pharmaceutical data integrity: critical...

Pharmaceutical Data Integrity: Critical Considerations

www.pharmatechassociates.com

Agenda• Introduction• Key words defining data integrity (DI)• Components of a DI Strategy• DI Case Study• Consequences of auditor finding the integrity issues

2Compliance Through Science ®

Introduction

• Data integrity is the accuracy and consistency of stored data, indicated by an absence of any alteration in data between two updates of a data record

• Data integrity is imposed within a system at its design stage through the use of standard rules and procedures, and is maintained through the use of error checking and validation routines

Is Data Integrity a New Requirement?

What is driving enforcement concerns?• Escalation of Virtual Business Models• Increasing Globalization• Evolving Documentation Practices• Smart devices, IoT

FDA Expectations• Agencies expect that pharmaceutical companies should

retain complete and accurate records and all raw data and to make that available to inspectors

• The integrity of data generated by a regulated pharmaceutical companies and laboratories matters most, because properly recorded information is the basis for manufacturers to assure product identity, strength, purity, and safety and non-compliances found in the integrity of data leads warning letters and a regulatory action from the agencies

How Do We Ensure Data Integrity- Key Words

Accurate no errors or editing without documented amendments

Attributable who acquired the data or performed an action and when

Available for review and audit or inspection over the lifetime of the record

Complete all data are present and available

Consistent all elements of the record, such as the sequence of events, follow on and are dated or time stamped in expected sequence

Contemporaneous documented at the time of the activity

Enduring on proven storage media (paper or electronic)

Legible data can be easily read

Original/Reliable written printout or observation or a certified copy thereof

Trustworthy the data and the record have not been tampered with 6Compliance Through Science ®

Just Remember ALCOA

Compliance Through Science ® 7

A L C A

rate• Clearly

indicates who recorded the data or performed the activity

• Signed / dated

• Who wrote it / when

• It must be possible to read or interpret the data after it is recorded

• Permanent• No

unexplained hieroglyphics

• Properly corrected if necessary

• Data must be recorded at the time it was generated

• Close proximity to occurrence

• Data must correctly reflect the action / observation made

• Data checked where necessary

• Modifications explained if not self-evident

• Data must be preserved in its unaltered state

• If not, why not

• Certified copies

ALCOA Principles

Metadata• Metadata means "data about data". It is defined as the

data providing information about one or more aspects of the data, it is used to summarize basic information about data which can make tracking and working with specific data easier. Some examples include:– Means of creation of the data– Purpose of the data– Time and date of creation– Creator or author of the data– Location on a computer network where the data was created– Standards used– File size

Regulatory Guidances and ReferencesUS FDA• 21 CFR Parts 11, 211, 803

[http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm]• FDA’s Application Integrity Policy at www.fda.gov • Guidance for Industry Part 11, Electronic Records; Electronic Signatures -

Scope and Application August 2003• Carmelo Rosa ISPE FDA 3rd Annual GMP conference June 2014

Baltimore MD: Current Inspectional and Compliance Issues in Data Integrity (www.ispe.org)

MHRA GMP Data Integrity Definitions and Guidance for Industry ---Published March 2015 • Eudralex-Volume 4 Good manufacturing practice (GMP) Guidelines

Regulatory Guidances and References• G. Heddell, “Data Integrity – An EU Perspective,”

ISPE/FDA Conference Baltimore, June 2014 (Director, Inspection, Enforcement and Standards, MHRA.)

• S. Wyn, “Data Integrity throughout the Computerized System Lifecycle,” ISPE/FDA Conference Baltimore, June 2014.

• GAMP® Good Practice Guide: “A Risk-based Approach to Compliant Electronic Records and Signatures”, 2005.

• GAMP® Good Practice Guide: “Electronic Data Archiving”, 2007.

