In recent years the subject of Data Integrity has become important mainly because of the many deficiencies that have been discovered by pharmaceutical inspectors and auditors around the world. Although Data Integrity is an old and basic issue in all types of manufacturing, the results of inspections and audits have revealed inconsistencies in the way that the subject is interpreted and applied in different regions and countries around the world. For the pharmaceutical industry, the subject is extremely important as it serves as the basis for judging the safety and quality of pharmaceutical products of all types and classes. The pharmacist, the caregiver, and the patient must depend on the integrity of the data that they are provided to judge the quality of the agents that are used in the treatment of the many different disease conditions. In addition, the manufacturer of the pharmaceutical is dependent on the integrity of the data that are used to judge the efficiency and safety of the manufacturing process.
Data Integrity is also a major issue for managing a manufacturer. It is critical for managers to have solid, reliable data to judge the efficiency of manufacturing processes and the quality of its products. The data are important in assessing the costs of production and planning for future company needs. Incorrect information has caused major problems for controlling processes and developing sound plans. For these reasons it would seem that management would be especially rigorous in insisting upon high quality data.
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Given the global nature of the pharmaceutical industry it is important that a uniform understanding of the need for data integrity be developed across the many regions and cultures of the world. Uniform procedures and techniques need to be applied so that caregivers and patients will not need to be concerned that the quality of a pharmaceutical product might be dependent on its manufacturer or country of origin. Consequently, major regulatory bodies have issued guidance documents in an attempt to produce globally applicable standards for data integrity.
The guidance documents and the subject itself have many broad impacts on the subjects of product quality, patient safety, and the need for manufacturing knowledge that is impossible to review the whole subject in a single paper. The material presented here will serve as an introduction to the subject and, hopefully, entice the pharmaceutical worker to pursue the subject in greater depth.
What has gone before
Data forms the base of the system that leads to knowledge about a product or process. For instance, the eyes gather data that inform us that we are looking at a tree; as more data are gathered we see that the tree looks healthy; this leads to the knowledge that the tree is not suffering from a disease, is adequately watered, and is living in a good environment for its growth. Consequently, many regulations were developed to assure the collection of good data. The phrase, “Garbage in, Garbage out” was well understood before the advent of computers. The issue of data integrity has been concentrated upon the activities of company laboratories as that is where most data are collected, and one must remember that manufacturing processes also generate data without employing laboratory facilities. Some of the most useful regulations are found in what is known as the “Good Laboratory Practices” (GLP).1 While many people believe that these regulations only apply to animal studies, its actual scope says that the regulations apply to the quality and integrity of data related to product safety in non-clinical laboratory studies on a wide range of products as required by several regulations.
The subparagraph referred to above1 relates to the collection of “raw data.” “Raw data means any laboratory work sheets, records, memoranda, notes, or exact copies thereof, that are the result of original observations and activities of a nonclinical laboratory study and are necessary for the reconstruction and evaluation of the report of that study.*
In the event that exact transcripts of raw data have been prepared (e.g., tapes which have been transcribed verbatim, dated, and verified accurate by signature), the exact copy or exact transcript may be substituted for the original source as raw data.
Note that this statement defines what may be considered to be an “exact copy or exact transcript.”
Raw data may include photographs, microfilm or microfiche copies, computer printouts, magnetic media, including dictated observations, and recorded data from automated instruments.
The regulations become more specific in a later paragraph.2 that states: All data generated during the conduct of a nonclinical laboratory study, except those that are generated by automated data collection systems, shall be recorded directly, promptly, and legibly in ink. All data entries shall be dated on the date of entry and signed or initialed by the person entering the data. Any change in entries shall be made so as not to obscure the original entry, shall indicate the reason for such change, and shall be dated and signed or identified at the time of the change. In automated data collection systems, the individual responsible for direct data input shall be identified at the time of data input. Any change in automated data entries shall be made so as not to obscure the original entry, shall indicate the reason for the change, shall be dated, and the responsible individual shall be identified.
This paragraph contains many regulations that have entered into common practice in reputable laboratories. The statement: shall be recorded directly, promptly, and legibly in ink, tells us that the first record of the data is the “original data.” There should be no intermediate recordings or copies. The reason for this is that each additional transcription of the data is subject to error and copying of data multiple times creates many opportunities for errors to be incorporated into the data. The term “promptly” means that the recording should be made as soon as possible after the generation of the data. Memory is a tricky thing and the longer it takes for data to be recorded, the greater the chance for error. And “legibly” means that employees with bad handwriting cannot be tolerated. Some laboratories have tried to remedy this situation by giving workers small computers so that data can be typed into a record. The statement “in ink” is an attempt to make the recording indelible. Many companies have banned the use of pencils or pens with erasable ink. If electronic recording is used a “track changes” function needs to be engaged so that changes or deletions of the original recording will be noted.
The second half of this paragraph is the origin of the requirement that only a single line through an entry may be employed when crossing out data. This practice has been carried over into word processors that utilize a “track changes” function. In all cases the individual responsible for the data entry or change must be identified and the date of the entry or change must be recorded. The part that is often ignored is the entry of the reason for a change. With word processors this entry should be easy as there is normally a function for inserting a comment, but with handwritten data in a cramped notebook it may be difficult. Many laboratories instruct workers to use symbols or numbers in parentheses to create footnotes at the bottom of a notebook page to record the reason for changes.
