Over the next five years, global data creation is projected to increase by more than 50% year-over-year.
Because of this growth and complexity, it is essential to categorize, weed out, contextualize, process, and visualize data. In doing it right, both humans and machines can use the data properly. This is increasingly challenging in life sciences across the value chain. After all, with all of todays accumulated data, questions about what to do with it, how to analyze it, and how to leverage its predictive power are a concern for many organizations. It’s one thing to collect data and another thing to make use of it.
Regulators are paying attention too. Year after year, the FDA finds more data integrity issues, costing manufacturers and, ultimately, patients who needs quality medication on time.
Improving pharma manufacturing and operations starts with using data well and using it right. The three key factors impacting and potentially complicating the process of acquiring, understanding, and using data are:
- The Five Vs: Data Volume, Variety, Velocity, Validity, and Veracity (still need to know more about those? Check out our eBook)
- The Paper Problem
- Data Integrity
The third piece of the puzzle, Data Integrity, is the focus of this blog, and is the crux of the challenge facing manufacturers. Data Integrity in pharmaceutical manufacturing is more than just a concept.
ALCOA+ principles intend to ensure that all of the data that the pharmaceutical industry generates, including data from clinical trials, manufacturing, and quality control meets the criteria listed above. It applies to any kind of data that has a GMP impact and that has been recorded electronically, manually in a paper-based format, or in a combination of the previous ones (hybrid format).
Let’s break it down…
- Attributable: Data must be traceable to the person who generated it and the time and date it was generated.
- Legible: Data must be written or recorded in a way that is easily readable.
- Contemporaneous: Data must be recorded at the time it is generated, rather than after the fact.
- Original: Data must be the original record, not a copy.
- Accurate: Data must be a true and accurate representation of what was observed or measured.
- Plus (+)
- Consistent: Data must be consistent with the study protocol, SOPs and regulatory requirements.
- Enduring: Data must be retained for the appropriate amount of time and in a manner that ensures its integrity.
- Available: Data must be accessible and retrievable for inspection and audit.
But DI does not apply only to data acquisition – it also applies to results and outputs. When AI Models support human decision-making in critical processes along the full drug life cycle, the letters A, L, C, O, and A perfectly define the prediction characteristics and recommendations that smart systems (like Aizon) provide. Any prediction, recommendation, cluster, pattern recognition, or detected anomaly must always have a link to the data used to create the model. It also must link to the corresponding accuracy and performance, units of measure, timestamp, and the input data used for the prediction. Finally, the result must be understandable and clearly interpretable.
Let’s take a look at a very specific case: Plasma derivatives constitute a specific class of drug biomanufacturing with very particular requirements. The most critical factor is that the raw material is human blood, donated by individuals and managed by donation centers. The raw material CQA is varied and heterogeneous, and its values determine the performance of the final product derived from its manufacturing process.
Spreadsheets, emails, PDFs, and applications (in the best cases), are examples of data sources that inform the CQA. When SMEs are required to prepare and set up the operations that transform plasma into intermediate and final products, they need the ability to manipulate different data sources and unify them into a single record point, which will ultimately be the source of truth for later actions.
Automizing data extraction from multiple origins and centralizing the information in a standard structure should be performed by systems like the Aizon platform. This guarantees data processing in a homogeneous structure that is consistent with Data Integrity standards.
If you’re ready to get started with better data management, take a look at the article: Four Reasons to Digitize your Life Sciences Data.