Data integrity, Pharmaceutical industry, Good Manufacturing Practice, GMP, Guidelines, Data management, DI and GMP Compliance, paper and electronic data, Archive and back up
The document discusses data integrity and recent regulatory approaches. It defines data integrity as the completeness, consistency, and accuracy of data throughout its lifecycle. Regulatory agencies are increasingly focused on data integrity due to its importance in ensuring product quality and safety. Common data integrity issues found by agencies include fabricated, falsified, or missing records. Ensuring data integrity requires effective quality management systems, risk management, technology solutions, and governance.
Trends changed from Non compliance to RR --> Gap to RR --> Data Integrity --> DIB --> Smart Audit & Smart Data.
RR = Regulatory Requirements
DIB = Data Integrity Breach
Take a serious Note for Data Integrity whether you are small or big organization. Your Data is the Heart of your business. Regulatory bodies are highly conscious about such issues. For beginners in this path, my small note can help you a lot.
Data integrity is critical throughout the CGMP data life cycle, including in the creation, modification, processing, maintenance, archival, retrieval, transmission, and disposition of data after the record’s retention period ends. It would be helpful for data management.
Presentation on data integrity in Pharmaceutical IndustrySathish Vemula
Presentation on data integrity in Pharmaceutical Industry
Contents:
- Definition & Basics
- Criteria for integrity of laboratory data
- Regulatory Requirements
- Barriers to Complete Data
- Possible data integrity problems
- Previous observations
- FDA Warning Letters – 2013
- FDA Warning Letters – 2014
- FDA 483’s related to data integrity
- EU – Non compliance Reports
- WHO - Notice of Concern
- Summary of Data Integrity issues
- Consequences- Rebuilding Trust
- Conclusion
The document discusses data integrity and provides guidance on ensuring data integrity through the ALCOA principles of Attributable, Legible, Contemporaneous, Original, and Accurate. It defines each of the ALCOA principles and provides examples of their application. Common data integrity issues found by the FDA during inspections are also summarized, including data manipulation, multiple sample runs, backdated documentation, unauthorized data access, logbook recording issues, and others.
Data integrity refers to the completeness, consistency, and accuracy of data. The data should follow the ALCOA principles: Attributable, Legible, Contemporaneous, Original, and Accurate. A lack of data integrity can lead to warning letters from regulators, import alerts, and no further product approvals. Typical contents of warning letters include failing to investigate complaints, falsifying documentation, and improperly recording batch information. Maintaining data integrity is important to avoid regulatory consequences.
This document discusses regulatory requirements and previous observations related to data integrity issues. It outlines criteria for integrity of laboratory data according to regulations. It also provides examples of possible data integrity problems that have been observed by regulators, such as altering raw data, manipulating test procedures to obtain passing results, and recording lab activities before they occur. FDA warning letters from 2013 are referenced that identified specific failures to record quality activities at the time they were performed.
- The document discusses data integrity, which refers to maintaining accurate and consistent data over its entire lifecycle. This is important for the regulated healthcare industry as quality decisions are based on data.
- The FDA uses the ALCOA criteria (Attributable, Legible, Contemporaneous, Original, Accurate) to define expectations for electronic data. Regulatory agencies now focus heavily on data integrity due to instances of fabricated documents and errors.
- Common data integrity issues found by agencies include non-contemporaneous recording, backdating records, re-running samples until desired results are obtained, and data fabrication. Ensuring data integrity helps prevent regulatory actions like warning letters or import bans against companies.
The document discusses data integrity and recent regulatory approaches. It defines data integrity as the completeness, consistency, and accuracy of data throughout its lifecycle. Regulatory agencies are increasingly focused on data integrity due to its importance in ensuring product quality and safety. Common data integrity issues found by agencies include fabricated, falsified, or missing records. Ensuring data integrity requires effective quality management systems, risk management, technology solutions, and governance.
Trends changed from Non compliance to RR --> Gap to RR --> Data Integrity --> DIB --> Smart Audit & Smart Data.