The point to the recording of these changes and reasons is that a reviewer can note the frequency of changes or mistaken entries and prescribe corrective actions to improve data entry. Questions such as: Is there a consistent pattern of changing failing results into passing ones? The date of the change must be close to the date of the original observation. Otherwise one may ask why it took so long to decide on the need for a change. If the same person is always making changes for the same reason, is there a training problem here? If many changes are made for the same reason, is that a sign that training is needed?
The requirements for signatures or initials to identify the responsible individuals creates a need for strong and well validated electronic signatures when employing computer-based systems. These signatures must be created and executed according to the requirements of the regulations for electronic records.3
The Guidance Documents
As noted previously, the major regulatory agencies have issued guidance documents for the purpose of creating a global understanding of what the agencies require in the area of data integrity. Although this article will concentrate on the guidance issued by the U.S. Food and Drug Administration (FDA),4 other guidance documents issued by the Medicines and Healthcare products Regulatory Agency (MHRA)5 and the Pharmaceutical Inspection Convention/Pharmaceutical Inspection Co-operation Scheme (PIC/S)6 are very important to companies and investigators operating internationally. While it will not be possible to cover all three documents adequately because of their wide scope, there are certain aspects that are held in common that are important here.
First, the FDA4 and the PIC/S6 documents are in the form of draft guidance. However it should be noted that the FDA document makes many references to U.S. GMP regulations, while the PIC/S guidance is based on the PIC/S and European GMP regulations. Therefore regulators can cite violations by referring to the actual regulations that underly the guidance documents, and the MHRA5 guidance is a final guidance that is based on several other international regulations and guidance documents. Note also that the U.S. FDA is also a member of PIC/S.
The FDA document4 appears to be strongly directed toward electronic systems, while the MHRA5 and PIC/S6 documents include more discussions of paper-based systems in addition to the electronic data. The PIC/S guidance is written from the point of view of inspectors and is therefore valuable to those who might receive an inspection.
As an introduction to these documents we might consider their definitions of what constitutes data integrity.
The FDA states:4 FDA expects that data be reliable and accurate. CGMP regulations and guidance allow for flexible and risk-based strategies to prevent and detect data integrity issues. Firms should implement meaningful and effective strategies to manage their data integrity risks based upon their process understanding and knowledge management of technologies and business models. For the purposes of this guidance, data integrity refers to the completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate (ALCOA).
The MHRA5 includes more terms to help to interpret the acronym. The guidance refers to the acronym ALCOA rather than ‘ALCOA+’. ALCOA being Attributable, Legible, Contemporaneous, Original, and Accurate and the ‘+’ referring to Complete, Consistent, Enduring, and Available. ALCOA was historically regarded as defining the attributes of data quality that are suitable for regulatory purposes. The ‘+’ has been subsequently added to emphasize the requirements. There is no difference in expectations regardless of which acronym is used since data governance measures should ensure that data is complete, consistent, enduring and available throughout the data lifecycle.
And PIC/S6 says: Data Integrity is defined as “the extent to which all data are complete, consistent and accurate, throughout the data lifecycle and is fundamental in a pharmaceutical quality system which ensures that medicines are of the required quality. Poor data integrity practices and vulnerabilities undermine the quality of records and evidence, and may ultimately undermine the quality of medicinal products. Data integrity applies to all elements of the Quality Management System and the principles herein apply equally to data generated by electronic and paper- based systems. There are many references to the Quality Management System as it is one of the quality elements that an inspector will want to assess.
The question to ask here is why would anyone want to have data that is not accurate, complete, and reliable? The elements of ALCOA and ALCOA+ would seem to be elements that anyone producing a product that can have an impact on the health of a human being would want for the data used to judge the safety and efficacy of that product. The answer, of course, is dependent on many social and even political factors that arise from the workers who generate and record the data. The current regulatory efforts are attempts to put several rules in place to make ALCOA a reality in pharmaceutical manufacturing
References
- U.S. Code of Federal Regulations, Title 21, Section 1, Part 58, Paragraph 3, Subparagraph k. (Abbreviated as 21 CFR 58.3(k)) This system of abbreviation will be employed for future references to the U.S. Code of Federal Regulations.
- ibid. 21 CFR 58.130(e).
- U.S. Code of Federal Regulations, Title 21, Section 1, Part 11, Paragraphs 50 and 70. 21 CFR 11. 50 & 70 and subpart C.
- “Data Integrity and Compliance with CGMP.” Draft Guidance for Industry, U.S. Department of Health and Human Services (DHHS), FDA, Centers for Drug Evaluation and Research (CDER), Biologics Evaluation and Research (CBER), Veterinary Medicine (CVM), April, 2016.
- “”GXP” Data Integrity Guidance and Definitions. ”Final Guidance, MHRA, March 2018.
- “Good Practices for Data Management and Integrity in Regulated GMP/GDP Environments.” Draft PIC/S Guidance, August, 2016.
Author Biography
Originally from Hawaii, the author holds degrees in biochemistry from Cornell University and the University of Wisconsin. In a career of over 40 years he has written over 60 articles and book chapters covering biochemical and pharmaceutical quality control topics. In recent years he has formed his own consulting company, gone into retirement, but continued his activities in the area of pharmaceutical quality control.
*Quotations from regulations or guidance documents are shown in italics.