Abstract
Preparative and process chromatography is a versatile unit operation for the capture, purification, and polishing of a broad variety of molecules, especially very similar and complex compounds such as sugars, isomers, enantiomers, diastereomers, plant extracts, and metal ions such as rare earth elements. Another steadily growing field of application is biochromatography, with a diversity of complex compounds such as peptides, proteins, mAbs, fragments, VLPs, and even mRNA vaccines. Aside from molecular diversity, separation mechanisms range from selective affinity ligands, hydrophobic interaction, ion exchange, and mixed modes. Biochromatography is utilized on a scale of a few kilograms to 100,000 tons annually at about 20 to 250 cm in column diameter. Hence, a versatile and fast tool is needed for process design as well as operation optimization and process control. Existing process modeling approaches have the obstacle of sophisticated laboratory scale experimental setups for model parameter determination and model validation. For a broader application in daily project work, the approach has to be faster and require less effort for non-chromatography experts. Through the extensive advances in the field of artificial intelligence, new methods have emerged to address this need. This paper proposes an artificial neural network-based approach which enables the identification of competitive Langmuir-isotherm parameters of arbitrary three-component mixtures on a previously specified column. This is realized by training an ANN with simulated chromatograms varying in isotherm parameters. In contrast to traditional parameter estimation techniques, the estimation time is reduced to milliseconds, and the need for expert or prior knowledge to obtain feasible estimates is reduced.
Highlights
IntroductionPreparative chromatography was developed through laboratory experiments that were both time and material consuming [2,3]
One of the core concepts in high-quality separation and purification is preparative chromatography as a well-established method in biopharmaceutical manufacturing [1].Traditionally, preparative chromatography was developed through laboratory experiments that were both time and material consuming [2,3]
Preparative chromatography was developed through laboratory experiments that were both time and material consuming [2,3]
Summary
Preparative chromatography was developed through laboratory experiments that were both time and material consuming [2,3]. These no longer meet today’s requirements for streamlined and personalized pharmaceutical production in ever-shorter development cycles [4,5]. Other opportunities are the reduction in process development time, and enabling model-based process control strategies, and optimization. If pursued further, this leads to the complete digitalization of the plant, the so-called digital twin [9,10,11]. The necessity for a fast and accurate low-effort model parameter determination emerges [12,13]
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