RR = Regulatory Requirements
DIB = Data Integrity Breach
Take a serious Note for Data Integrity whether you are small or big organization. Your Data is the Heart of your business. Regulatory bodies are highly conscious about such issues. For beginners in this path, my small note can help you a lot.
Data integrity is critical throughout the CGMP data life cycle, including in the creation, modification, processing, maintenance, archival, retrieval, transmission, and disposition of data after the record’s retention period ends. It would be helpful for data management.
Presentation on data integrity in Pharmaceutical IndustrySathish Vemula
Presentation on data integrity in Pharmaceutical Industry
Contents:
- Definition & Basics
- Criteria for integrity of laboratory data
- Regulatory Requirements
- Barriers to Complete Data
- Possible data integrity problems
- Previous observations
- FDA Warning Letters – 2013
- FDA Warning Letters – 2014
- FDA 483’s related to data integrity
- EU – Non compliance Reports
- WHO - Notice of Concern
- Summary of Data Integrity issues
- Consequences- Rebuilding Trust
- Conclusion
The document discusses data integrity and provides guidance on ensuring data integrity through the ALCOA principles of Attributable, Legible, Contemporaneous, Original, and Accurate. It defines each of the ALCOA principles and provides examples of their application. Common data integrity issues found by the FDA during inspections are also summarized, including data manipulation, multiple sample runs, backdated documentation, unauthorized data access, logbook recording issues, and others.
Data integrity refers to the completeness, consistency, and accuracy of data. The data should follow the ALCOA principles: Attributable, Legible, Contemporaneous, Original, and Accurate. A lack of data integrity can lead to warning letters from regulators, import alerts, and no further product approvals. Typical contents of warning letters include failing to investigate complaints, falsifying documentation, and improperly recording batch information. Maintaining data integrity is important to avoid regulatory consequences.
This document discusses regulatory requirements and previous observations related to data integrity issues. It outlines criteria for integrity of laboratory data according to regulations. It also provides examples of possible data integrity problems that have been observed by regulators, such as altering raw data, manipulating test procedures to obtain passing results, and recording lab activities before they occur. FDA warning letters from 2013 are referenced that identified specific failures to record quality activities at the time they were performed.
- The document discusses data integrity, which refers to maintaining accurate and consistent data over its entire lifecycle. This is important for the regulated healthcare industry as quality decisions are based on data.
- The FDA uses the ALCOA criteria (Attributable, Legible, Contemporaneous, Original, Accurate) to define expectations for electronic data. Regulatory agencies now focus heavily on data integrity due to instances of fabricated documents and errors.
- Common data integrity issues found by agencies include non-contemporaneous recording, backdating records, re-running samples until desired results are obtained, and data fabrication. Ensuring data integrity helps prevent regulatory actions like warning letters or import bans against companies.
Data Integrity Training by Dr. A. AmsavelDr. Amsavel A
This document provides an overview of a training presentation on data integrity. The presentation covers the objectives of data integrity in ensuring patient safety and quality. It defines key terms like data, data integrity, data governance and data lifecycle. It discusses regulatory requirements for data integrity and principles like ALCOA+. It also describes examples of data integrity breaches found by regulators like falsification and backdating. The presentation notes the consequences of data integrity issues can be severe including regulatory warnings, fines, and loss of trust. It emphasizes the importance of a quality culture and management awareness to ensure data integrity.
This presentation is compiled by Drug Regulations, a nonprofit organization that provides online pharmaceutical resources. It discusses FDA guidance on data integrity and compliance with cGMP regulations. The guidance clarifies FDA's expectations around the creation and handling of data to ensure its reliability and accuracy according to cGMP standards.
FDA Data Integrity Issues - DMS hot fixesVidyasagar P
The document discusses data integrity, including popular causes of integrity issues, consequences, and fixes related to document management systems. It provides definitions of data integrity and discusses regulatory requirements around integrity from agencies like the FDA. Specifically, it summarizes the FDA's 21 CFR Part 11 regulation, which considers electronic records equivalent to paper if certain controls are in place. It also discusses application integrity policies and concludes that ensuring data integrity is important to rebuild regulatory trust if issues are found.
