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Data Sciences

The importance of data integrity in the drug development process

Chart on a piece of paper

Data integrity is crucial when developing new medicines to ensure their safety and effectiveness for patients.

What is data integrity & why is it so important in drug development and clinical trials?

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.

Data integrity is essential because it ensures that the raw data collected is valid, complete, and well-documented. Data integrity's goal is to ensure that all data in the form of original records and observations, as well as,  any other documented activity that would be required to reconstruct the clinical study and assure the safety, efficacy, and quality of the product is available, complete, accurate and authentic.

How does data integrity differ from data security?

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. 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. Data security is extremely important, as unauthorized access to sensitive data can lead to the changing of records and data loss.

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, including electronic data entered in software and computerized systems, data entered exclusively on paper sources, and also hybrid systems which have both paper & electronic data.  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"). 

ALOCA + stands for Attributable, Legible, Contemporaneous, Original, Accurate.  (+) Complete, Enduring, Available, Consistent, and all data must follow the ALCOA + principle.

Attributable data collection - must include the place of origin, and the date of data collection should be noted down. 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.

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 the 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. Furthermore, they 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 violations of data integrity do regulatory agencies look for?

All regulatory authorities have similar expectations on data integrity and 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

What are the benefits of Good Documentation Practices?

Good Documentation Practices (GDP) are part of data integrity and 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.

Anything else that we should know about Data Integrity?

If a task or event is not documented, it does not happen. To avoid miscommunication, assumptions, or the appearance of fraud, document the tasks immediately and follow established procedures or protocols.

Take a few seconds to review your work, assuring the document complies with ALCOA + and maintains the integrity of the data. Data integrity is everyone's responsibility.

At Quotient Sciences, data integrity is central to everything we do.  We have robust and stringent quality systems in place across our global network of facilities to ensure that the data we collect from our development, manufacturing, and clinical study programs is accurate and consistent to ensure volunteer and patient safety and meet regulatory requirements.

Contact us for more information about our approach to data integrity.

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