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Vaskó D, Pantea E, Domján J, Fehér C, Mózner O, Sarkadi B, Nagy ZK, Marosi GJ, Hirsch E. Raman and NIR spectroscopy-based real-time monitoring of the membrane filtration process of a recombinant protein for the diagnosis of SARS-CoV-2. Int J Pharm 2024; 660:124251. [PMID: 38797253 DOI: 10.1016/j.ijpharm.2024.124251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/27/2024] [Accepted: 05/19/2024] [Indexed: 05/29/2024]
Abstract
This research shows the detailed comparison of Raman and near-infrared (NIR) spectroscopy as Process Analytical Technology tools for the real-time monitoring of a protein purification process. A comprehensive investigation of the application and model development of Raman and NIR spectroscopy was carried out for the real-time monitoring of a process-related impurity, imidazole, during the tangential flow filtration of Receptor-Binding Domain (RBD) of the SARS-CoV-2 Spike protein. The fast development of Raman and NIR spectroscopy-based calibration models was achieved using offline calibration data, resulting in low calibration and cross-validation errors. Raman model had an RMSEC of 1.53 mM, and an RMSECV of 1.78 mM, and the NIR model had an RMSEC of 1.87 mM and an RMSECV of 2.97 mM. Furthermore, Raman models had good robustness when applied in an inline measurement system, but on the contrary NIR spectroscopy was sensitive to the changes in the measurement environment. By utilizing the developed models, inline Raman and NIR spectroscopy were successfully applied for the real-time monitoring of a process-related impurity during the membrane filtration of a recombinant protein. The results enhance the importance of implementing real-time monitoring approaches for the broader field of diagnostic and therapeutic protein purification and underscore its potential to revolutionize the rapid development of biological products.
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Affiliation(s)
- Dorottya Vaskó
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H-1111, Hungary
| | - Eszter Pantea
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H-1111, Hungary
| | - Júlia Domján
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H-1111, Hungary
| | - Csaba Fehér
- Department of Applied Biotechnology and Food Science, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H-1111, Hungary
| | - Orsolya Mózner
- Research Centre for Natural Sciences, Budapest 1117, Hungary; Semmelweis University Doctoral School, Semmelweis University, Budapest 1085, Hungary; CelluVir Biotechnology Ltd., Budapest 1094, Hungary
| | - Balázs Sarkadi
- Research Centre for Natural Sciences, Budapest 1117, Hungary; Semmelweis University Doctoral School, Semmelweis University, Budapest 1085, Hungary; CelluVir Biotechnology Ltd., Budapest 1094, Hungary
| | - Zsombor K Nagy
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H-1111, Hungary
| | - György J Marosi
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H-1111, Hungary
| | - Edit Hirsch
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest H-1111, Hungary.
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2
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Dietrich A, Schiemer R, Kurmann J, Zhang S, Hubbuch J. Raman-based PAT for VLP precipitation: systematic data diversification and preprocessing pipeline identification. Front Bioeng Biotechnol 2024; 12:1399938. [PMID: 38882637 PMCID: PMC11177211 DOI: 10.3389/fbioe.2024.1399938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/13/2024] [Indexed: 06/18/2024] Open
Abstract
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 R 2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R 2 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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Affiliation(s)
- Annabelle Dietrich
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Robin Schiemer
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Jasper Kurmann
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Shiqi Zhang
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Jürgen Hubbuch
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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3
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Schiemer R, Rüdt M, Hubbuch J. Generative data augmentation and automated optimization of convolutional neural networks for process monitoring. Front Bioeng Biotechnol 2024; 12:1228846. [PMID: 38357704 PMCID: PMC10864647 DOI: 10.3389/fbioe.2024.1228846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024] Open
Abstract
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.
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Affiliation(s)
- Robin Schiemer
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Matthias Rüdt
- Institute of Life Technologies, HES-SO Valais-Wallis, Sion, Switzerland
| | - Jürgen Hubbuch
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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4
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Medl M, Leisch F, Dürauer A, Scharl T. Explainable deep learning enhances robust and reliable real-time monitoring of a chromatographic protein A capture step. Biotechnol J 2024; 19:e2300554. [PMID: 38385524 DOI: 10.1002/biot.202300554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 02/23/2024]
Abstract
The application of model-based real-time monitoring in biopharmaceutical production is a major step toward quality-by-design and the fundament for model predictive control. Data-driven models have proven to be a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness, and reliability. That is only possible when (i) the data used for modeling is of high quality and sufficient size, (ii) state-of-the-art modeling algorithms are employed, and (iii) the input-output mapping of the model has been characterized. In this study, we evaluate the accuracy of multiple data-driven models in predicting the monoclonal antibody (mAb) concentration, double stranded DNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. The models achieved high-quality predictions with a normalized root mean squared error of <4% for the mAb concentration and of ≈10% for the other process variables. Furthermore, we demonstrate how permutation/occlusion-based methods can be used to gain an understanding of dependencies learned by one of the most complex data-driven models, convolutional neural network ensembles. We observed that the models generally exhibited dependencies on correlations that agreed with first principles knowledge, thereby bolstering confidence in model reliability. Finally, we present a workflow to assess the model behavior in case of systematic measurement errors that may result from sensor fouling or failure. This study represents a major step toward improved viability of data-driven models in biopharmaceutical manufacturing.
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Affiliation(s)
- Matthias Medl
- Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Friedrich Leisch
- Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Astrid Dürauer
- Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Theresa Scharl
- Institute of Statistics, University of Natural Resources and Life Sciences, Vienna, Austria
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5
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Rathore AS, Nikita S, Thakur G, Mishra S. Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends Biotechnol 2023; 41:497-510. [PMID: 36117026 DOI: 10.1016/j.tibtech.2022.08.007] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/08/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022]
Abstract
Artificial intelligence and machine learning (AI-ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI-ML techniques include the growing global demand for biotherapeutics and the shift toward Industry 4.0, spurring the rise of integrated process platforms and continuous processes that require intelligent, automated supervision. This review summarizes AI-ML applications in biopharmaceutical manufacturing, with a focus on the most used AI-ML algorithms, including multivariate data analysis, artificial neural networks, and reinforcement learning. Perspectives on the future growth of AI-ML applications in the area and the challenges of implementing these techniques at manufacturing scale are also presented.
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Affiliation(s)
- Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
| | - Saxena Nikita
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Garima Thakur
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Somesh Mishra
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
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6
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Bayer B, Duerkop M, Pörtner R, Möller J. Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths. Biotechnol J 2023; 18:e2200381. [PMID: 36382343 DOI: 10.1002/biot.202200381] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/17/2022]
Abstract
Despite the advantages of mathematical bioprocess modeling, successful model implementation already starts with experimental planning and accordingly can fail at this early stage. For this study, two different modeling approaches (mechanistic and hybrid) based on a four-dimensional antibody-producing CHO fed-batch process are compared. Overall, 33 experiments are performed in the fractional factorial four-dimensional design space and separated into four different complex data partitions subsequently used for model comparison and evaluation. The mechanistic model demonstrates the advantage of prior knowledge (i.e., known equations) to get informative value relatively independently of the utilized data partition. The hybrid approach displayes a higher data dependency but simultaneously yielded a higher accuracy on all data partitions. Furthermore, our results demonstrate that independent of the chosen modeling framework, a smart selection of only four initial experiments can already yield a very good representation of a full design space independent of the chosen modeling structure. Academic and industry researchers are recommended to pay more attention to experimental planning to maximize the process understanding obtained from mathematical modeling.
