Fast and Versatile Chromatography Process Design and Operation Optimization with the Aid of Artificial Intelligence

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.

The use of predictive models to develop chromatography-based purification processes

“…Unexpected mass transfer limitations due to the restricted pore diffusion may reflect the presence of a hitchhiking protein because the protein–protein interaction will increase the apparent size of the target protein. The modeling of hitchhiking phenomena would be useful in the context of biopharmaceutical process development because the abundance of hitchhiking HCPs in a purified recombinant protein can form part of the quality product profile, representing a critical quality attribute ( Baik et al, 2019 ; Mouellef et al, 2021 ). Accounting for hitchhiking would require that protein–protein interactions are routinely included in chromatography models.…”

“…Accordingly, these models are currently used mainly for late-stage downstream process characterization but their widespread use in academia and industry is limited by the complexity of model calibration and implementation (Saleh et al, 2020). Nevertheless, there is a growing commercial interest in the topic because it can accelerate process development (Mouellef et al, 2021), for example, the German start-up company GoSilico has developed chromatography modeling software (Hahn et al, 2012) and was acquired by Cytiva (formerly GE Healthcare) in 2021 1 . Specifically, mechanistic modelling can improve holistic process understanding (Close 2015), increase transferability to new processes, and simplify change management (Djuris and Djuric 2017).…”

Benefits and Limitations of Artificial Neural Networks in Process Chromatography Design and Operation

“…Instead of only exploiting existing methods, new methods like the utilization of machine learning algorithms are explored. These range from common data analysis tools like the Partial Least Squares (PLS) algorithm to the utilization of artificial neural networks (ANN) [21][22][23][24], which are considered universal approximators [25].…”

“…It is necessary to consider the model input, which affects the input of the ANN, in the training dataset. The outputs are also produced in fractions of a second [22,[36][37][38][39][40][41].…”

Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning

“…For instance, Meleiro et al (2009) [13] propose modeling a chemical plant through ANN tools. Mouellef et al (2021) [14] presents a chromatography process design and operation optimization aided by artificial intelligence. Rahnama et al…”