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Rashedi M, Khodabandehlou H, Wang T, Demers M, Tulsyan A, Garvin C, Undey C. Integration of just-in-time learning with variational autoencoder for cell culture process monitoring based on Raman spectroscopy. Biotechnol Bioeng 2024; 121:2205-2224. [PMID: 38654549 DOI: 10.1002/bit.28713] [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: 11/21/2023] [Revised: 03/09/2024] [Accepted: 04/04/2024] [Indexed: 04/26/2024]
Abstract
Protein production in the biopharmaceutical industry necessitates the utilization of multiple analytical techniques and control methodologies to ensure both safety and consistency. To facilitate real-time monitoring and control of cell culture processes, Raman spectroscopy has emerged as a versatile analytical technology. This technique, categorized as a Process Analytical Technology, employs chemometric models to establish correlations between Raman signals and key variables of interest. One notable approach for achieving real-time monitoring is through the application of just-in-time learning (JITL), an industrial soft sensor modeling technique that utilizes Raman signals to estimate process variables promptly. The conventional Raman-based JITL method relies on the K-nearest neighbor (KNN) algorithm with Euclidean distance as the similarity measure. However, it falls short of addressing the impact of data uncertainties. To rectify this limitation, this study endeavors to integrate JITL with a variational autoencoder (VAE). This integration aims to extract dominant Raman features in a nonlinear fashion, which are expressed as multivariate Gaussian distributions. Three experimental runs using different cell lines were chosen to compare the performance of the proposed algorithm with commonly utilized methods in the literature. The findings indicate that the VAE-JITL approach consistently outperforms partial least squares, convolutional neural network, and JITL with KNN similarity measure in accurately predicting key process variables.
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Affiliation(s)
- Mohammad Rashedi
- Digital Integration & Predictive Technologies, Amgen Inc., Thousand Oaks, California, USA
| | - Hamid Khodabandehlou
- Digital Integration & Predictive Technologies, Amgen Inc., Thousand Oaks, California, USA
| | - Tony Wang
- Digital Integration & Predictive Technologies, Amgen Inc., West Greenwich, Rhode Island, USA
| | - Matthew Demers
- Digital Integration & Predictive Technologies, Amgen Inc., West Greenwich, Rhode Island, USA
| | - Aditya Tulsyan
- Digital Integration & Predictive Technologies, Amgen Inc., West Greenwich, Rhode Island, USA
| | - Christopher Garvin
- Digital Integration & Predictive Technologies, Amgen Inc., West Greenwich, Rhode Island, USA
| | - Cenk Undey
- Digital Integration & Predictive Technologies, Amgen Inc., Thousand Oaks, California, USA
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2
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Sripada SA, Hosseini M, Ramesh S, Wang J, Ritola K, Menegatti S, Daniele MA. Advances and opportunities in process analytical technologies for viral vector manufacturing. Biotechnol Adv 2024; 74:108391. [PMID: 38848795 DOI: 10.1016/j.biotechadv.2024.108391] [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: 11/14/2023] [Revised: 03/14/2024] [Accepted: 05/29/2024] [Indexed: 06/09/2024]
Abstract
Viral vectors are an emerging, exciting class of biologics whose application in vaccines, oncology, and gene therapy has grown exponentially in recent years. Following first regulatory approval, this class of therapeutics has been vigorously pursued to treat monogenic disorders including orphan diseases, entering hundreds of new products into pipelines. Viral vector manufacturing supporting clinical efforts has spurred the introduction of a broad swath of analytical techniques dedicated to assessing the diverse and evolving panel of Critical Quality Attributes (CQAs) of these products. Herein, we provide an overview of the current state of analytics enabling measurement of CQAs such as capsid and vector identities, product titer, transduction efficiency, impurity clearance etc. We highlight orthogonal methods and discuss the advantages and limitations of these techniques while evaluating their adaptation as process analytical technologies. Finally, we identify gaps and propose opportunities in enabling existing technologies for real-time monitoring from hardware, software, and data analysis viewpoints for technology development within viral vector biomanufacturing.
