Int J App Pharm, Vol 12, Issue 6, 2020, 19-23Review Article



1Department of Pharmaceutical Regulatory Affairs, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, 643001, 2Department of Pharmaceutics, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, 643001

Received: 09 Jul 2020, Revised and Accepted: 27 Aug 2020


The pharmaceutical industry is currently one of the most dynamic among all industries. At present, it is striking with various compliance challenges like never before there is increased regulation, acquisitions, push toward harmonization and endemic in a Data Integrity (DI) concern. DI weakness is identified, either as a result of an audit or a regulatory inspection, companies with multiple sites should ensure that appropriate corrective and preventive actions are implemented across the organizations and appropriate notification to regulatory authorities should be made wherever applicable. The objective of the study carries the number of issues involved within data integrity in current GMP aspects, the root causes were addressed based on warning letters. This review intends to study the concept of data integrity holistically in all aspects, regulatory expectations and to evaluate the state of compliance and challenges that explore to suggest appropriate remedial and proactive measures to avoid DI issues. There were many challenges involved to overcome the issues, which are all about the one's handling by maintaining good documentation practice. The importance, strategies and recommendations were discussed to overcome from the repeated data integrity mistakes.

This review was carried out by systematic searches of data integrity in relevant guidelines, published articles, reviews and abstracts in Google scholar, Pubmed, Science direct, Embase, Web of science, Cochrane database of systematic reviews of articles up to March 2020. The keywords used for gathering information were listed below.

Keywords: Data integrity, Root causes, ALCOA, Recommendations


Data integrity (DI) is the assurance of data records that are complete, accurate, intact, and maintained within the original circumstance, including their connectivity to relevant data records and focuses to prevent unwanted changes to the required information [1]. DI is much important for the management of a quality system in the pharmaceutical industry, which ensures that medicines are safe based on the evidence of data. Due to issues with DI, there was a considerable increase in enforcement actions taken by regulatory bodies [2]. With faster growth in the generics market, economical and regulations on pharmaceutical manufacturers is increasing nowadays. In recent years there has been a significant increase in the number and types of data integrity issues that have been cited in regulatory inspections [3]. The DI related cGMP violations have leads to several regulatory actions, including warning letters, import alerts and seizures. There were many uncovered serious cases on DI related problems. Companies often have DI issues, which are hazardous to the company's long-term prospects and a demoralizing effect on the company culture [4]. Managing data is challenging in the pharmaceutical industry, especially when a firm's growth was emerging on a volume of data at a rapid rate. Distrustful data quality can result in severe consequences for the responsible organization that destroys the reputation of the organization [5]. Implementing controls and management of data without understanding the regulatory and business processes can result in the questionable validity of data and may lead to regulatory action. DI is the map of maintaining and ensuring the accuracy and consistency of data over its lifecycle [6]. Good data storage and record management are vital elements of the pharmaceutical quality system. DI refers to maintaining and assuring the accuracy and consistency of data over its entire life cycle in compliance with its suitable regulatory requirements [7]. Organizations expect that pharmaceutical organizations have to hold exact records and every single data will be accessible to controllers [8]. There are many chances of getting corrupted results if there is no proper measures are taken to ensure the safety of data. Errors of DI generally arise from human error, uncooperative operating procedures, data transfers, defects in software and physical negotiation to devices [9]. DI maintenance is an essential part of the industry’s accountability to ensure the safety, effectiveness and quality of the drug products. Data integrity is a serious part of regulatory compliance [10].

Errors involved in the DI system were classified in fig. 1.

Fig. 1: Lapses in data integrity [11]

Common data integrity issues

1. Personnel

2. Task preparation and execution

3. Materials

4. Procedures

5. Data collection (Capture/Interpretation/Review)

6. Handling issues

7. Data management and archiving

Consequences of data integrity noncompliance

Elements of data integrity

The regulated bodies and industries followed a term called ALCOA (Attributable, Legible, Contemporaneous, Original and Accurate) a USFDA guidance since 1990, for ensuring data integrity and which is a key to handling Good Documentation Practice (GDP) [20].

