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

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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

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H A

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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

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