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Affiliation(s)
| | | | - Ralf Pörtner
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany
| | - Johannes Möller
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg, Germany
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7
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Narvekar A, Puranik A, Kulkarni B, Jagtap D, Jain R, Dandekar P. FcγRIIIA affinity chromatography complements conventional functional characterization of rituximab. Biotechnol Prog 2023; 39:e3304. [PMID: 36181372 DOI: 10.1002/btpr.3304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 09/26/2022] [Accepted: 09/29/2022] [Indexed: 11/06/2022]
Abstract
Analytical and functional characterization of batches of biologics/biosimilar products are imperative towards qualifying them for pre-clinical and clinical investigations. Several orthogonal strategies are employed to characterize the functional attributes of these drugs. However, the use of conventional techniques for online monitoring of functional attributes is not feasible. Liquid chromatography is one of the crucial unit operations during the downstream processing of biopharmaceuticals. In this work, we have demonstrated the utility of FcγRIIIA affinity chromatography as an independent quantitative functional characterization tool. FcγRIIIA affinity chromatography aided in sequential elution of Rituximab glycoform mixtures, based on varying levels of galactosylation, and thereby the affinity for the receptor protein. The predominant glycans present in the three Rituximab glycoform mixture peaks were G0F, G1F, and G2F, respectively. Dissociation rate constants were derived from the chromatographic elution profiles by the peak profiling method, for the control and glucose stress conditions. The glucose stress conditions did not result in unfavorable binding kinetics of Rituximab and FcγRIIIA. The dissociation rate constants of the glycoform mixture 2, predominantly consisting of G1F, were similar to the dissociation rate constants obtained by surface plasmon resonance. Moreover, the glycosylation profiles obtained from chromatographic estimation can be corroborated with the ADCC activity. However, the ex vivo ADCC reporter assay indicated that there was an increase in the effector activity with increasing glucose stress. Thus, FcγRIIIA affinity chromatography permitted three independent assessments via a single analysis. Such approaches can be utilized as potential process analytical technology (PAT) tools in the biosimilar development process.
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Affiliation(s)
- Aditya Narvekar
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Mumbai, India
| | - Amita Puranik
- Department of Chemical Engineering, Institute of Chemical Technology, Mumbai, India
| | - Bhalchandra Kulkarni
- Division of Structural Biology, National Institute for Research in Reproductive and Child Health, Mumbai, India
| | - Dhanashree Jagtap
- Division of Structural Biology, National Institute for Research in Reproductive and Child Health, Mumbai, India
| | - Ratnesh Jain
- Department of Chemical Engineering, Institute of Chemical Technology, Mumbai, India
| | - Prajakta Dandekar
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Mumbai, India
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8
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Duong-Trung N, Born S, Kim JW, Schermeyer MT, Paulick K, Borisyak M, Cruz-Bournazou MN, Werner T, Scholz R, Schmidt-Thieme L, Neubauer P, Martinez E. When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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9
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PAT strategies and applications for cell therapy processing. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2022. [DOI: 10.1016/j.cobme.2022.100405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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10
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McCorry MC, Reardon KF, Black M, Williams C, Babakhanova G, Halpern JM, Sarkar S, Swami NS, Mirica KA, Boermeester S, Underhill A. Sensor technologies for quality control in engineered tissue manufacturing. Biofabrication 2022; 15:10.1088/1758-5090/ac94a1. [PMID: 36150372 PMCID: PMC10283157 DOI: 10.1088/1758-5090/ac94a1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 09/23/2022] [Indexed: 11/11/2022]
Abstract
The use of engineered cells, tissues, and organs has the opportunity to change the way injuries and diseases are treated. Commercialization of these groundbreaking technologies has been limited in part by the complex and costly nature of their manufacture. Process-related variability and even small changes in the manufacturing process of a living product will impact its quality. Without real-time integrated detection, the magnitude and mechanism of that impact are largely unknown. Real-time and non-destructive sensor technologies are key for in-process insight and ensuring a consistent product throughout commercial scale-up and/or scale-out. The application of a measurement technology into a manufacturing process requires cell and tissue developers to understand the best way to apply a sensor to their process, and for sensor manufacturers to understand the design requirements and end-user needs. Furthermore, sensors to monitor component cells' health and phenotype need to be compatible with novel integrated and automated manufacturing equipment. This review summarizes commercially relevant sensor technologies that can detect meaningful quality attributes during the manufacturing of regenerative medicine products, the gaps within each technology, and sensor considerations for manufacturing.
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Affiliation(s)
- Mary Clare McCorry
- Advanced Regenerative Manufacturing Institute, Manchester, NH 03101, United States of America
| | - Kenneth F Reardon
- Chemical and Biological Engineering and Biomedical Engineering, Colorado State University, Fort Collins, CO 80521, United States of America
| | - Marcie Black
- Advanced Silicon Group, Lowell, MA 01854, United States of America
| | - Chrysanthi Williams
- Access Biomedical Solutions, Trinity, Florida 34655, United States of America
| | - Greta Babakhanova
- National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
| | - Jeffrey M Halpern
- Department of Chemical Engineering, University of New Hampshire, Durham, NH 03824, United States of America
- Materials Science and Engineering Program, University of New Hampshire, Durham, NH 03824, United States of America
| | - Sumona Sarkar
- National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America
| | - Nathan S Swami
- Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, United States of America
| | - Katherine A Mirica
- Department of Chemistry, Dartmouth College, Hanover, NH 03755, United States of America
| | - Sarah Boermeester
- Advanced Regenerative Manufacturing Institute, Manchester, NH 03101, United States of America
| | - Abbie Underhill
- Scientific Bioprocessing Inc., Pittsburgh, PA 15238, United States of America
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11
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Thakur G, Bansode V, Rathore AS. Continuous manufacturing of monoclonal antibodies: Automated downstream control strategy for dynamic handling of titer variations. J Chromatogr A 2022; 1682:463496. [PMID: 36126561 DOI: 10.1016/j.chroma.2022.463496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 11/16/2022]
Abstract
Handling long-term dynamic variability in harvest titer is a critical challenge in continuous downstream manufacturing. This challenge is becoming increasingly important with the advent of high-titer clones and modern upstream perfusion processes where the titer can vary significantly across the course of a campaign. In this paper, we present a strategy for real-time, dynamic adjustment of the entire downstream train, including capture chromatography, viral inactivation, depth filtration, polishing chromatography, and single-pass formulation, to accommodate variations in titer from 1-7 g/L. The strategy was tested in real time in a continuous downstream purification process of 36 h duration with induced titer variations. The dynamic control strategy leverages real-time NIR-based concentration sensors in the harvest material to continuously track the titer, integrated with an in-house Python-based control system that operates a BioSMB for carrying out capture and polishing chromatography, as well as a series of pumps and solenoid valves for carrying out viral inactivation and formulation. A set of 9 different methods, corresponding to the different harvest titers have been coded onto the Python controller. The methods have a varying number of chromatography columns (3-6 for Protein A and 2-10 for CEX), designed to ensure proper scheduling and optimize productivity across the entire titer variation space. The approach allows for a wide range of titers to be processed on a single integrated setup without having to change equipment or to re-design each time. The strategy also overcomes a key unexplored challenge in continuous processing, namely hand-shaking the downstream train to upstream conditions with long-term titer variability while maintaining automated operation with high productivity and robustness.
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Affiliation(s)
- Garima Thakur
- Department of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India
| | - Vikrant Bansode
- Department of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India.
