The Promise of Converging Disruptive Technologies in Advanced Therapy Production

Yasser El-Sherbini

by Yasser El-Sherbini

Advanced therapeutic medicinal products (ATMPs), better known as cell and gene therapy (CGT) in the United States, are changing the landscape in many disease areas thanks to scientific and technical advances. However, translating scientific discoveries into sustainable and scalable manufacturing processes is crucial to bringing CGT innovations from bench to bedside.

Employing disruptive technologies in the production of ATMPs/CGTs will pave the way toward transforming global CGT production strategies, which in turn will ensure populations who will benefit from CGT will gain access to these products faster.

These disruptive technologies are well described by the World Economic Forum in their definition of the Fourth Industrial Revolution as “merging the physical, digital and biological worlds in ways that create both great promise and potential peril.” This is clearly projected in technologies such as artificial intelligence (AI), internet of things (IoT), advanced robotics (AR), 3D printing and big data informatics.

Disruptive technologies can converge with traditional manufacturing technologies in a way that empowers mass customized production, adding new capabilities while maintaining fixed (and perhaps reduced) overhead costs as well as sustainable quality.

The Fourth Industrial Revolution technologies can impact every single segment of the CGT production value chain (both upstream and downstream production and analytics) in a way that will create comprehensive strategic options for decision makers to generate more opportunities, faster and more cost-effectively.

The pace at which biotech companies are adopting such disruptive technologies will determine their own fate in a fiercely competitive and rapidly evolving field. Therefore, careful understanding of these technologies and considered investment in development of disruptive technologies is needed to ensure success.

The ultimate goal should be employing disruptive technologies to achieve a data-driven & self-learning “continuous bioprocessing” platform that encompasses: 1) Human-robotic interaction, 2) self-learning process development; and 3) a data-driven control strategy.

Let’s consider the impact of these technologies on CGT production.

Advanced robotics and single use technologies

AR has significantly changed process development, with some segments of the process are now fully automated (for example, liquid handling and cell culturing). These technologies not only eliminate human errors (which can be an issue in process sustainability and tech transfer) but also enable process scalability through the use of large-scale platforms beyond human handling capacity. The next generation of such technologies should deliver robotics that can work alongside humans in a collaborative, intuitive, self-monitoring and agile manner.

In the same context, using automated single use technologies (SUT) reduces the manufacturer’s equipment footprint and results in a better controlled process as well as lower capital investment.

Regulatory bodies are in favour of using SUT because they eliminate human error and ensure better quality by reducing the risk of contamination, improving operational efficiencies and reducing the need for robust validation processes, which in turn improves processing time. However, the current challenge with employing SUT into process development is the availability of fully validated scale-down models for all manufacturing segments (both upstream and downstream). This can be partially achieved in production segments such as bioreactors (with Ambr 250 providing a revolutionary solution) and also in downstream purification and filtration. However with other segments such as centrifugation, it can be more challenging to achieve fully validated SDM.

Artificial intelligence, Internet of Things and Big Data analyses

Advances in AR and SUT, combined with the current efficient data acquisition platforms (such as supervisory controlled and data acquisition [SCADA]) will generate an enormous quantity of data. AI is a valuable tool for transforming this “big data” into meaningful data in order to drive real-time self-learning, “data driven” decision-making strategies for the manufacturing process. AI will make it possible to transform big data into “knowledge” then into “wisdom”, and this wisdom will constitute the basis for the self-learning process.

AI, together with IoT, will enable greater automation of the process, which will improve quality management and reduce the need for capital investments especially in process development and validation.

However, to achieve this goal, process analytical techniques (PAT) need to become more robust to “feed” more insightful information for AI processing. This will require sensors that can detect parameters beyond dissolved oxygen and pH (such as metabolites, cell viability, viral titer, protein concentration, aggregate formation, ionic strength, etc). Real-time monitoring of process and key product quality attributes will enable improved self-learning control strategy and compliance.

It is also worth mentioning that the current advances in cloud computing provides quicker and more efficient data processing over the cloud which is perfectly represented by applications in next generation sequencing (NGS) data processing.

3D printing

3D technology is becoming a cornerstone for many industries because 3D printing enables the use of materials that are compatible with both the process and the product (biologically, electrically, chemically, and mechanically) while supporting the production of complex geometries, such as three-dimensional structures with undercuts or cavities.

3D printing doesn’t replace traditional manufacturing techniques, but rather complements and perfects these in terms of value and design. When it comes to CGT production, 3D printing can be employed to mimic human body niches to help develop neural network, potency assays and the environment for cells that can’t be differentiated on traditional 2D tissue culture plates.

QbD and disruptive technologies

Quality by Design (QbD) is a risk-based framework for ensuring the manufacturing process is consistent in terms of sustainability and scalability to make a product to the pre-defined criteria. This framework requires robust monitoring and control, which can best be achieved through integrated continuous bioprocessing, together with disruptive technologies, as opposed to traditional batch production.

To achieve continuous bioprocessing there will need to be better integration of upstream and downstream operations, and that will require the involvement of disruptive technologies of the Fourth Industry Revolution.

From a regulatory perspective, health authorities are starting to show greater interest in innovative technologies to support CGT production in a full QbD approach, with the U.S. Food and Drug Administration and the European Medicines Agency establishing expert teams as contact points for manufacturers and technology providers.

Nevertheless, there is still a need for broader breakthroughs and understanding of such disruptive technologies by the developers and the end users respectively.

Stakeholders across the CGT industry (regulatory bodies, technology developers and biotechnology companies) are demonstrating eagerness to embrace disruptive technologies in QbD, which will lead to a more innovative, collaborative approach within the industry.

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