BLOG SERIES: Data management in cell and gene therapy manufacturing

In our interactions with professionals in the cell and gene therapy manufacturing field, we witnessed firsthand the struggles with streamlined data management and process analytics. 

This series of articles is MyCellHub’s response to these issues: a practical guide so you can cut through the complexity and effortlessly optimize the most crucial aspects of manufacturing. 

This guide dives into the untapped potential of streamlined data collection, processing, and management as a catalyst for efficiency and agility in the manufacturing process. We describe practical and actionable steps like selective data collection, directly affecting product quality. We also detail the importance of focusing on critical parameters of your production and using the power of analytics for continuous process improvement. 

Find out how to elevate your production process with our insights and innovative strategies for data management in cell and gene therapy manufacturing.

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PART 01:

4 key challenges of data management in cell and gene therapy production

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The rapidly evolving landscape of cell and gene therapy (CGT) manufacturing demands innovative solutions for ensuring efficiency, quality, and cost-effectiveness.

In response to the digitalization challenges, we examined the critical aspects of streamlining data management and analytics for professionals working in the cell and gene therapy production sector, including bioprocess engineers, manufacturing supervisors, and quality control personnel.

With this article, we launch a series that collects the practical insights we’ve gathered from over four years of working with cell and gene therapy manufacturers. Here’s a preview of the topics covering data in CGT manufacturing that we’ll shed light on: 

1. Challenges of data management
2. Tips for strategic data selection
3. Streamlining data management 
4. Mastering data analytics
5. Benefits of streamlined data management and process analytics 

But before we propose solutions, let’s dive deep into the heart of the data-related challenges faced by biopharmaceutical manufacturing.

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Why is it important to recognize data challenges in GMP? 

We’re now undergoing the fourth industrial revolution (known as Industry 4.0). It’s a transformative era marked by the integration of advanced digital technologies such as AI and big data analytics into manufacturing processes. Pharma 4.0 is an initiative by The International Society for Pharmaceutical Engineering (ISPE) to bring the Industry 4.0 principles to the pharmaceutical sector [1]. 

Pharma 4.0 brings hope for innovation and productivity in cell and gene therapy manufacturing. The novelty of these personalized therapies demands a fresh, evolved approach to data management. Nevertheless, our firsthand knowledge of digital transformation in GMP-regulated environments shows that data management poses challenges for manufacturers. 

We believe that the complexity of the manufacturing processes makes it hard to identify these challenges. Through pinpointing and understanding them, we can better tackle them with practical strategies. 

So, buckle up and get ready to uncover the data challenges your cell and gene therapy production might be facing.

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Data management challenges: grasping the complexity.

Cell and gene therapy is an exciting and rapidly growing field that is revolutionizing medicine. The approval of six groundbreaking cell and gene therapies in 2022 signified a significant milestone. Fast forward to 2023, the momentum continues to build, with an impressive number of 13 promising therapies poised for approval.

With more leading pharmaceutical companies getting involved, the potential for breakthrough therapies is higher than ever before. It’s a promising prospect, but the development of more complex therapies, plus the increasing amount of data associated with them, brings its own set of challenges.

Let’s take a look at the difficulties of data management that the industry faces or will face in the coming years: 

1. Increasing time costs of data-related tasks.
2. Inefficiency caused by lack of standardization.
3. Increasing complexity of processes.
4. Limited implementation of Quality by Design principles.   

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1. Growth in production volume, leading to an increase in time spent on data-related tasks:

The growth in production volume leads to a direct increase in the amount of data collected. With this, the collection of in-process and release testing data, batch records, supply chain and sample tracking data, audit trails, stability date, etc., can pose a significant data management challenge.

And it's not just about gathering data; validating, analyzing, and interpreting this wealth of information is equally vital. Unfortunately, due to the large volume of data, this process often leads to bottlenecks during the review and approval stages. While a “Review by Exception” approach is proposed to facilitate this process, it’s still not widely used in biopharmaceutics manufacturing. 

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2. The inefficiency caused by a lack of standardized assays and data management tools:

The cell and gene therapy industry often grapples with the inefficiencies stemming from the lack of standardized assays and unified data management tools. This absence of uniformity leads to a scattered and fragmented data landscape, introducing layers of complexity and redundancy that slow down the process. 

This lack of standardized tools disrupts the flow of information and makes data management a laborious task, burdening teams with inefficient data collection, validation, and interpretation processes. 

All this creates additional aspects to navigate in order to fulfill GMP audit requirements. It can also lead to crucial data slipping through the cracks, potentially affecting the quality and safety of therapies. 

By standardizing assays and streamlining data management tools, we pave the way for a more cohesive and streamlined data landscape.‍

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3. Expanding complexity in cell and gene therapies, challenging current manufacturing methods:

Cell and gene therapies have witnessed substantial growth over the last decade. With maturing of the field, we use new technologies and gain more understanding of biological processes which, in turn, allows for more precise manufacturing of biotherapeutics. However, it also complicates the manufacturing processes.

The growing complexity of processes highlights the shortcomings of the current manufacturing methods. The advancements in cell and gene therapy manufacturing require improved, sophisticated data management, analytics, and production techniques to ensure consistent quality and efficacy. 

A prominent example of increasing process complexity can be observed in autologous therapies (treatments that involve using a patient's own cells or tissues). This highly personalized approach involves production of a unique batch of a biotherapeutic per each patient. 

While autologous therapies are incredibly successful in battling certain diseases, the regulatory paperwork and data management processes need to be done individually as well. As an example, the batch record for a single batch of an autologous CAR-T therapy can take up to 1200 pages. This increases the costs of the therapies and exposes the limitations of the manual documentation methods. 

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4. The challenge of implementing Quality by Design (QbD) principles for improved consistency:

QbD principles ensure that while the process conditions might vary (within validated limits, e.g. to compensate for differences in the starting cell material) the final product and its effect in the patient are robust and reproducible [2]. 

The adoption of QbD principles is essential for addressing process variability and improving consistency in product quality and effectiveness. This approach allows for agile manufacturing processes that can deal with variations. By applying QbD principles, the cell and gene therapy industry can move away from manufacturing procedures based on static recipes and design more personalized, adaptive, and data-driven processes [3]. 

While being highly beneficial, the implementation of QbD is a resource-intensive process, especially at the initial stages. It requires robust data collection and analysis, training of the employees, as well as the development and implementation of detailed process models. This is often challenging, especially for organizations accustomed to traditional methods.

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‍What can we do with the data flood? 

The challenges we've laid out shed light on biopharma's growing reliance on data. 

But in a setting as complex as cell and gene therapy manufacturing, data is only valuable if it’s managed correctly. It’s becoming clear that the quantities of data that we're currently grappling with cannot be effectively collected, managed, and processed using manual methods alone. 

If these processes aren't streamlined and digitalized, we risk missing out on the significant benefits that this data can offer. That’s why the introduction of new technologies for optimized data management and analysis is not just a possibility, it's a reality and necessity that's reshaping our industry.

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In the upcoming parts of our Data Management in Cell and Gene Therapy Manufacturing collection, you’ll learn about actionable strategies in three main data-related fields in cell and gene therapy manufacturing: 

1. Data collection prioritizing, not to drown in information, but surf on it. READ NOW >>
2. Data management, to efficiently bring the raw data to the analytics table. 
3. Data analytics, to ensure your decisions are backed by robust evidence.

Don't miss out on these valuable insights to transform your manufacturing process.

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Ready to tackle the data challenges and try digital workflows in cell and gene therapy manufacturing? 

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