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Data integrity is a critical aspect of the pharmaceutical industry, ensuring that data generated, recorded, and reported is accurate, complete, and reliable. Maintaining data integrity is essential for meeting regulatory requirements, ensuring patient safety, and upholding the credibility of pharmaceutical products. Here are key considerations and measures related to data integrity in the pharmaceutical sector:

  1. Regulatory Requirements:
    • Regulatory bodies, such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA), have specific guidelines and regulations concerning data integrity in the pharmaceutical industry. Companies must comply with these regulations to obtain and maintain approvals for their products.
  2. Good Documentation Practices (GDP):
    • Implementing good documentation practices is crucial for ensuring data integrity. This includes accurate and detailed recording of data, as well as the use of controlled and standardized documentation processes.
  3. Data Lifecycle Management:
    • Pharmaceutical companies need to manage the entire data lifecycle, from generation to archival. This involves defining processes for data creation, review, modification, storage, retrieval, and disposal. A well-defined and controlled data lifecycle helps prevent data manipulation and ensures traceability.
  4. Electronic Data Integrity:
    • With the increasing use of electronic systems, maintaining the integrity of electronic data is vital. Companies should implement secure and validated electronic data management systems that include features such as audit trails, user access controls, and data encryption.
  5. Training and Awareness:
    • Employees involved in data generation and management should receive proper training on data integrity principles and practices. Awareness programs help instill a culture of data integrity within the organization.
  6. Data Security:
    • Robust cybersecurity measures are essential to protect data from unauthorized access, tampering, or loss. This includes implementing firewalls, encryption, access controls, and regular security audits.
  7. Audit Trails and Version Control:
    • Implementing audit trails allows tracking of changes made to data, providing transparency and accountability. Version control ensures that only the most recent and authorized versions of documents are used.
  8. Quality Risk Management:
    • Employing quality risk management principles helps identify and mitigate potential risks to data integrity. This involves assessing processes, systems, and potential vulnerabilities to prevent or detect issues that could compromise data quality.
  9. Supplier and Vendor Oversight:
    • When outsourcing certain processes or relying on external suppliers, pharmaceutical companies should establish robust agreements and oversight mechanisms to ensure data integrity throughout the supply chain.
  10. Documentation Review and Approval:
    • Establishing clear procedures for the review and approval of documentation is essential. This ensures that data is reviewed by authorized personnel for accuracy and completeness before it is considered final.
  11. Continuous Improvement:
    • Implementing a culture of continuous improvement allows organizations to adapt to evolving technologies and regulatory requirements. Regularly reviewing and updating data integrity practices ensures ongoing compliance and effectiveness.
Data Integrity 2023
Data Integrity

A. Introduction

  1. In recent years, the number of observations made regarding the integrity of
    data, documentation and record management practices during inspections of following GxP

      1. good manufacturing practice (GMP) (2),
      2. good clinical practice (GCP),
        good laboratory practice (GLP) and
      3. Good Trade and Distribution Practices
        (GTDP) have been increasing.
  2. The possible causes for this may include
      • Reliance on inadequate human practices;
      • Poorly defined procedures;
      • Resource constraints;
      • The use of computerized systems that are not capable of meeting regulatory requirements or are inappropriately managed and validated.
      • Inappropriate and inadequate control of data flow;
      • Failure to adequately review and manage original data
        and records.
  3. Data governance and related measures should be part of a quality system,
    and are important to ensure the reliability of data and records in good
    practice (GxP) activities and regulatory submissions.
  4. “ALCOA+”
    • The data and records should be ‘attributable, legible, contemporaneous, original’ and accurate,
      complete, consistent, enduring, and available; commonly referred to as

B. Diffination & synoms

Some definitions given below apply to the terms used in these guidelines. They
may have different meanings in other contexts.

