Comparison of chemometric models using Raman spectroscopy for offline biochemical monitoring throughout the VLP-making upstream process

Guardalini, Luis Giovani Oliveira; Dias, Viviane Miranda; Leme, Jaci; Bernardino, Thaissa Consoni; Astray, Renato Mancini; Silveira, Suellen Regina da; Ho, Paulo Lee; Tonso, Aldo; Jorge, Soraia Attie Calil; Núñez, Eutímio Gustavo Fernández

doi:10.1016/j.bej.2023.109013

Cited by 7 publications

(23 citation statements)

References 47 publications

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“…This method has proven successful in monitoring mammalian cell cultures in bioreactors. Several successful examples can be found in recent literature [18,34,36]. Subsequent studies have extended this application from developmental scales of 3 to 15 L [27,34] to clinical production scales of 2000 L [37], demonstrating the scaling potential of this approach.…”

Section: Introductionmentioning

confidence: 92%

“…For more complex data sets or non-linear relationships, machine learning techniques such as Random Forest or SVM can be used. Advanced deep learning techniques such as Convolutional Neural Networks (CNN) are particularly effective for processing highdimensional spectral data, as they can automatically extract meaningful features and improve prediction accuracy [18]. However, one must be aware that such a method of creating a model requires a large database, which is not always available.…”

Section: Model Constructionmentioning

confidence: 99%

“…In this technique, the attributes of a data set are transformed into uncorrelated principal components, which allows a reduction in data dimensions with minimal loss of information. PCA-based techniques complemented by machine learning methods such as decision trees [15], Support Vector Machine (SVM) [16] and artificial neural networks (ANN) [17,18] allow for an even finer analysis. Additional preprocessing steps can be implemented, including normalisation and smoothing via k-th order Savitzky-Golay derivative [19], while model accuracy can be assessed by the standard error of calibration, factors used and coefficient of determination (R 2 ).…”

Section: Introductionmentioning

confidence: 99%

“…These properties make it particularly suitable for the study of cell cultures and tissues [27]. It is widely used for the study of polysaccharides, amino acids, alcohols and metabolites and has secured its position as an important process analytical technology (PAT) in the bioprocess industry [18,28,29]. The ability of inline Raman spectroscopy to monitor and adjust critical parameters in real time ensures consistent drug production.…”

Section: Introductionmentioning

confidence: 99%

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Halfway to Automated Feeding of Chinese Hamster Ovary Cells

Tomažič

Škrjanc

2023

Sensors

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This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction. It also describes the development of a simulation environment whose core is a model of CHO cell kinetics. The latter allows the development of advanced control algorithms for nutrient dosing and the observation of the effects of different parameters on the growth and productivity of CHO cells. All developed models were validated and demonstrated to have a high robustness and predictive accuracy, which were reflected in a 40% reduction in the root mean square error compared to established methods. The results of this study provide valuable insights into the practical application of these methods in the field of monitoring and automated cell feeding and make an important contribution to the further development of process analytical technology in the bioprocess industry.

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Section: Introductionmentioning

confidence: 92%

Section: Model Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Halfway to Automated Feeding of Chinese Hamster Ovary Cells

Tomažič

Škrjanc

2023

Sensors

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“…Besides the optical spectroscopic techniques ultraviolet-visible (UV/Vis) and infrared (IR) spectroscopy, Raman spectroscopy coupled with chemometrics has found extensive applications in monitoring various processes for biopharmaceutical products, including raw material testing (Li et al, 2010), cell culture (Abu-Absi et al, 2011;Berry et al, 2015;Golabgir and Herwig, 2016), chromatography (Feidl et al, 2019;Rolinger et al, 2021;Wang et al, 2023), filtration (Rolinger et al, 2023), freezing (Roessl et al, 2014;Weber and Hubbuch, 2021), or formulation (Wei et al, 2022). For VLPs in particular, recent studies demonstrated the real-time monitoring of a baculovirus cultivation for the production of rabies VLPs (Guardalini et al, 2023a;b; as well as the cross-flow filtrationbased polishing operations such as dis-and reassembly of the HBcAg-VLPs (Rüdt et al, 2019;Hillebrandt et al, 2022). Despite the broad applicability of spectroscopic methods in biopharmaceutical processing, their application to precipitation processes is rather unexplored.…”

Section: Introductionmentioning

confidence: 99%

Raman-based PAT for VLP precipitation: systematic data diversification and preprocessing pipeline identification

Dietrich,

Schiemer,

Kurmann

et al. 2024

Front. Bioeng. Biotechnol.

