Data Integrity's Impact in Drug Substance Testing

Published on: 
Pharmaceutical Technology, Pharmaceutical Technology, September 2023, Volume 47, Issue 9
Pages: 23-28, 29

Interpreting data and understanding the various components of biologic drug substance testing is an important skillset to know as a lab personnel.

The healthcare space is at its peak in technology and other digital transformations: machine learning, artificial intelligence (AI), and other aspects are showing promise for helping patients and improving efficiency in the labs. However, there have been significant data integrity lapses that have caused consequences in the pipeline, such as manufacturing plants closing, drug shortages, and US Food and Drug Administration (FDA) approvals not going through (1).

Shailesh Vengurlekar, senior vice president of Quality & Regulatory Affairs, LGM Pharma, echoes this challenge in today’s world of drug substance testing. “Some of the biggest challenges facing drug substance testing include supply chain deficiencies, and data integrity,” he says. “Post-pandemic supply chains are still recovering, and the pharmaceutical industry is no exception. Data integrity continues to be a major topic of concern as well and is one of the factors driving the industry’s embrace of digital transformation.”

Another major challenge is the lack of significance when large data sets are being interpreted over a sustained period. Quality-by-design (QBD) approaches are a way to ensure proper data interpretation in this type of scenario to help monitor and analyze the trends of biologics and other drugs (2).


Regulatory attempts

Regulatory action was taken to try and improve this hurdle. FDA released a data integrity guidance in December 2018 due to the increased data integrity violations during current good manufacturing practice (CGMP) inspections in recent years. This included more regulatory actions, like warning letters, import alerts, and consent decrees to attempt to ensure the safety, efficacy, and quality of drugs (3).

Data interpretation is crucial

Interpreting data and understanding the various components of biologic drug substance testing is an important skillset to know as a lab personnel. According to Khanh Ngo Courtney, senior director of Biologics at Element, the characterization and routine testing of biological therapeutics, with cell and gene therapies being a specific focus, require advanced analytical techniques and an understanding of the biology itself. Mahesh Bhalgat, chief operating officer of Syngene International, said on the fact that although many labs and lab personnel are technically sound, there is still a lack of understanding and decision-making on the use of analysis models for biological product characterization and for the mechanism of action (MoA) studies (2).

Possible solutions

A way to solve this is by making sure the lab can provide as many assurances of product safety, strength, purity, quality, as possible, all of which depend on the data being discussed at the lab itself.Any data-driven decisions must be reproducible and based upon data that can support those outcomes (1).

Further, following the ALCOA method is another way that personnel can consider making sure they are on track with the right data:

  • Attributable: This is specific to staff members via audit trails and e-signatures and includes no password sharing and compliance to Part 11.
  • Legible: Make sure your data is usable and readable during internal audits.
  • Contemporaneous: Keep records of the tasks at hand during the performance of a task, not before or later.
  • Original: Store data in its original format, not manipulated, stored elsewhere, or shared with others.
  • Accurate: Use a system that minimizes errors, raw data and analytical results presented in a proper format (1).

This method also highlights the elements of making sure data are complete, consistent, enduring, and available, which can help ensure proper interpretation and lessening the hurdles that have come with data integrity.

Looking ahead

Even with the challenge of data integrity at hand, there is hope for drug substance testing to continue adapting to the technological transformations that are growing at a fast pace. Additionally, as more process knowledge and analytical method data become available, optimization of process and analytical methods will continue to be required as part of continuous improvement concepts.

Vengurlekar feels that in the next 5–10 years, drug substance testing will see increased efforts to standardize testing protocols and ensure that the quality of results across laboratories improve, leading to more consistent and reliable testing outcomes. As for Courtney, many current methods all have their own difficulties, but finding the right analytical solution for the intended purpose is an unmet need for many quality-defining methods for advanced therapeutic molecules in this current day and age.


  1. Chapman, J. Data Integrity 101: Why is it important? Redica Systems. 22 Oct 2020.
  2. Mirasol, F. Biologics Testing Highlights Need for Analytical Skills. Pharm. Technol. 2022, 46 (1) 38–40.
  3. UW-Madison School of Pharmacy. The Trouble with Biologics: Analyzing Large Molecules and Data Integrity. University of Wisconsin-Madison (accessed 11 August 2023).

About the Author

Jill Murphy is an editor for Pharmaceutical Technology.

Article Details

Pharmaceutical Technology Europe

Volume 35, No.9
September 2023
Pages 23-28, 29


When referring to this article, please cite it as Murphy, J. Data Integrity’s Impact in Drug Substance Testing. Pharmaceutical Technology 35 (9) 2023.