• Nycomed requirements• Determine QC measures in
detail
Balance
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Contents
1. Integrating clinical trial data
2. Working with CROs
3. Current operating model in Data Integration Standards
4. Conclusion and experience
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Contracting out data standardisation tasks
The planning process and documentation for the data standardisation task is Nycomed SOP controlled
This SOP is mandatory for the CRO
The SOP basically describes the Data Integration Plan (DIP). This is the ‘Data Management Plan’ for data standardisation tasks
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The Data integration plan (DIP)
DIP content
General: Management plan for data standardisation
• Contacts and communication details• Agreements about CDISC standards • Details about deliverables and
responsibilities such as• Timelines• SOPs to be used • QC planning and documentation
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specifications
Datasets (SDTM ADaM)
Define files
QC plan and report
Integration Report
Deliverables
QC and oversight on deliverables
Checks primary and secondary endpoint variables(correct application of rules as specified in SAP and ADaM specifications)
Compliance (check opendcdisc.org report after receipt of data)
Consistency(cross check of aCRF, SDTM specs , SDM, Adam specs, Adams, define files)
Completeness (QC and SAS programs, according to agreements in the DIP)
GCP compliant SAS programming according to CRO SOP.
Validation / QC documentation
Compliance checks(run opendcdisc.org checks and deliver report for SDTMs / ADaMs)
VerificationOversightValidation / QC
Slide 20
CRO Nycomed DIM Nycomed Biostatistician
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Contents
1. Integrating clinical trial data
2. Working with CROs
3. Current operating model in Data Integration Standards
4. Conclusion and experience
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Examples of issues
Deliverable Potential issues Nycomed measures
Data Integration Plan
Relevant CRO SOPs are not available to Nycomed
As a minimum, SOP list is required as well as a trial specific QC plan
SDTM and ADaM datasets
CRO starts programming without approved specifications
Nycomed will not accept datasets
SAS programs No clear agreement on deliveryCRO claims intellectual property
Clear specification in MSA, contracts, SOPs and DIP
QC documentation Qualification documentation is not provided/available or only on general level
DIP requires proofed validation evidence per program
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Lessons learned
• Mature CDISC standards and CDISC knowledge on both side is crucial • Sponsor standards and QC concept are available and aligned with
CRO • Clear definition of deliverables and QC concepts• Communication and partnership building – preferred provider concept
• Contracting CROs for CDISC data standardisation is more than just referring to the current SDTM/ADaM standards
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Thank you
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Backup slides
Working with CROs
CRO
Issue log, …….Issue log, ……..Issue log, ……..
SDTM aCRF Specs Data +Define.xml
Ny
co
me
d r
es
ou
rce
sC
RO
re
so
urc
es
Review +approval
Review +approval
Data Integration plan (DIP)
DIM
QCReview +approval
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