Emerging Trends in Clinical Data Management

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

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• 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

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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)

http://www.sciencemag.org/products/ddbt_0207_Final.dtl

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

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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


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


http://74.41.95.83/resdyncgweb/RDCG_EDC_Paper.pdf



http://www.thefreelibrary.com/DATATRAK+International+Releases+Value+Proposition+of+EDC+to+the...-a078554673



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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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.


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)


http://www.iptonline.com/articles/public/Formedix1.pdf

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


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


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


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White Paper for your reading

15 Published: September 2010


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


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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Applications of Data Analytics

Data Trends & Outliers

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0

4

8

12

16

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Site-1 Site-2 Site-3 Site-4 Site-5

10

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6

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Dis

cre

pan

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s /

pat

ien

t

Discrepancies /Site compared to number of patients.

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1

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1 1.2 1.5

0.9

3

Ave

rag

e T

ime

(h

r)

Average time from patient admission to performing ECG

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5

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15

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25

30


10 9 12

8

25

Qu

erie

s /

100

eC

RF

Pag

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Query Rate Across Sites

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12

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5 4.2 6 5.4

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Ave

rage

Lag

Tim

e (D

ays)

Lag Time Between DE & Visit Date


Advanced Analytics

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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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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


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


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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Emerging Trends in CDM

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Accelerated adoption of

EDC

Data Standards

Data Integration

Data Analytics

Cross Functional

Collaboration


Thank you

[email protected]

[email protected]

Quintiles CDM, Bangalore

Disclaimer: The views in this presentation are of the authors and not necessarily of Quintiles

Emerging Trends in Clinical Data Management

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