This document outlines good documentation practices for cGMP documents. It states that all documents produced by a company may be reviewed by regulatory agencies. It provides guidelines for writing in cGMP documents, such as using blue ink and initialing and dating all entries. It describes types of cGMP documents and proper ways to record information, make corrections, document deviations, and void or recreate records. The overall purpose is to ensure strict documentation rules are followed to maintain compliance with regulatory agencies.
CCK Discussion Forum held at ICCBS, University of Karachi, attended by over hundred of registered experienced pharmaceutical professionals participants belonging from dozen of pharmaceutical manufacturing facilities
Good documentation practice (commonly abbreviated GDP, recommended to abbreviate as GDocP to distinguish from "good distribution practice" also abbreviated GDP) is a term in the pharmaceutical industry to describe standards by which documents are created and maintained. While some GDocP standards are codified by various competent authorities, others are not but are considered cGMP (with emphasis on the "c", or "current"). Some competent authorities release or adopt guidelines, and they may include non-codified GDocP expectations. While authorities will inspect against these guidelines and cGMP expectations in addition to the legal requirements and make comments or observations if departures are seen. In the past years, the application of GDocP is also expanding to cosmetic industry, excipient and ingredient manufacturers.
Data Integrity app Link: https://play.google.com/store/apps/details?id=com.innovativeapps.dataintegrity&hl=en
One Step Ahead in Pharma Compliance
Across the internet, there are millions of resources are available which provide information about Computer System Validation.
Refer above Data Integrity app which helps you to understand current regulatory agencies thinking on Data Integrity.
Data Integrity in pharmaceutical laboratories is a must, the attached ppt shall help the QC members to understand and develop an integral analytical culture
Data integrity is crucial in the pharmaceutical industry to ensure patient safety and product quality. It refers to data being complete, consistent, and accurate throughout its lifecycle, from generation to storage. Issues can arise from falsification, poor documentation practices, lack of system controls, and not reviewing for errors. To minimize risks, companies should follow good documentation practices like ALCOA+, implement audit trails and quality management systems, and conduct training and system validation. Maintaining data integrity builds trust between regulators and industry.
Good Documentation Practice (GDocP) is an essential part of the quality assurance and such, related to all aspects of GMP” this definition is based on WHO. It is a systematic procedure of preparation, reviewing, approving, issuing, recording, storing and archival of document.
This document discusses data integrity and its importance in the pharmaceutical industry. It defines data integrity as the extent to which all data is complete, consistent and accurate throughout its lifecycle. Regulatory agencies like the FDA and MHRA require data integrity to ensure patient safety. Lack of data integrity can result in warning letters or consent decrees from regulators. The document outlines principles like ALCOA+ to ensure data integrity. It discusses examples of data integrity issues found in warning letters and gives recommendations for proper implementation of data integrity controls and quality culture.
Data Integrity Issues in Pharmaceutical CompaniesPiyush Tripathi
Data integrity refers to maintaining and assuring the accuracy and consistency of data over its entire life-cycle, and is a critical aspect to the design, implementation and usage of any system which stores, processes, or retrieves data.
This slide is related to Good documentation Practice in Pharmaceutical Industries. It was presented in the pharmaceutical industry (Chemidrug Industry Private Ltd.) during the training session.
This document discusses data integrity and good documentation practices. It begins with an introduction to the ALCOA principles for data quality, which stand for Attributable, Legible, Contemporaneous, Original, and Accurate. It then defines data integrity and lists some common data integrity issues seen by regulators. The rest of the document outlines good documentation practices, including the importance of conforming to standards, maintaining document versions and revisions, and avoiding issues like undocumented changes. It emphasizes that poor documentation can undermine drug safety and efficacy and break regulatory trust.
21 CFR Part 11 outlines FDA regulations for electronic records and electronic signatures. It requires that electronic records be trustworthy, reliable, and equivalent to paper records. It also requires that electronic signatures be equivalent to handwritten signatures. The document then outlines the key subparts and sections of 21 CFR Part 11, including requirements for controls on closed and open systems, electronic signature components and controls, and validation of electronic records and signatures.
