emerging trends in clinical data management
DESCRIPTION
Appalla Venkataprabhakar and I presented this at the Oracle\'s Annual Clinical Development and Safety Conference 2010 at Hyderabad, India on 6th October 2010.TRANSCRIPT
Emerging Trends in Data
Management
A. V. Prabhakar, PhD
Senior Manager, Clinical Data Management
Dr. Arshad Mohammed
Director, Clinical Data Management
Disclaimer: The views in this presentation are of the authors and not necessarily of Quintiles
Thalidomide: Revived interest
Thalidomide became infamous in 1960s as one of the biggest drug disasters
About 10,000 children born deformed since their mothers used Thalidomide for morning sickness during pregnancy
2
• 1998: FDA approved for treatment and suppression of cutaneous manifestations of erythema nodosum leprosum (ENL).
• 2006: Accelerated approval for thalidomide (Thalomid, Celgene Corporation) in combination with dexamethasone for the treatment of newly diagnosed multiple myeloma
• STEPS* program
FDA Approval
Brazilian physicians
Drug of choice for the
treatment of severe ENL
Since 1965
*System for Thalidomide Education and Prescribing Safety (S.T.E.P.S.) oversight program
Continued Industry Challenges
• Drug R&D costs have rocketed 23 folds in last 28 years, touching an all time high of up to $1.25 billion per new molecular entity (NME).
• Reducing patent protected market life as drug development time up from 11.6 years in 1970s to about 14 years
Time and money in R&D
• Even with 20 years patent protection, some companies are unable to get their drug to market before the patent’s expiration date.
Returns and Profits
• Optimizing the clinical trial process
• Rationalize research pipelines
R&D budgets falling and patent expiries looming: Urgent Priority
• Relying on real time technologies including CTMS, EDC, Automation of processes, shrinking timelines especially start up and close out
Industry is examining alternative ways for brining drug to market
3
References: 1. Drug Discovery and Biotechnology Trends: Recent Developments in Drug Discovery : Improvements in Efficiency http://www.sciencemag.org/products/ddbt_0207_Final.dtl)
2. The productivity tiger - time and cost benefits of clinical drug development in India. (http://pharmalicensing.com/public/articles/view/1153412098_44bfac02291f1)
Current Scenario in Clinical Research
Submissions team
Medical Writing
Biostatistics
Data Management
Clinical Operations
End Result for Biopharmaceutical
Industry
A Safe and Effective compound that can
be marketed
Generation of Clinical Data
Clean Data
Analyzed Data
Clinical Study Report
Regulatory Submission
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Emerging Scenario
5
Data Advanced
Technology enabled Transformation
Meaningful Information (Asset)
Maximizing asset value, data turned into information and used
before during after
a clinical trial program
Impact on Bio-Pharmaceutical Industry
• Creates better compounds
• Designs better study protocols
• Makes faster go or no-go decisions
• Alters assessments on compounds in development
Protocol design Adaptive design Meta analysis
Emerging Trends in CDM
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Accelerated adoption of
EDC
Data Standards
Data Integration
Data Analytics
Cross Functional
Collaboration
Key Functional Collaboration
Clinical & DM
DM & BIOS
Lab, ECG, IVRS, Safety etc
Cross Trial, Across Programs
EDC Standards Integration Analytics Collaboration
Accelerated adoption of EDC
Despite its slow start, the use of EDC is on the rise at a rapid pace
“By 2012 the expected number of EDC studies would be greater than
70%” - By David Handelsman
EDC can help to reduce the clinical research cost by ~20 – 28%
With skyrocketing costs – up to $1.25 billion to bring a new drug to
market, $500 - $700 million of which is spent on clinical trials – companies are seeking faster access to cleaner
clinical data
7 Reference: “Effective Clinical Trial Monitoring Using EDC Metrics” , Appalla Venkataprabhakar, Data Basics – Spring 2009
Quintiles Bangalore, Sept 10
EDC Standards Integration Analytics Collaboration
Advantages of using EDC
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References
1. EDC Advantage : Shrinking LPO-DBL Timelines in EDC Study”, Appalla Venkataprabhakar, Data Basics – Spring 2010
2. Achieving cost savings using EDC effectively ” (http://74.41.95.83/resdyncgweb/RDCG_EDC_Paper.pdf)
3. DATATRAK International Releases Value Proposition of EDC to the Pharmaceutical Industry - Part II
(http://www.thefreelibrary.com/DATATRAK+International+Releases+Value+Proposition+of+EDC+to+the...-a078554673)
Saving Time
Time to DBL could be
reduced by 43% &
number of queries by
86%
• 25-30% savings realized using EDC from decreasing traditional monitoring / DDE budgets
• PWC: Shift from paper to EDC will bring 35-50% reductions time & cost
• Cost savings alone with EDC vs. Paper estimated about $60 million per drug
Saving Money
• EDC provides better data accuracy
• Data standardization
• Centralized work flow
• Real time study results
• Low operations cost
Overall Improvement
EDC Standards Integration Analytics Collaboration
Process and role changes
• Sponsors looking at 5, 8, 15 weeks, etc for start ups
Crunched EDC start up timelines
• Follow the sun methodology in Database builds
• Global EDC testing hubs
• Centralized UAT
Global EDC Build teams
• Sponsor
• CRO
• Industry wide?
