maintaining quality in clinical research optimizing ......organization (iso 9000, 9001, 9004)...

Maintaining Quality in Clinical Research – Optimizing Planning

and Oversight Activities

Jean Mulinde, M.D.

Senior Policy Advisor

Division of Clinical Compliance Evaluation

Office of Scientific Investigations, CDER, FDA

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FDA Disclaimer

• The views and opinions presented here represent those of the speaker and should not be considered to represent advice or guidance on behalf of the U.S. Food and Drug Administration.

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Today’s Journey

• Quality in Clinical Research

• FDA Regulation and Guidance

• Study Oversight – Centralized Monitoring Examples

• FDA Oversight Activities – CDER’s Clinical Investigator Site Selection Tool

• Final Thoughts

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Quality in Clinical Research

• Clinical research is the means by which preventive, diagnostic, and therapeutic interventions are evaluated

• Relied on for decision making by

– Patients and Caretakers

– Physicians/Medical Personnel

– Industry

– Regulators

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Reminder: Clinical Trials of Quality

• Scientific question is important; there is a need to produce high-quality evidence to inform decision making on use of a preventive, diagnostic, or therapeutic intervention

• Trial design is adequate to answer this scientific question; if study well conducted the results will be credible

• Data produced are sufficiently accurate and reliable (fit for purpose) so that they may be used for decision making

• The rights, safety, and welfare of trial participants have been adequately protected

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Why an addendum to ICH E6?

• Since the 1996 adoption of ICH E6 GCP, clinical trials have evolved substantially – Increases in globalization

– Increases in study complexity

– Increases in technological capabilities

• Good Clinical Practice (GCP) approaches needed modernization to keep pace with the scale and complexity of clinical trials and to ensure appropriate use of technology

Source: Concept Paper, ICH E6, www.ICH.org

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How Do We Get from Here…

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To Here….Proactive Quality and Risk Based Approaches

Quality Management Systems

Quality by Design

Protocol

Vendor and Investigator

Oversight Plans

Risk Based Monitoring Data Management Plan

Safety Monitoring Plan

Plans, Plans, Plans, Etc.

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Quality Management System

• A Quality Management System (QMS) is an integrated system through which organizations can systematically plan and achieve quality objectives linked to their broader strategic goals

• Well described – International Standards Organization (ISO 9000, 9001, 9004)

• Fully implemented across many industries, though use in clinical development arena lagging

• A reasonable assumption that principles and experience from other sectors may be leveraged to inform development of quality management systems in the clinical development arena

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Quality by Design

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Quality by Design (QbD)

• Premise: Quality in clinical trials is defined as the absence of errors that matter.

What do we really need to get right to ensure reliability of results and patient protection? (Risk based thinking)

• Assumption: Likelihood of a successful, quality trial can be improved through prospective attention to preventing important errors that could undermine our ability to obtain meaningful information from a trial.

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Clinical Trials Transformation Initiative (CTTI)

• Public-private partnership initiated to develop and drive adoption of practices that increase the quality and efficiency of clinical trials.

• Duke University serves as host of CTTI with infrastructure expenses and projects funded by grant funding from FDA and annual fees of member organizations.

• More than 80 organizations from across clinical trial enterprise (regulators, industry, patient advocacy groups, professional societies, investigator groups, academic institutions)

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CTTI QbD Project

• Identify best practices and develop methods and tools to apply principles of QbD to the scientific and operational design of clinical trials.

• “Principles document” – Describes Critical to Quality (CTQ) factors generally relevant

to most clinical trials – Emphasizes that the criticality of different CTQ factors is

based on the type and design of trial being conducted – Emphasizes the importance of engaging all stakeholders in

study development – Emphasizes the importance of not falling into check list

mentality, but use of interactive discussion when consider CTQ factors

– Provides considerations and example questions for each CTQ factor to spur cross-functional group discussion

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Principles Document – CTQ Factors

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CTQ Factor – Protocol Design

https://www.ctti-clinicaltrials.org/projects/quality-design






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Quality by Design

Protocol


Oversight Plans

Risk Based Monitoring Data Management Plan



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Leveraging Resources for Success

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Quality by Design

Protocol


Oversight Plans Data Management Plan



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FDA Requirements Clinical Trial Quality and Monitoring

• 21 CFR 312 broadly describes sponsor responsibilities for clinical trials – Selection of qualified investigators

– Monitoring trial progress

– Ensuring the trial is conducted according to the investigational plan

– Review and analysis of accumulating evidence relating to product’s safety and effectiveness