The document provides guidelines for good documentation practices at a company. It states that the purpose is to standardize ways to correct errors in documentation. All persons involved in preparing, completing or reviewing documents are responsible for following these practices. This includes checking for mistakes, ensuring documents are complete before forwarding, treating documentation as important rather than an inconvenience, writing clearly, and only the person responsible should correct errors. Proper documentation is important for traceability and accountability.
Phụ lục 11 về Hệ thống máy tính trong bộ tiêu chuẩn GMP EU. Xem thêm các tài liệu khác trên kênh Slideshare của Công ty cổ phần Tư vấn thiết kế GMP EU.
Data integrity is the degree to which data are complete, consistent, accurate, trustworthy, reliable, and that these characteristics of the data are maintained throughout the data life cycle.
Data Integrity Training by Dr. A. AmsavelDr. Amsavel A
This document provides an overview of a training presentation on data integrity. The presentation covers the objectives of data integrity in ensuring patient safety and quality. It defines key terms like data, data integrity, data governance and data lifecycle. It discusses regulatory requirements for data integrity and principles like ALCOA+. It also describes examples of data integrity breaches found by regulators like falsification and backdating. The presentation notes the consequences of data integrity issues can be severe including regulatory warnings, fines, and loss of trust. It emphasizes the importance of a quality culture and management awareness to ensure data integrity.
This presentation is compiled by Drug Regulations, a nonprofit organization that provides online pharmaceutical resources. It discusses FDA guidance on data integrity and compliance with cGMP regulations. The guidance clarifies FDA's expectations around the creation and handling of data to ensure its reliability and accuracy according to cGMP standards.
FDA Data Integrity Issues - DMS hot fixesVidyasagar P
The document discusses data integrity, including popular causes of integrity issues, consequences, and fixes related to document management systems. It provides definitions of data integrity and discusses regulatory requirements around integrity from agencies like the FDA. Specifically, it summarizes the FDA's 21 CFR Part 11 regulation, which considers electronic records equivalent to paper if certain controls are in place. It also discusses application integrity policies and concludes that ensuring data integrity is important to rebuild regulatory trust if issues are found.
This document outlines good documentation practices for cGMP documents. It states that all documents produced by a company may be reviewed by regulatory agencies. It provides guidelines for writing in cGMP documents, such as using blue ink and initialing and dating all entries. It describes types of cGMP documents and proper ways to record information, make corrections, document deviations, and void or recreate records. The overall purpose is to ensure strict documentation rules are followed to maintain compliance with regulatory agencies.
CCK Discussion Forum held at ICCBS, University of Karachi, attended by over hundred of registered experienced pharmaceutical professionals participants belonging from dozen of pharmaceutical manufacturing facilities
Good documentation practice (commonly abbreviated GDP, recommended to abbreviate as GDocP to distinguish from "good distribution practice" also abbreviated GDP) is a term in the pharmaceutical industry to describe standards by which documents are created and maintained. While some GDocP standards are codified by various competent authorities, others are not but are considered cGMP (with emphasis on the "c", or "current"). Some competent authorities release or adopt guidelines, and they may include non-codified GDocP expectations. While authorities will inspect against these guidelines and cGMP expectations in addition to the legal requirements and make comments or observations if departures are seen. In the past years, the application of GDocP is also expanding to cosmetic industry, excipient and ingredient manufacturers.
Data Integrity app Link: https://play.google.com/store/apps/details?id=com.innovativeapps.dataintegrity&hl=en
One Step Ahead in Pharma Compliance
Across the internet, there are millions of resources are available which provide information about Computer System Validation.
Refer above Data Integrity app which helps you to understand current regulatory agencies thinking on Data Integrity.
