Manufacturing
How AI and ML can avoid the problems that plagued QbD and PAT
Nikolai Makaranka advises companies to: ■ start with narrow, focused use cases that solve specific pain points ■ define clear evaluation criteria and success metrics before deployment ■ invest in data infrastructure, as algorithms are largely commoditised, but clean, contextualised data makes AI useful
■ build cross-functional teams combining AI expertise with deep domain knowledge – those who understand both data and process
Missed opportunities
Cronin says despite early adoption, QbD and PAT’s full potential remains underutilised, with technical, cultural and regulatory challenges. Using regression models to predict shelf life to speed time to file was previously limited by a lack of integration between predictive models and regulatory filings. Similarly, PAT technologies like pH control are widely used, but broader deployment across unit operations was hindered by validation complexity and regulatory uncertainty. However, QbD principles such as process understanding, risk management and continuous improvement remain important and increasingly supported by digital and AI tools, which may help realise its full potential. As Cronin recounts, AI quickly analyses large amounts of data to find patterns, helping better understanding of processes and predicting outcomes. It can aid with collecting and analysing data automatically, saving time and mistakes. During development and scale-up, AI can optimise different process settings, making it easier to find the best ways to make products. AI tools can organise and share research data, making knowledge more accessible and streamlining regulatory documentation. In manufacturing, AI could watch processes in real time, spot problems early and make corrections. Overall, says Cronin, AI makes QbD “more practical and effective by automating tasks, improving understanding and enabling real-time quality control, helping companies build quality into products from the start and keep improving”. Louie points out that QbD and PAT are primarily applied during early stages of a product’s life cycle. While QbD establishes a design space, scientifically and statistically justified ranges for process parameters, PAT enables direct collect of real-time data from manufacturing operations. QbD is also used to define test methods for in-process analysis, quality control, product release and stability testing. Over the past 15 years, Louie notices these approaches have successfully streamlined the regulatory review process by enabling efficient communication between applicants and regulators, and allowing flexible management of process variations within the established design space. However, he argues many organisations “fail to allocate sufficient resources to extend QbD and PAT practices beyond initial drug registration”. Largely,
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it’s down to the difficulty in curating and maintaining process and testing knowledge. This is often distributed across multiple outsourced entities, such as contract development and manufacturing organisations and testing laboratories – not supported by robust knowledge management tools. Much of this information remains unstructured, residing in technical reports prepared for technology transfer and regulatory submissions. Recently, data science techniques have been employed to improve the curation and retrieval of this information. With the integration of AI and machine learning (ML), it’s now possible to model complex data relationships, correlate diverse data sources and develop comprehensive knowledge management systems. These tools can be used to generate insights, simulate manufacturing conditions and predict potential failings.
The future looks promising
Louie is particularly keen for the application of AI/ML for managing and controlling neurodegenerative disorders. Of pharmaceuticals’ capability, he says: “As advances in medical sciences continue to extend life expectancy, preserving quality of life becomes increasingly important. Neurodegenerative disorders remain major causes of memory loss and dementia, yet the underlying causes, diagnoses and early detections remain unsatisfactory. Especially promising is the correlation between Alzheimer’s progression and retinal imaging.”
At its manufacturing sites, BMS is investing in AI to help enable digital twins, real-time release testing, and advanced analytics for biologics and small molecules. The vision includes AI-assisted hybrid models of its manufacturing processes and predictive maintenance. BMS has also established joint Centres of Excellence in Process Data Analytics and Process Modelling. Cronin acknowledges the rapidly changing technological landscape, feeling success hinges on “upskilling teams, establishing clear governance, and embedding AI into existing workflows”, plus “investing in domain-specific AI tools”.
She predicts AI will become a foundational layer across biopharma, with “increased use of generative models, real-time analytics and autonomous process control”, Cronin is most excited about AI’s “potential to accelerate innovation while improving quality and compliance”, and “move from reactive to proactive decision-making and use the extensive data” BMS collects. The current focus is efficiency, but ultimately it will change BMS’s development and manufacturing. AI isn’t a “plug-and-play solution”, but requires “robust data infrastructure, cross-functional buy-in, and continuous validation”, Cronin would tell manufacturers. “Proactive engagement and transparent documentation can pave the way for broader adoption.” ●
www.worldpharmaceuticals.net
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