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12
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Ralbovsky NM, Smith JP. Process analytical technology and its recent applications for asymmetric synthesis. Talanta 2022; 252:123787. [DOI: 10.1016/j.talanta.2022.123787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/25/2022] [Indexed: 11/27/2022]
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13
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Narayanan H, Luna M, Sokolov M, Butté A, Morbidelli M. Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Cell Culture Processes. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Harini Narayanan
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Martin Luna
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | | | | | - Massimo Morbidelli
- DataHow AG, Zürichstrasse 137, 8600 Dübendorf, Switzerland
- Dipartimento di Chimica, Materiali e Ingegneria Chimica, Giulio Natta, Politecnico di Milano, 20131 Milano, Italy
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14
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Du Y, Tong L, Wang Y, Liu M, Yuan L, Mu X, He S, Wei S, Zhang Y, Chen Z, Zhang Z, Guo D. Development of a kinetics‐integrated
CFD
model for the industrial scale‐up of
DHA
fermentation using
Schizochytrium
sp. AIChE J 2022. [DOI: 10.1002/aic.17750] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Yuan‐Hang Du
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
| | - Ling‐Ling Tong
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
| | - Yue Wang
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
| | - Meng‐Zhen Liu
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
| | - Li Yuan
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
| | - Xin‐Ya Mu
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
| | - Shao‐Jie He
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
| | - Shi‐Xiang Wei
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
| | - Yi‐Dan Zhang
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
| | - Zi‐Lei Chen
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
| | - Zhi‐Dong Zhang
- Institute of Applied Microbiology Xinjiang Academy of Agricultural Sciences/Xinjiang Laboratory of Special Environmental Microbiology Urumqi Xinjiang China
| | - Dong‐Sheng Guo
- School of Food Science and Pharmaceutical Engineering Nanjing Normal University Nanjing China
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15
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Ihling N, Munkler LP, Paul R, Berg C, Reichenbächer B, Kadisch M, Lang D, Büchs J. Non-invasive and time-resolved measurement of the respiration activity of Chinese hamster ovary cells enables prediction of key culture parameters in shake flasks. Biotechnol J 2022; 17:e2100677. [PMID: 35377965 DOI: 10.1002/biot.202100677] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/27/2022] [Accepted: 04/01/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Shake flasks are frequently used for mammalian cell suspension cultures. For process development and routine culture monitoring, information on culture behaviour is needed early on. MAIN METHODS AND MAJOR RESULTS Here, cell-specific oxygen uptake rates (qO2 ) of two CHO cell lines were determined from shake flask experiments by simultaneous measurement of oxygen transfer rates (OTR) and viable cell concentrations (VCC). For cell line one, qO2 decreased from 2.38∙10-10 mmol cell-1 h-1 to 1.02∙10-10 mmol cell-1 h-1 during batch growth. For cell line two, qO2 was constant (1.90∙10-10 mmol h-1 ). Determined qO2 values were used to calculate the VCC from OTR data. Cumulated oxygen consumption and glucose consumption were correlated for both cell lines and enabled calculation of glucose concentrations from OTR data. IgG producing cell line one had an oxygen demand of ∼15 mmoloxygen gglucose -1 , cell line two consumed ∼5 mmoloxygen gglucose -1 . The established correlations for determination of VCC and glucose were successfully transferred to subsequent cultivations for both cell lines. Combined measurement of the OTR and the carbon dioxide transfer rate enabled quantitative determination of the lactate concentration (production and consumption) without sampling. CONCLUSIONS AND IMPLICATIONS Taken together, non-invasive measurement of the respiration activity enabled time-resolved determination of key culture parameters for increased process understanding in shake flasks. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Nina Ihling
- AVT - Biochemical Engineering, RWTH Aachen University, Forckenbeckstr. 51, Aachen, D-52074, Germany
| | - Lara Pauline Munkler
- AVT - Biochemical Engineering, RWTH Aachen University, Forckenbeckstr. 51, Aachen, D-52074, Germany
| | - Richard Paul
- AVT - Biochemical Engineering, RWTH Aachen University, Forckenbeckstr. 51, Aachen, D-52074, Germany
| | - Christoph Berg
- AVT - Biochemical Engineering, RWTH Aachen University, Forckenbeckstr. 51, Aachen, D-52074, Germany
| | | | - Marvin Kadisch
- Rentschler Biopharma SE, Erwin-Rentschler-Str. 21, Laupheim, 88471, Germany
| | - Dietmar Lang
- Rentschler Biopharma SE, Erwin-Rentschler-Str. 21, Laupheim, 88471, Germany
| | - Jochen Büchs
- AVT - Biochemical Engineering, RWTH Aachen University, Forckenbeckstr. 51, Aachen, D-52074, Germany
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16
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Mandenius C. Realization of user-friendly bioanalytical tools to quantify and monitor critical components in bio-industrial processes through conceptual design. Eng Life Sci 2022; 22:217-228. [PMID: 35382530 PMCID: PMC8961037 DOI: 10.1002/elsc.202100116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 09/29/2021] [Accepted: 10/10/2021] [Indexed: 11/22/2022] Open
Abstract
This minireview suggests a conceptual and user-oriented approach for the design of process monitoring systems in bioprocessing. Advancement of process analytical techniques for quantification of critical analytes can take advantage of basic conceptual process design to support reasoning, reconsidering and ranking solutions. Issues on analysis in complex bio-industrial media, sensitivity and selectivity are highlighted from users' perspectives. Meeting challenging analytical demands for understanding the critical interplay between the emerging bioprocesses, their biomolecular complexity and the needs for user-friendly analytical tools are discussed. By that, a thorough design approach is suggested based on a holistic design thinking in the quest for better analytical opportunities to solve established and emerging analytical needs.
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Affiliation(s)
- Carl‐Fredrik Mandenius
- Unit of BiotechnologyBiophysics and BioengineeringIFMLinköping UniversityLinköpingSweden
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17
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18
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Domján J, Pantea E, Gyürkés M, Madarász L, Kozák D, Farkas A, Horváth B, Benkő Z, Nagy ZK, Marosi G, Hirsch E. Real-time amino acid and glucose monitoring system for the automatic control of nutrient feeding in CHO cell culture using raman spectroscopy. Biotechnol J 2022; 17:e2100395. [PMID: 35084785 DOI: 10.1002/biot.202100395] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 01/09/2022] [Accepted: 01/10/2022] [Indexed: 11/06/2022]
Abstract
An innovative, Raman spectroscopy-based monitoring and control system is introduced in this paper for designing dynamic feeding strategies that allow the maintenance of key cellular nutrients at an ideal level in Chinese hamster ovary cell culture. The Partial Least Squares calibration models built for glucose, lactate and 16 (out of 20) individual amino acids had very good predictive power with low root mean square errors values and high square correlation coefficients. The developed models used for real-time measurement of nutrient and by-product concentrations allowed us to gain better insight into the metabolic behavior and nutritional consumption of cells. To establish a more beneficial nutritional environment for the cells, two types of dynamic feeding strategies were used to control the delivery of two-part multi-component feed media according to the prediction of Raman models (glucose or arginine). As a result, instead of high fluctuations, the nutrients (glucose together with amino acids) were maintained at the desired level providing a more balanced environment for the cells. Moreover, the use of amino acid-based feeding control enabled to prevent the excessive nutrient replenishment and was economically beneficial by significantly reducing the amount of supplied feed medium compared to the glucose-based dynamic fed culture. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Júlia Domján
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary
| | - Eszter Pantea
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary
| | - Martin Gyürkés
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary
| | - Lajos Madarász
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary
| | - Dóra Kozák
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary
| | - Attila Farkas
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary
| | - Balázs Horváth
- Gedeon Richter Plc., Gyömröi út 19-21, Budapest, H-1103, Hungary
| | - Zsuzsa Benkő
- Gedeon Richter Plc., Gyömröi út 19-21, Budapest, H-1103, Hungary
| | - Zsombor Kristóf Nagy
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary
| | - György Marosi
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary
| | - Edit Hirsch
- Department of Organic Chemistry and Technology, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary
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19
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Walsh I, Myint M, Nguyen-Khuong T, Ho YS, Ng SK, Lakshmanan M. Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing. MAbs 2022; 14:2013593. [PMID: 35000555 PMCID: PMC8744891 DOI: 10.1080/19420862.2021.2013593] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.