Global Libraries
• Protocol
• CRF
• DMP documents, Edit Checks
• UAT
Technology for Standardization
• DM: partial to total outsourcing (FSP)
• Outcomes based
• Partner DM staff at sponsor offices
• Shared Risks and Benefits
Partnerships of next level
• Enhanced Project management skills required
• Metrics driven
• Zero tolerance: Quality and Compliance
• Project Reviews
Management of CDM
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EDC Standards Integration Analytics Collaboration
Scenario due to Lack of Data
(Standard & Integration)
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Example of sophisticated review process of an FDA reviewer Reference
1.http://www.globalsubmit.com/home/LinkClick.aspx?fileticket=ta1z74CpCQw=&tabid=260.
EDC Standards Integration Analytics Collaboration
Data Standards
• Standardization helps improve efficiencies in trials by reusability of tools & ability to combine data across clinical studies
• Data standards make inter department & inter organizational collaboration possible
Data Standards are agreed upon set of rules that allow
information to be shared and processed in uniform &
consistent manner
• Lack of globally accepted pharmaceutical data formats believed to cost pharmaceutical industry in excess of US $ 156 million per annum
Financial Impact
• CDISC at the forefront of partnering with industry and defining standards
• HL7 is accepted messaging standard for communicating clinical data & supported by most major medical informatics system vendors
Leading Organizations
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Reference
1. Facilitating the use of CDISC standards in clinical trials “ – http://www.iptonline.com/articles/public/Formedix1.pdf)
EDC Standards Integration Analytics Collaboration
CDISC* Standards Table & Purpose
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Model / Standard Purpose
Operational Data Model (ODM) XML specification supporting interchange of data, metadata or updates of both between clinical systems
Clinical Data Acquisition Standards
Harmonization (CDASH) Data model for a core set of global data collection fields (element name, definition, metadata)
Submissions Data Tabulation Model
(SDTM) Data model supporting the submission of data to the FDA including standard domains, variables, and rules
Analysis Dataset Models (ADaM) Data model closely related to SDTM to support the statistical reviewer
Define.xml XML Specification to contain the metadata associated with a clinical study for submission
Standards for the Exchange of non-
clinical data (SEND) Data model extending SDTM to support the submission of animal toxicity studies
Protocol Representation Model (PRM) Metadata model focused on the characteristics of a study
and the definition and association of activities within the
protocols, including "arms" and "epochs".
* Clinical Data Interchange Standards Consortium
EDC Standards Integration Analytics Collaboration
Data Integration
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• Bringing data from multiple sources (IVRS, Diary, Lab, Randomization, Coding) eliminates redundant tasks like reconciliation, same data entry into multiple systems
• Accelerates flow of critical information to key stakeholders that aids faster decisions
Integration
• Out of box integrations
• Life Sciences Data Hub
• IVRS & EDC integration
• EDC & Safety integration
• Quintiles Data Factory
• Quintiles white paper for your reading
Examples of Data Integration
• Expedites data cleaning & reconciliation process
• Enhancing patient safety
• Strengthening quality
• Reduce the risk of data entry errors
• Accelerating timelines
Advantages of Data Integration
EDC Standards Integration Analytics Collaboration
Clinical Trial Data Integration
Clinical Research
Organizations / Partners
EDC / RDC / CDMS
IVRS
Hand Held Device Data
Regulatory Compliant
Integration & Reporting
Environment
CTMS
Financials
Clinical Trials
Progress Review
Data Exports,
PDF / HTML Reports
Business Process
Automation with Workflow
Central Labs
Clinical
Data Review
Data Analytics and
Online Reports
Courtesy: Oracle LSH presentation
EDC Standards Integration Analytics Collaboration
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White Paper for your reading
15 Published: September 2010
EDC Standards Integration Analytics Collaboration
Data Analytics in New Health Landscape
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• Make better cross functional business decisions - identify risks & mitigate them in timely manner e.g. need of additional trainings for staff at a site, fraud detection, signal detection, protocol deviations.
