Manufacturing AI now:
What QbD and PAT missed
While AI is transforming pharmaceutical R&D and manufacturing, legacy frameworks like quality by design (QbD) and process analytical technology (PAT) have
fallen short of expectations. Digital life sciences advisor Michael Louie, pharma AI start-up founder Nikolai
Makaranka and Grace Cronin of Bristol Myers Squibb talk to Ellie Philpotts about exploring how AI could succeed where others stalled – if data quality, infrastructure and cross- functional collaboration are prioritised from the start.
A
lthough elementary AI arguably emerged in the 1950s with the Turing Test, its influence on pharmaceuticals is a recent acceleration. The sector has “historically collected data from manufacturing and clinical operations to inform decisions and monitor the state of control”, says digital life sciences advisor Michael Louie. Now, however, there is a “shift towards using near real-time data to proactively adjust manufacturing parameters” – a role where AI’s capabilities are set to expand. In clinical development, AI is already helping optimise inclusion and exclusion criteria in trial protocols, which can reduce patient dropout rates. Yet in manufacturing, it is arriving in an environment where established frameworks such as quality by design (QbD) and process analytical technology (PAT) are already deeply embedded. The question for
40
the industry now is not whether these tools remain relevant, but how they are changing – and whether AI can help drive their next phase.
Introducing QbD and PAT
In 2002, the US Food and Drug Administration (FDA) released ‘Pharmaceutical Current Good Manufacturing Practices for the 21st Century’. This advanced QbD principles by promoting better scientific and risk-based regulatory approaches. PAT, meanwhile, is a regulatory framework and systematic approach to designing, analysing and
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