An Analysis of 2019 FDA Warning Letters Citing Data Integrity Failures
Data integrity is a fundamental aspect of ensuring the quality, safety, and efficacy of pharmaceutical products. It refers to the completeness, consistency, and accuracy of data throughout the data lifecycle. Data integrity failures can compromise the reliability of data and lead to serious consequences for patients, manufacturers, and regulators. Therefore, the FDA and other health authorities have been increasingly focusing on data integrity issues in their inspections and enforcement actions. In this article, I will analyze the FDA warning letters issued in 2019 that cite data integrity deficiencies. Warning letters issued to pharmaceutical API/drug manufacturing and testing facilities are included in this analysis.
This article will present the following information:
- Warning letters that include data integrity citations in 2019
- The trend of data integrity-associated warning letters over the past 12 years
- Distribution of data integrity-associated warning letters by country
- Frequency of different types of data integrity observations.
Warning Letters That Include Data Integrity Citations in 2019
In 2019, the FDA issued and posted a total of 137 warning letters to pharmaceutical API/drug manufacturers and/or testing laboratories. Data integrity (DI) issues were cited in 33 warning letters, which is 25% of the total warning letters. The table below shows the warning letters that cited DI issues, along with the date of issuance, the receiving country name, and the company name.
Trend of Data Integrity Associated Warning Letters Over the Past 12 Years
The chart below shows the trend of data integrity (DI) warning letters issued by the FDA from 2008 to 2019. It can be seen that there was a significant increase in the number of DI warning letters after 2013, reaching a peak in 2017. Since then, there has been a slight decrease in the number of DI warning letters, but they are still very high compared to the previous years. This indicates that data integrity remains a major challenge for the pharmaceutical industry and a key focus area for the FDA.
Distribution of Data Integrity Associated Warning Letters by Country
The table below shows the number of data integrity (DI) related warning letters issued by the FDA to different countries from 2008 to 2019. It can be seen that the number of DI warning letters was low (4-6) until 2012, and then increased significantly afterwards. The majority of DI warning letters were issued to China, India, and USA during this period. USA received the highest number of DI warning letters, followed by India and China in 2019.
Source of data:
2008-2018: The data has been taken from Pharmaceutical Online.
2019: The data has been taken from FDA website.
Frequency of Different Types of Data Integrity Observations
The chart below shows the frequency of different data integrity (DI) observations cited in the FDA warning letters issued in 2019. The top five observations are:
- Incomplete/Missing Records: This observation was cited 13 times (18%) and refers to the lack of proper documentation or retention of records that are required by the GMP regulations.
- User Access Deficiencies: This observation was cited 11 times (16%) and refers to the failure to control or restrict the access of users to computerized systems or data files that are used for GMP purposes.
- Deleting/Destroying Original GMP Records: This observation was cited 10 times (14%) and refers to the intentional or unintentional removal or alteration of original records that are essential for ensuring data integrity and product quality.
- Falsified Data: This observation was cited 9 times (13%) and refers to the fabrication or manipulation of data that are intended to deceive or mislead the regulators or customers.
- Audit Trail Deficiency: This observation was cited 6 times (9%) and refers to the absence or inadequacy of audit trails that are used to track and verify the changes made to data or records.
Note that some warning letters may include multiple DI observations. Therefore, the total number of observations may exceed the total number of warning letters.
Best Practices for Companies in Case of Data Integrity Observation
In many cases, companies only address the reported data integrity issue and define an action to fix it. However, this is not enough to ensure the quality and reliability of the data. The root cause of the data integrity issue should be identified, the investigation should be expanded to all affected areas, and the risk assessment should be performed for all affected data. These steps are essential to prevent the recurrence of data integrity problems and to comply with the regulatory expectations.
For example, see the below observation:
“Our investigator observed your laboratory equipment lacked appropriate controls. For example, from January 1, 2018, to June 25, 2019, audit trails from Agilent 1260 Infinity Series II high-performance liquid chromatography (HPLC) instruments showed a pattern of aborted runs and single run entries. Single run entries included analyses of multiple peaks or split peaks without documented investigations or adequate scientific justifications. Your employees used the Agilent Service Account login, with full administrative privileges, to abort HPLC testing runs without being attributable to a specific individual.
Your response identified the number of deleted, aborted, and single runs during your HPLC testing. However, your response did not provide adequate investigations or evidence of corrective actions put in place to prevent these data integrity issues from recurring.”
The following best practices should be applied to remediate data integrity (DI) observation:
- Create a deviation record addressing the DI observation as soon as possible. This will help to document the issue and initiate the investigation process.
- Conduct a thorough investigation using a detailed investigation protocol. Define the scope of your investigation covering all affected manufacturing and laboratory operations, batches, equipment, facilities, areas, etc. Consider the time frame when the data were affected and document the justification for excluding any part from the investigation.
- Quarantine the employees who are suspected of DI violation. Quarantining means removing them from GMP activities but not terminating them from the job. You may give them leave or assign them to non-GMP activities. After the investigation, define appropriate actions for them. For example, provide them with required training on DI to return to GMP activities or take administrative actions as applicable.
- Interview the employees to understand the nature and extent of the problem and to identify the root cause, including any quality culture deficiencies.
- Perform a comprehensive risk assessment for all affected data (tested data, manufactured batches, released batches, distributed batches, etc.). If necessary, perform testing using retain samples and take actions accordingly.
- Define corrective and preventive actions (CAPA) to eliminate the root causes and prevent recurrence. Ensure that the CAPA are effective, timely, and verifiable.
- Define actions for the affected data or batches based on the risk assessment. For example, retest, rework, recall, or destroy as appropriate.
- Define actions as long-term measures describing any remediation efforts and enhancements to procedures, processes, methods, controls, systems, management oversight, and human resources (e.g., training, staffing improvements) designed to ensure the integrity of your company’s data.
Conclusion
Data integrity is a vital aspect of the pharmaceutical industry that affects the quality, safety, and efficacy of the products. The FDA and other health authorities have been issuing warning letters to manufacturers who fail to comply with the data integrity standards and regulations. This analysis of the 2019 warning letters revealed that data integrity deficiencies were prevalent and varied across different countries and types of observations. The most common data integrity problems were related to incomplete or missing records, user access deficiencies, deleting or destroying original GMP records, falsified data, and audit trail deficiency. These problems indicate a lack of proper data management practices, training, and culture among the pharmaceutical companies. Therefore, I suggest that pharmaceutical companies should implement effective data governance systems, conduct regular data integrity audits and assessments, provide adequate training and education to their staff, and foster a culture of data integrity and quality throughout their organization. By doing so, they can improve their data integrity performance and avoid regulatory actions.
About the Author:
Mohammed Raihan Chowdhury is a seasoned pharma professional who has been working in quality assurance of pharma for 16 years. He is the Head of Quality Systems and Services at One Quality Solutions Ltd., a leading pharma consulting and services company in Bangladesh.
Before joining One Quality Solutions Ltd., Mr. Chowdhury was the Manager of Quality Assurance at Square Pharmaceuticals Ltd. He has also worked at Novartis (Bangladesh) Limited for more than 13 years, where he held various roles such as Quality Operations Lead, Data Integrity Lead, and Site GMP Auditor.
Mr. Chowdhury has extensive experience in managing regulatory inspections from FDA, EU & ANVISA, implementing data integrity programs, and simplifying quality systems. You can contact him at raihan.chowdhury@1qualitysolutions.com
