How To Be Compliant To Data Integrity Expectations

Supervision System How To Be Compliant To Data Integrity Expectations

2023-11-30 A guest post by IMA 4 min Reading Time

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The pharmaceutical industry has a critical responsibility to produce safe and effective drugs that meet regulatory requirements. Compliance with regulatory requirements, including those related to data integrity, is crucial to ensure patient safety and the efficacy of the drugs produced. How IMA helps pharmaceutical manufacturers to be inline with the regulatory requirements.

Allows the view of production data: IMA Active CDC Line HMI(Source: IMA) — Allows the view of production data: IMA Active CDC Line HMI
(Source: IMA)

Data integrity over its entire lifecycle of products an processes is one of the crucial requirement pharmaceutical manufacturers have to fulfill. In the pharmaceutical industry, data integrity is essential to ensure that the data used for product quality and safety assessments are accurate, reliable, and traceable.

Regulatory bodies, including the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA), have issued guidelines and regulations requiring that pharmaceutical manufacturers implement appropriate controls and processes to ensure data integrity throughout the data lifecycle, including data collection, processing, analysis, and reporting. Failure to comply with data integrity requirements can result in regulatory action, including product recalls, warning letters, and even criminal charges.

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Which Guiding Principles Have To Consider?

One of the guiding principle established in 2018 by the FDA is called ALCOA as guidance for manufacturers responsible for data integrity as outlined by 21 CFR Part 11. The term ALCOA is an acronym standing for Attributable, Legible, Contemporaneous, Original, and Accurate.

Every pharmaceutical company has to develop and implement a comprehensive data integrity governance, which is the sum total of arrangements which provide assurance of data integrity. This ensures that data, irrespective of the process, format or technology in which it is generated, recorded, processed, retained, retrieved and used will inline with the ALCOA principles, ensure an attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available record throughout the data lifecycle.

With this requirements in mind the experts of IMA Active have developed a supervision systems, named Kortex/Maestro. The system is developed by the Data Integrity by design principles, refering to the proactive approach of incorporating data integrity principles and controls into the design and development of systems, processes, and workflows. It involves building data integrity measures into the core architecture and functionality of systems to ensure the integrity, reliability, and security of data throughout its lifecycle.

By integrating Data Integrity by design, organizations can proactively mitigate risks, detect and prevent data integrity issues, and establish a robust foundation for maintaining the accuracy, completeness, and reliability of data throughout its lifecycle.

What Are The Key Aspects of Implementing Data Integrity By Design

1. System architecture: Designing systems with a focus on data integrity by implementing appropriate data structures, data capture mechanisms, and data flow controls. This includes defining data relationships, validation rules, and error-checking mechanisms to ensure the accuracy and completeness of data.

2. User access controls: Implementing robust user authentication and authorization mechanisms to ensure that only authorized individuals have access to sensitive data. Role-based access controls and user permissions should be defined and enforced to restrict unauthorized changes to data.

3. Audit trails and logging: incorporating comprehensive audit trail functionality to track all data-related activities and system events. This includes recording user actions, system changes, data modifications, and access attempts. Logs should be protected from tampering and made easily accessible for review.

4. Data validation and verification: implementing automated data validation and verification checks at various stages of data entry, processing, and storage. This helps ensure that data adheres to predefined rules, standards, and quality requirements. Validation checks should be performed on input data, calculations, and data transformations.

5. Data backup and recovery: designing systems with robust data backup and recovery mechanisms to prevent data loss or corruption. Regular backups should be taken and stored securely to ensure the availability and integrity of data in the event of system failures, disasters, or cyber-attacks.

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Date: 08.12.2025

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6. Cybersecurity: designing measures and controls to protect data from unauthorized access, breaches, or tampering. This can be achieved by designing secure network architectures that segment and isolate critical systems and sensitive data and applying encryption techniques to protect data during transit and at rest.

What Does Key Components of a Data Integrity Governance in the Pharma Industry Should Include?

1. Management commitment: Senior management should be committed to ensuring data integrity, and should provide resources, guidance, and oversight to support data integrity efforts.

2. Risk assessment: A risk-based approach should be taken to identify areas of the business where data integrity risks are highest, and where controls should be implemented to mitigate these risks.

3. Policies and procedures: Clear and comprehensive policies and procedures should be in place to guide employees on data integrity requirements, including the ALCOA CCEA principles.

4. Training: All employees involved in data generation and management should receive regular training on data integrity requirements and best practices.

5. Quality control: Quality control processes should be implemented to ensure that data are complete, accurate, and reliable, including regular review of data and audit trails.

6. Audit and monitoring: Regular audits and monitoring should be conducted to identify potential data integrity issues, and to verify compliance with data integrity requirements.

7. Continuous improvement: Ongoing monitoring and review of data integrity processes should be conducted to identify areas for improvement and to ensure that the program remains effective and up-to-date.

IMA as a supplier of high-quality pharmaceutical equipment, is aware that their control systems play a critical role in data collection and management, and that any errors or inaccuracies in the data collected can have severe consequences, including compromised product quality and safety. Therefore, IMA has adopted appropriate Data Integrity by design technical arrangements (such as audit trail, user authentication, access controls, etc.) to support regulated companies’ data governance.

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