Manufacturing vaccines, digital twins and lessons learned: part I

Using a digital replica of the manufacturing process, the pharmaceutical industry is exploring the ability of virtual technology to improve the efficiency and agility of the production chain and expedite time-to-market

The COVID-19 pandemic has been a sharp reminder to the world of the fact that when there’s need for a vaccine, lives are on the line and time is of the essence.

To speed up vaccine development and manufacturing, life sciences companies are increasingly leveraging a type of technology that’s been used for years in industries such as automotive manufacturing and the aerospace world. Digital twins are making a difference … and in more ways than expected.

GSK, which makes approximately 2 million doses of vaccines per day, is one of the companies leading the way. It’s partnered with digital technology leaders Siemens and Atos to pilot a digital twin to create a complete and real-time simulation of the entire vaccine manufacturing process.

At the one-year anniversary of this partnership, I spoke to Matt Harrison, Head of Sciences, Digital Innovation and Business Strategy at GSK, about what the company has learned, what it might mean for pharmaceutical science and business, and what he predicts is the future of vaccine development.

Perhaps tackling the future first, I ask Matt whether we’re now looking at the demise of small-scale production equipment. “Not anytime soon would be my view,” he says.

Matt Harrison

“No, I think that this is a great example of Industry 4.0 and represents the progress that can be made when you integrate physical-world stainless steel with computers, software, data and advanced algorithms together to create opportunities for change in the pharmaceutical for manufacturing industry.”

“What we’re doing with digital twins goes beyond small-scale research and development (R&D) and flows through into scale-up and production and provides a way to control the key quality attributes of the manufacturing process. It’s certainly a step forward, and we're really happy with that; we also believe that it’s an informative story and something that benefits the industry.”

“Does that spell the end of traditional or batch manufacturing? No, it doesn’t. Much like other industries, it means we can use a blend of different technologies, some of which are fully autonomous, some of which are operator-dependent. That’s the overall model, based on what we’ve been learning, that we’re starting to propose.”

I suggest that a key driver is process optimisation and, as a consequence, a way to reduce development times and accelerate time-to-market.

“Absolutely yes,” agrees Matt: “If what you could do, rather than being in a lab all the time to develop your experimental plans, solve a particular problem or increase your understanding of a process — which might take weeks or months to run those tests, collate that data and work out what you do next, etc. — is use a digital twin, then you’re able to run multiple simulations at once, from anywhere in the world 24 hours a day.”

“What this does is accelerate the rate at which you gain a deeper comprehension of that particular process. In terms of critical quality attributes, the greater the interdependency of the key process parameters, the more complex the procedure and the more important it is to explore how each one affects the others."

"Currently, achieving that level of awareness means having humans in the lab and going through a process of learning, understanding and validating their findings.”

“Digital twins can expedite that progression and improve the flow through rate at which drug development happens. Of course, that’s only one part of the story; all vaccines and medicines are contingent on the success of clinical trials."

"So, yes, you can speed up human understanding, drug development, the manufacturing process, etc., but you also need to fast-track the clinical study timeframe to get the full effect.”

“Ultimately,” Matt continues, “the more you know about the interrelationships between the process parameters, the greater the potential is to make commercial-scale production more flexible.

Although there are benefits to be gained by accelerating the development process, the true value manifests itself with the manufacturing setting if and when you can move from a quality control perspective to a quality assurance perspective with time.”

“Then, you’re into the realms of real-time release and, potentially, actually being able to predict the final quality of the product before you even make it. That, for me, is where the real value of this lies."

"It’s something that, with our operations partners, we’re working out how to take this vision — and the results of the pilot-scale experiments — from R&D into a manufacturing setting."

"At the same time, we’re running a number of other twins to look at the different platforms that we have in vaccines, examine how that [research] flows through into operations and full-scale production. It’s work that’s ongoing, but we think the application is truly exciting.”

Multiple benefits

I can’t help but agree and suggest that, particularly in the development space, the use of digital twin technology means you can investigate aggressive or edge points and push the parameters of any process without fear of wasting any active ingredient or blowing up the equipment because it's all a digital simulation.

“Very much so,” says Matt: “There are all kinds of benefits, from sustainability to waste reduction and not having to do as many experiments. There are all kinds of angles you could work in this context; ultimately, the digital twin testbed enables you to push harder, do more in the time that you have and do it faster. It allows you to truly understand the parameter interdependencies in a way that may not be possible in a real-world environment."

There’s more freedom to experiment, fewer time constraints and less risk, which brings more flexibility into the design and development space in all kinds of different ways.” GSK has found the benefits of digital twin technology to include the following:

  • speed: you can run simulations in hours instead of having to build a test plant, which could take years
  • sustainability: reduces the amount of materials and energy required for real experimentation activities
  • safety: assurance of quality and yield predictions improve the reliability of supply
  • manufacturing agility: automation makes process robust and transfer between sites simpler
  • education: an offline version of the digital twin can be used as a simulator for training.

Big data

The ongoing evolution and implementation of Industry 4.0 means that data is everywhere. Surely the abundance of real-time information provides a fuller and more comprehensive view of the process or product under development when it comes to optimising operational excellence or aligning the CQAs and being able to move confidently onto the next phase, I posit.

“Yes,” notes Matt, “we’re very much able to control different unit operations using this twin — or simulate them — but we also use the system to physically control a process in a scaled-up setting.”

“When you're developing a vaccine — or a small molecule — and/or doing any kind of process improvement, then CMC (chemistry and manufacturing controls) has two roles; it generates the data that enables us to elucidate the process to a stage whereby it can be transferred to operations; and, essentially, it provides the knowledge we need to be able to tell the regulators how we do, actually, control the process.”

“With digital twins, we generate an awful lot of data, which has an inherent value to an organisation in terms of further insight into how to develop the next generation of products."

"And what this technology does is brings all of that awareness together in a way that allows you to benefit from almost any experiment you do. You gain more from that experiment than you would do if it was done in isolation because you have access to reams of data that augment and drive the development process. At the same time, you're using it to propel corporate knowledge and understanding as well.”

In conclusion, Matt adds: “The potential is huge! Our long-term plan at GSK is to expand and replicate this model in some of our discovery activities and the future production of all new vaccines. Ultimately, the goal is to deliver more vaccines and medicines faster to the people who need them.”

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