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Affiliation(s)
- Ian Walsh
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Matthew Myint
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Terry Nguyen-Khuong
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ying Swan Ho
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Say Kong Ng
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.,Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore
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20
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Ralbovsky NM, Lomont JP, Ruccolo S, Konietzko J, McHugh PM, Wang SC, Mangion I, Smith JP. Utilizing in situ spectroscopic tools to monitor ketal deprotection processes. Int J Pharm 2022; 611:121324. [PMID: 34848366 DOI: 10.1016/j.ijpharm.2021.121324] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/19/2021] [Accepted: 11/24/2021] [Indexed: 11/15/2022]
Abstract
The use of protection groups to shield a functional group during a synthesis is employed throughout many reactions and organic syntheses. The role of a protection group can be vital to the success of a reaction, as well as increase reaction yield and selectivity. Although much work has been done to investigate the addition of a protection group, the removal of the protection group is just as important - however, there is a lack of methods employed within the literature for monitoring the removal of a protection group in real time. In this work, the process of removing, or deprotecting, a ketal protecting group is investigated. Process analytical technology tools are incorporated for in situ analysis of the deprotection reaction of a small molecule model compound. Specifically, Raman spectroscopy and Fourier transform infrared spectroscopy show that characteristic bands can be used to track the decrease of the reactant and the increase of the expected products over time. To the best of our knowledge, this is the first report of process analytical technology being used to monitor a ketal deprotection reaction in real time. This information can be capitalized on in the future for understanding and optimizing pharmaceutically-relevant deprotection processes and downstream reactions.
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Affiliation(s)
- Nicole M Ralbovsky
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA
| | - Justin P Lomont
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA
| | - Serge Ruccolo
- Process Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA
| | - Janelle Konietzko
- Process Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA
| | - Patrick M McHugh
- Process Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA
| | - Sheng-Ching Wang
- Process Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA
| | - Ian Mangion
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA
| | - Joseph P Smith
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA.
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21
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Ralbovsky NM, Soukup RJ, Lomont JP, Lauro ML, Gulasarian A, Saha-Shah A, Winters MA, Richardson DD, Wang SC, Mangion I, Smith JP. In situ real time monitoring of emulsification and homogenization processes for vaccine adjuvants. Analyst 2021; 147:378-386. [PMID: 34908043 DOI: 10.1039/d1an01797g] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Adjuvants are commonly employed to enhance the efficacy of a vaccine and thereby increase the resulting immune response in a patient. The activity and effectiveness of emulsion-based adjuvants has been heavily studied throughout pharmaceuticals; however, there exists a lack in research which monitors the formation of a stable emulsion in real time. Process analytical technology (PAT) provides a solution to meet this need. PAT involves the collection of in situ data, thereby providing real time information about the monitored process as well as increasing understanding of that process. Here, three separate PAT tools - optical particle imaging, in situ particle analysis, and Raman spectroscopy - were used to monitor two key steps involved in the formation of a stable emulsion product, emulsification and homogenization, as well as perform a stability assessment. The obtained results provided new insights-particle size decreases during emulsification and homogenization, and molecular changes do not occur during either the emulsification or homogenization steps. Further, the stability assessment indicated that the coarse emulsion product obtained from the emulsification step is stable over the course of 24 hours when mixed. To the best of our knowledge, this is the first report of an analytical methodology for in situ, real time analysis of emulsification and homogenization processes for vaccine adjuvants. Using our proposed analytical methodology, an improved understanding of emulsion-based vaccine adjuvants can now be achieved, ultimately impacting the ability to develop and deliver successful pharmaceuticals.
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Affiliation(s)
- Nicole M Ralbovsky
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA.
| | - Randal J Soukup
- Process Research & Development, MRL, Merck & Co., Inc., West Point, PA, 19486, USA
| | - Justin P Lomont
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA.
| | - Mackenzie L Lauro
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA.
| | - Amanda Gulasarian
- Process Research & Development, MRL, Merck & Co., Inc., West Point, PA, 19486, USA
| | - Anumita Saha-Shah
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA.
| | - Michael A Winters
- Process Research & Development, MRL, Merck & Co., Inc., West Point, PA, 19486, USA
| | - Douglas D Richardson
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA.
| | - Sheng-Ching Wang
- Process Research & Development, MRL, Merck & Co., Inc., West Point, PA, 19486, USA
| | - Ian Mangion
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA.
| | - Joseph P Smith
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA 19486, USA.
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22
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Zalai D, Kopp J, Kozma B, Küchler M, Herwig C, Kager J. Microbial technologies for biotherapeutics production: Key tools for advanced biopharmaceutical process development and control. DRUG DISCOVERY TODAY. TECHNOLOGIES 2021; 38:9-24. [PMID: 34895644 DOI: 10.1016/j.ddtec.2021.04.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 03/14/2021] [Accepted: 04/06/2021] [Indexed: 12/26/2022]
Abstract
Current trends in the biopharmaceutical market such as the diversification of therapies as well as the increasing time-to-market pressure will trigger the rethinking of bioprocess development and production approaches. Thereby, the importance of development time and manufacturing costs will increase, especially for microbial production. In the present review, we investigate three technological approaches which, to our opinion, will play a key role in the future of biopharmaceutical production. The first cornerstone of process development is the generation and effective utilization of platform knowledge. Building processes on well understood microbial and technological platforms allows to accelerate early-stage bioprocess development and to better condense this knowledge into multi-purpose technologies and applicable mathematical models. Second, the application of verified scale down systems and in silico models for process design and characterization will reduce the required number of large scale batches before dossier submission. Third, the broader availability of mathematical process models and the improvement of process analytical technologies will increase the applicability and acceptance of advanced control and process automation in the manufacturing scale. This will reduce process failure rates and subsequently cost of goods. Along these three aspects we give an overview of recently developed key tools and their potential integration into bioprocess development strategies.
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Affiliation(s)
- Denes Zalai
- Richter-Helm BioLogics GmbH & Co. KG, Suhrenkamp 59, 22335 Hamburg, Germany.
| | - Julian Kopp
- Research Division Biochemical Engineering, Institute of Chemical Environmental and Bioscience Engineering, Vienna University of Technology, Vienna, Austria
| | - Bence Kozma
- Research Division Biochemical Engineering, Institute of Chemical Environmental and Bioscience Engineering, Vienna University of Technology, Vienna, Austria
| | - Michael Küchler
- Richter-Helm BioLogics GmbH & Co. KG, Suhrenkamp 59, 22335 Hamburg, Germany
| | - Christoph Herwig
- Research Division Biochemical Engineering, Institute of Chemical Environmental and Bioscience Engineering, Vienna University of Technology, Vienna, Austria; Competence Center CHASE GmbH, Altenbergerstraße 69, 4040 Linz, Austria
| | - Julian Kager
- Research Division Biochemical Engineering, Institute of Chemical Environmental and Bioscience Engineering, Vienna University of Technology, Vienna, Austria
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23
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São Pedro MN, Silva TC, Patil R, Ottens M. White paper on high-throughput process development for integrated continuous biomanufacturing. Biotechnol Bioeng 2021; 118:3275-3286. [PMID: 33749840 PMCID: PMC8451798 DOI: 10.1002/bit.27757] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/15/2021] [Accepted: 03/12/2021] [Indexed: 12/25/2022]
Abstract
Continuous manufacturing is an indicator of a maturing industry, as can be seen by the example of the petrochemical industry. Patent expiry promotes a price competition between manufacturing companies, and more efficient and cheaper processes are needed to achieve lower production costs. Over the last decade, continuous biomanufacturing has had significant breakthroughs, with regulatory agencies encouraging the industry to implement this processing mode. Process development is resource and time consuming and, although it is increasingly becoming less expensive and faster through high-throughput process development (HTPD) implementation, reliable HTPD technology for integrated and continuous biomanufacturing is still lacking and is considered to be an emerging field. Therefore, this paper aims to illustrate the major gaps in HTPD and to discuss the major needs and possible solutions to achieve an end-to-end Integrated Continuous Biomanufacturing, as discussed in the context of the 2019 Integrated Continuous Biomanufacturing conference. The current HTPD state-of-the-art for several unit operations is discussed, as well as the emerging technologies which will expedite a shift to continuous biomanufacturing.