Data Integrity in pharmaceutical laboratories is a must, the attached ppt shall help the QC members to understand and develop an integral analytical culture
Data integrity is crucial in the pharmaceutical industry to ensure patient safety and product quality. It refers to data being complete, consistent, and accurate throughout its lifecycle, from generation to storage. Issues can arise from falsification, poor documentation practices, lack of system controls, and not reviewing for errors. To minimize risks, companies should follow good documentation practices like ALCOA+, implement audit trails and quality management systems, and conduct training and system validation. Maintaining data integrity builds trust between regulators and industry.
Good Documentation Practice (GDocP) is an essential part of the quality assurance and such, related to all aspects of GMP” this definition is based on WHO. It is a systematic procedure of preparation, reviewing, approving, issuing, recording, storing and archival of document.
This document discusses data integrity and its importance in the pharmaceutical industry. It defines data integrity as the extent to which all data is complete, consistent and accurate throughout its lifecycle. Regulatory agencies like the FDA and MHRA require data integrity to ensure patient safety. Lack of data integrity can result in warning letters or consent decrees from regulators. The document outlines principles like ALCOA+ to ensure data integrity. It discusses examples of data integrity issues found in warning letters and gives recommendations for proper implementation of data integrity controls and quality culture.
Data Integrity Issues in Pharmaceutical CompaniesPiyush Tripathi
Data integrity refers to maintaining and assuring the accuracy and consistency of data over its entire life-cycle, and is a critical aspect to the design, implementation and usage of any system which stores, processes, or retrieves data.
This slide is related to Good documentation Practice in Pharmaceutical Industries. It was presented in the pharmaceutical industry (Chemidrug Industry Private Ltd.) during the training session.
This document discusses data integrity and good documentation practices. It begins with an introduction to the ALCOA principles for data quality, which stand for Attributable, Legible, Contemporaneous, Original, and Accurate. It then defines data integrity and lists some common data integrity issues seen by regulators. The rest of the document outlines good documentation practices, including the importance of conforming to standards, maintaining document versions and revisions, and avoiding issues like undocumented changes. It emphasizes that poor documentation can undermine drug safety and efficacy and break regulatory trust.
21 CFR Part 11 outlines FDA regulations for electronic records and electronic signatures. It requires that electronic records be trustworthy, reliable, and equivalent to paper records. It also requires that electronic signatures be equivalent to handwritten signatures. The document then outlines the key subparts and sections of 21 CFR Part 11, including requirements for controls on closed and open systems, electronic signature components and controls, and validation of electronic records and signatures.
The document provides guidelines for good documentation practices at a company. It states that the purpose is to standardize ways to correct errors in documentation. All persons involved in preparing, completing or reviewing documents are responsible for following these practices. This includes checking for mistakes, ensuring documents are complete before forwarding, treating documentation as important rather than an inconvenience, writing clearly, and only the person responsible should correct errors. Proper documentation is important for traceability and accountability.
Phụ lục 11 về Hệ thống máy tính trong bộ tiêu chuẩn GMP EU. Xem thêm các tài liệu khác trên kênh Slideshare của Công ty cổ phần Tư vấn thiết kế GMP EU.
Data integrity is the degree to which data are complete, consistent, accurate, trustworthy, reliable, and that these characteristics of the data are maintained throughout the data life cycle.
This document discusses data integrity in pharmaceutical quality systems. It defines data and explains its importance in decision making and continual improvement. It notes that the FDA and EMA have issued warning letters and non-compliance reports for data integrity issues. Examples are provided of warning letters issued to two companies for violations like unauthorized data manipulation, lack of audit trails, and insufficient investigations. The causes of data integrity breaches are discussed. It emphasizes establishing a culture of integrity, security protocols, and complying with cGMP guidelines. It provides details on how to properly manage system access, data storage, backups, and recording data according to ALCOA principles. The importance of audit trail software is also covered.
This document discusses management information systems and decision support systems. It defines management information as data that has been processed for decision making purposes. It also discusses the different components and structures of management information systems, including physical components like hardware and software, information processing functions like transaction processing and report generation, different levels for decision support, and how MIS can be structured according to organizational functions. The goal of MIS is to provide relevant information to managers at different levels to aid in decision making.