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Virus-like particles (VLPs) are a promising class of biopharmaceuticals for vaccines and targeted delivery. Starting from clarified lysate, VLPs are typically captured by selective precipitation. While VLP precipitation is induced by step-wise or continuous precipitant addition, current monitoring approaches do not support the direct product quantification, and analytical methods usually require various, time-consuming processing and sample preparation steps. Here, the application of Raman spectroscopy combined with chemometric methods may allow the simultaneous quantification of the precipitated VLPs and precipitant owing to its demonstrated advantages in analyzing crude, complex mixtures. In this study, we present a Raman spectroscopy-based Process Analytical Technology (PAT) tool developed on batch and fed-batch precipitation experiments of Hepatitis B core Antigen VLPs. We conducted small-scale precipitation experiments providing a diversified data set with varying precipitation dynamics and backgrounds induced by initial dilution or spiking of clarified Escherichia coli-derived lysates. For the Raman spectroscopy data, various preprocessing operations were systematically combined allowing the identification of a preprocessing pipeline, which proved to effectively eliminate initial lysate composition variations as well as most interferences attributed to precipitates and the precipitant present in solution. The calibrated partial least squares models seamlessly predicted the precipitant concentration with R2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R2 of 0.74 and 0.64. Although the resolution of fine differences between experiments was limited due to the observed non-linear relationship between spectral data and the VLP concentration, this study provides a foundation for employing Raman spectroscopy as a PAT sensor for monitoring VLP precipitation processes with the potential to extend its applicability to other phase-behavior dependent processes or molecules.

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Physics‐informed neural networks guided modelling and multiobjective optimization of a mAb production process

Alam,

Anurag,

Gangwar

et al. 2024

Can J Chem Eng

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In this paper, we aim to correlate various process and product quality attributes of a mammalian cell culture process with process parameters. To achieve this, we employed physics‐informed neural networks that solve the governing ordinary differential equations comprising independent variables (inputs‐ time, flow rates, and volume) and dependent variables (outputs‐ viable cell density, dead cell density, glucose concentration, lactate concentration, and monoclonal antibody concentration). The proposed model surpasses the prediction and accuracy capabilities of other commonly used modelling approaches, such as the multilayer perceptron model. It has higher R‐squared (R2), lower root mean square error, and lower mean absolute error than the multilayer perceptron model for all output variables (viable cell density, viability, glucose concentration, lactate concentration, and monoclonal antibody concentration). Furthermore, we incorporate a Bayesian optimization study to maximize viable cell density and monoclonal antibody concentration. Single objective optimization and weighted sum multiobjective optimization were carried out for viable cell density and monoclonal antibody concentration in separate (single objective optimization) and combined (multiobjective optimization) forms. An increment of 13.01% and 18.57% for viable cell density and monoclonal antibody concentration, respectively, were projected under single objective optimization, and 46.32% and 67.86%, respectively, for multiobjective optimization as compared to the base case. This study highlights the potential of the physics‐informed neural networks‐based modelling and optimization of upstream processing of mammalian cell‐based monoclonal antibodies in biopharmaceutical operations.

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Comparison of chemometric models using Raman spectroscopy for offline biochemical monitoring throughout the VLP-making upstream process

Cited by 7 publications

References 47 publications

Halfway to Automated Feeding of Chinese Hamster Ovary Cells

Halfway to Automated Feeding of Chinese Hamster Ovary Cells

Raman-based PAT for VLP precipitation: systematic data diversification and preprocessing pipeline identification

Physics‐informed neural networks guided modelling and multiobjective optimization of a mAb production process

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