Handling of Electronic Raw Data

In the pharmaceutical industry, Electronic Raw Data refers to data in digital format that is first generated during manufacturing, testing, or research processes. Representative examples include measurement results collected directly from laboratory analytical instruments, and process data entered into Laboratory Information Management Systems (LIMS).

Electronic raw data possesses characteristics that differ significantly from paper records. The most important characteristic is that even when copies are created, each copy holds equal value as raw data. Therefore, it is critically important to clearly define in advance, through standard operating procedures (SOPs) or other documentation, which point in time the acquired data should be treated as electronic raw data. Generally, data at the point of initial recording in measuring instruments or systems is defined as electronic raw data.

Example of Electronic Raw Data Handling When Using EDC

The “original electronic case report form” should be defined chronologically in the protocol (or SOP):

1. During ASP utilization: Electronic records on the ASP server
2. After ASP termination: Electronic records burned onto CD-R (PDF format)
3. After in-house storage: Electronic records registered in EDMS (Electronic Document Management System)
4. During EDC revision: Electronic records after migration
5. In case of disaster: Official backups of the above electronic records

Ensuring Data Reliability

To ensure the reliability of electronic raw data, regulatory requirements adopt the fundamental concept known as “ALCOA+ principles.” ALCOA originally stood for Attributable (it is clear who created it), Legible (the content can be clearly read), Contemporaneous (recorded at the time the data was generated), Original (maintains the format in which it was first recorded), and Accurate (a correct record without errors). In recent years, this has been expanded to ALCOA+, with additional elements including Complete (all data is present), Consistent (data is in proper sequence and temporal order), Enduring (data is preserved throughout its required retention period), and Available (data can be retrieved for review when needed).

To satisfy these principles, systems handling electronic raw data require functionality to prevent data tampering or inappropriate deletion. Particularly important is the functionality called Audit Trail, which automatically saves records of all operations on data. An audit trail must capture who performed what action, when, and why, creating a complete and unalterable history of all data interactions.

System reliability itself is also crucial. According to quality standards called GxP (Good Practice), systems must be validated to ensure they operate appropriately, and their performance must be maintained continuously. Validation includes Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ), demonstrating that the system functions as intended in its operational environment. Furthermore, to appropriately manage data access, user authorization settings and authentication functions are indispensable. Modern systems typically implement role-based access control (RBAC) to ensure users can only access and modify data appropriate to their responsibilities.

Differences from Paper Records

There are significant differences in how paper records and electronic raw data are handled. In the case of paper records, when using a copy of the original as an official record, a procedure called “True Copy” (certified copy) is required. This requires verification of identity with the original, notation that it is a True Copy, the creator’s signature and date, and if necessary, confirmation by a reviewer’s signature.

On the other hand, for electronic raw data, complete replication by the system is possible, and if appropriate access management and audit trails are maintained, all copies possess equal authenticity. This is fundamentally different from paper records where there is a clear original. However, regarding long-term preservation, unique challenges exist, such as measures against system obsolescence. Organizations must implement data migration strategies and maintain the ability to read data even as technology evolves. This may include maintaining legacy systems, migrating data to new formats, or using vendor-neutral archive formats.

There are also major differences regarding data correction. With paper records, it is necessary to handwrite the correction content, corrector’s signature, date, and reason for correction, while keeping the original record legible. In contrast, with electronic raw data, the system automatically records change history, and detailed audit trails including access logs are retained. The electronic approach provides several advantages: it is impossible to make corrections without leaving a trace, the complete history is preserved indefinitely, and the system can enforce business rules such as requiring electronic signatures and reasons for changes.

Comparison of Paper and Electronic Records

Aspect	Paper Records	Electronic Raw Data
Original Definition	Single physical original	All identical copies are originals if properly controlled
Copies	Require True Copy certification with signatures	System-generated copies are equivalent if audit trails maintained
Corrections	Manual strikethrough, signature, date, reason (original must remain legible)	Automated audit trail with complete change history
Audit Trail	Manual annotations and sign-offs	Automatic, comprehensive, and unalterable logging
Access Control	Physical security and sign-out procedures	Electronic authentication and role-based permissions
Long-term Storage	Physical degradation concerns	Technology obsolescence and migration challenges
Searchability	Manual review required	Electronic search and data mining capabilities
Backup	Physical copies or microfilm	Redundant digital backups with integrity verification

Response to Regulatory Requirements

Regarding the handling of electronic raw data, regulatory authorities worldwide have established various requirements. The FDA’s (Food and Drug Administration) 21 CFR Part 11 stipulates requirements for the use of electronic records and electronic signatures, requiring system validation and maintenance of audit trails. However, it is important to note that FDA has issued guidance clarifying a risk-based approach to Part 11 compliance, focusing on records required to be maintained under predicate rules.

