Data Integrity Challenges in Validation Activities in Pharmaceuticals

Within the validation processes of the pharmaceutical industry, a considerable amount of data is created through activities such as equipment qualification, process validation and computerized system validation. All validation activities must have access to accurate and reliable data; therefore, this data becomes a key component in making critical business decisions including but not limited to product quality determination, regulatory compliance and patient safety.

Because of these issues, it is increasingly challenging to maintain data integrity throughout the validation process. As companies become more reliant upon digital systems and validation processes have become much more complex, maintaining accurate, complete and secure data has also become much more difficult. As a result, data integrity has become a topic of major concern; multiple regulatory agencies around the world have adopted strict enforcement of data integrity principles and Good Manufacturing Practices (GMP).

This article will discuss some of the common data integrity challenges that occur in validation activities and how they can be resolved.

What is Data Integrity?

Data Integrity is essential to the completeness, consistency and accuracy of data during its entire lifecycle. Pharmaceutical validation assures that all recorded information can be trusted and reflects true observations.
Data integrity is typically explained according to ALCOA+.
- Attributable
- Legible
- Contemporaneous
- Original
- Accurate
The ALCOA+ extends to completeness, consistency, durability and availability.

The proper execution of these principles is critical to obtaining trustworthy and acceptable validation results that will meet regulatory expectations.

Importance of Data Integrity for Validation Activities

Validation is the process of establishing that a system or process produces results that are as expected; it can be thought of as the way to give a company documented evidence that it is complying with regulations. If the validation data is in question, all validation work will be in question.
There are significant consequences for a company due to poor Data Integrity, including:

• Incorrect conclusions from completed validations
• Regulatory actions, such as a 483 (Form 483) or Warning Letter
• Quality risks to product
• Loss of credibility

As an example, the FDA and other regulatory agencies demand documentation from the pharmaceutical companies on how the Data Integrity of their data is controlled — particularly in their validation processes.

Common Data Integrity Challenges

1. Manual Data Entry Errors

The most frequent occurrence of errors in validating manually recorded data is during the manual entry of that data. Issues with manual data entry can arise:

• Incorrectly entered data.
• Data that is missed.
• Data that has been incorrectly transcribed.

All of these errors may lead to increased inconsistency when transferring from one record to another.

2. Lack of Real Time Data Entry

While all validation actions should be entered in real-time as they occur, data is sometimes entered at a later date. This can lead to:

• Entries based on memory.
• Entries that are not recorded on time.
• Unable to establish a trace of re-entries.

Any of these situations would be violations of the ALCOA principles.

3. Poor Documentation

For validating purposes, missing documentation is one of the most common problems.
Examples of missing documentation include:

• Documents that lack entries.
• Documents that are difficult to read due to illegible handwriting.
• Un-signed Dates.
• Documents that lack supporting documentation.

Regardless of whether or not validation has been conducted correctly, the absence of appropriate documentation leads to compliance issues.

4. Inadequate Audit Trails

An audit trail is necessary to identify what changes are made and how they were made to data, especially in systems using electronic data. Some of the issues related to audit trails include:

• Audit trail that is either incomplete or not functional.
• Data changes that were made that were not authorized.
• Audit trail data that is not reviewed.

Establishing data validity becomes difficult in the absence of adequate audit trails.

5. Data Manipulation and Integrity Breaches

In particular, it may happen that data is modified intentionally in order to prove that the data meets acceptance criteria. Some examples of this behavior are:

• Deleting results that failed their respective tests
• Backdating data entries from previous dates
• Repeating tests without any justifiable reason

The above behaviors are violations that warrant regulatory action.

6. Inconsistent Data Across Systems

Validation is often done using multiple systems which consist of, but are not limited to, LIMS, MES and manual record keeping. This creates:

• Data that is inconsistent with one another
• Data that is not synchronized between the different systems
• Data that is wrong or in disagreement with one another

Data contains inconsistencies creates doubt about how reliable the data is.

7. Inadequate Control of Electronic Records

Due to the increasing number of digital systems, electronic data management is becoming much more important. Weak controls over electronic data management can come in many forms including:

• Weak access control to electronic records
• Sharing of electronic user accounts
• Lack of management of electronic records
• Poor validation of electronic systems

Compliance with 21 CFR Part 11 is necessary in order to address this issue.

8. Insufficient Training of Personnel
Data integrity is not simply the result of an organization's systems; it is also a function of its people.
A lack of training can lead to:

• Improper management of data
• Documentation that is not accurate
• Misunderstanding the compliance responsibilities of their respective positions

The greatest contributor to data integrity issues continues to be human error.

