About this Research Topic
Computational models are increasingly used in all areas of biopharmaceutical process engineering. The advantages of using in-silico tools include reduced experimental effort, process transparency, clear rationality behind decisions, and increased process robustness. Mathematical models are now used in the areas of experimental design, process characterization, process design, monitoring and control. Furthermore, model-based methods can assist in the implementation of regulatory requirements such as those proposed in recent Quality by Design and Validation initiatives.
Although computational models are increasingly used for the optimization, development and manufacturing of biopharmaceutical processes, such models have not been yet fully incorporated into the rather conservative biopharmaceutical industry. In order to demonstrate recent advances and the power of computational tools to decrease development time, increase product quality assurance, enhance controls of complex bioprocesses and even support regulatory submissions and validation concepts, this research article aims to collect a variety of model-based approaches to help incorporate computational models into the biopharmaceutical industry. The ability to apply methods from different scientific fields such as traditional chemical engineering or novel machine learning algorithms from computer science is highly encouraged.
The aim of this Research Topic is to provide recent developments of model-based methods for biopharmaceutical development and manufacturing. Computational Fluid Dynamics (CFD) for upstream and downstream units, mechanistic and hybrid models for cellular processes, mathematical modeling of downstream units such as chromatography or filtration, chemometric approaches for spectroscopic data, control approaches in upstream and downstream processing and machine learning algorithms for any of the aforementioned topics are all within scope of this research topic. All studies must contribute insights into the process for the development and manufacturing of biopharmaceutical products using mathematical modeling and/or (bio)chemical engineering principles or methodologies. Studies dealing with pure sciences (microbiology, cell biology, genetics, etc.) without any engineering or modeling elements do not fall within the scope of this Research Topic. Reports of using conventional experimental design to optimize a process without providing any mathematical modeling of the (bio)process studied should be submitted to more specialized journals.
Although computational models are increasingly used for the optimization, development and manufacturing of biopharmaceutical processes, such models have not been yet fully incorporated into the rather conservative biopharmaceutical industry. In order to demonstrate recent advances and the power of computational tools to decrease development time, increase product quality assurance, enhance controls of complex bioprocesses and even support regulatory submissions and validation concepts, this research article aims to collect a variety of model-based approaches to help incorporate computational models into the biopharmaceutical industry. The ability to apply methods from different scientific fields such as traditional chemical engineering or novel machine learning algorithms from computer science is highly encouraged.
The aim of this Research Topic is to provide recent developments of model-based methods for biopharmaceutical development and manufacturing. Computational Fluid Dynamics (CFD) for upstream and downstream units, mechanistic and hybrid models for cellular processes, mathematical modeling of downstream units such as chromatography or filtration, chemometric approaches for spectroscopic data, control approaches in upstream and downstream processing and machine learning algorithms for any of the aforementioned topics are all within scope of this research topic. All studies must contribute insights into the process for the development and manufacturing of biopharmaceutical products using mathematical modeling and/or (bio)chemical engineering principles or methodologies. Studies dealing with pure sciences (microbiology, cell biology, genetics, etc.) without any engineering or modeling elements do not fall within the scope of this Research Topic. Reports of using conventional experimental design to optimize a process without providing any mathematical modeling of the (bio)process studied should be submitted to more specialized journals.
Keywords: Computational Fluid Dynamics, Mechanistic models, Hybrid modeling, Machine learning, Biopharmaceuticals, Spectroscopy, Model-based Design Space, Quality by Design, Multivariate Modeling, Statistical modeling, Model-based control, Process optimization, Scale-up, Process control
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