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Data Integrity & Its Criticality in Quality Systems & Data Automation

Deva H. Puranam
Data Integrity & Its Criticality in Quality Systems & Data Automation

The manufacture of pharmaceutical products (drugs, devices, active ingredients, etc.) is a highly complex process that involves advanced scientific analysis and instrumentation at all stages of manufacturing, analysis, packaging, transportation and storage.

Data provides direct evidence that products are safe and effective – telling the story of drug products, long after they have been manufactured and shipped. As part of its mission to ensure the safety, efficacy and quality of products produced by the pharmaceutical industry, the FDA expects that all data submitted to the agency to obtain market approval is both reliable and accurate. The FDA first identified failures in data governance and data integrity in the year 2000 with a warning letter issued to Schein Pharmaceuticals that cited lack of proper controls over computerized laboratory systems. In the years since, the FDA focus on compliance with data integrity regulations in facility inspections has increased significantly. They consider the integrity of data, from the moment it is generated, and extending through to the end of its life cycle, to be a critical component of ensuring that only high-quality and safe drugs are manufactured. Enforcement actions by the FDA due to noncompliance with data integrity regulations can result in serious financial consequences for an organization (e.g., facility shutdowns, drug shortages, product recalls, import and/or distribution bans, delayed or denied drug approvals, substantial remediation costs, and loss of customers due to a damaged reputation). In addition, manufacturers who are found in violation of data integrity regulations may lose the trust of the FDA and face more frequent and in-depth inspections in the future.

Good practices, systems, procedures, and training are important not only due to global regulatory agencies focus on data but also it is the right thing to do. Business process simplification and standardization is an important step to lay the good groundwork for data generation, acquisition, storage and retrieval process. Technology complexity has been on an increasing trend and making investment in training and people is very much needed for the firms across the globe than ever before. Automation and IT systems configured, operated, and maintained “correctly” can address many data integrity challenges. Automated audit trail reports as part of the standard application software is a real opportunity to help industry.

Pharmaceutical Industry Trends Citing Data Integrity Violations

Regardless of company size, roughly 50% of all global drug 483s that have been issued over the 5 year period from 2014-2018 cite data integrity concerns. Data integrity violations are even more prevalent in warning letters, with 79% of global drug warning letters during 2014-2018 period citing data integrity issues. Additionally, the total number of FDA warning letters referencing data integrity deficiencies has increased significantly in recent years.

While 21 CFR Part 11 is known as the data integrity rule, deficiencies in Part 11 are rarely cited in 483s or warning letters. Two predicate rules that are frequently cited for data integrity are 211.68 and 211.194. While part 211.68 specifies requirements for “Automatic, Mechanical and Electronic Equipment”, part 211.194 is cited when firms do not review and include all relevant data when making lot release decisions.

E.g., for 211.68: The firm did not implement effective computer system controls to ensure only authorized individuals had access to the systems

E.g., for 211.194: The firm failed to review critical data and / or metadata that would allow them to identify out of specification events that require investigation in lot release decisions.

Also the most common theme in recent warning letters has been the clear and consistent encouragement by the FDA to employ “independent” data integrity assessments as part of the strategy for remediating identified issues, another clear indication of DI and its importance.

The ALCOA acronym, which has been around since the 1990’s is used as a good framework for ensuring data integrity. These simple principles should be part of the data life cycle, GDP and data integrity initiatives. In a nutshell, the path to data integrity should be “complete, consistent and enduring”. The governance and ethics in an organization should trickle down from management behaviors to an employee on the shop floor.

In our session on Data Integrity in Quality Systems and Automated Technologies, at the 2020 ISPE Asia Pacific Pharmaceutical Manufacturing Virtual Conference and Executive Forum on 21 – 23 September, we will take a deeper dive to learn how organizations are prioritizing Data reliability strengthening the oversight on Data Integrity related issues both in quality systems (QMS elements) and Automation systems. From the presentations one can learn on how organizations have learned and got better over a period of time from internal as well as external audits, how they have ventured into automating their data acquisition systems, validated them, employed them in every critical aspect of product life cycle and how they have improved inspection outcomes.

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