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Affiliation(s)
- Sobhana A Sripada
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA
| | - Mahshid Hosseini
- Joint Department of Biomedical Engineering, North Carolina State University, and University of North Carolina, Chapel Hill, 911 Oval Dr., Raleigh, NC 27695, USA
| | - Srivatsan Ramesh
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA
| | - Junhyeong Wang
- Joint Department of Biomedical Engineering, North Carolina State University, and University of North Carolina, Chapel Hill, 911 Oval Dr., Raleigh, NC 27695, USA
| | - Kimberly Ritola
- North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA; Neuroscience Center, Brain Initiative Neurotools Vector Core, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stefano Menegatti
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, NC, 27695, USA; North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA; Biomanufacturing Training and Education Center, North Carolina State University, 890 Main Campus Dr, Raleigh, NC 27695, USA.
| | - Michael A Daniele
- Joint Department of Biomedical Engineering, North Carolina State University, and University of North Carolina, Chapel Hill, 911 Oval Dr., Raleigh, NC 27695, USA; North Carolina Viral Vector Initiative in Research and Learning (NC-VVIRAL), North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA; Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC 27695, USA.
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3
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Khodabandehlou H, Rashedi M, Wang T, Tulsyan A, Schorner G, Garvin C, Undey C. Cell culture product quality attribute prediction using convolutional neural networks and Raman spectroscopy. Biotechnol Bioeng 2024; 121:1231-1243. [PMID: 38284180 DOI: 10.1002/bit.28646] [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: 07/31/2023] [Revised: 11/14/2023] [Accepted: 12/19/2023] [Indexed: 01/30/2024]
Abstract
Advanced process control in the biopharmaceutical industry often lacks real-time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real-time. In spite of the ease of training, the accuracy of the PLS model is impacted if it is not used to predict quality attributes for the cell lines it is trained on. To address this issue, a deep convolutional neural network (CNN) is proposed for offline modeling of metabolites using Raman spectroscopy. By utilizing asymmetric least squares smoothing to adjust Raman spectra baselines, a generic training data set is created by amalgamating spectra from various cell lines and operating conditions. This data set, combined with their derivatives, forms a two-dimensional model input. The CNN model is developed and validated for predicting different quality variables against measurements from various continuous and fed-batch experimental runs. Validation results confirm that the deep CNN model is an accurate generic model of the process to predict real-time quality attributes, even in experimental runs not included in the training data. This model is robust and versatile, requiring no recalibration when deployed at different sites to monitor various cell lines and experimental runs.
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Affiliation(s)
- Hamid Khodabandehlou
- Digital Integration & Predictive Technologies, Process Development Department, Amgen Inc., Thousand Oaks, California, USA
| | - Mohammad Rashedi
- Digital Integration & Predictive Technologies, Process Development Department, Amgen Inc., Thousand Oaks, California, USA
| | - Tony Wang
- Digital Integration & Predictive Technologies, Process Development Department, Amgen Inc., West Greenwich, Rhode Island, USA
| | - Aditya Tulsyan
- Digital Integration & Predictive Technologies, Process Development Department, Amgen Inc., West Greenwich, Rhode Island, USA
| | - Gregg Schorner
- Digital Integration & Predictive Technologies, Process Development Department, Amgen Inc., West Greenwich, Rhode Island, USA
| | - Christopher Garvin
- Digital Integration & Predictive Technologies, Process Development Department, Amgen Inc., West Greenwich, Rhode Island, USA
| | - Cenk Undey
- Digital Integration & Predictive Technologies, Process Development Department, Amgen Inc., Thousand Oaks, California, USA
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4
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Tanemura H, Kitamura R, Yamada Y, Hoshino M, Kakihara H, Nonaka K. Comprehensive modeling of cell culture profile using Raman spectroscopy and machine learning. Sci Rep 2023; 13:21805. [PMID: 38071246 PMCID: PMC10710501 DOI: 10.1038/s41598-023-49257-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 12/06/2023] [Indexed: 12/18/2023] Open
Abstract
Chinese hamster ovary (CHO) cells are widely utilized in the production of antibody drugs. To ensure the production of large quantities of antibodies that meet the required specifications, it is crucial to monitor and control the levels of metabolites comprehensively during CHO cell culture. In recent years, continuous analysis methods employing on-line/in-line techniques using Raman spectroscopy have attracted attention. While these analytical methods can nondestructively monitor culture data, constructing a highly accurate measurement model for numerous components is time-consuming, making it challenging to implement in the rapid research and development of pharmaceutical manufacturing processes. In this study, we developed a comprehensive, simple, and automated method for constructing a Raman model of various components measured by LC-MS and other techniques using machine learning with Python. Preprocessing and spectral-range optimization of data for model construction (partial least square (PLS) regression) were automated and accelerated using Bayes optimization. Subsequently, models were constructed for each component using various model construction techniques, including linear regression, ridge regression, XGBoost, and neural network. This enabled the model accuracy to be improved compared with PLS regression. This automated approach allows continuous monitoring of various parameters for over 100 components, facilitating process optimization and process monitoring of CHO cells.