Presentation: Data Integrity – an international regulatory perspectiveTGA Australia
This presentation will provide an overview of the international regulatory perspective on data integrity and discuss some of the key points highlighted in recently released guidance documents from across the globe.
TGA presentation: Data Integrity - an international regulatory perspectiveTGA Australia
This document discusses data integrity from an international regulatory perspective. It provides an overview of guidance documents from various regulatory agencies on data integrity expectations. Key points covered include that data integrity is not a new requirement, but rather has always been expected under good manufacturing practices. Regulators are increasing their focus and collaboration around data integrity inspections. The document also addresses common misconceptions, such as thinking data integrity only applies to fraudulent data or is difficult to comply with. Effective strategies for ensuring data integrity involve a lifecycle approach considering culture, governance, procedures, systems, and behavior.
1) Clinical trials rely on effective data collection and management systems. Computers have revolutionized these processes by making them timely, reliable and effective.
2) 21 CFR Part 11 provides regulations for computer systems used in clinical trials. It focuses on electronic records, signatures and controls to ensure accuracy, reliability and protect human health.
3) A risk assessment is important to classify computer system risks and ensure compliance with 21 CFR Part 11. This involves analyzing the probability and severity of dangers to determine regulatory requirements.
This document discusses the effects of information technology on internal controls. It begins by outlining the principles of a reliable system according to the AICPA Trust Services framework. It then discusses control environment issues like segregation of duties for IT roles. The document also covers risk assessment, information and communication considerations, monitoring activities, and various control activities for general computer controls, application controls, and manual exception report follow up. Overall, it provides an overview of how IT impacts internal controls across different components of the COSO framework.
MPH07 Computers in clinical development.pptxBhuminJain1
My topic is computers in clinical development. There are various ways pf collecting data like pure paper based system, electronic based system and communication.
05 Duplication and Preservation of Digital evidence - NotesKranthi
The document discusses best practices for preserving digital evidence from a crime scene, including:
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The document discusses documentation integrity and data integrity principles. It covers concepts like ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate), ensuring records are complete, accurate, and consistent. It provides examples of data integrity issues like backdating, fabricating records, or deleting records. Overall, the document emphasizes that data integrity is crucial for public safety and regulatory compliance.
A Pharma/CRO Partnership in the Design and Execution of Paperless Clinical Tr...Target Health, Inc.
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Quick Overview: Pharmaceutical Data IntegrityPeter Dellva
Brief overview of the most important aspects of pharmaceutical data integrity. Slideshare includes pharmaceutical and biopharmaceutical industry key resources.
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2. Overview
1. Guidelines
2. Principles of DI
3. Data Integrity Risk Assessment (DIRA)
4. Data Criticality and Risk
5. Design Robust Compliance System
6. Data interpretation and Good Documentation Practices (GDocP)
3. Principles of Data Integrity (DI)
1. Data recorded as per GMP requirement and ensuring data is complete, consistent and
accurate throughout the lifecycle i.e paper or electronic
2. Organisational culture, behaviour encouraging to create right environment to
implement effective Data integrity on site
3. Data governance policy should be effectively endorsed at the highest level
4. Data Integrity Risk Assessment (DIRA)
5. Develop system to maintain and control data, periodic DI audit
6. Contingency procedure should not impact on DI controls. Apply to automated
computerized system to paper-based system, vice versa.
7. Holistic approach for any identified DI weakness
8. Notification to regulatory authority where significant DI incident identify.
9. Effectively implementation of ALCOA+
4. Data Integrity Risk Assessment (DIRA)
1. Consider factors impacting on process or function on the system
2. Common factors are: Computerised system, People, SOP, training, Quality system
3. Automated validated system reduce DI risk
4. Human intervention influence on data record, report, retain, verification, poor
systematic control, poor validation plan increase DI risk
5. DI risk remediation action should be documented, discuss and review by Quality
management
6. DI risk remediation actions should categorise by long term and short term. Where
long term action require, short term action has clearly target problem
5. Data Criticality and Risk
1. Critical data influence on Quality, Safety and Efficacy decision
2. Risk of data deletion, amendment, exclusion without authorisation and lost of
traceability
3. Process complexity, inconsistency, inconclusive outcome increase data risk
4. Common data sources are Paper, electronic, Hybrid, other
4.1. Paper Data: Hand written, printed data require second independent verification (risk
reducing measure)
4.2. Electronic: Complexity of the electronic system. Validation of electronic system to
reduce risk e.g. functionality, configuration, user intervention, data lifecycle
4.3. Hybrid: Combination of paper and electronic data.
4.4. Other: photograph, image, photocopy where original copy fade control storage of
data.