• ISPE, “GAMP5: Risk Based Approach to Compliant GxP Computerized Systems”, 200

Data/Information Controls

External Environment

External Environment(Societal, Political, Legal)

Internal Environment(QMS, IT Governance)

Data Life Cycle(ALCOA)

Good Documentation Practices

Fully supporting the different

steps in the lifecycle puts

demands on metadata, standards,tools and people.

Compliance Through Science ®

Managing the Data Life Cycle• Do I have all my data?

– Design of data collection: protocol, process, method– Data Life Cycle controls for data + metadata

• Has my data been objectively processed?– Controls to Prevent & Detect Testing Toward Outcome

• Am I reviewing all my data?– Printouts versus Source Electronic Records– Review of Audit Trails

• Am I reporting all my data?– Controls to Prevent & Detect Selective Reporting

Risks to Data Integrity• Overseas Testing and Manufacturing Supply Chain• Out‐sourcing of Operations (e.g., QC Labs, Manufacturing)• Economic Stressors—cutting corners• Incomplete Quality Sensibility (Quality Maturity)• Data Review Practices• Increasing use of Electronic Systems without

commensurate understanding and implementation of risk‐based controls for Electronic Data Integrity, e.g. MES, LIMS, EBR– Controls to Prevent Data Integrity Issues– Controls to Detect Data Integrity Issues

Data Life Cycle (DLC)• How do we think about “data” and how do we design our

business processes?• How do we validate systems that generate source data

with direct impact on patient safety, product quality, application integrity…?

• How do we manage risks across the entire DLC?• We must evolve our understanding of original data-

printout vs. raw data/metadata

Functional Hierarchy of Information Systems ISA 95

Business vs. Data Processes

CreateData

TransferData

StoreData

RetrieveData

ProcessData

Create Data

Review Data

BusinessProcess

Report Data

DataProcess

Risk Management Framework

ICH Q9/Q10- Quality Risk Management Principles

Applying a Risk Based Approach to Data Review

• Critical” Thinking Skills for Data Reviewer– What about ERROR PATTERNS?

• Frequency• Pattern• Determinate or Indeterminate• Failure Mode• Failure Effect

Attacks Take Planning

DDoS: DDoS is a type of DOS attack where multiple compromised systems, which are often infected with a Trojan, are used to target a single system causing a Denial of Service(DoS) attack

Current Security Strategies

• Current solutions are defensive (reactive)• Designed to defend after attack points have been

identified (attack reconnaissance)

– Password Access– Data Encryption– Network Firewall– Security Overhead

Business vs. Data Processes

CreateData

TransferData

StoreData

RetrieveData

ProcessData

Create Data

Review Data

BusinessProcess

Report Data

DataProcess

The Network-Data Security Elements

Data BreachTarget Areas

Data Integrity - The property that data hasnot been altered in an unauthorizedmanner. Data integrity covers data instorage, during processing, and while intransit. (NIST SP 800-33)

The Network-Data Security Elements

Data BreachTarget Areas

• Annex 11-1, Risk Management

• Annex 11-4.4, Requirements Document

• Annex 11-12, Security• Annex 11-13, Incident

Management • Annex 11-16, Business

Continuity• Annex 11-3, Suppliers

and Service Providers.

Data in Motion• Establishing a secure network

security strategy is one critical piece of preventing data breaches

• LIMS, Clinical and ERP systemsmust have a solution in place to monitor and prevent unauthorizedaccess and data corruption threats

Motion

Data in Motion• IT Infrastructure should be

qualified.Annex 11-5, Data• Computerized systems

exchanging data electronically with other systems should include appropriate built-in checks for the correct and secure entry and processing of data, in order to minimize the risks.

Motion

Data in MotionAnnex 11-6, Accuracy Checks• For critical data entered manually,

there should be an additional check on the accuracy of the data.

• This check may be done by a second operator or by validated electronic means.

• The criticality and the potential consequences of erroneous or incorrectly entered data to a system should be covered by risk management.

