Data management for advanced therapies, Part 2: unique data features
by Heidi Hagen and Christophe Suchet | December 4, 2019
Note: this post is the second of an eight-part series on data management strategies for personalized therapies, such as cell therapies, gene therapies, or neoantigen cancer vaccines. You can find the first installment, on the need for new strategies in advanced therapies, here.
Advanced therapies have introduced a new personalized supply chain into the biopharma industry. With that comes new and unique requirements for collecting data – as well as a whole lot more data in general.
The volume of data collected and documented in a Biologics License Application (BLA) for a traditional therapy is already sizable, often adding up to several thousand pages. This number skyrockets for personalized therapies, however, due to their patient- and donor-specific supply chains.
Advanced therapies also put new demands on the quality and characteristics of data that, all together, require an entirely novel approach to the collection, management, and analysis of information.
Here is a quick look at the top four unique data needs across the advanced therapy supply chain, as well as resulting challenges. (To find out how to tackle these challenges with effective data management strategies, download Vineti’s data management whitepaper.)
Increased volume of data
While batch sizes of advanced therapies are typically much smaller than seen in traditional drug products, the amount of data generated is often larger. In the case of autologous therapies, for example, every patient equals a “batch of one.” But for each batch, data must be captured at every point in the supply chain, and then used both in connection with the patient’s clinical data as well as pooled to support overall manufacturing consistency and robustness.
This could result in an extremely large data pool; for example, a single gene therapy BLA could consist of up to 60,000 pages. All that data must be managed and analyzed for regulatory review.
The challenge: Traditional data management strategies cannot effectively collect, process, and analyze the quantity of data generated from advanced therapies.
Different types of data and their emphasis
The main component of personalized treatments is living, patient-based starting material. Working with living cells generates new types of data and requires precise and compliant procedures for tracking, handling, storing, and transporting patient material in a real-time manner. All of these procedures and the data they generate need to be documented on both a per-patient and a pooled basis in the CMC section of any BLA.
As a result, the CMC section of an advanced therapy BLA is much larger than typically seen with traditional therapies, and takes on increased importance. One FDA leader estimated that the amount of time spent reviewing Clinical data versus CMC data (80%/20% for traditional products) is reversed when it comes to advanced therapies (20%/80%).
The challenge: Creating a strategy that can account for new types of data, track a variety of new data points, handle large amounts of CMC data, and evolve in real-time.
Quality of data
As with traditional therapeutics, data quality matters for every drug product. But the importance of CMC data in advanced therapies demands enhanced quality control throughout the entire production and reporting processes. You also must take into consideration that the complexity and volume of data produced for each batch leave more room for error, making it necessary to precisely define “quality.”
Data quality can be assessed with the following questions:
- Did data collection follow validation protocols?
- Is the data complete without any gaps?
- Is the data accurate and without error?
- Is the data captured in a standard format?
- Is the data available in real-time?
The challenge: Understanding the value of and assigning quality assurance to different data types.
Increased data sources
The distributed nature of the advanced therapy supply chain equates to significantly more data collection points. These can include Healthcare Providers, transportation providers, clinical operations support teams, manufacturing facilities, and potentially storage facilities or additional patient treatment centers. And each facility has its own processes and systems, opening the door for potential conflicts around data variability and accessibility.
Adding to the complexity, some stakeholders still use manual systems to log patient data. This can increase the rate of error that is inevitable with disparate and disconnected systems.
The challenge: Identifying a strategy that streamlines collaboration and applies systems that can integrate with stakeholders and harmonize data.
We’ll be covering these strategies in this series of blog posts. If you’d rather access them all at once, they’re also discussed in our “Advanced Therapies Guide: Proven data management strategies for greater speed and simplicity in operations and filings. ”
Download the Whitepaper
Next in this series, we’ll be looking at the unique data demands that arise from working with “living,” cell-based patient material. If you have data management questions for us in the meantime, please contact us.
Data management for advanced therapies series:
Part 1: the need for new strategies
Part 3: working with living cells
Part 4: working with multiple stakeholders
Part 5: traceability
Part 6: efficiencies across the supply chain
Part 7: summing up the strategies
Part 8: the benefits of a strategic approach
Heidi Hagen is the Chief Strategy Officer and a Co-founder of Vineti. Over the course of her career, she has overseen the operations and delivery for more than 100,000 doses of cell therapy. Christophe Suchet is the Chief Product and Compliance Officer of Vineti, and previously led IT systems for cell therapy pioneer Kite Pharma. If you'd like to see how Vineti's Personalized Therapy Management (PTM) platform can help you solve your advanced therapy data challenges, please contact us to schedule a demo.