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Affiliation(s)
| | - Tiago C. Silva
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
| | - Rohan Patil
- Global CMC DevelopmentSanofiFraminghamMassachusettsUSA
| | - Marcel Ottens
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
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24
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Ryckaert A, Stauffer F, Funke A, Djuric D, Vanhoorne V, Vervaet C, De Beer T. Evaluation of torque as an in-process control for granule size during twin-screw wet granulation. Int J Pharm 2021; 602:120642. [PMID: 33933640 DOI: 10.1016/j.ijpharm.2021.120642] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/20/2021] [Accepted: 04/21/2021] [Indexed: 10/21/2022]
Abstract
The potential of torque as in-process control (IPC) to monitor granule size in twin-screw wet granulation (TSG) was investigated. An experimental set-up allowing the collection of granules at four different locations (i.e., in the wetting zone, after the first and second kneading zone and at the end of the granulator) of the granulator screws was used to determine the change in granule size, granule temperature and the contribution of each compartment to the overall torque for varying screw speed, mass feed rate and liquid-to-solid ratio. The only observed correlation was between the granule size and torque increase after the first kneading zone because the torque increase was an indication of the degree in granule growth which was consistently observed with all applied granulation process parameters. No correlation was observed in the other locations as changes of torque were accompanied to either granule breakage and/or growth. Moreover, torque increase was correlated to higher granule temperature, suggesting that energy put into the granulator was partly used to heat up the material being processed and explains additionally the lack of correlation between granule size and torque. Therefore, this study showed that torque could not be used as IPC to monitor granule size during TSG.
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Affiliation(s)
- A Ryckaert
- Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical analysis, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium.
| | - F Stauffer
- Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical analysis, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium.
| | - A Funke
- Chemical & Pharmaceutical Development, Pharma R&D, Bayer AG, Friedrich-Ebert-Straße 475, 42369, Wuppertal, Germany.
| | - D Djuric
- Chemical & Pharmaceutical Development, Pharma R&D, Bayer AG, Friedrich-Ebert-Straße 475, 42369, Wuppertal, Germany.
| | - V Vanhoorne
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutics Ghent University, Ottergemsesteenweg 460, Ghent, Belgium.
| | - C Vervaet
- Laboratory of Pharmaceutical Technology, Department of Pharmaceutics Ghent University, Ottergemsesteenweg 460, Ghent, Belgium.
| | - T De Beer
- Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical analysis, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium.
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25
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Chiappini FA, Azcarate S, Alcaraz MR, Forno ÁG, Goicoechea HC. Prospective inference of bioprocess cell viability through chemometric modeling of fluorescence multiway data. Biotechnol Prog 2021; 37:e3173. [PMID: 33969945 DOI: 10.1002/btpr.3173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/19/2021] [Accepted: 05/06/2021] [Indexed: 11/11/2022]
Abstract
In this investigation, the fermentation step of a standard mammalian cell-based industrial bioprocess for the production of a therapeutic protein was studied, with particular emphasis on the evolution of cell viability. This parameter constitutes one of the critical variables for bioprocess monitoring since it can affect downstream operations and the quality of the final product. In addition, when the cells experiment an unpredictable drop in viability, the assessment of this variable through classic off-line methods may not provide information sufficiently in advance to take corrective actions. In this context, Process Analytical Technology (PAT) framework aims to develop novel strategies for more efficient monitoring of critical variables, in order to improve the bioprocess performance. Thus, in this work, a set of chemometric tools were integrated to establish a PAT strategy to monitor cell viability, based on fluorescence multiway data obtained from fermentation samples of a particular bioprocess, in two different scales of operation. The spectral information, together with data regarding process variables, was integrated through chemometric exploratory tools to characterize the bioprocess and stablish novel criteria for the monitoring of cell viability. These findings motivated the development of a multivariate classification model, aiming to obtain predictive tools for the monitoring of future lots of the same bioprocess. The model could be satisfactorily fitted, showing the non-error rate of prediction of 100%.
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Affiliation(s)
- Fabricio A Chiappini
- Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe, Argentina.,Argentinian national institution of research, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz, Argentina
| | - Silvana Azcarate
- Argentinian national institution of research, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz, Argentina.,Facultad de Ciencias Exactas y Naturales, Universidad Nacional de La Pampa, Santa Rosa, Argentina
| | - Mirta R Alcaraz
- Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe, Argentina.,Argentinian national institution of research, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz, Argentina
| | - Ángela G Forno
- Zelltek SA, Parque Tecnológico Litoral Centro - CCT Conicet Santa Fe (C1425FQB), Santa Fe, Argentina
| | - Hector C Goicoechea
- Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe, Argentina.,Argentinian national institution of research, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz, Argentina
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26
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Duran‐Villalobos CA, Ogonah O, Melinek B, Bracewell DG, Hallam T, Lennox B. Multivariate statistical data analysis of cell‐free protein synthesis toward monitoring and control. AIChE J 2021. [DOI: 10.1002/aic.17257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Olotu Ogonah
- Department of Biochemical Engineering University College London London UK
| | - Beatrice Melinek
- Department of Biochemical Engineering University College London London UK
| | | | - Trevor Hallam
- Sutro Biopharma, Inc. South San Francisco California USA
| | - Barry Lennox
- Department of Electrical and Electronic Engineering The University of Manchester Manchester UK
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27
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Pauk JN, Raju Palanisamy J, Kager J, Koczka K, Berghammer G, Herwig C, Veiter L. Advances in monitoring and control of refolding kinetics combining PAT and modeling. Appl Microbiol Biotechnol 2021; 105:2243-2260. [PMID: 33598720 PMCID: PMC7954745 DOI: 10.1007/s00253-021-11151-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 01/19/2021] [Accepted: 01/27/2021] [Indexed: 12/21/2022]
Abstract
Overexpression of recombinant proteins in Escherichia coli results in misfolded and non-active protein aggregates in the cytoplasm, so-called inclusion bodies (IB). In recent years, a change in the mindset regarding IBs could be observed: IBs are no longer considered an unwanted waste product, but a valid alternative to produce a product with high yield, purity, and stability in short process times. However, solubilization of IBs and subsequent refolding is necessary to obtain a correctly folded and active product. This protein refolding process is a crucial downstream unit operation-commonly done as a dilution in batch or fed-batch mode. Drawbacks of the state-of-the-art include the following: the large volume of buffers and capacities of refolding tanks, issues with uniform mixing, challenging analytics at low protein concentrations, reaction kinetics in non-usable aggregates, and generally low re-folding yields. There is no generic platform procedure available and a lack of robust control strategies. The introduction of Quality by Design (QbD) is the method-of-choice to provide a controlled and reproducible refolding environment. However, reliable online monitoring techniques to describe the refolding kinetics in real-time are scarce. In our view, only monitoring and control of re-folding kinetics can ensure a productive, scalable, and versatile platform technology for re-folding processes. For this review, we screened the current literature for a combination of online process analytical technology (PAT) and modeling techniques to ensure a controlled refolding process. Based on our research, we propose an integrated approach based on the idea that all aspects that cannot be monitored directly are estimated via digital twins and used in real-time for process control. KEY POINTS: • Monitoring and a thorough understanding of refolding kinetics are essential for model-based control of refolding processes. • The introduction of Quality by Design combining Process Analytical Technology and modeling ensures a robust platform for inclusion body refolding.