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• 21 CFR 314.126 broadly describes what constitutes an adequate and well-controlled study – Design permits a valid comparison with a control to

provide a quantitative assessment of drug effect

– Method of selection provides adequate assurance that subjects have the disease or condition being studied

– Method of assigning patients to treatment and control groups minimizes bias and assures comparability

– Adequate measures are taken to minimize bias

– Methods of assessment of subjects' response are well-defined and reliable

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• 21 CFR 312.120 describes acceptance of foreign data from studies not conducted under IND – Study well designed and conducted

– Performed by qualified investigators

– Conducted in accordance with Good Clinical Practices

– FDA is able to validate data through onsite inspection if necessary

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FDA Guidance on Oversight

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FDA Guidance on Monitoring

• Quality is an overarching objective that must be built into the clinical trial

• Monitoring, or oversight, alone cannot ensure quality

• Monitoring is a quality control tool for determining whether study activities are being carried out as planned, so that deficiencies can be identified and corrected

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Considerations When Developing Monitoring Plans

• Complexity of study design/study endpoints

• Types of study endpoints

• Clinical complexity of study population

• Geography where will be conducted

• Relative experience of clinical investigators

• Stage of study

• Relative safety of investigational product

• Electronic systems to be used in conduct of study

• Quantity of data

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Considerations When Developing Monitoring Plans

• Discourages “One Size Fits All” approach to monitoring

• Encourages use of a variety of monitoring techniques:

• Centralized

• Remote

• On-site

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Centralized Monitoring (Including Centralized Statistical Monitoring)

• Systematic central monitoring of clinical trial data has the potential to: – Reduce on-site monitoring needs

– Enhance focus of on-site monitoring

– Increase data quality

– Enhance protection of trial participants

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Centralized Monitoring (Including Centralized Statistical Monitoring)

• Capability to more readily identify some types of significant issues that impact data integrity

• Findings must be considered in context and determination of clinical relevance not always straightforward

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Importance of Context - Example 1

Pulse for “0” “2” “4” “6” “8”

• Protocol does not define method of collection

• Plausible based on clinical assessment practices

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Importance of Context - Example 2

Blood Pressure “0” “5”

• Manual reading with rounding?

• Protocol Design

• Impact

• Criticality of data

element?

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Importance of Context – Example 3

Blood Pressure “0”

• Further evaluation

needed!!!!

• Impact

- Criticality data element?

• Regulatory review – Adequacy and reliability

data from site

– Adequacy sponsor

oversight (monitoring)

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• Subject 2301-001: – ICF signed 01Dec2016 – Screen Failure 15Jan2017

• Subject 2303-049: – ICF signed 24Feb2017 – Randomized 23Mar2017 – Completed 23Aug2017

Subject Duplicate Enrollment?

Study Site

Location Subject Date Birth Gender Race Height Weight

2301 Mega City, NY 2301-001 1951-02-17 M WHITE 179 cm 85 kg

2303 Mega City, NY

2303-049 1951-02-17 M WHITE 179 cm 85.3 kg

• Protocol permits screen failure subjects to be re-enrolled >30 days post last assessment

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• Subject 2964-001:

– ICF signed 01Dec2016

– Randomized15Jan2017

– Completed 15Jun2017

• Subject 2964-014:

– ICF signed 21Jun2017

– Randomized 19Jul2017

– Discontinued 01Sept2017

Subject Duplicate Enrollment?

Study Site

Location Subject Date Birth Gender Race Height Weight

2964 Mini City, SD 2964-001 1926-01-10 F ASIAN 139 cm 54 kg

2964 Mini City, SD 2964-014 1926-01-10 F ASIAN 139 cm 54.1 kg

• Protocol does not permit re-enrollment in investigations of same investigational product

• Further evaluation indicated!!!!

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Inspection Readiness Risk Management

• Risk identification and evaluation process

• Critical processes/procedures identified

• Plans for risk control (i.e., mitigation, monitoring, etc.)