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Affiliation(s)
- Hiroki Tanemura
- Biologics Technology Research Laboratories I, Biologics Division, Daiichi Sankyo Co., Ltd., 2716-1, Aza Kurakake, Oaza Akaiwa, Chiyoda-Machi, Oura-Gun, Gunma, 370-0503, Japan.
| | - Ryunosuke Kitamura
- Biologics Technology Research Laboratories I, Biologics Division, Daiichi Sankyo Co., Ltd., 2716-1, Aza Kurakake, Oaza Akaiwa, Chiyoda-Machi, Oura-Gun, Gunma, 370-0503, Japan
| | - Yasuko Yamada
- Analytical & Quality Evaluation Research Laboratories, Pharmaceutical Technology Division, Daiichi Sankyo Co., Ltd., 1-12-1, Shinomiya, Hiratsuka, Kanagawa, 254-0014, Japan
| | - Masato Hoshino
- Biologics Technology Research Laboratories I, Biologics Division, Daiichi Sankyo Co., Ltd., 2716-1, Aza Kurakake, Oaza Akaiwa, Chiyoda-Machi, Oura-Gun, Gunma, 370-0503, Japan
| | - Hirofumi Kakihara
- Biologics Technology Research Laboratories I, Biologics Division, Daiichi Sankyo Co., Ltd., 2716-1, Aza Kurakake, Oaza Akaiwa, Chiyoda-Machi, Oura-Gun, Gunma, 370-0503, Japan
| | - Koichi Nonaka
- Biologics Division, Daiichi Sankyo Co., Ltd., 2716-1, Aza Kurakake, Oaza Akaiwa, Chiyoda-Machi, Oura-Gun, Gunma, 370-0503, Japan
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Müller DH, Börger M, Thien J, Koß HJ. The Good pH probe: non-invasive pH in-line monitoring using Good buffers and Raman spectroscopy. Anal Bioanal Chem 2023; 415:7247-7258. [PMID: 37982845 PMCID: PMC10684429 DOI: 10.1007/s00216-023-04993-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/04/2023] [Accepted: 10/09/2023] [Indexed: 11/21/2023]
Abstract
In bioprocesses, the pH value is a critical process parameter that requires monitoring and control. For pH monitoring, potentiometric methods such as pH electrodes are state of the art. However, they are invasive and show measurement value drift. Spectroscopic pH monitoring is a non-invasive alternative to potentiometric methods avoiding this measurement value drift. In this study, we developed the Good pH probe, which is an approach for spectroscopic pH monitoring in bioprocesses with an effective working range between pH 6 and pH 8 that does not require the estimation of activity coefficients. The Good pH probe combines for the first time the Good buffer 3-(N-morpholino)propanesulfonic acid (MOPS) as pH indicator with Raman spectroscopy as spectroscopic technique, and Indirect Hard Modeling (IHM) for the spectral evaluation. During a detailed characterization, we proved that the Good pH probe is reversible, exhibits no temperature dependence between 15 and 40 °C, has low sensitivity to the ionic strength up to 1100 mM, and is applicable in more complex systems, in which other components significantly superimpose the spectral features of MOPS. Finally, the Good pH probe was successfully used for non-invasive pH in-line monitoring during an industrially relevant enzyme-catalyzed reaction with a root mean square error of prediction (RMSEP) of 0.04 pH levels. Thus, the Good pH probe extends the list of critical process parameters monitorable using Raman spectroscopy and IHM by the pH value.
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Affiliation(s)
- David Heinrich Müller
- Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062, Aachen, Germany
| | - Marieke Börger
- Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062, Aachen, Germany
| | - Julia Thien
- Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062, Aachen, Germany
| | - Hans-Jürgen Koß
- Institute of Technical Thermodynamics, RWTH Aachen University, Schinkelstraße 8, 52062, Aachen, Germany.
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Nikita S, Mishra S, Gupta K, Runkana V, Gomes J, Rathore AS. Advances in bioreactor control for production of biotherapeutic products. Biotechnol Bioeng 2023; 120:1189-1214. [PMID: 36760086 DOI: 10.1002/bit.28346] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 02/11/2023]
Abstract
Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.