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Affiliation(s)
- Jan Niklas Pauk
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Gumpendorferstrasse 1a/166, 1060, Vienna, Austria
- Competence Center CHASE GmbH, Altenbergerstraße 69, 4040, Linz, Austria
| | - Janani Raju Palanisamy
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Gumpendorferstrasse 1a/166, 1060, Vienna, Austria
| | - Julian Kager
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Gumpendorferstrasse 1a/166, 1060, Vienna, Austria
| | - Krisztina Koczka
- Bilfinger Industrietechnik Salzburg GmbH, Mooslackengasse 17, 1190, Vienna, Austria
| | - Gerald Berghammer
- Bilfinger Industrietechnik Salzburg GmbH, Mooslackengasse 17, 1190, Vienna, Austria
| | - Christoph Herwig
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Gumpendorferstrasse 1a/166, 1060, Vienna, Austria.
| | - Lukas Veiter
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Gumpendorferstrasse 1a/166, 1060, Vienna, Austria
- Competence Center CHASE GmbH, Altenbergerstraße 69, 4040, Linz, Austria
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28
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Gerstweiler L, Bi J, Middelberg AP. Continuous downstream bioprocessing for intensified manufacture of biopharmaceuticals and antibodies. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2020.116272] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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29
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Lin YK, Leong HY, Ling TC, Lin DQ, Yao SJ. Raman spectroscopy as process analytical tool in downstream processing of biotechnology. Chin J Chem Eng 2021. [DOI: 10.1016/j.cjche.2020.12.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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30
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Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses. Bioprocess Biosyst Eng 2021; 44:683-700. [PMID: 33471162 PMCID: PMC7997827 DOI: 10.1007/s00449-020-02478-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 11/05/2020] [Indexed: 10/26/2022]
Abstract
Bioprocess development and optimization are still cost- and time-intensive due to the enormous number of experiments involved. In this study, the recently introduced model-assisted Design of Experiments (mDoE) concept (Möller et al. in Bioproc Biosyst Eng 42(5):867, https://doi.org/10.1007/s00449-019-02089-7 , 2019) was extended and implemented into a software ("mDoE-toolbox") to significantly reduce the number of required cultivations. The application of the toolbox is exemplary shown in two case studies with Saccharomyces cerevisiae. In the first case study, a fed-batch process was optimized with respect to the pH value and linearly rising feeding rates of glucose and nitrogen source. Using the mDoE-toolbox, the biomass concentration was increased by 30% compared to previously performed experiments. The second case study was the whole-cell biocatalysis of ethyl acetoacetate (EAA) to (S)-ethyl-3-hydroxybutyrate (E3HB), for which the feeding rates of glucose, nitrogen source, and EAA were optimized. An increase of 80% compared to a previously performed experiment with similar initial conditions was achieved for the E3HB concentration.
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Abstract
This special issue is devoted to new developments in measurement technologies for upstream and downstream bioprocessing [...]
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32
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Facco P, Zomer S, Rowland-Jones RC, Marsh D, Diaz-Fernandez P, Finka G, Bezzo F, Barolo M. Using data analytics to accelerate biopharmaceutical process scale-up. Biochem Eng J 2020. [DOI: 10.1016/j.bej.2020.107791] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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33
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Thakur G, Thori S, Rathore AS. Implementing PAT for single-pass tangential flow ultrafiltration for continuous manufacturing of monoclonal antibodies. J Memb Sci 2020. [DOI: 10.1016/j.memsci.2020.118492] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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34
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Abstract
Real-time monitoring of product titers during process development and production of biotherapeutics facilitate implementation of quality-by-design principles and enable rapid bioprocess decision and optimization of the production process. Conventional analytical methods are generally performed offline/at-line and, therefore, are not capable of generating real-time data. In this study, a novel fiber optical nanoplasmonic sensor technology was explored for rapid IgG titer measurements. The sensor combines localized surface plasmon resonance transduction and robust single use Protein A-modified sensor chips, housed in a flexible flow cell, for specific IgG detection. The sensor requires small sample volumes (1–150 µL) and shows a reproducibility and sensitivity comparable to Protein G high performance liquid chromatography-ultraviolet (HPLC-UV). The dynamic range of the sensor system can be tuned by varying the sample volume, which enables quantification of IgG samples ranging from 0.0015 to 10 mg/mL, without need for sample dilution. The sensor shows limited interference from the sample matrix and negligible unspecific protein binding. IgG titers can be rapidly determined in samples from filtered unpurified Chinese hamster ovary (CHO) cell cultures and show good correlation with enzyme-linked immunosorbent assay (ELISA).
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35
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Continuous Fc detection for protein A capture process control. Biosens Bioelectron 2020; 165:112327. [DOI: 10.1016/j.bios.2020.112327] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 05/16/2020] [Accepted: 05/23/2020] [Indexed: 11/19/2022]
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36
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Emerson J, Kara B, Glassey J. Multivariate data analysis in cell gene therapy manufacturing. Biotechnol Adv 2020; 45:107637. [PMID: 32980438 DOI: 10.1016/j.biotechadv.2020.107637] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 07/27/2020] [Accepted: 09/22/2020] [Indexed: 01/26/2023]
Abstract
The emergence of cell gene therapy (CGT) as a safe and efficacious treatment for numerous severe inherited and acquired human diseases has led to growing interest and investment in new CGT products. The most successful of these have been autologous viral vector-based treatments. The development of viral vector manufacturing processes and ex vivo patient cell processing capabilities is a pressing issue in the advancement of autologous viral vector-based CGT treatments. In viral vector production, scale-up is a critical task due to the limited scalability of traditional laboratory systems and the demand for high volumes of viral vector manufactured in accordance with current good manufacturing practice. Ex vivo cell processing methods require optimisation and automation before they can be scaled out, and several other manufacturing challenges are prevalent such as high levels of raw material and process variability, difficulty characterising complex materials, and a lack of knowledge of critical process parameters and their effect on critical quality attributes of the viral vector and cell drug products. Multivariate data analysis (MVDA) has been leveraged successfully in a variety of applications in the chemical and biochemical industries, including for tasks such as bioprocess monitoring, identification of critical process parameters and assessment of process variability and comparability during process development, scale-up and technology transfer. Henceforth, MVDA is reviewed here as a suitable tool for tackling some of the challenges faced in the development of CGT manufacturing processes. A summary of some key CGT manufacturing challenges is provided along with a review of MVDA applications to mammalian and microbial processes, and an exploration of the potential benefits, requirements and pre-requisites of MVDA applications in the development of CGT manufacturing processes.
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Affiliation(s)
- Joseph Emerson
- School of Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
| | - Bo Kara
- Currently, Evox Therapeutics, Medawar Centre, Oxford OX4 4HG, UK.
| | - Jarka Glassey
- School of Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
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37
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Richelle A, Lee BW, Portela RMC, Raley J, Stosch M. Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation. Biotechnol J 2020; 15:e2000113. [DOI: 10.1002/biot.202000113] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 05/30/2020] [Indexed: 12/19/2022]
Affiliation(s)
- Anne Richelle
- Process Systems Biology and Engineering Center of Excellence Technical Research and Development, GSK Rixensart 1330 Belgium
| | - Boung Wook Lee
- Microbial and Cell Culture Development Biopharm Product Development & Supply, GSK King of Prussia PA 19406 USA
| | - Rui M. C. Portela
- Process Systems Biology and Engineering Center of Excellence Technical Research and Development, GSK Rixensart 1330 Belgium
| | - Jonathan Raley
- Microbial and Cell Culture Development Biopharm Product Development & Supply, GSK King of Prussia PA 19406 USA
| | - Moritz Stosch
- Process Systems Biology and Engineering Center of Excellence Technical Research and Development, GSK Rixensart 1330 Belgium
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38
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Wang G, Haringa C, Noorman H, Chu J, Zhuang Y. Developing a Computational Framework To Advance Bioprocess Scale-Up. Trends Biotechnol 2020; 38:846-856. [DOI: 10.1016/j.tibtech.2020.01.009] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 01/27/2020] [Accepted: 01/29/2020] [Indexed: 01/10/2023]
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39
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Hebbi V, Kumar D, Rathore AS. Process Analytical Technology Implementation for Peptide Manufacturing: Cleavage Reaction of Recombinant Lethal Toxin Neutralizing Factor Concatemer as a Case Study. Anal Chem 2020; 92:5676-5681. [PMID: 32191451 DOI: 10.1021/acs.analchem.9b05273] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The α-chymotrypsin-based cleavage reaction is necessary for manufacturing peptides using rDNA technology with tandem repeats. The current work showcases application of process analytical technology (PAT) tools for monitoring and control of this reaction, using recombinant Lethal Toxin Neutralizing Factor (rLTNF) as a case study. At-line Fourier Transform infrared spectroscopy (ATR-FTIR) combined with attenuated total internal reflectance sampling accessory was exploited to monitor the reaction. PLS spectral calibration models were created for real-time quantification of concentrations of rLTNF concatemer and urea in the reaction mixture. An end-to-end PAT monitoring and control strategy was developed to address potential deviations and ensure that targets for yield, purity, and impurity profile are met for each batch. The impact of various deviations of process parameters outside the operating space, such as deviations in the reaction buffer, concentration of concatemer in the IBs, enzyme loading relative to protein concentration, and reaction time with late quenching were investigated. Variation in impurity profile over time in the case of late reaction quenching was determined through HPLC and mass spectrometry. It has been demonstrated how process signatures from the PAT tools across various batches and campaigns can be analyzed to facilitate real-time process monitoring and control.
