Why Raw Data is Essential in Pharmaceutical Manufacturing

Pharmaceuticals safeguard human health and life. In their manufacturing processes, records known as “raw data” play an extremely critical role. Why is raw data so important?

What is Raw Data?

Raw data refers to “as-is” numerical values and observational results recorded during pharmaceutical manufacturing. Examples include the weight of raw materials when measured, temperature during manufacturing, the color of finished medicines, and results from quality inspections.

Why Raw Data is Important

1. Ensuring Safety

Pharmaceuticals involve human life. Therefore, it is necessary to be able to confirm whether any problems occurred during the manufacturing process. With raw data, it becomes possible to verify afterward that the medicine was manufactured correctly.

2. Quality Assurance

Raw data serves as evidence that the medicine meets established quality standards. For example, it can demonstrate that “this medicine contains the correct amount of active ingredient.”

3. Root Cause Analysis When Problems Occur

If any problem is discovered in a medicine, it is possible to identify the cause by reviewing the raw data. This is important for preventing similar problems from recurring.

4. Prevention of Falsification

Raw data is preserved in its originally recorded form. This makes it impossible to alter numerical values afterward, thereby maintaining the reliability of manufacturing records.

Specific Examples of Raw Data

Consider a middle school science experiment as an analogy. In an experiment notebook, the following records would be taken: the date and time when the experiment was performed, instruments used, measured values, and observational results.

Similarly, in pharmaceutical manufacturing, the following types of raw data are recorded: raw material weighing values such as “10.156kg,” room temperature during manufacturing such as “20.5°C,” the name of the manufacturing staff member such as “Taro Yamada,” and manufacturing start time such as “April 1, 2024 at 9:30.”

Procedures for Changing Raw Data and Their Importance

1. Rules for Changing Raw Data

When changing raw data, it is necessary to follow strict rules. The basic principles for changes are: the original data must never be erased; a single line should be drawn through the changed portion so it remains readable; the reason for the change must be recorded; the date and time of the change must be entered; and the signature of the person making the change must be included.

2. Why Such Strict Rules are Necessary

Ensuring Data Traceability

It becomes clear who changed what, when, why, and how. The appropriateness of the change can be confirmed afterward. Fraudulent falsification can be prevented.

Quality Assurance Perspective

Both the data before the change and the data after the change can be confirmed. It is possible to reflect on whether the decision to make the change was correct. The continuity of manufacturing history is maintained.

3. Specific Change Example

Changes are made according to established procedures in cases such as the following.

Before Change: Raw material weighing value: 10.156kg

After Change: Raw material weighing value: ~~10.156kg~~ 10.165kg Reason for change: Numerical error discovered upon re-weighing Date and time of change: April 1, 2024 at 9:45 Person making change: Taro Yamada (signature)

4. Change Management in Electronic Recording Systems

In recent years, an increasing number of pharmaceutical companies have been introducing electronic recording systems, but even in such cases, the following principles remain unchanged.

System Requirements

A function to automatically record change history, a function to make entry of reasons for changes mandatory, an access authority management function, and a function to preserve audit trails.

Ensuring Reliability of Electronic Records

Thorough management of system login, implementation of regular backups, and system qualification validation.

Regulatory Authority Expectations

1. Ensuring Data Integrity

Regulatory authorities emphasize the management of raw data from the following perspectives.

Compliance with ALCOA Principles

The original ALCOA framework consists of five fundamental principles:

• Attributable (Accountability): It is clear who recorded the data
• Legible (Readability): Records are clearly readable
• Contemporaneous (Simultaneity): Data is recorded in real-time
• Original (Originality): Original data is retained
• Accurate (Accuracy): Data is accurate

Additional Requirements of ALCOA+

As regulatory expectations evolved, ALCOA was enhanced to ALCOA+ by incorporating four additional elements. These additions address the comprehensive nature of data management required in modern manufacturing and clinical research environments:

• Complete (Completeness): There are no omissions in the data; the data must be whole and documented fully
• Consistent (Consistency): There are no contradictions in the data; documentation should be chronological and orderly
• Enduring (Durability): Data is preserved throughout its lifecycle; records must be maintained for the duration specified by regulatory authorities
• Available (Accessibility): Data can be retrieved when needed; documents must be accessible for reference or audit purposes

The progression from ALCOA to ALCOA+ reflects the pharmaceutical industry’s response to increasing regulatory scrutiny and the need for more comprehensive data integrity measures. These enhanced principles have been adopted by regulatory authorities worldwide, including the U.S. Food and Drug Administration (FDA), the UK Medicines and Healthcare Products Regulatory Agency (MHRA), the European Medicines Agency (EMA), the World Health Organization (WHO), and the Pharmaceutical Inspection Convention/Pharmaceutical Inspection Co-operation Scheme (PIC/S).