• Ongoing risk tracking and review process (planned and actual)

• Decision process used to determine need for CAPA versus ongoing plans to track issue

• Reporting on deviations occurring from planned risk management plans

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Inspection Readiness Evidence of Oversight

• Who conducted monitoring, when, how (on-site, remote, central)

• Data and/or activities reviewed

• Description of non-compliance, data irregularities, other deficiencies identified

• Description of any actions taken as result monitoring findings

• When needed, results of root cause analysis

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Inspection Readiness Evidence of Oversight

• When needed, Corrective and Preventive Action (CAPA) plan implemented and impact tracked When corrective actions unsuccessful action taken (e.g., increased oversight, site closed)

• Issue escalation and communication to appropriate parties, in a timely manner

• When appropriate, consideration as to whether identified issue(s) may similarly impact other sites, whether represent systemic study issue, or have broader product or cross product development implication

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CDER’s Clinical Investigator Site Selection Tool

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Challenges

• Finite resources limit the number of inspections that can be conducted

• Identification of sites from which subject protection and/or data integrity risks are greatest to inform decisions on how to best expend limited inspectional resources

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Application-Inspections Overseen by OSI/OSIS* (CDER, FY2008 - FY2017)

*Based on inspection start date – [Complis database as of December 29, 2017]

• Sponsor (GCP) includes Sponsor/CRO/Sponsor-Investigator

• BEQ Application-Inspections accomplished with 289 FY17 Site Visits

• Good Laboratory Practice and Bioequivalence inspection programs operated by OSIS as of January 2015

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Clinical Investigator: Data Audit versus Referral* (CDER, FY2017)

* Based on inspection start date – [Complis database as of December 29, 2017]

• Referrals include Complaints, Required Reports, IRB/Sponsor Notifications, and other referrals-internal and external for All OSI Branches

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Additional Challenges

• Increasing numbers of – Sites per clinical trial

– Foreign trial sites

• Volume data and inadequate data standards increase analysis time

• PDUFA/BSUFA timelines require high level of efficiency

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Development Goals

• Enable deployment of limited resources towards sites that pose the potentially greatest risk to public health

• Facilitate early site selection/issuance of inspection assignments

• Develop a more consistent, science-based approach to selection of clinical sites for inspection

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Development

• Collaborative effort across – Office of Biostatistics – Office of Compliance – Office of New Drugs – Office of Planning and Informatics

• Attributes – Identified through series expert interviews across offices – Attributes then ranked as to importance in second round

interviews – 21 attributes used in tool (application, study, site level) – Data included

• Summary site level data provided by applicant (clinsite.xpt)

• FDA internal sources

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“Clinsite” Dataset

Cross Application/Study Information

IND #, NDA #, BLA #, Supplement #, Number of Sponsors, Study Number (Identifier), Study Title, Domain, Study Arms, Endpoint, Endpoint Type

CI Contact Information Last name, First name, Middle initial, Street, City, State, Country, Postal Code, Phone, Fax, E-mail

Site Specific Data Site ID, Under IND?, Enroll #, Screen #, Discontinued #, Non-serious adverse event #, Serious adverse event #, Death #, Protocol violation #, Max financial disclosure (single investigator), Max financial disclosure (site total), Treatment efficacy, Standard deviation treatment efficacy, Site efficacy, Standard deviation site efficacy

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Guidance on Providing clinsite.xpt

https://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/FormsSubmissionRequirements/UCM332466.pdf

https://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/FormsSubmissionRequirements/UCM332468.pdf

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Inputted Data Processed via Decision Tree Algorithm

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CDER 21st Century Review Process

Identification of sites for inspection due

Loaded CISST released to reviewers

21st Century Site Selection Meeting

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High Level Site Ranking View

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Regional and Country Views

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Outlier Displays

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Site Specific Displays

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CISST Provides • Risk ranking of sites provides a

framework for site selection • Provides standard data exploration

methodology • Assembles many site characteristics in

one tool and gives the user the ability to choose sites based on site views of: – Data Irregularities – Outliers analysis with filters – Inspection history and Investigator

experience – Clinical investigator cross-study

participation – Regional and country-specific summaries – Comparison of variables across

treatment arms – Raw data versus converted data views

• Easy navigation and functionalities • Improves data analysis time • Automated documentation and form

generation

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Benefits Realized

• Clinical sites identified earlier in review clock

• Enhanced identification of clinical sites with GCP non-compliance

• Archived clinsite data available for data mining

Initiate inspection process earlier

Inspection results sooner

Increased time within review cycle for resolution of issues identified

during inspections

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Final Thoughts

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Successful Quality Management and Risk Based Approaches

• Maintain data integrity and the safety of trial participants (and patients in the post approval realm) within, and across, clinical development programs

• Improve efficiencies and optimize resource utilization (Industry, Regulators, Healthcare system)

• Make available beneficial new therapies for patients based on a foundation of high-quality evidence

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“Quality is never an accident; it is

always the result of high intention,

sincere effort, intelligent direction and

skillful execution; it represents the

wise choice of many alternatives”

- William A. Foster

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