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Does Your Risk-Based Monitoring (RBM) Meet FDA Guidelines?
19 September 2013
Presenters
Dr. Malcolm N BurgessExecutive Vice President, ICONIK
Dr. Burgess is responsible for the on-going design and development of ICON’s revolutionary integrated IT platform, ICONIK. Prior to taking this position, Malcolm was based in Hong Kong with responsibility for the management, growth and development of ICON’s Asia Pacific organization. Earlier positions included head of ICON’s Global Clinical Research strategy, Chief Operating Officer for the US Clinical Research Division (2006-2009), and global leadership roles involving Biometrics and IVRS.
Prior to joining ICON, Dr. Burgess held the position of Executive Director, Global Electronic Data Capture Logistics for Novartis. He has over 30 years’ experience within the Pharmaceutical sector having held various Research and Development positions within Novartis, Hoechst Marion Roussel and SmithKline Beecham, playing a key role in numerous significant regulatory submissions.
Malcolm holds a BSc in Chemistry from University College, London and a Doctorate in Biochemistry and Physiology from Bath University, UK.
Presenters
Mike LukerSenior Advisor, Clinical Development InnovationEli Lilly and Company
Mike is a 23 year pharmaceutical industry veteran with a track record of mobilizing and leading teams to pursue better ways of working across various disciplines including data sciences, information technology, clinical operations, and human resources. Mike currently leads a diverse team of clinical research professionals dedicated to transforming clinical development at Eli Lilly and Company – and more broadly across the industry – via a portfolio of progressive and highly collaborative innovation initiatives.
Agenda
• Introductions
• Industry Direction
• TransCelerate and Regulatory opinions
• Surfacing the real issues: integrity of the data
• Risk-based monitoring model and its benefits
• Eli-Lilly's experience implementing a risk-
based monitoring strategy
• Q&A
Industry direction – acceptance of RBM
• HSP/BIMO concept paper 2007− Quality in FDA-Regulated Clinical Research. Human Subject
Protection (HSP)/Bioresearch Monitoring (BIMO) Initiative Workshop April 2007
• EMA Reflection Paper 2011− Reflection paper on risk based quality management in clinical trials
August 2011• MHRA Risk Adapted Approaches
− Risk-adapted Approaches to the Management of Clinical Trials of Investigational Medicinal Products October 2011
• TransCelerate RBM Position Paper 2013− Position Paper: Risk-Based Monitoring Methodology May 2013
• FDA Guidance – August 2013− FDA. Guidance for industry oversight of clinical investigations – a risk-
based approach to monitoring August 2013
TransCelerate: Catalyst for Alignment
TransCelerate Position Paper (May 2013)
• Reflects the movement within the industry that is driven by health authorities to transition to RBM
• TransCelerate RBM methodology ‘improves efficiency by changing the focus to central or off-site monitoring activities’
− Identifies potential issues sooner than a monitoring strategy that relies primarily on site monitoring visits
− The methodology is being developed in parallel with the transition to risk-based inspection processes by health authorities
• Central and Off-site Monitoring Activities serve as the foundation of monitoring efforts and are complemented by targeted On-site Monitoring Activities based on a defined risk level
• Monitoring activities can be increased in response to issues and risks identified
• Risk Assessments should be initiated prior to the finalization of protocols and CRFs to minimize risks in advance of starting the trial.