Motion

Data in Use

• Built-in Checks (Annex 11-5)• Accuracy Checks (Annex 11-6)• Data Storage (Annex 11-7)• Printouts (Annex 11-8)• Audit Trails (Annex 11-9)• Security (Annex 11-12)• Electronic Signature (11-14)• Archiving (Annex 11-17)• Operational Checks (21 CFR

Part 11)

In Use

Validation (Annex 11-4) +Periodic Evaluation (Annex11-11)

Components of the DI Strategy

1• Education and Communication

2• Detection and Risk Mitigation

3• Technology and IT Systems

4• Governance and Data Integrity

Establishing the Data Integrity Mindset• Establish a DI Culture through consistent messaging

(Communication) and education• Programs could include:

– Executive Awareness Training– Auditors Training– Process Owners– Highlight Data Integrity Risks and Impact– Change Management– Data Integrity Checklists and Monitoring Programs

Detection and Mitigation Risks

• Assess current data repository and management systems– Spreadsheets: Version Control, Access Control,

Traceability, Part 11 Compliance– Stand Alone Systems: Audit Trail, Data Archiving and

Retrieval– System Access: Access Control, Accountability,

Traceability

Process Mapping

Map the transformation and transmittance of data in order to identify system risks which have not been addressed and optimize the process

Create Electronic

Generate Paper

Printout

Place Printout

in Notebook

Review and

Approve Notebook

Review Electronic

Technology and IT System Roadmap

Establish formal requirements for all IT systems

Establish IT operational standards which segregate those who create data from those that can change it

Control access to systems

Governance and Data Integrity Structure

DI Subject Matter Experts provide guidance, recommend improvements

Division

LocalLocal

Division

DivisionDivision

Global

Define DI strategy and standards Monitor developments internally and externally

Global

Owners of business processes and systems

Expect Issues To Rise with Awareness

Build Your Tactical Framework

EXAMPLE TEXT

CharacterizeMonitor

Detect Identify

Tactical Framework

What is the DI Issue? What is the

Root Cause?

What is the Solution?

Has the solutionBeen effective?

Data Integrity Case Study• A DI audit reveals an issue• Current users aren't properly managing the raw data

created as part of the IT system• Additional GDP errors are identified- data transcription,

verification

Practical Data Integrity Roadmap

What Quality Systems are Impacted? • Data and Record Management • Document Management & Change Control • Quality Assurance • Management Oversight

Data Integrity Audit Readiness (Method/Roadmap):

Workshops/Training • How to conduct an Audit Trail Review and Group

Account Review • System Inventory • Assessment Tool • Audit Guides and Training • Remediation Plan Template • Review Findings Regularly

• Assessment of Controls Related to Data Management • To provide an overview of the data collection systems and the level

of electronic and or management controls in place • Used to determine follow-up items, as needed • Applicable to all points of data collection for GMP and GLP systems

in the laboratory, development and production (manufacturing) environments

• Consisted of a series of questions related to the inventory of electronic systems or processes involving data and the state of controls which are required

• The objective of the assessment is to identify controls and data integrity

Data Integrity Audit Readiness (Assessment):

• Audit trail – active and reviewed • Part 11 Compliance – how determined • Raw Data (Manf) - is data contained with the batch

record and subject to review as part of the release process

• Raw Data (Lab) – is data contained with the analytical record and subject to review as part of the release process

• Log Book – audited or verified • Qualification Status

User Accounts • Passwords controlled and access rights reviewed • Accounts personalized • Administrator accounts - access restricted according to

its business function • Are system administrators able to generate, change or

even delete data • Training

Non-networked Stand-alone Systems • Data management and control practices • Is raw data in the system considered an electronic

record and handled/retained accordingly? • Can reported results be fully traced to source data

whether or not it is in paper or electronic form? • Is data availability ensured throughout defined retention

period even after system retirement? • Is data backed up and verification ensured?

QA unit relationship to production management • QA Unit

– Describe conditions under which data can be altered, updated, changed, etc., or when equipment controls can be overridden or shut off. How is this communicated to management and documented?