• Greater transparency into the status of a clinical trial subject
• Enhanced safety and efficacy monitoring via a holistic review of individual and aggregated subject data
• Increased operational efficiency and quality made possible through a transparent and holistic view of data
Benefits of Clinical Data Analytics
Science of examining raw data with the purpose of drawing conclusions about that information
EDC Standards Integration Analytics Collaboration
90
92
94
96
98
100
Visit-1 Visit-2 Visit-3 Visit-4 Visit-5
Tem
per
atu
re (C
)
Trend of Temperature vs. Visit
160
165
170
175
180
185
190
195
200
Visit-1 Visit-2 Visit-3 Visit-4 Visit-5
Hei
ght (
Cm
s)
Trend of Height vs. Visit
Applications of Data Analytics
Data Inconsistency
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EDC Standards Integration Analytics Collaboration
Applications of Data Analytics
Data Trends & Outliers
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0
4
8
12
16
20
Site-1 Site-2 Site-3 Site-4 Site-5
10
7
10
6
18
Dis
cre
pan
cie
s /
pat
ien
t
Discrepancies /Site compared to number of patients.
0
1
2
3
4
Site-1 Site-2 Site-3 Site-4 Site-5
1 1.2 1.5
0.9
3
Ave
rag
e T
ime
(h
r)
Average time from patient admission to performing ECG
0
5
10
15
20
25
30
Site-1 Site-2 Site-3 Site-4 Site-5
10 9 12
8
25
Qu
erie
s /
100
eC
RF
Pag
es
Query Rate Across Sites
0
4
8
12
16
20
Site-1 Site-2 Site-3 Site-4 Site-5
5 4.2 6 5.4
15
Ave
rage
Lag
Tim
e (D
ays)
Lag Time Between DE & Visit Date
EDC Standards Integration Analytics Collaboration
Advanced Analytics
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EDC Standards Integration Analytics Collaboration
DM-BIOS Collaboration
Till recently biostatisticians were
involved at later part of study when the data was available to them for final
statistical analysis
Resulted in lot of rework on databases (including
locked) for the unidentified data errors
identified by biostatisticians
Data errors identified so late incur additional time,
costs and annoyed customer (internal /
external)
Involvement of a biostatistician from start of the study significantly helps the DM team avoid a lot of potential rework.
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EDC Standards Integration Analytics Collaboration
Best Practice of DM-BIOS Collaboration
During Start up
Kick off Meeting
Protocol & CRF Preparation / Annotation
Early review of completed CRF
Edit Check Document Review
During Conduct
Data Transfer & Non-CRF Data Guidelines Preparation
Review of data at subsequent intervals
Ensure BIOS feedback
During Close Out
Interim transfers and early BIOS feedback
Completes data issues log & provides final copy of the
same to the data team lead.
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Key factor for early DB Locks: Effective working relationship between DM & Bios
EDC Standards Integration Analytics Collaboration
Clinical-DM Collaboration
Start-Up Phase
Inputs during designing of CRF per study protocol
CRF completion guidelines
Review of edit check document
Conduct Phase
CDM & BIOS inputs if SDV < 100%
Clinical share Monitoring Visit plan with DM
Triggered Monitoring Visit
DM should share milestone dates with Clinical
Monthly calls* between CDM and Clinical
Close Out Phase
Weekly calls* between CDM and Clinical
CDM should share the status updates or dashboards - live
CRF’s entered, Queries in open status, SDV, Freezing,
Locking, PI Signature etc
Start about 2 months before the final DB lock
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* Discuss issues or updates related to data points / queries / site response / site training / milestones, etc
EDC Standards Integration Analytics Collaboration
Loyalty Scores
Start up: 96.7%
Close out: 96.7%
Overall I am very impressed with the management of the project. The whole team has been extremely accessible. Of particular note was the data management team in India
who seemed to work around the clock on this study. - Clinical Operations Manager, Product Development,
Quintiles Case Study
Therapeutic Area: Anti Infective
Indication: Typhoid Fever
Vaccine
Patients: 329
Sites: 3 (All Sites in US)
Duration of Study: 1 year
Platform: Inform 4.5
Go Live within 6 weeks
Last Patient Last Visit-Database lock in 5 days
TOP 5 in terms of study performance on Quintiles
Inform Dashboard
All major deliverables achieved before time
Customer Audit: No critical or major findings
Project Management
Clinical Operations
Data Management
Lab
BIOS
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EDC Standards Integration Analytics Collaboration
Emerging Trends in CDM
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Accelerated adoption of
EDC
Data Standards
Data Integration
Data Analytics
Cross Functional
Collaboration
EDC Standards Integration Analytics Collaboration
Thank you
Quintiles CDM, Bangalore
Disclaimer: The views in this presentation are of the authors and not necessarily of Quintiles