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Combining AI & PAT: leaner and small scale drug manufacturing

AI-assisted identification and drug tests is on the rise in pharmaceuticals with all eyes on accuracy and rapid go-to-market. What if AI and process analytical technology (PAT )are combined? Can we expect leaner processes? And small-scale drug making facilities?

Raised expectations for accelerating commercialisation follow the benchmark set by Covid-19 vaccinations and groundbreaking pharma technology such as mRNA.

These developments also raised the bar of trust in the life sciences sector and its ability to leverage AI to do so. With AI already making a huge impact, the spotlight is now on processes within drug manufacturing. AI can fuel rapid identification and testing of new drugs or diagnostic techniques such as process analytical technology (PAT) used manufacturing processes.

Deploying AI technology in conjunction with PAT could have a major impact on pharmaceutical engineering, and huge implications for the size, location and management of pharmaceutical manufacturing facilities.

What can PAT do for pharmaceuticals?

PAT improves process understanding, increase efficiency and ensures quality of output. Real-time monitoring and control of conditions allows the collection of vast amounts of data to support analysis and help identify opportunities for improvement.

PAT enables sample analysis throughout the pharmaceutical manufacturing process rather than having to end of cycle. Rapid adjustments and corrections to the batch, then save time, expenditure and waste.

High automation levels of allow pharmaceutical manufacturers to reduce the physical demands on staff and reduce human error. Capacity for continuous testing and monitoring also enhances consumer confidence. It could also speed up the lengthy drug approval process and allow rapid and efficient adjustments to meet different regulatory requirements around the globe.

How will AI increase PAT’s popularity?

The lag in PAT’s uptake within the industry, might be due to inertia and an unwillingness to change where the current processes meet expectations. There might also be concerns about the different skills required for operating staff or a need to see further and more long-term testing to prove results.

AI could shift the gear in uptake thanks to rapidly run extensive modelling and testing to confirm its effectiveness. This will help build the body of research and results, encouraging confidence and demonstrating the impact it could have on all drug-related processes.

Continuous improvement on PAT tools reduce the need for highly specialised operators and lower CapEx for manufacturers considering PAT implementation.

AI could be a catalyst throughout with data generated analysed by machine learning algorithms, resulting in continual improvement in product and the efficiency of the manufacturing process.

New Models: Leaner & Small-Scale pharma manufacturing?

The elimination of superfluous processes could easily lead to slimmed down plants with far smaller footprints than current manufacturing facilities. Standardised processes could lead to small scale pharmaceutical plants set up in remote locations where drugs can be manufactured to order. With the reduced need for human input, rather than being shipped out from a large central site, drug manufacturing could follow the model of global charities with in-country units ready for emergency deployment or the way that small, localised wastewater treatment plants in vulnerable regions to improve sanitation and water supply.

Reduced footprint and fewer location constraints could have a significant impact on the design of manufacturing facilities, real estate strategy and operating costs. And could support sustainability goals too.

Repurposing offices into modular pharma manufacturers

Deerns is helping data centre clients explore the potential of smaller, edge locations. Are there comparisons to be drawn in life sciences? Could improving the pharmaceutical manufacturing process signal a move away from huge manufacturing plants? There’s long been talk about the potential to repurpose vacant office buildings into life sciences facilities (or data centres).

Deerns understands the impact these developments on engineering projects, real estate strategy and ultimately your bottom line. If PAT leads to a downsizing in footprint requirements of manufacturing plants, this could be the moment that we see pharmaceutical companies move into more urban areas – particularly the newer, more agile industry players.

Simpler, more standardised process also increases the potential for the design and creation of modular manufacturing plants. Manufacturing off site and installation at the required location could be a big step towards a hub and spoke model and a more flexible approach for drug manufacture.

The impact of a combination of AI and PAT on pharmaceutical manufacturing models could be groundbreaking. We can’t afford to stand back and watch the progress of AI in drug research and development, and then wonder how we’re going to catch up in drug manufacturing.

Let’s talk

Rui Dinis

UK Life Science Lead - Associate Director

Array