• In Process Testing – Describe how data is collected and what information is

maintained with the batch record and what is maintained elsewhere.

• Availability of Procedures and General Controls – Are the relevant SOPs in place for data handling, management,

record retention and good documentation practices?

• Manufacturing/Production questions relating to Electronic Signature (ES) and Records (ER)

• eCompliance– Is ER/ES handled and appropriately managed at the local,

operational and equipment level? • User Accounts

– Describe process for maintenance of password controls. • Non-networked standalone systems • Calibration Management – the process • Incident Management – the process • Process Validation – the process • Change Management – the process

Lessons LearnedControls must be in place to ensure the integrity of data • A well prepared GxP document provides objective

evidence of an “action” and the result of an “action” • Why it is critical to ensure data is accurate and controlled • Data must be safe from manipulation or loss, intentional

or unintentional • It is critical to educate personnel on data integrity and its

overall impact on product identity, strength, purity and safety

Lessons Learned

Data Handling is key to Data Integrity • We must consider:

– How data is collected and reported – How data is reviewed – How the integrity of data is protected – How calculation errors are handled – How alarms are managed – Who has the authority to invalidate data

• What happens to this data? (i.e., discarded, archived with sample analysis package, etc.)

– How electronic data is protected from editing, changing, deletion? • How are passwords assigned and protected?

Data Integrity EnforcementActivity

Compliance Through Science ® 47Compliance Through Science ®

Common Data Integrity Failures• Lack of controlled access to computer systems• “Trial” HPLC injections

• Trial injections in stand alone equipment, outside a quality structure

• Deleted data• Not recording activities contemporaneously• Backdating• Fabricating data• Copying existing data as new data• Re-running samples

Recent Warning Letters• Completed batch production records days after

operations ended. Also released lots before Quality Unit approvals – July 2015

• Failure to maintain original manufacturing data, contained in “rough notes” – July 2015

• Failure to control access to data systems – July 2015• Lack of access controls to prevent manipulation of data –

April 2015• Lack of audit trails for lab instruments – April 2015• Turning off audit trail – April 2015• Altered results of identity test results – April 2015

Keys to Successful Data Integrity Assurance Program

• Management Commitment & Governance• Quality Risk Management• Critical Thinking Skills• Embracing Innovation

What are your next steps? Get ready!• Data Integrity enforcement is on the rise • Accuracy, reliable design, consistent intended

performance of record systems, both paper document systems and computerized systems

• Data Controls (both paper and electronic) to ensure authenticity, integrity, confidentiality, readily retrievable, accuracy, consistency, completeness throughout Data Life Cycle

• Signature Controls (both hand‐written and electronic) to ensure legally‐binding Quality Systems and Management Governance in place to assure data integrity

References used in Presentation

• Carmelo Rosa ISPE FDA 3rd Annual GMP conference June 2014 Baltimore MD: Current Inspectional and Compliance Issues in Data Integrity (www.ispe.org)

• Eudralex-Volume 4 Good manufacturing practice (GMP) Guidelines • 2015 PDA/FDA Joint Regulatory Conference Enforcement Trends CDER

Office of Compliance Tom Cosgrove, Office of Manufacturing Quality Director, September 29, 2015, Washington, DC

• Data Integrity Training Lessons Learned & Case Studies, Monica J. Cahilly, M.S. Green Mountain Quality Assurance LLC

• Designing Data Integrity into your Practices, Orlando López, SME -GAMP Data Integrity SIG, PTEA Meeting, September 18, 2014,Overland Park Convention Center, KS

Contact Information

Bikash Chatterjeebchatterjee@pharmatechassociates.com

Pharmatech Associates, Inc.22320 Foothill Blvd. #330Hayward, California 94541Telephone: 510-732-0177Toll Free: 877-787-0177

Visit our website at:www.pharmatechassociates.com

53Compliance Through Science®

pharmaceutical data integrity: critical...

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