    • ALCOA+. A commonly used acronym for “attributable, legible, contemporaneous,
      original and accurate” which puts additional emphasis on the attributes of being
      complete, consistent, enduring and available throughout the data life cycle for
      the defined retention period.
    • Archiving. Archiving is the process of long-term storage and protection of
      records from the possibility of deterioration, and being altered or deleted,
      throughout the required retention period. Archived records should include
      the complete data, for example, paper records, electronic records including
      associated metadata such as audit trails and electronic signatures. Within a GLP
      context, the archived records should be under the control of independent data
      management personnel throughout the required retention period.
    • Audit trail. The audit trail is a form of metadata containing information
      associated with actions that relate to the creation, modification or deletion of
      GxP records. An audit trail provides for a secure recording of life cycle details
      such as creation, additions, deletions or alterations of information in a record,
      either paper or electronic, without obscuring or overwriting the original record.
      An audit trail facilitates the reconstruction of the history of such events relating
      to the record regardless of its medium, including the “who, what, when and
      why” of the action.
    • Backup. The copying of live electronic data, at defined intervals, in a secure
      manner to ensure that the data are available for restoration.
      Certified true copy or true copy. A copy (irrespective of the type of media
      used) of the original record that has been verified (i.e. by a dated signature or by
      generation through a validated process) to have the same information, including
      data that describe the context, content, and structure, as the original.
      Data. All original records and true copies of original records, including source
      data and metadata, and all subsequent transformations and reports of these
      data which are generated or recorded at the time of the GMP activity and whichallow full and complete reconstruction and evaluation of the GMP activity. Data
      should be accurately recorded by permanent means at the time of the activity.
      Data may be contained in paper records (such as worksheets and logbooks),
      electronic records and audit trails, photographs, microfilm or microfiche, audio
      or video files or any other media whereby information related to GMP activities
      is recorded.
    • Data criticality. This is defined by the importance of the data for the quality and
      safety of the product and how important data are for a quality decision within
      production or quality control.
    • Data governance. The sum total of arrangements which provide assurance of
      data quality. These arrangements ensure that data, irrespective of the process,
      format or technology in which it is generated, recorded, processed, retained,
      retrieved and used will ensure an attributable, legible, contemporaneous, original,
      accurate, complete, consistent, enduring and available record throughout the
      data life cycle.
    • Data integrity risk assessment (DIRA). The process to map out procedures,
      systems and other components that generate or obtain data; to identify and
      assess risks and implement appropriate controls to prevent or minimize lapses
      in the integrity of the data.
    • Data life cycle. All phases of the process by which data are created, recorded,
      processed, reviewed, analysed and reported, transferred, stored and retrieved and
      monitored, until retirement and disposal. There should be a planned approach
      to assessing, monitoring and managing the data and the risks to those data, in
      a manner commensurate with the potential impact on patient safety, product
      quality and/or the reliability of the decisions made throughout all phases of the
      data life cycle.
    • Dynamic data. Dynamic formats, such as electronic records, allow an interactive
      relationship between the user and the record content. For example, electronic
      records in database formats allow the user to track, trend and query data;
      chromatography records maintained as electronic records allow the user or
      reviewer (with appropriate access permissions) to reprocess the data and expand
      the baseline to view the integration more clearly.
    • Electronic signatures. A signature in digital form (bio-metric or non-biometric)
      that represents the signatory. In legal terms, it is the equivalent of the handwritten
      signature of the signatory.
    • Good practices (GxP). An acronym for the group of good practice guides governing the preclinical, clinical, manufacturing, testing, storage, distribution and post-market activities for regulated pharmaceuticals, biologicals and medical devices, such as GLP, GCP, GMP, good pharmacovigilance practices (GVP) and good distribution practices (GDP).
    • Hybrid system. The use of a combination of electronic systems and paper systems.
    • Medical product. A term that includes medicines, vaccines, diagnostics and medical devices.
    • Metadata. Metadata are data that provide the contextual information required to understand other data. These include structural and descriptive metadata, which describe the structure, data elements, interrelationships and other characteristics of data. They also permit data to be attributable to an individual. Metadata that are necessary to evaluate the meaning of data should be securely linked to the data and subject to adequate review. For example, in the measurement of weight, the number 8 is meaningless without metadata, such as, the unit, milligram, gram, kilogram, and so on. Other examples of metadata include the time or date stamp of an activity, the operator identification (ID) of the person who performed an activity, the instrument ID used, processing parameters, sequence files, audit trails and other data required to understand data and reconstruct activities.
    • Raw data. The original record (data) which can be described as the firstcapture of information, whether recorded on paper or electronically. Raw data is synonymous with source data.
    • Static data. A static record format, such as a paper or electronic record, that is fixed and allows little or no interaction between the user and the record content. For example, once printed or converted to static electronic format chromatography records lose the capability of being reprocessed or enabling more detailed viewing of baseline.

Good Documentation Practices


Data Integrity 2023
Data Integrity

To be continued………………………………

pingback:-Data deviation in microbiology

Read more:-Laboratory incident

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