Abstract
Active pharmaceutical ingredients (APIs) are highly valuable, highly sensitive products resulting from production processes with strict quality control specifications and regulations that are required for the safety of patients [...]
Highlights
Active pharmaceutical ingredients (APIs) are highly valuable, highly sensitive products resulting from production processes with strict quality control specifications and regulations that are required for the safety of patients
To ensure a profitable and growing pharmaceutical industry of significant societal benefits and low environmental footprint, model-based tools are fundamental to advancing the basic understanding, design, and optimization of pharmaceutical manufacturing processes in accordance with the United Nations “2030 sustainable development goals”
The foregoing model-based concepts, and combinations of them, are key to exploring the full potential of innovative, highly effective pharmaceutical manufacturing processes. These are some of the grand challenges that can be tackled by process systems engineering (PSE), and they have been catalyzed by an unprecedented advent of established methodologies and algorithmic tools that are either available via open access environments or incorporated in commercial software/databases for a plethora of purposes
Summary
Before complex pharmaceutical manufacturing processes can be simulated holistically, dedicated unit operations have to be studied first, including upstream and downstream processes. The resulting dynamic model might be used to identify optimal operating conditions of the enzymatic synthesis process, and can be extended to multi-enzyme reactions too. To quantify the impact of different but interacting process parameters, a model-based global sensitivity analysis with a low computational cost was implemented, contributing to QbD and the identification of critical process parameters. These essential parameters of the process might be relevant for the development of future control strategies that can result in significant robustness for the spray drying process
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