ALCOA++ and Beyond

Some regulatory frameworks have further extended these principles to ALCOA++, which may include additional attributes such as traceability, risk-based oversight, and comprehensive audit trail reviews. These enhancements emphasize that data integrity is not merely a technical requirement but an organizational commitment that requires proper governance, staff training, and a culture of quality.

2. Focus Points During Inspections

Regulatory authorities focus intensively on the following points during inspections.

Record Management Structure

Methods for storing raw data, status of change history management, and status of access authority settings.

Data Reliability

Consistency of records, compliance with change procedures, and status of backup implementation.

Training and Education

Employee education regarding record creation, level of understanding of change procedures, and awareness of the importance of data integrity.

3. Expected Quality Culture

Regulatory authorities expect the cultivation of the following quality culture.

Management Involvement

Clear policies regarding data integrity, securing necessary resources, and implementation of regular reviews.

Employee Awareness

Understanding the importance of records, habits of honest record creation, and a culture of early problem reporting.

Contemporary Regulatory Context and Industry Trends

Digital Transformation and Data Integrity Challenges

As the pharmaceutical industry continues its digital transformation, the volume and complexity of data generated during manufacturing, clinical trials, and quality control processes have dramatically increased. Managing and ensuring the integrity of such vast datasets requires increasingly sophisticated systems and controls.

Modern pharmaceutical companies gather data from diverse sources including laboratory equipment, digital platforms, automated systems, sensors, and various computerized systems. Integrating, harmonizing, and maintaining the integrity of this diverse data pool adds significant complexity to data governance.

Electronic Systems and Enhanced Controls

Electronic-based systems offer numerous advantages over traditional paper-based record systems. Many modern electronic systems include security suites with audit trail capabilities, electronic signature functions, data verification at both input and output stages, and robust backup, recovery, and data transfer processes.

However, the shift from paper-based to electronic systems has also created new risks, including cybersecurity threats, unauthorized access, and the potential for data manipulation. Regulatory authorities have recognized these challenges and issued specific guidance for electronic records and computerized systems, including the FDA’s 21 CFR Part 11 regulations and the EU GMP Annex 11.

Risk-Based Approach to Data Management

Current regulatory guidance promotes a risk-based approach to data management that considers data risk, criticality, and lifecycle. This approach recognizes that reduced effort or frequency of control measures may be justified for data that has lesser impact on product quality or patient safety, while data critical to safety and efficacy decisions requires the most rigorous controls.

Recent Regulatory Actions and Industry Impact

Analysis of FDA warning letters from recent years shows that a substantial proportion cite data integrity violations. Common findings include:

• Missing or inadequate audit trails
• Use of shared or generic login accounts
• Inadequate controls over blank forms and templates
• Poor documentation practices
• Uncontrolled data modifications
• Insufficient segregation of duties
• Lack of proper system validation
• Inadequate staff training on data integrity

These enforcement actions can result in severe consequences including facility shutdowns, delayed or denied drug approvals, significant remediation costs, product recalls, import alerts, and loss of customer trust.

The Importance of Data Governance

Effective data integrity requires robust data governance frameworks that include:

• Clear policies and procedures at the organizational level
• Defined roles and responsibilities
• Regular management review and oversight
• Comprehensive staff training programs
• Appropriate system controls and validation
• Regular internal audits and self-inspections
• A culture that emphasizes quality and compliance

Organizations must ensure that data integrity considerations are integrated throughout the entire quality management system and product lifecycle, from development through manufacturing, distribution, and post-market surveillance.

International Harmonization Efforts

Regulatory authorities worldwide are working toward greater harmonization of data integrity expectations. The International Council for Harmonisation (ICH) and organizations such as PIC/S facilitate this convergence through collaborative guidance development and mutual recognition agreements.

This international cooperation helps ensure that pharmaceutical manufacturers operating globally can implement consistent data integrity practices across multiple jurisdictions, though organizations must remain aware of specific regional requirements and nuances.

Conclusion

Raw data forms the foundation of pharmaceutical quality assurance. By properly creating, managing, and preserving raw data according to established principles and regulatory expectations, pharmaceutical manufacturers can ensure product safety and efficacy while maintaining regulatory compliance.

The evolution from basic record-keeping to sophisticated data integrity frameworks such as ALCOA+ reflects the maturation of the pharmaceutical industry and the increasing importance placed on data reliability by regulators worldwide. As technology continues to advance and data systems become more complex, the fundamental principles of data integrity remain constant: data must be attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available.

Organizations that successfully implement these principles through robust governance, appropriate systems, proper training, and a strong quality culture will be well-positioned to meet regulatory expectations, protect patient safety, and maintain the trust of healthcare providers and patients who depend on their products.