The European Union’s EMA (European Medicines Agency) Annex 1 to EU GMP Guidelines addresses contamination control, while Annex 11 provides comprehensive requirements for computerized systems and electronic records management. The MHRA (Medicines and Healthcare products Regulatory Agency) has published detailed guidance on data integrity, emphasizing the ALCOA+ principles and providing practical examples of good practices.

Furthermore, the PIC/S (Pharmaceutical Inspection Co-operation Scheme) guidelines stipulate more detailed data integrity requirements. PIC/S PI 041-1 “Good Practices for Data Management and Integrity in Regulated GMP/GDP Environments” is recognized as one of the most comprehensive guidance documents, providing clear expectations for data lifecycle management, computerized system validation, and data governance.

The WHO (World Health Organization) has also published guidance on data integrity that is particularly relevant for emerging markets. In Japan, the MHLW (Ministry of Health, Labour and Welfare) and PMDA (Pharmaceuticals and Medical Devices Agency) have incorporated these international standards into domestic regulations, with increasing emphasis on data integrity during regulatory inspections.

Data Governance and Organizational Culture

Beyond technical controls, regulatory authorities increasingly emphasize the importance of data governance and organizational culture. A robust data governance framework should include clear policies and procedures, defined roles and responsibilities, regular training programs, and management oversight. Organizations must establish a “quality culture” where data integrity is everyone’s responsibility, not just that of the quality assurance department.

Senior management must demonstrate commitment to data integrity through allocation of appropriate resources, establishment of clear expectations, and implementation of effective quality metrics. When data integrity issues are discovered, the focus should be on root cause analysis and corrective and preventive actions (CAPA) rather than solely on punitive measures, encouraging open reporting of problems.

Emerging Technologies and Future Considerations

The pharmaceutical industry is increasingly adopting advanced technologies that present both opportunities and challenges for electronic raw data management. Cloud computing offers scalability and disaster recovery benefits but requires careful consideration of data sovereignty, security, and vendor qualifications. Blockchain technology is being explored for creating immutable audit trails and ensuring data integrity across distributed systems.

Artificial intelligence and machine learning are being applied to detect data anomalies and predict quality issues, but regulatory frameworks for validating AI/ML systems are still evolving. The industry must balance innovation with the fundamental requirement to ensure data reliability and patient safety.

Conclusion

Managing electronic raw data involves unique challenges that differ from paper records. However, by establishing appropriate management systems and implementing system validation, it becomes possible to ensure even higher reliability than paper records. What is important is to clearly define the point of data generation and establish subsequent management methods. This enables simultaneous achievement of quality assurance in pharmaceutical processes and compliance with regulatory requirements.

Ensuring data reliability in the pharmaceutical industry is ultimately a critical issue directly connected to patient safety. Understanding the respective characteristics of electronic raw data and paper records, and constructing appropriate management strategies, forms the foundation that supports the manufacture and supply of high-quality pharmaceuticals.

In today’s increasingly digital pharmaceutical landscape, organizations must view data integrity not as a compliance burden but as a fundamental component of quality assurance. The investment in robust electronic systems, comprehensive training, and strong data governance pays dividends through improved product quality, enhanced regulatory confidence, and ultimately, better patient outcomes. As technology continues to evolve, the principles of data integrity remain constant: ensuring that data is reliable, attributable, and trustworthy throughout its entire lifecycle.

Note: This document reflects regulatory requirements and industry best practices as of January 2025. Organizations should consult with regulatory experts and monitor guidance updates from relevant authorities including FDA, EMA, MHRA, PIC/S, WHO, and local regulatory agencies to ensure ongoing compliance.