Chinese hamster ovary (CHO) cells are widely utilized in the production of antibody drugs. To ensure the production of large quantities of antibodies that meet the required specifications, it is crucial to monitor and control the levels of metabolites comprehensively during CHO cell culture. In recent years, continuous analysis methods employing on-line/in-line techniques using Raman spectroscopy have attracted attention. While these analytical methods can nondestructively monitor culture data, constructing a highly accurate measurement model for numerous components is time-consuming, making it challenging to implement in the rapid research and development of pharmaceutical manufacturing processes. In this study, we developed a comprehensive, simple, and automated method for constructing a Raman model of various components measured by LC-MS and other techniques using machine learning with Python. Preprocessing and spectral-range optimization of data for model construction (partial least square (PLS) regression) were automated and accelerated using Bayes optimization. Subsequently, models were constructed for each component using various model construction techniques, including linear regression, ridge regression, XGBoost, and neural network. This enabled the model accuracy to be improved compared with PLS regression. This automated approach allows continuous monitoring of various parameters for over 100 components, facilitating process optimization and process monitoring of CHO cells. 

Comprehensive modeling of cell culture profile using Raman spectroscopy and machine learning

Bioprocessing 4.0 in biomanufacturing: paving the way for sustainable bioeconomy

This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess. 

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic in silico samples, so-called data augmentation, may provide the means to alleviate this challenge. In this study, we develop and implement a novel data augmentation method for generating in silico spectral data based on local estimation of pure component profiles for training convolutional neural network (CNN) models using four data sets. We simultaneously tune hyperparameters associated with data augmentation and the neural network architecture using Bayesian optimization. Finally, we compare the optimized CNN models with partial least-squares regression models (PLS) in terms of accuracy, robustness, and interpretability. The proposed data augmentation method is shown to produce highly realistic spectral data by adapting the estimates of the pure component profiles to the sampled concentration regimes. Augmenting CNNs with the in silico spectral data is shown to improve the prediction accuracy for the quantification of monoclonal antibody (mAb) size variants by up to 50% in comparison to single-response PLS models. Bayesian structure optimization suggests that multiple convolutional blocks are beneficial for model accuracy and enable transfer across different data sets. Model-agnostic feature importance methods and synthetic noise perturbation are used to directly compare the optimized CNNs with PLS models. This enables the identification of wavelength regions critical for model performance and suggests increased robustness against Gaussian white noise and wavelength shifts of the CNNs compared to the PLS models.

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring

Harnessing Raman spectroscopy for cell therapy bioprocessing

Raman spectroscopy has gained popularity to monitor multiple process indicators simultaneously in biopharmaceutical processes. However, robust and specific model calibration remains a challenge due to insufficient analyte variability to train the models and high cross‐correlation of various media components and artifacts throughout the process.

Advancing Raman model calibration for perfusion bioprocesses using spiked harvest libraries

BACKGROUND
Despite technological advances ensuring stable cell culture perfusion operation over prolonged time, reaching a cellular steady-state metabolism remains a challenge for certain manufacturing cell lines. This study investigated the stabilization of a steady-state perfusion process producing a bispecific antibody with drifting product quality attributes, caused by shifting metabolic activity in the cell culture.


MAIN METHODS
A novel on-demand pyruvate feeding strategy was developed, leveraging lactate as an indicator for tricarboxylic acid (TCA) cycle saturation. Real-time lactate monitoring was achieved through in-line Raman spectroscopy, enabling accurate control at predefined target setpoints.


MAJOR RESULTS
The implemented feedback control strategy resulted in a 3-fold reduction of ammonium accumulation and stabilized product quality profiles. Stable and flat glycosylation profiles were achieved with standard deviations below 0.2% for high mannose and fucosylation. Whereas galactosylation and sialylation were stabilized in a similar manner, varying lactate setpoints might allow for fine tuning of these glycan forms.


IMPLICATION
The Raman-controlled pyruvate feeding strategy represents a valuable tool for continuous manufacturing, stabilizing metabolic activity and preventing product quality drifting in perfusion cell cultures. Additionally, this approach effectively reduced high mannose, helping to mitigate increases associated with process intensification, such as extended culture durations or elevated culture densities. This article is protected by copyright. All rights reserved.

Raman-controlled pyruvate feeding to control metabolic activity and product quality in continuous biomanufacturing.

Adeno‐associated viruses (AAVs) are non‐pathogenic viruses that have become promising delivery vehicles in cell and gene therapy, with several AAV‐based therapeutics receiving approval in recent years. However, a critical challenge is the separation of full and empty viral capsids, as empty capsids lack therapeutic DNA and may compromise product efficacy and safety. This study describes a proof‐of‐concept of a twin‐column multicolumn countercurrent solvent gradient purification (MCSGP) process to enhance the ratio of full to empty capsids during AAV purification. Starting from a batch method with anion exchange chromatography, the process was adapted to continuous operation. The implementation of MCSGP, with six cycles, increased full capsid content in the final product pool from 30% to 68% (ddPCR) and 27% to 61% (cryo‐TEM) respectively. Moreover, MCSGP was capable of overcoming the inherent yield‐purity trade‐off in the polishing step by enhancing full capsid recovery. Additionally, the MCSGP process improved productivity by up to 23% and reduced buffer consumption up to 27% compared to the batch method (ddPCR). The increased productivity and reduced buffer consumption offer both economic and environmental benefits. This study demonstrates the potential of MCSGP to meet the rising demand for high‐quality AAV products in gene therapy.

Enrichment of Full AAV2 Using Multicolumn Countercurrent Solvent Gradient Purification (MCSGP).

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Advancing Raman model calibration for perfusion bioprocesses using spiked harvest libraries

Raman-controlled pyruvate feeding to control metabolic activity and product quality in continuous biomanufacturing.

Enrichment of Full AAV2 Using Multicolumn Countercurrent Solvent Gradient Purification (MCSGP).