• Monitoring strategies are adapted to ensure oversight to what is not prevented via protocol or CRF design
Regulatory agencies– acceptance of RBM
FDA guidance – August 2013• ‘FDA believes that risk-based monitoring could improve sponsor
oversight of clinical investigations’
Current guidance• Makes it clear that ‘sponsors can use a variety of approaches to fulfil their
responsibilities for monitoring clinical investigator conduct and performance in IND studies’
• Describes strategies for monitoring activities that reflect a ‘modern, risk-based approach’ that focuses on critical study parameters and relies on a combination of monitoring activities to oversee a study effectively
• Encourages greater use of centralized monitoring methods where appropriate
ICONIK Background & Strategy
ICON’s integrated information platform
TransparencyIncreased requirement for Information throughout the development process
VisibilityAccurate information is required to
proactively manage studies
EfficiencyAutomate and reduce manual effort to
produce information
QualityIncreased focus on data integrity and control
of clinical data
ICONIK : Operational Process
STEP 1: Centralise all of the data• Global repositories for all study data
STEP 2: Standardise the data• Standardise the data structure• ICONIK Study Data Mapper
STEP 3: Analyse & Report• Single Study• Multiple Studies & Programs• Compound• Therapeutic area analysis
PO
RTA
LR
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oll
abo
rati
on
Re
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sit
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es
Sy
ste
ms CTMS ePRO
Project Management
Investigator Payments
ICONIK : Our integrated information platform
Repositories
Safety Underperformingsites
Balanced Scorecard
Outstanding Queries
Patient Recruitment
Document Sharing& Team Sites
LaboratoryEDC Firecrest
Operational Metrics Repository
Clinical DataRepository
Master Data
ICONIK : Centralise
ICONIK : Standardise
Patent pending clinical data processing environment
standardisation
EDCs
Study DataMapper
Repository
File Sources
Other DBs
Study Properties
Clinical Data (Raw & Coded)
“System” & Discrepancy
Data
Clinical Reference
Tables
Study Metadata
Clinical Metric Model
Visualisation Models
Clinical Dimensional
Model
CDISC Delivery
Structures
Integrated Program DBs
sources
LS
H A
da
pters
staging delivery
targetslsh EDW
LS
H A
da
ptersEDW
Study DataMapper
Repository
ICONIK Clinical System Data
Model
ICONIK Clinical Data Model
ICONIK Clinical Metadata Model
ICONIK : Reports
ICONIK Monitoring Services
Technology
People
Scientific
Process
Optimize resources
Reduces SDV
Cost effective
Transform
Event triggers
Volume triggers
Risk triggers
Adaptive
Designed to meet regulatory and quality requirements for GCP
SDV of critical data
Increased centralized monitoring:
ICONIK
Targeted on site monitoring
ICONIK Monitoring Process
Data integration & analytics factory
Central Data Analysis Group
Site risk triggers
Volume triggers
Event triggers
100% SDV of critical data only
Planning Analysis Decision Action
Targeted Monitoring Interventions
Site management call
Off-site monitoring visit
On-site monitoring visit
Critical data definition for study
Findings review by
study team
Risk Management & Monitoring Plan
Protocol
Stakeholders
Clinical Data Analyst Group
Clinical Data Analyst
• Monitors trial data: site trends and signals
• Provides detailed guidance to CRA
• “Monitoring Enablement Reports”
• New Core Team Member
• Specialized Skills Required
Primary role is risk detection and mitigation
Baseline Monitoring of Critical Data
• Informed consent (includes subject existence)
• Eligibility (inclusion and exclusion criteria)
• SAE
– 100% of the first SAE (whether related or unrelated to IP)
– 100% of subsequent SAE (considered related to IP or procedures)
• Primary endpoints
– 100% of data associated with the endpoint
Triggered Monitoring
• Triggered monitoring hierarchy
Volume triggers
Event triggers
Risk triggersSite risk triggers
Volume triggersEvent triggers
Triggered Monitoring – Risk Triggers
• Identification of 17 site performance metrics across four findings categories:
– Recruitment– Reporting diligence– Data quality– Other
• Possible monitoring interventions are:– On site visit, including SDV increase– Off site visit– Telephone contact, targeting discussion on the finding(s)– Other specific intervention in case of major/repeated findings, i.e.:
• Site closure, Site re-training, co-monitoring / QC visit, For cause audit, CTM/Sponsor contact to site
Composite Risk Score
ICONIK Monitoring: Interventions
Escalation pathway
•Formal call to site
•Routine or scheduled
•Site performance and activity
•Monitoring Enablement Report
•Motivational
•Trip report
•Formal visit to site
•Quality or performance
•Directed by Monitoring Enablement Report