6. Design Robust Compliance System
1. Synchronised clock system for traceable time stamp with correct time zone
2. Control over raw data recording and it’s accessibility
3. Use of controlled logbook with number page to prevent recreation of primary data
4. Access control of computerised system to prevent amendment of data, audit trail
5. Use of interface e.g. Barcode scanner, Automated weighing Printer, Badge access
system to eliminate manual entries and reduce human interaction
6. Trained people, validated system
7. Access of record , reconciliation of printouts to Data verification team
8. Promote work environment that provide sufficient time, equipment, clear
instruction
9. SME involvement in Risk assessment, Quality Metrics for DI
7. Design Robust Compliance System
1. Use of scriber to record data should be justifiable
Contemporaneous recording
Clearly identify who is performing task
All involved persons should trained to carry out activity
Task briefing should be given to all persons involved
Document should be singed by all persons involved
8. Data Interpretation and GDocP
1. What information are consider as Data
Original records
True copies of original records
Sourced data (Printout from HMI e.g. SIP, CIP, Autocalve, Balance, HPLC, FTIR)
Metadata
Reports printouts
Any information recorded at the time of activity perform
Data allows to reconstruct and evaluate carried out GxP activity.
9. Data Interpretation and GDocP
GDocP are those measures that collectively and individually ensure Documentation,
whether paper or electronic are following the principles of ‘ALCOA+’
Documents use for GMP purpose should comply with GDocP i.e. BMR, logbook,
Specification, SOP, Analytical method, Protocol, Qualification Doc, CofA, TA, PQR
Document handling procedure i.e Retention, Archiving, Periodic review
ALCOA + (Plus)
Attributable Consistent
Legible Complete
Contemporaneous Enduring
Original Available
Accurate
10. Data Interpretation and GDocP
ALCOA
Attributable – clear who has capture and
document the data
Legible – readable and stay in permanent
format throughout life cycle
Contemporaneous – recorded at the time
of activity
Original – first recorded data, not ‘True
copy’
Accurate – True representation of Fact
ALCOA+
Consistent – recorded in same way
Complete – Complete pack of data
including metadata
Enduring – long lasting durable storage
(paper, electronic)
Available – Retrievable and accessible for
review
11. Data Interpretation and GDocP
2. What is Raw data?
Original report, first captured information and should endure during lifecycle of data
Raw data could be paper or electronic
If data capture electronically and print as per requirement then electronic data is a
raw
raw data which can reproduce
If electronic system does not store data then print out is the raw data
If electronic system have limited storage capacity then data should be periodically
review and paper copy to keep
Electronic data storage, back up and periodic review to carry out as per site
procedure
12. Data Interpretation and GDocP
3. What is Metadata?
Describe the attribute of other data and provide context and meaning.
It is a part of original data but original data has no meaning if context not provide
by metadata.
4. What is Data Governance?
Any form of data generation are recorded, processed, retain and available to use
for
data lifecycle.
Data ownership and accountability
Data access restriction, handling, encourage reporting of errors, omissions, OOS
13. Data Interpretation and GDocP
5. What is Data lifecycle?
Data to retain and accessible in original form from generation, recording, use,
retention, archive/retrieval and destruction to ensure DI.
