Data integrity is crucial when developing new medicines to ensure their safety and effectiveness for patients.
Data integrity is central to our operations. Our rigorous quality systems across all our facilities ensure the accuracy and consistency of data collected from our development, manufacturing, and clinical programs. This commitment safeguards volunteer and patient safety and ensures compliance with regulatory requirements.
In this article, learn about data integrity in pharma and clinical trial data integrity from our team.
What is data integrity in drug development?
Data integrity is the maintenance and assurance of data accuracy and consistency over the lifecycle of a drug product/study. It is a critical aspect of the design, implementation, and usage of any system that stores, processes, or retrieves data.
Data integrity must follow global mandatory requirements for regulated healthcare industries for developing and bringing a new medical product to market. In addition, data integrity must comply with Good Manufacturing Practices (GMP), Good Clinical Practices (GCP), and Good Laboratory Practices (GLP), often collectively termed GxP.
Why is data integrity so important in drug development and clinical trials?
Data integrity and clinical trial data integrity is essential because it ensures that the raw data collected is valid, complete, and well-documented.
The goal of data integrity is to ensure that all data—original records, observations, and other documented activity— required to reconstruct the clinical study is available, complete, accurate, and authentic. This is to assure the safety, efficacy, and quality of the trial as well as the product being evaluated.
How does data integrity differ from data security in drug development?
Data integrity and data security are related terms, each playing an important role in the successful achievement of the other.
Data security is the protection of data against unauthorized access or corruption and is necessary to ensure data integrity. It is extremely important, as unauthorized access to sensitive data can lead to the changing of records and data loss.
Data integrity is a desired result of data security, but the term data integrity refers only to the validity and accuracy of data rather than the act of protecting data.
How is raw data defined and how does it relate directly to data integrity?
Raw data is the original and first documentation of the captured data. It is essential that the integrity of raw data be maintained. The documentation of raw data can be done in different formats: electronic data entered in software and computerized systems, data entered exclusively on paper sources, and hybrid systems which have both paper and electronic data entry.
Data integrity requirements apply to each of these formats.
What does ALCOA stand for and how does that relate to data integrity?
Regulators wanted to make certain that the integrity of data is preserved during the drug development lifecycle and through commercialization, so they established the ALCOA principle (later revised to include the “plus").
ALCOA ++ stands for Attributable, Legible, Contemporaneous, Original, and Accurate:
• Attributable data collection—including the place of origin and the date of data collection. Any alterations to the data should be noted, and clear identification of the person making the correction should be available.
• Legible—data should be easily read
• Contemporaneous—time and date of data collection should correspond accurately with the time and date of data recording.
• Original—original data should be preserved and maintained. In case of duplications/copies of the original data, the creator of the copy should confirm the authenticity of the copies (True Copy).
• Accurate—data should be error-free, and in case of any updates or corrections, a clear note/comment should be noted to support such change.
The Plus (+) in ALCOA ++:
• Complete—data should be complete in nature (no omissions), including any changes that have been made during the life of the data.
• Consistent—data should be chronologically arranged, with an audit trail available for any updates or changes to the data.
• Enduring—the manner used to record the data should be one that will last a long time without losing readability.
• Available—data should be accessible whenever needed, over the life of the data, and after study/protocol completion as per regulatory requirements.
• Integrity—emphasizes honesty and ethical behavior in data handling, encourages a culture of transparency and accountability.
• Transparency—all data processes should be open to scrutiny, encourages audit readiness and traceability.
How is ALCOA++ applied to GxP?
GxP is a collection of quality guidelines and regulations established to ensure the safety and efficacy of drug products. Collectively these define the Good Practices, where “x” may stand for laboratory, clinical, manufacturing, or distribution.
Independent of the environment, all regulatory agencies have a statutory obligation to ensure that the drugs available in their specific country fulfill the necessary requirements for safety, quality, and efficacy. They are responsible for effectively reviewing all documents containing both clinical and non-clinical data before giving permission for the marketing of a new drug to ensure the efficacy, quality, and safety of the drug in humans.
Additionally, regulatory agencies encourage manufacturers, clinical sites, and sponsors to implement effective and robust strategies to ensure that accurate and secure data management systems are in place and routinely monitored by the quality unit.
What types of data integrity violations do regulatory agencies monitor? What are the consequences of data integrity violations?
All regulatory authorities have similar expectations on data integrity and clinical trial data integrity. Some examples of violations that have been reported by the FDA, include:
• Deletion or manipulation of data
• Aborted sample analysis without justification
• Invalidated results without justification
• Destruction or loss of data
• Failure to document work contemporaneously
• Uncontrolled documentation
Consequences of poor data integrity and data security can be severe. They can include harm to the company’s reputation, financial losses, vulnerability to hacking or other cyberattacks, fines, legal action, and risks to patient safety.
What are the benefits of Good Documentation Practices?
Good Documentation Practices (GDP) are part of data integrity and clinical trial data integrity to help ensure that the recording of raw data meets ALCOA++ principles.
Some benefits of Good Documentation Practices include:
• The creation of legal evidence
• The determination of responsibility
• The conservation of acquired skills
• The facilitation of communication and the ability to provide a story of the events
• The establishment of an audit trail for clear visibility
• The accurate reconstruction of events
An inspector or auditor must be able to reconstruct the series of a product’s or project’s events and confirm the integrity of the related data using paper or electronic documents.
What are audit trails and why are they important?
Per FDA, audit trail means a secure, computer-generated, time-stamped electronic record that allows for reconstruction of the course of events relating to the creation, modification, or deletion of an electronic record. Audit trails include those that track the creation, modification, or deletion of data (such as processing parameters and results) and those that track actions at the record or system level (such as attempts to access the system or rename or delete a file).
Audit trails are important because they provide a means of verifying the data's accuracy and completeness by, providing a chronological sequence of events via a clear view of the documentation and record updates to confirm data integrity.
Ultimately, in any regulatory environment, audit trails are crucial to show record compliance and data integrity. If a task or event is not documented, it does not happen.
In a pharmaceutical manufacturing or clinical setting, data integrity is everyone's responsibility. Best practice is to document tasks immediately and follow established procedures or protocols to avoid risks of miscommunication, assumptions, or worse: the appearance of fraud. It only takes a few seconds to review work to ensure the document complies with ALCOA ++ and maintains data integrity.