9. Poor Review and Approval Practices

All data should be reviewed prior to being approved, but commonly this is not done at all.
The types of data that should be reviewed prior to being approved include:

• Discrepancies that are not discovered
• Data that is incomplete prior to approving it
• Independent verification is not performed

Weak review processes allow mistakes to occur and will continue to do so.

Impact of Data Integrity Issues on Validation

Data integrity issues will impact validations substantially as validations cannot be valid if the data is not complete or trustworthy. Some consequences of data integrity problems include:

• Repeating validation studies
• Delays in product approvals
• Regulatory observations
• Increased cost of operation

In extreme cases, data integrity issues can potentially lead to a product recall or shutting down operations.

Strategies to Overcome Data Integrity Challenges

1. Implement Strong Data Governance

Having a well-defined governance framework with clear roles/responsibilities/procedures for managing all data.

2. Use Automated Systems

To Maximize Efficiency Reducing reliance on manual entry through automation reduces the chance of error. Automated systems with built-in controls increase accuracy and reliability of your data.

3. Comply With All Regulations

Obtaining all the required certifications from regulatory agencies will aid in your ability to digitally record and store data in compliance with 21 CFR Part 11.

4. Maintain Audit Trails

Audit trails should be enabled, secure and reviewed on a regular basis. These actions will provide and ensure a transparent process that holds people accountable for their actions.

5. Train Your People

Employees should be trained to:
- understand the principles related to data quality
- understand how to document their work
- understand their regulatory obligations
Employees that receive proper training will reduce risks associated with making errors.

6. Use Review Systems

Develop review systems with multiple stages of review to ensure the data you are producing is complete and accurate.

7. Perform Regular Audits

Conducting internal audits will identify system weaknesses and improve overall data integrity practices.

8. Foster an Environment of Quality

Create an environment in which the integrity and compliance are of the utmost importance and not the short-cut.

Role of Technology in Improving Data Integrity

New technology has provided solutions to the challenges that is faced when trying to maintain data integrity today. Examples of new technologies include:
- Electronic Batch Records (EBRs)
- Laboratory Information Management Systems (LIMS)
- Data Analytics Tools
- Cloud Storage Systems
By implementing new technologies, data accuracy, accessibility and traceability has improved.

Regulatory Expectations

Pharmaceutical companies are expected by Regulatory Agencies to adhere to strict standards with regard to maintaining data integrity. Some of these standards are:
- Maintain complete and accurate records
- Maintain secure data storage
- Maintain an appropriate audit trail
- Can trace the origination of data (trace data)
Meeting the Good Manufacturing Practices (cGMP) standards is required to meet these regulatory standards.

Data integrity is essential for ensuring that validation processes are effective and can be used to demonstrate compliance with regulations and quality of drug products; without truthfulness in data, the validation is useless.

There are many causes of data integrity issues including manual keying errors, poor record keeping and limitations within systems, but these can all be addressed via appropriate preparation, training and utilizing technology.

Pharmaceutical companies that implement solid data governance principles and develop a culture of integrity will have validation activities that are accurate, reliable, compliant and trustworthy.

Frequently Asked Questions (FAQs) on Data Integrity Challenges in Validation Activities

Q1. What is data integrity in pharmaceuticals?

Answer: Data Integrity refers to the confidence that a data item maintains its intended accuracy, completeness and reliability from its beginning through its end.

Q2. What are ALCOA+ principles?

Answer: ALCOA+ is a set of principles that specify the major characteristics of quality records such as accuracy, legibility and traceability among others.

Q3. Why is data integrity important in validation?

Answer: Data Integrity is crucial to validating data as it preserves the validation data in an acceptable and approved State for future use.

Q4. What causes data integrity issues?

Answer: The main reasons for Data Integrity Problems include manual data entry errors, poor data documentation and weak computer System controls.

Q5. What is 21 CFR Part 11?

Answer: 21 CFR Part 11 is the FDA regulation that governs how electronic records and how electronic signatures are controlled.

Q6. How can data integrity be improved?

Answer: Data integrity can be increased by using automated systems, through training and through mechanisms that provide for strong review processes.

Q7. What is an audit trail?

Answer: Audit trail is a collection of electronic records that show changes made to data and who made those changes.

Q8. What happens if data integrity is compromised?

Answer: You would experience compliance problems, product risks and possibly regulatory consequences.

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