Quality by digital design to accelerate sustainable medicines development

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.

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Publication Details

Journal
International Journal of Pharmaceutics
Published
2025-04-24
DOI
https://doi.org/10.1016/j.ijpharm.2025.125625
Citations
15
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
22.56
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Quality by digital design to accelerate sustainable medicines development

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International Journal of Pharmaceutics
Computational Drug Discovery Methods
22.56
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Quality by digital design to accelerate sustainable medicines development

Humera Siddique, Gavin Reynolds, Sven L. M. Schroeder, Mark J. Henson, Magdalene W. S. Chong, Massimo Bresciani, Alastair J. Florence, Ettore Settanni, Sara Ottoboni, Blair F. Johnston, Javier Cardona, Chantal L. Mustoe, Mohammed M. Al Qaraghuli, Alice Turner, Andrew S. Dunn, Jérôme Mantanus, Cameron John Brown, Marianne Langston, Daniel Markl, Claire S. Adjiman, Christopher L. Burcham, David Fradet, Alpana Ankush Thorat, Hannah Batchelor, Chris D. Rielly, Ken Smith, John Robertson, Alison Nordon, Antony D. Vassileiou, Murray N. Robertson, Walkiria Schlindwein, Stephanie J. Urwin, Jagjit Singh Srai, Martin Rowland, Gavin Halbert, Per-Ola Quist, John McGinty, Ian Houson, Ján Šefčı́k, Elke Prasad, Brahim Benyahia, Rachel M. Smith, Helen Feilden, Wei Li, Bhavik A. Mehta, Ye Seol Lee, Ciro Cottini, Pirmin Hidber, Tabbasum Naz
article en
15 citations

Abstract

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.

International Journal of PharmaceuticsVol. 681
Roche (Switzerland) (CH), Loughborough University (GB), Eli Lilly (United States) (US), Pfizer (United States) (US), University of Strathclyde (GB), Pfizer (United Kingdom) (GB), Diamond Light Source (GB), AstraZeneca (Singapore) (SG), AstraZeneca (Poland) (PL), AstraZeneca (Sweden) (SE), Chiesi (Italy) (IT), Takeda (Japan) (JP), University College London (GB), De Montfort University (GB), UCB Pharma (Belgium) (BE), University of Sheffield (GB)
Openalex Percentile: Top 1%
Computational Drug Discovery Methods
22.56
DOIOpen Access PDFOpenAlex
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