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Affiliation(s)
- Vishwanath Hebbi
- Department of Chemical Engineering, Indian Institute of Technology, 110016, Hauz Khas, India
| | - Devendra Kumar
- Department of Chemical Engineering, Indian Institute of Technology, 110016, Hauz Khas, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology, 110016, Hauz Khas, India
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40
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Salem DP, Gong X, Lee H, Zeng A, Xue G, Schacherl J, Gibson S, Strano MS. Characterization of Protein Aggregation Using Hydrogel-Encapsulated nIR Fluorescent Nanoparticle Sensors. ACS Sens 2020; 5:327-337. [PMID: 31989811 DOI: 10.1021/acssensors.9b01586] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The monitoring of biopharmaceutical critical quality attributes in-process, at both the process development and manufacturing stages, is necessary for the implementation of process analytical technology and quality-by-design principles. Among these attributes, it is important to monitor and control protein aggregation during the manufacturing of biological therapeutics to prevent adverse immunogenic responses and minimize negative impacts on drug deliverability. In this work, we explore hydrogel-encapsulated, label-free fluorescent nanosensors for the characterization of protein aggregation. A mathematical model is used to describe the diffusion and binding of a series of stressed pharmaceutical samples to such sensors, describing their dynamic response. We use mathematical modeling to map the influence of hydrogel properties on the separation performance, given the composition of UV-stressed IgG1 samples. Using this modified model, the compositions of light-stressed IgG1 samples were fit to experimental data and correlated with size-exclusion chromatography data. The results demonstrate the ability to detect the presence of high-molecular-weight protein species at a concentration as low as 1%. This work represents a significant step toward the development and deployment of rapid process analytical technologies for biopharmaceutical characterization.
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Affiliation(s)
- Daniel P. Salem
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Xun Gong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heejin Lee
- Process Development, Amgen Inc., 360 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Alicia Zeng
- Process Development, Amgen Inc., 360 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Gang Xue
- Process Development, Amgen Inc., 360 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Jeff Schacherl
- Process Development, Amgen Inc., 360 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Scott Gibson
- Process Development, Amgen Inc., 360 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Michael S. Strano
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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41
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Feidl F, Vogg S, Wolf M, Podobnik M, Ruggeri C, Ulmer N, Wälchli R, Souquet J, Broly H, Butté A, Morbidelli M. Process‐wide control and automation of an integrated continuous manufacturing platform for antibodies. Biotechnol Bioeng 2020; 117:1367-1380. [DOI: 10.1002/bit.27296] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 02/03/2020] [Indexed: 12/17/2022]
Affiliation(s)
- Fabian Feidl
- Institute for Chemical and BioengineeringDepartment of Chemistry and Applied BiosciencesZurich Switzerland
| | - Sebastian Vogg
- Institute for Chemical and BioengineeringDepartment of Chemistry and Applied BiosciencesZurich Switzerland
| | - Moritz Wolf
- Institute for Chemical and BioengineeringDepartment of Chemistry and Applied BiosciencesZurich Switzerland
| | - Matevz Podobnik
- Institute for Chemical and BioengineeringDepartment of Chemistry and Applied BiosciencesZurich Switzerland
| | - Caterina Ruggeri
- Institute for Chemical and BioengineeringDepartment of Chemistry and Applied BiosciencesZurich Switzerland
| | - Nicole Ulmer
- Institute for Chemical and BioengineeringDepartment of Chemistry and Applied BiosciencesZurich Switzerland
| | - Ruben Wälchli
- Institute for Chemical and BioengineeringDepartment of Chemistry and Applied BiosciencesZurich Switzerland
| | - Jonathan Souquet
- Merck Serono S.A. Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Hervé Broly
- Merck Serono S.A. Biotech Process Sciences Corsier‐sur‐Vevey Switzerland
| | - Alessandro Butté
- Institute for Chemical and BioengineeringDepartment of Chemistry and Applied BiosciencesZurich Switzerland
| | - Massimo Morbidelli
- Institute for Chemical and BioengineeringDepartment of Chemistry and Applied BiosciencesZurich Switzerland
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42
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Bayer B, von Stosch M, Melcher M, Duerkop M, Striedner G. Soft sensor based on 2D-fluorescence and process data enabling real-time estimation of biomass in Escherichia coli cultivations. Eng Life Sci 2019; 20:26-35. [PMID: 32625044 PMCID: PMC6999058 DOI: 10.1002/elsc.201900076] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 09/03/2019] [Accepted: 10/18/2019] [Indexed: 11/09/2022] Open
Abstract
In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on‐line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real‐time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D‐fluorescence data and process data, was developed for a comprehensive study with a 20‐L experimental design, for Escherichia coli fed‐batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D‐fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D‐fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression‐model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on‐line, enabling Quality by Design.
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Affiliation(s)
- Benjamin Bayer
- Department of Biotechnology University of Natural Resources and Life Sciences Vienna Austria
| | - Moritz von Stosch
- School of Chemical Engineering and Advanced Materials Newcastle University Newcastle upon Tyne United Kingdom
| | - Michael Melcher
- Institute of Applied Statistics and Computing University of Natural Resources and Life Sciences Vienna Austria.,Austrian Centre of Industrial Biotechnology Graz Austria
| | - Mark Duerkop
- Department of Biotechnology University of Natural Resources and Life Sciences Vienna Austria.,Novasign GmbH Vienna Austria
| | - Gerald Striedner
- Department of Biotechnology University of Natural Resources and Life Sciences Vienna Austria
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43
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Spann R, Gernaey KV, Sin G. A compartment model for risk-based monitoring of lactic acid bacteria cultivations. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2019.107293] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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44
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Handl A, López-Lorente ÁI, Handrick R, Mizaikoff B, Hesse F. Infrared attenuated total reflection and 2D fluorescence spectroscopy for the discrimination of differently aggregated monoclonal antibodies. Analyst 2019; 144:6334-6341. [PMID: 31553337 DOI: 10.1039/c9an00424f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Antibody aggregates may occur as undesirable by-products during the manufacturing process of biopharmaceutical proteins since parameters such as pH, temperature, ionic strength, protein concentration, oxygen, and shear forces can lead to aggregate formation. These aggregates have to be detected, quantified and removed cost extensively, since they may reduce the safety and efficacy of the product. Protein aggregates can range from small soluble dimers up to large visible agglomerates. Differently aggregated antibody samples were characterized for their soluble and insoluble aggregate concentration by size exclusion chromatography and fluorescence microscopy, respectively. The samples exhibited a high diversity of protein aggregates, which varied in amount, size and shape. For secondary structure characterization, infrared attenuated total reflection (IR-ATR) and two-dimensional fluorescence (2D-FL) spectroscopy were applied. Using direct spectroscopy, only marginal differences of various antibody aggregates were evident. However, using appropriate chemometric strategies, the evaluation of IR-ATR and 2D-FL spectra yielded the discrimination of differently aggregated antibody samples with yet unprecedented precision.
