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 me...
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.
The present paper contributes to the issues of batch process modelling and monitoring by proposing a time-varying state space (TVSS) model for the evaporative sugar crystallization industrial process. The study is foc...
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The present paper contributes to the issues of batch process modelling and monitoring by proposing a time-varying state space (TVSS) model for the evaporative sugar crystallization industrial process. The study is foc...
详细信息
The present paper contributes to the issues of batch process modelling and monitoring by proposing a time-varying state space (TVSS) model for the evaporative sugar crystallization industrial process. The study is focused on issues of on-line detection of changes in crystallization process operation, the early warning of process malfunctions and potential production failures; problems that have not been directly addressed by existing statistical monitoring schemes. The TVSS methodology is compared with current state-of-the-art techniques and the results obtained demonstrate the superior performance of the TVSS approach to successfully detect abnormal events and periods of bad operation.
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