•CRA uses on-site judgement
•Trip report
• Informal” calls to site
• Issue-focused
•Designed to motivate sites and drive quality
Quality call Off-Site Monitoring Visit On-Site Monitoring Visit
Reduced SDV enabled by ICONIK Monitoring
Critical Data SDV
Risk-Based SDV
% SDV
Relationship ManagementCurrent Future
ICONIK Monitoring: Impact
sites
% SDV & On-Site Activity
Eli-Lilly’s Experience
Monitoring Progression @ LiLLY
Historical• 100%
SDV
2003-2011
• Statistical sampling
2011-2013• Critical data focus
Next Step (2013-15)
• TransCelerate BioPharma methodology• Comprehensive, risk-driven monitoring; centralized monitoring capability
• Centralized data-driven surveillance (predict and prevent)• Enablers: Digital source; analytics platforms
• Internal CRAs, supplemental contract staff
• Internal project managers
•Every 1st, 3rd, 5th & 5th subject•100% SDV, ICF, I/E criteria
Functional sourcing of CRA staff
Three preferred partners
Regional alignment
•Targeted SDV (critical data)•Study-specific monitoring plans
Long-Term Vision: From / To
From To
Paper Digital
Distributed Centralized
Verification Analytics
Detection Prediction
Correction Prevention
Lilly/ICON Retrospective Analysis
Objective
Hypothesis
Method
Trial
Assess the capability of ICONIK site performance metrics to predict critical site risks previously identified by the existing monitoring process
ICON’s site performance analysis metrics are able to predict the need for at least one pre-defined CAPA with >90% sensitivity and >70% specificity
• Comparative site performance analysis on all study sites, evaluating 17 metrics
• Collection of 7 key monitoring outcomes data• Evaluate the diagnostic performance of each metric against
each of the 7 key outcomes; determine which metrics provide the greatest predictive performance
Depressive disorder study, 608 randomized subjects, 56 sites, 7 countries
Lilly/ICON Retrospective Analysis
1
• Several individual and cluster metrics achieved the sensitivity of >90% and specificity >70% for various monitoring outcome events
2
• The confidence intervals around the point estimates were wide (exploratory trial, sample size)
3• Experiment identified performance metrics for
evaluation in an expanded evaluation
Results
Application to Ongoing Trials
Approach
Objectives
Trials
• Apply ICONIK to ongoing trials− supplement existing trial monitoring plan
• Evaluate capability of ICONIK to detect data quality and site performance issues not detectable via traditional monitoring methods
• Evaluate the capability to adjust monitoring approach based on insight gained through ICONIK reporting
• Depressive disorder study, 608 randomized subjects, 56 sites, 7 countries
• Diabetes cardiovascular events study, ~6000 randomized subjects, 350 sites
Example 1
ICONIK Signal Unusually consistent vital signs reporting for BP and pulse; fewer than half of readings were unique
Site Findings •CTM attended next SMV with CRA •Staff were using approximation for vital signs, not per protocol
Mitigation •Sub-investigator re-trained•SDV increased to 50% of enrolled subjects•GCP deviation reported
Outcome Demonstrated improvement in site vital signs data reporting
ICONIK Signal Relatively low concomitant medication and adverse event reporting
Site Findings •Lead CRA co-monitored next SMV with CRA•Non-reported concomitant medications and adverse events confirmed
Mitigation •Site placed on enrollment hold; staff re-retraining delivered•SDV increased to 100% for 50% of enrolled subjects
Outcome Demonstrated improvement in both concomitant medication and adverse event reporting for randomized subjects
Example 2
Net Learning, Next Steps
• Retrospective analysis demonstrated promising predictive capability
• The use of ICONIK enabled detection of sites with data quality issues which were unlikely to be detected via traditional monitoring methods
• Expanded retrospective analysis
• Identifying additional opportunities to apply ICONIK in 2013
Net Learning
Next Steps
ICONIK Monitoring Benefits
• Efficiency− New monitoring paradigm - targeted and
directed onsite activity, supplemented by centralized site management team
• Quality− Improved quality through detection of risk,
re-directed study team interventions accordingly
• Performance − Proactively manage study and site activity
across subject recruitment, subject retention, protocol violations, data quality, data variability, reporting diligence, potentially fraudulent data detection, CRA performance
• Transparency− Increased visibility of activity and status
• Cost− Opportunities to reduce trial execution
costs, dependent on process changes and study design choices
Benefits
Efficiency
Quality
CostTransparency
Performance
Like to know more?
Enquiries@iconplc.com
Twitter handle: @ICONplc
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