Table 1: Required elements for ensuring data integrity

Elements Abbreviation Explanation Comments
A Attributable Action that who performed and when? If anyone was changed the record, who did it and why? Who did it?
L Legible Recorded data must be permanent, readable and durable medium Can you read it?
C Contemporaneous Date and time should be affixed whenever the recorded data was performed Was it done in real-time?
O Original Is the obtained results are original data? Is it original or true copy?
A Accurate Without documents amendments no editing/modification to be done Is it accurate data?

Example for attributable

Note: It is imperative to guarantee a mark log is kept up to recognize the marks, initials or potentially assumed names of individuals finishing paper records.

Example for legible

Note: Should be readable and permanent

Example for contemporaneous

Note: Data should never be backdated, documented at the time of activity

Fig. 2: Legible criteria [23]

Example for original

Note: Reliable written printout and certified data

Example for accurate

Note: Should complies with actual value and error-free

USFDA prohibits the following

Minimum data integrity requirements

Why DI issues happen (Root causes)?

Shortage of manpower

Shortage of staff and unnecessary work weight can prompt off base and inadequate documentation [30].

Quantity over quality

Employees might be compelled to bargain the adequate quality levels to meet creation targets or dispatch courses of events [31].

Lack of awareness

Often, representatives are not prepared or insufficiently prepared to understand GMPs. This makes workers consider exercises as a task as opposed to understanding their significance by considering GMP [32].

Effectiveness of training

While the organization may procure the best worldwide mentors, representatives referenced that there were language and highlight obstructions, which kept the workers from understanding the substance, in this manner making the preparation repetitive [33].

Preventing data integrity issues

Information respectability disappointments have prompted organizations losing their licenses, consent orders, cautioning letters, import alarms, summon of the application uprightness arrangement, terrible exposure when issues become newsworthy and more. Assessing at any rate the components introduced in these 5 significant regions in the process will help recognize where any likely information and honesty issues may exist, just as giving plans to upgrades [34].

Personnel preparation

The personnel involving in generating, reviewing and approving data must have adequate knowledge and skills to generate GMP environment and keep up to the data integrity expectations, which includes:


One should ensure that the procedure that,


The key elements that guarantee the accuracy and control of equipment and systems should be verified, which includes:

Being proactive

Being proactive in detecting the potential data integrity issues and upholds the civilizing elements supports data integrity in the organization. Frequent audits on data integrity should be performed and issues in potential or questionable practices should be identified [38].


Based on the detailed study of various aspects of DI issues, regulatory guidelines, expectations and learning from various regulatory inspections/warning letters, the below following are the recommendations to prevent and proactively avoid DI issues to safeguard the company’s image and reputation for long-lasting, sustainable business [39]. Recommendations were determined below:


The integrity of data performed by any pharmaceutical organization is a topmost factor for trustworthiness. The finding of a solitary example where information respectability is undermined throws a shadow over the entire of the information produced. Discovering the occurrence of adulteration brings up the reason for more number of cases for such non-compliance on guidelines. Therefore ensuring data integrity is a major importance to any pharmaceutical organization as the consequences of getting it wrong are very costly and it will take a long time to rebuild the trust. Among 1000+manufacturers in generic pharmaceuticals across the globe, it is unclear how many operate in such a way that ensures compliance with current and future regulatory agencies in data integrity expectations. Getting data integrity right is a huge credit. It needs a concentrated, continuous effort to improve and maintain the policies involved and discipline culture to avoid regulatory issues. The time, hard costs, open door costs and key interruption of fixing a DI administrative lack fundamentally exceed the venture of time and vitality to make proper DI frameworks and controls. By putting up resources into an arrangement of precise, compelling and practical consistency will secure productivity and integrity in maintaining the quality of data standards.




I am thankful to my guide Dr. GNK Ganesh, who given me an idea for writing a review on this topic.




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