Mustoe Chantal L, Turner Alice J, Urwin Stephanie J, Houson Ian, Feilden Helen, Markl Daniel, Al Qaraghuli Mohammed M, Chong Magdalene W S, Robertson Murray, Nordon Alison, Johnston Blair F, Brown Cameron J, Robertson John, Adjiman Claire, Batchelor Hannah, Benyahia Brahim, Bresciani Massimo, Burcham Christopher L, Cardona Javier, Cottini Ciro, Dunn Andrew S, Fradet David, Halbert Gavin W, Henson Mark, Hidber Pirmin, Langston Marianne, Lee Ye Seol, Li Wei, Mantanus Jérôme, McGinty John, Mehta Bhavik, Naz Tabbasum, Ottoboni Sara, Prasad Elke, Quist Per-Ola, Reynolds Gavin K, Rielly Chris, Rowland Martin, Schlindwein Walkiria, Schroeder Sven L M, Sefcik Jan, Settanni Ettore, Siddique Humera, Smith Kenneth, Smith Rachel, Srai Jagjit Singh, Thorat Alpana A, Vassileiou Antony, Florence Alastair J
CMAC, University of Strathclyde, Glasgow G1 1RD, United Kingdom; Strathclyde Institute of Pharmacy & Biomedical Science, University of Strathclyde, Glasgow G4 0RE, United Kingdom.
CMAC, University of Strathclyde, Glasgow G1 1RD, United Kingdom; WestCHEM, Department of Pure and Applied Chemistry and Centre for Process Analytics and Control Technology (CPACT), University of Strathclyde, Glasgow G1 1XL, United Kingdom.
Int J Pharm. 2025 Aug 20;681:125625. doi: 10.1016/j.ijpharm.2025.125625. Epub 2025 Apr 24.
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.
我们提出了一种产学研共享的观点，即数字设计质量（QbDD）作为加速药物开发和推动新药审批监管创新的框架的原则和机遇。这种方法利用工业数字技术中不断涌现的能力，以实现强大的控制策略，确保产品质量和患者安全，同时减少开发时间/成本，提高研发效率，将可持续性融入新产品和流程，并提升供应链弹性。QbDD的关键驱动因素包括新科学理解、先进模拟以及模型驱动的自动化实验方法带来的机遇。QbDD加速了对更稳健设计空间的识别和探索。在路线选择、可制造性和可持续性方面出现了优化多个目标的机遇，同时确保产品质量。采用QbDD面临的挑战包括跨开发阶段的数据和信息源分散、预测能力的差距以及目前对经验知识和判断的广泛依赖。这些挑战可以通过QbDD工作流程、模型驱动的实验设计来收集和构建可查找、可访问、可互操作和可重用（FAIR）的数据，以及用于可共享和可重用知识的化学、制造和控制本体来解决。此外，必须开发和利用改进的产品、过程和性能预测工具，以提供整体的端到端开发方法。
