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Automating Phase One Trials
November 5, 2012
Param Singh
Vice President,
Clinical Trial Management Solutions
BioPharm Systems, Inc.
Ed Dougherty
Senior Sales Consultant
Oracle Health Sciences GBU
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Welcome & Introductions
Param Singh
Vice President of Clinical Trial Management Solutions
BioPharm Systems, Inc.
• CTMS practice director since 2007
– Expertise in managing all phases and styles of clinical trials
– Leads the team that implements, supports, and enhances Oracle’s
LabPas and Siebel Clinical solutions
• Extensive Siebel Clinical implementation experience
– 11+ years of experience implementing Siebel Clinical
– 15+ implementations
– Spearheaded the creation of the Siebel Clinical “accelerator”
ASCEND
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Welcome & Introductions (cont.)
Ed Dougherty
Senior Sales Consultant
Oracle Health Sciences Global Business Unit
• 17+ years of experience in Life Sciences
– Primary focus: clinical development
– Facilitates client understanding of Oracle Health Sciences
applications through presentations, demonstrations, and workshops
– Prior to joining Oracle, worked for Medidata, leading
implementations of their EDC solution and helping to define their
product strategy
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Agenda
• Historical approach to managing phase I trials
• Challenges of historical approach
• Emerging trend toward automation
• Overview of LabPas, Oracle’s phase I clinic
automation solution
• Minimum requirements for phase I solution
• Time for questions
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Current State of Phase I
Since 2008, phase I costs have increased:
• 32% for subject recruitment
• 68% for each subject per trial
• 108% for staffing
The biggest challenge, according to sponsors, is finding “trial
sites and clinical research organizations that can yield
reliable, high quality data.”
-- Clinical Trial Costs Are Rising Rapidly
Pharmalot, Pharmalive.com, 26 July 2011
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Phase I Trial Management, Historically
Almost entirely paper-based
1. Volunteer recruiters manually verify study eligibility
2. Nurse/Technician records screening and study data by hand
on paper
3. Human “checkers” stand behind Nurse/Technician and verify
data is recorded accurately
4. Data entry personnel manually transcribe data from paper
to database
5. Monitor compares database to paper records
6. Data entry personnel investigate and correct errors/discrepancies
7. PI reviews data
8. Reports are manually generated for each sponsor
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Challenges of Historical Approach
• Recruiting volunteers
– Failure to recruit full subject groups can delay a trial and
increase costs
• Complex workflow
– Data is collected at a rapid pace and in a highly structured manner,
and trial design often changes
• Scheduling studies
– Lack of visibility into the availability of beds and staff to conduct
each trial makes it difficult to bid correctly and maximize revenue
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Challenges of Historical Approach (cont.)
• Sample integrity
– Need to collect samples on time, track their location, and store
them properly to preserve data integrity
• Evidence of processes
– SOPs show planned processes; need contemporaneous data to
provide evidence
• Accurate data capture
– Missing or erroneous data can invalidate results and necessitate a
partial or complete repetition
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Emerging Trend Toward Automation
“Historically, these challenges have been addressed with
increasingly intricate paper processes. These studies are,
however, becoming more complex. The unit must
accommodate the requirement that early phase trials deliver
a first look at efficacy and stakeholders' desire to share data
rapidly. Meanwhile, the units aim to take on larger numbers of
studies. All of these factors are leading to the simple
conclusion that paper processes are no longer practical.”
-- Automating Phase I Trials,
Applied Clinical Trials, 1 August 2010
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Oracle Health Sciences LabPas
• Flexibility
– Early phase study protocols can change quickly
– Last-minute setup, schedule, and barcode label changes easy to
manage (even after study has begun)
• Configurability
– Define all the major elements of a study
• Sample processing requirements, configuring edit checks, etc.
– Expansive reporting and data export structure
• Control
– Designed to reduce errors and queries
• Direct data capture
• Sample processing automation
• Real-time edit checks and alerts
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Key Benefits of LabPas
• Subject Recruitment
– Study volunteers recruited quickly with an easy-to-use
customizable, scripted data collection wizard and appointment
scheduling tools
– Volunteers automatically evaluated based on configurable inclusion
and exclusion rules
• Direct Data Capture
– Automation dramatically reduces the need for paper from clinical
trials
– Real-time study data collected and checked when the technician is
with the subject; no delay in data entry or verification
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Key Benefits of LabPas (cont.)
• Sample Management
– Barcode-driven sample management efficiently captures data and
controls sample process
– Customizable edit checks and alerts reduce input and sample
processing errors
• Data Management
– Data visualization, data review/approval, metrics and query
management allow effective qualification of electronic source
– Key decisions made earlier when real-time data is available to both
investigators and sponsors
• Reporting
– Data can be evaluated and easily transferred to the sponsor with
flexible reporting capabilities
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Features & Functions of LabPas
• Volunteer lookup based on demographics and clinical
history
• Volunteer screening, appointment scheduling, and
screening data entry
• Flexible study and data capture setup enables rapid
configuration of study protocols
• Barcodes labels (subjects, dosing, tubes, instruments)
improve workflow
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Features & Functions of LabPas (cont.)
• Define multiple clinics to enable global early phase
deployment strategies
• Configurable security roles, electronic signatures, and a
complete audit trail
• Sample storage management and temperature monitoring
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Anticipated Phase I Trend
• Phase I clinics will continue to automate their operations to
differentiate themselves
– More competitive bids
– Higher quality data
– Faster data sharing
• Sponsors will continue to seek out phase I clinics that have
automated processes
– Time and cost savings
– Increased compliance
– Greater confidence in data, leading to better go/no-go decisions
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Recommendations for a Phase I Solution
• Minimum requirements should include:
– Robust volunteer database
– Call center scripts / decision trees
– Automatic verification of study eligibility
– Staff scheduling functionality
– Screening event tracking
– Electronic data capture on the clinic floor
– Automatic edit checks
– Sample tracking
– Portal for sponsors to review data
– Trusted/reliable software vendor
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Thank you for attending!
Questions?
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Contact Us
• North America Sales Contact:
– Rod Roderick
– rroderick@biopharm.com
– +1 877 654 0033
• Europe/Middle East/Africa Sales Contact:
– Rudolf Coetzee
– rcoetzee@biopharm.com
– +44 (0) 1865 910200
• General Inquiries:
– info@biopharm.com
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Presenter – Param Singh
• Contact
– psingh@biopharm.com
– +1 210 454 5192
Param has been working in the life sciences industry his
entire career. As vice president of CTMS at BioPharm, he
developed the CTMS practice to become one of the best in
the industry.
With a knack for resource and project management, Param
leads a highly skilled team of implementation specialists and
continues to build lasting relationships with clients.
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Presenter – Ed Dougherty
• Contact
– ed.dougherty@oracle.com
– +1 610 579 3705
Ed has over 17 years of experience in the life sciences
industry, primarily focused on the clinical development space.
For the past four years, he has been part of the Oracle Health
Sciences Global Business Unit, supporting various clinical
solutions, such as LabPas.
Ed facilitates client discussions surrounding the benefits of
Oracle Health Sciences applications through detailed
presentations and workshops.
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