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Affiliation(s)
- Alina Handl
- Biberach University, Institute of Applied Biotechnology, Hubertus-Liebrecht-Str. 35, 88400 Biberach, Germany.
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45
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Combining Mechanistic Modeling and Raman Spectroscopy for Monitoring Antibody Chromatographic Purification. Processes (Basel) 2019. [DOI: 10.3390/pr7100683] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Chromatography is widely used in biotherapeutics manufacturing, and the corresponding underlying mechanisms are well understood. To enable process control and automation, spectroscopic techniques are very convenient as on-line sensors, but their application is often limited by their sensitivity. In this work, we investigate the implementation of Raman spectroscopy to monitor monoclonal antibody (mAb) breakthrough (BT) curves in chromatographic operations with a low titer harvest. A state estimation procedure is developed by combining information coming from a lumped kinetic model (LKM) and a Raman analyzer in the frame of an extended Kalman filter approach (EKF). A comparison with suitable experimental data shows that this approach allows for the obtainment of reliable estimates of antibody concentrations with reduced noise and increased robustness.
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46
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Bayer B, Sissolak B, Duerkop M, von Stosch M, Striedner G. The shortcomings of accurate rate estimations in cultivation processes and a solution for precise and robust process modeling. Bioprocess Biosyst Eng 2019; 43:169-178. [PMID: 31541314 PMCID: PMC6960212 DOI: 10.1007/s00449-019-02214-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 06/21/2019] [Accepted: 09/10/2019] [Indexed: 11/27/2022]
Abstract
The accurate estimation of cell growth or the substrate consumption rate is crucial for the understanding of the current state of a bioprocess. Rates unveil the actual cell status, making them valuable for quality-by-design concepts. However, in bioprocesses, the real rates are commonly not accessible due to analytical errors. We simulated Escherichia coli fed-batch fermentations, sampled at four different intervals and added five levels of noise to mimic analytical inaccuracy. We computed stepwise integral estimations with and without using moving average estimations, and smoothing spline interpolations to compare the accuracy and precision of each method to calculate the rates. We demonstrate that stepwise integration results in low accuracy and precision, especially at higher sampling frequencies. Contrary, a simple smoothing spline function displayed both the highest accuracy and precision regardless of the chosen sampling interval. Based on this, we tested three different options for substrate uptake rate estimations.
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Affiliation(s)
- B Bayer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.
| | - B Sissolak
- Bilfinger Industrietechnik Salzburg GmbH, Salzburg, Austria.
| | - M Duerkop
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
| | - M von Stosch
- School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, UK
| | - G Striedner
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
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47
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Hernández Rodríguez T, Posch C, Schmutzhard J, Stettner J, Weihs C, Pörtner R, Frahm B. Predicting industrial‐scale cell culture seed trains–A Bayesian framework for model fitting and parameter estimation, dealing with uncertainty in measurements and model parameters, applied to a nonlinear kinetic cell culture model, using an MCMC method. Biotechnol Bioeng 2019; 116:2944-2959. [DOI: 10.1002/bit.27125] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 06/20/2019] [Accepted: 07/19/2019] [Indexed: 12/30/2022]
Affiliation(s)
- Tanja Hernández Rodríguez
- Biotechnology & Bioprocess EngineeringOstwestfalen‐Lippe University of Applied Sciences and Arts Lemgo Germany
| | - Christoph Posch
- Novartis Technical Research & DevelopmentSandoz GmbH Langkampfen Austria
| | - Julia Schmutzhard
- Novartis Technical Research & DevelopmentSandoz GmbH Langkampfen Austria
| | - Josef Stettner
- Novartis Technical Research & DevelopmentSandoz GmbH Langkampfen Austria
| | - Claus Weihs
- Faculty of StatisticsTU Dortmund University Dortmund Germany
| | - Ralf Pörtner
- Institute for Bioprocess‐ and Biosystems EngineeringHamburg University of Technology Germany
| | - Björn Frahm
- Biotechnology & Bioprocess EngineeringOstwestfalen‐Lippe University of Applied Sciences and Arts Lemgo Germany
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48
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Shekhawat LK, Rathore AS. An overview of mechanistic modeling of liquid chromatography. Prep Biochem Biotechnol 2019; 49:623-638. [DOI: 10.1080/10826068.2019.1615504] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Lalita K. Shekhawat
- Department of Chemical Engineering, Indian Institute of Technology, New Delhi, India
| | - Anurag S. Rathore
- Department of Chemical Engineering, Indian Institute of Technology, New Delhi, India
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49
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Jenzsch M, Bell C, Buziol S, Kepert F, Wegele H, Hakemeyer C. Trends in Process Analytical Technology: Present State in Bioprocessing. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2019; 165:211-252. [PMID: 28776065 DOI: 10.1007/10_2017_18] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Process analytical technology (PAT), the regulatory initiative for incorporating quality in pharmaceutical manufacturing, is an area of intense research and interest. If PAT is effectively applied to bioprocesses, this can increase process understanding and control, and mitigate the risk from substandard drug products to both manufacturer and patient. To optimize the benefits of PAT, the entire PAT framework must be considered and each elements of PAT must be carefully selected, including sensor and analytical technology, data analysis techniques, control strategies and algorithms, and process optimization routines. This chapter discusses the current state of PAT in the biopharmaceutical industry, including several case studies demonstrating the degree of maturity of various PAT tools. Graphical Abstract Hierarchy of QbD components.
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Affiliation(s)
- Marco Jenzsch
- Roche Pharma Technical Operations - Biologics Manufacturing, Nonnenwald 2, 82377, Penzberg, Germany.
| | - Christian Bell
- Roche Pharma Technical Operations - Biologics Analytical Development Europe, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Stefan Buziol
- Roche Pharma Technical Operations - Bioprocess Development Europe, Nonnenwald 2, 82377, Penzberg, Germany
| | - Felix Kepert
- Roche Pharma Technical Operations - Biologics Analytical Development Europe, Nonnenwald 2, 82377, Penzberg, Germany
| | - Harald Wegele
- Roche Pharma Technical Operations - Biologics Analytical Development Europe, Nonnenwald 2, 82377, Penzberg, Germany
| | - Christian Hakemeyer
- Roche Pharma Technical Operations - Biologics Global Manufacturing Science and Technology, Sandhofer Strasse 116, 68305, Mannheim, Germany
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50
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Narayanan H, Sokolov M, Butté A, Morbidelli M. Decision Tree-PLS (DT-PLS) algorithm for the development of process: Specific local prediction models. Biotechnol Prog 2019; 35:e2818. [PMID: 30969466 DOI: 10.1002/btpr.2818] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 03/15/2019] [Accepted: 03/25/2019] [Indexed: 12/26/2022]
Abstract
This work presents a novel multivariate statistical algorithm, Decision Tree-PLS (DT-PLS), to improve the prediction and understanding of dynamic processes based on local partial least square regression (PLSR) models for characteristic process groups defined based on Decision Tree (DT) analysis. The DT-PLS algorithm is successfully applied to two different cell culture data sets, one obtained from bioreactors of 3.5 L lab scale and the other obtained from the 15 ml ambr microbioreactor system. Substantial improvement in the predictive capabilities of the model can be achieved based on the localization compared to the classical PLSR approach, which is implemented in the commercially available packages. Additionally, the differences in the model parameters of the local models suggest that the governing process variables vary for the different process regimes indicating the different states of the cell under different process conditions.
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Affiliation(s)
- Harini Narayanan
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland
| | - Michael Sokolov
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland.,DataHow AG, Zurich, Switzerland
| | - Alessandro Butté
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland.,DataHow AG, Zurich, Switzerland
| | - Massimo Morbidelli
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland.,DataHow AG, Zurich, Switzerland
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