6. Recording data
Should meet ALCOA+ principles
Recording methods should be controlled and traceable
7. Data transfer / migration
Storage media can change without changing the original data
Storage media, data transfer process should be validated, audit trail availability
Electronic worksheet should be controlled
14. Data Interpretation and GDocP
8. Data Processing
Original data should available throughout life cycle
Data process should not manipulate data to get desirable result e.g. Chromatogram
9. Excluding Data
Should demonstrate with valid scientific justification and recorded with original data
Excluded data should retain with throughout lifecycle
10. Original Record
First source of information cab ne paper, observation, electronic record including
any
supportive data require to re-generate the GxP activity.
Original record can be static and dynamic
15. Data Interpretation and GDocP
11. True copy
A verified copy by second person which contains same data, information as
original
copy. This can be paper, electronic and retain throughout the data lifecycle.
12. Computerised system transaction
It can be single or series of operation to perform the transaction. This can be
perform manually or control by automated system
Critical steps should perform, record contemporaneously and save before make
any change
E-signature, audit trail should introduce to capture data even
16. Data Interpretation and GDocP
13. Audit trail
A form of metadata containing information associated with actions that relate
to creation, modification or deletion of GxP records.
Secure recording of any type modification of data such as creation, additions,
deletions, alterations e.g. Paper or Electronic
Audit trail facilitate the reconstruction of historical event during investigation
System should be risk assessed for clearly identified use access level
Computerised system should always provide retention of audit trail to review
changes
Where Audit trail not possible, alternative method should in in place e.g.
logbook
Audit trail should review periodically, part of data approval by trained reviewer
17. Data Interpretation and GDocP
14. Electronic signature (e-sign)
Digital form of signature equivalent in legal terms of hand written signature
Control of e-sign: attributable, no manipulation, legible, security of access by
owner
Where pdf, paper copy e-sign, metadata should be documented with original
Method of authentication of e-sign should be risk assess
Only inserted image , footnote without metadata is not adequate for e-sign
18. Data Interpretation and GDocP
15. Data review and approval
Should be a SOP to describe review and approve data
Error should be addressed as per procedure e.g. Comments, Deviation
Correction, recording, process should follow ALCOA+
Electronic report should customise to provide all necessary information require
for
review and approval of report and related documents
If data provided by other organisation, then receiving organise should evaluate
the data
Data integrity of data supplier and generator
Clearly identify category of routine and periodic data review
19. Data Interpretation and GDocP
16. Computerised system user access/system administrator roles
Access as per functionality appropriate to job role
Company must able to demonstrate regarding user access level
Individual login require for audit trail to ensure data integrity
Generic user access should not be used, additional license purchase should be
available
System not use for GxP purpose but contain GxP data must demonstrate control
over user access
System admin rights should not conflict interest of data use
20. Data Interpretation and GDocP
17. Data retention
Data archive , back up procedure and policy should available
Data retention should ensure to protect data from deliberate, inadvertent
alteration or loss
Data security should ensure by controlling measure or validate system
Original/true copy paper data can be stored by validating scanning process
and control storage location
Data destruction procedure should consider criticality and applicable legislative
retention requirements
21. Data Interpretation and GDocP
18. Archive and back up
Designated secure area for long term, retention of data and metadata for the
purpose of verification of the process or activity
Should be protected for any alteration and event like fire, flood, pest
Should be an arrangement to permit recovery of data, able to perform full
verification
Virtual system can be use for legacy system which are no longer supported
If full data migration of original is not achievable, risk based approach in place to
protect data
Original data pack including metadata configure to maintain for recovery including
disaster recovery
Backup and recovery process should be validated, periodically tested, functionality
Back up policy and procedure should be in place
Back up policy should not affect retention policy
22. Data Interpretation and GDocP
19. File structure
DI risk assessment should define GxP use, software functionality for intended use,
control and security over generated file
20. Validation for intended purpose
Comply with regulatory requirement
Functional verification should be demonstrated
internal requalification schedule should be available
21. IT supplier and service provider
For ‘cloud’ and ‘virtual’ service provider ownership, retrieval, retention, data security
should consider
Impact of law application of geographical location where data are physically stored
Technical agreement should clearly define responsibilities of contract giver and
acceptor
Software/system restoration should be made as per GxP