TY - JOUR
T1 - Quality by digital design to accelerate sustainable medicines development
AU - Mustoe, Chantal L.
AU - Turner, Alice J.
AU - Urwin, Stephanie J.
AU - Houson, Ian
AU - Feilden, Helen
AU - Markl, Daniel
AU - Al Qaraghuli, Mohammed M.
AU - Chong, Magdalene W.S.
AU - Robertson, Murray
AU - Nordon, Alison
AU - Johnston, Blair F.
AU - Brown, Cameron J.
AU - Robertson, John
AU - Adjiman, Claire
AU - Batchelor, Hannah
AU - Benyahia, Brahim
AU - Bresciani, Massimo
AU - Burcham, Christopher L.
AU - Cardona, Javier
AU - Cottini, Ciro
AU - Dunn, Andrew S.
AU - Fradet, David
AU - Halbert, Gavin W.
AU - Henson, Mark
AU - Hidber, Pirmin
AU - Langston, Marianne
AU - Lee, Ye Seol
AU - Li, Wei
AU - Mantanus, Jérôme
AU - McGinty, John
AU - Mehta, Bhavik
AU - Naz, Tabbasum
AU - Ottoboni, Sara
AU - Prasad, Elke
AU - Quist, Per-Ola
AU - Reynolds, Gavin K.
AU - Rielly, Chris
AU - Rowland, Martin
AU - Schlindwein, Walkiria
AU - Schroeder, Sven L.M.
AU - Sefcik, Jan
AU - Settanni, Ettore
AU - Siddique, Humera
AU - Smith, Kenneth
AU - Smith, Rachel
AU - Srai, Jagjit Singh
AU - Thorat, Alpana A.
AU - Vassileiou, Antony
AU - Florence, Alastair J.
PY - 2025/4/24
Y1 - 2025/4/24
N2 - We present a shared industry-academic perspective on the principles and opportunities for Quality by Digital Design (QbDD) as a framework to accelerate medicines development and enable regulatory innovation for new medicines approvals. This approach exploits emerging capabilities in industrial digital technologies to achieve robust control strategies assuring product quality and patient safety whilst reducing development time/costs, improving research and development efficiency, embedding sustainability into new products and processes, and promoting supply chain resilience. Key QbDD drivers include the opportunity for new scientific understanding and advanced simulation and model-driven, automated experimental approaches. QbDD accelerates the identification and exploration of more robust design spaces. Opportunities to optimise multiple objectives emerge in route selection, manufacturability and sustainability whilst assuring product quality. Challenges to QbDD adoption include siloed data and information sources across development stages, gaps in predictive capabilities, and the current extensive reliance on empirical knowledge and judgement. These challenges can be addressed via QbDD workflows; model-driven experimental design to collect and structure findable, accessible, interoperable and reusable (FAIR) data; and chemistry, manufacturing and control ontologies for shareable and reusable knowledge. Additionally, improved product, process, and performance predictive tools must be developed and exploited to provide a holistic end-to-end development approach.
AB - We present a shared industry-academic perspective on the principles and opportunities for Quality by Digital Design (QbDD) as a framework to accelerate medicines development and enable regulatory innovation for new medicines approvals. This approach exploits emerging capabilities in industrial digital technologies to achieve robust control strategies assuring product quality and patient safety whilst reducing development time/costs, improving research and development efficiency, embedding sustainability into new products and processes, and promoting supply chain resilience. Key QbDD drivers include the opportunity for new scientific understanding and advanced simulation and model-driven, automated experimental approaches. QbDD accelerates the identification and exploration of more robust design spaces. Opportunities to optimise multiple objectives emerge in route selection, manufacturability and sustainability whilst assuring product quality. Challenges to QbDD adoption include siloed data and information sources across development stages, gaps in predictive capabilities, and the current extensive reliance on empirical knowledge and judgement. These challenges can be addressed via QbDD workflows; model-driven experimental design to collect and structure findable, accessible, interoperable and reusable (FAIR) data; and chemistry, manufacturing and control ontologies for shareable and reusable knowledge. Additionally, improved product, process, and performance predictive tools must be developed and exploited to provide a holistic end-to-end development approach.
KW - pharmaceutical development
KW - sustainable medicine developments
KW - digital design
U2 - 10.1016/j.ijpharm.2025.125625
DO - 10.1016/j.ijpharm.2025.125625
M3 - Article
SN - 0378-5173
JO - International Journal of Pharmaceutics
JF - International Journal of Pharmaceutics
M1 - 125625
ER -