Data management for advanced therapies, Part 5: traceability
by Heidi Hagen and Christophe Suchet | February 11, 2020
Note: this post is the fifth in an eight-part series on data management strategies for personalized therapies, such as cell therapies, gene therapies, or neoantigen cancer vaccines. You’ll find the prior installment, on data considerations when working with multiple stakeholders, here .
Personalized medicine has introduced a lot of “firsts” to the healthcare landscape -- including new requirements for supply chain traceability systems. When the FDA approved the first autologous therapies, agency regulators did so with the stipulation that the biopharma companies involved implement a robust digital orchestration and record-keeping system to ensure traceability and protect patient safety.
The same goes for the EMA (European Medicines Agency), the EU’s pharmaceutical regulator. Both the EMA and FDA require sponsors to have a robust next-gen traceability system in place and include a detailed description of their systems and methods in their filings. The traceability solution must be able to capture, connect, and manage data points across the entire supply chain, from intake and manufacturing to long-term monitoring and patient care.
Data sets such as the patient-centric Chain of Identity (COI) and patient-material-centric Chain of Custody (COC) are essential. There is also a large volume of other necessary information, such as data related to starting material collection and condition, that can improve safety and streamline the manufacturing process.
Here are a few excerpts from Vineti’s Advanced Therapy Guide: From Complex to Controlled, with key considerations for why and how to incorporate traceability data into your strategy.
Robust traceability reduces the risk of error
As discussed earlier in this series, the manufacturing process for advanced therapies is medicine’s most complex yet. The distributed nature of the journey across multiple locations significantly increases the risk of patient-specific starting material or finished drug product being delivered to the wrong place or, worse yet, being administered to the wrong patient.
Automated COI and COC information reduces this risk. Together, these data chains create greater traceability, quality, and confidence.
What to consider in your data strategy: different facilities, such as hospitals or drug product storage depots, may have unique systems in place. Your strategy should include the ability to integrate with multiple systems and harmonize disparate data sets in a compliant fashion.
Traceability data can help you plan ahead
Traceability data, analyzed collectively, can provide valuable insights into daily operations and quality management. This analysis can uncover inefficiencies, deviations, and trends happening across the personalized therapy supply chain, and help stakeholders navigate potential issues that would otherwise prevent short- and long-term scalability.
Since understanding data relationships is key to scaling your clinical trials and preparing for commercialization, the sooner you develop an automated strategy, the better. The threshold for implementing a digital platform could be as low as 10 to 20 trial subjects, depending on the complexity of the workflow involved. Getting it right early will help prepare your therapy for a successful license application.
What to consider in your data strategy: early adoption of a digital solution with robust reporting functionality will leverage your data to support growth.
Automated compliance can be an advantage
Regulatory agencies now have specific requirements for what data must be captured and how it is managed. COI and COC, for instance, often involve standardizing a number of different data types in a consistent format that should be readily available for regulatory inspections.
How efficiently you capture traceability data -- and utilize it -- is where you can maximize its value. The industry does not yet have established data standards in many cases. The biopharma team is often responsible for developing a standardization model for key data -- such as patient identifiers, starting material data, and in-process labeling variables -- that will both satisfy CMC requirements and enhance analysis. The more you automate traceability and related aspects of compliance, the more you can do with your data, such as internal analysis to improve your R&D.
What to consider in your data strategy: implement a solution that automates compliance so you can turn your data into an advantage.
Next in this series, we’ll discuss data strategies that help improve supply chain efficiency. If you have data-related questions for us in the meantime, please contact us.
Wondering how to turn traceability data into an value-add? Download Vineti’s Advanced Therapies Guide: From Complex to Controlled.
Data management for advanced therapies series:
Part 1: the need for new strategies
Part 2: unique data features
Part 3: working with living cells
Part 4: working with multiple stakeholders
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.