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Affiliation(s)
- Saxena Nikita
- Department of Chemical Engineering, DBT Centre of Excellence for Biopharmaceutical Technology, Indian Institute of Technology, Hauz Khas, Delhi, India
| | - Somesh Mishra
- Department of Chemical Engineering, DBT Centre of Excellence for Biopharmaceutical Technology, Indian Institute of Technology, Hauz Khas, Delhi, India
| | - Keshari Gupta
- TCS Research, Tata Consultancy Services Limited, Pune, India
| | | | - James Gomes
- Kusuma School of Biological Sciences, Indian Institute of Technology, Hauz Khas, Delhi, India
| | - Anurag S Rathore
- Department of Chemical Engineering, DBT Centre of Excellence for Biopharmaceutical Technology, Indian Institute of Technology, Hauz Khas, Delhi, India
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7
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Rathore AS, Thakur G, Kateja N. Continuous integrated manufacturing for biopharmaceuticals: A new paradigm or an empty promise? Biotechnol Bioeng 2023; 120:333-351. [PMID: 36111450 DOI: 10.1002/bit.28235] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 01/13/2023]
Abstract
Continuous integrated bioprocessing has elicited considerable interest from the biopharma industry for the many purported benefits it promises. Today many major biopharma manufacturers around the world are engaged in the development of continuous process platforms for their products. In spite of great potential, the path toward continuous integrated bioprocessing remains unclear for the biologics industry due to legacy infrastructure, process integration challenges, vague regulatory guidelines, and a diverging focus toward novel therapies. In this article, we present a review and perspective on this topic. We explore the status of the implementation of continuous integrated bioprocessing among biopharmaceutical manufacturers. We also present some of the key hurdles that manufacturers are likely to face during this implementation. Finally, we hypothesize that the real impact of continuous manufacturing is likely to come when the cost of manufacturing is a substantial portion of the cost of product development, such as in the case of biosimilar manufacturing and emerging economies.
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Affiliation(s)
- Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology, New Delhi, India
| | - Garima Thakur
- Department of Chemical Engineering, Indian Institute of Technology, New Delhi, India
| | - Nikhil Kateja
- Department of Chemical Engineering, Indian Institute of Technology, New Delhi, India
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8
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Min R, Wang Z, Zhuang Y, Yi X. Application of Semi-Supervised Convolutional Neural Network Regression Model Based on Data Augmentation and Process Spectral Labeling in Raman Predictive Modeling of Cell Culture Processes. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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9
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Schwarz H, Mäkinen ME, Castan A, Chotteau V. Monitoring of Amino Acids and Antibody N-Glycosylation in High Cell Density Perfusion Culture based on Raman Spectroscopy. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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10
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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: 12] [Impact Index Per Article: 6.0] [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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11
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Modern Sensor Tools and Techniques for Monitoring, Controlling, and Improving Cell Culture Processes. Processes (Basel) 2022. [DOI: 10.3390/pr10020189] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
The growing biopharmaceutical industry has reached a level of maturity that allows for the monitoring of numerous key variables for both process characterization and outcome predictions. Sensors were historically used in order to maintain an optimal environment within the reactor to optimize process performance. However, technological innovation has pushed towards on-line in situ continuous monitoring of quality attributes that could previously only be estimated off-line. These new sensing technologies when coupled with software models have shown promise for unique fingerprinting, smart process control, outcome improvement, and prediction. All this can be done without requiring invasive sampling or intervention on the system. In this paper, the state-of-the-art sensing technologies and their applications in the context of cell culture monitoring are reviewed with emphasis on the coming push towards industry 4.0 and smart manufacturing within the biopharmaceutical sector. Additionally, perspectives as to how this can be leveraged to improve both understanding and outcomes of cell culture processes are discussed.
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12
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Cui X, Tang M, Wang M, Zhu T. Water as a probe for pH measurement in individual particles using micro-Raman spectroscopy. Anal Chim Acta 2021; 1186:339089. [PMID: 34756261 DOI: 10.1016/j.aca.2021.339089] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 09/16/2021] [Accepted: 09/20/2021] [Indexed: 10/20/2022]
Abstract
Atmospheric aerosol acidity impacts numerous physicochemical processes, but the determination of particle pH remains a significant challenge due to the nonconservative nature of the H+ concentration ([H+]). Traditional measurements have difficulty in describing the practical state of an aerosol because they comprise chemical components or hypotheses that change the nature of the particles. In this work, we present a direct pH measurement that uses water as a general probe to detect [H+] in individual particles by micro-Raman spectroscopy. Containing the vibrational bands of ions and water influenced by ions, the spectra of hydrated ion were decomposed from the solution spectra as standard spectra by multivariate curve resolution analysis. Meanwhile, ratios of hydrated ions were calculated between the Raman spectra and standard spectra to evaluate concentration profiles of each ion. It demonstrated that good quantitative models between the ratio and concentration for all ions including H+ can be built with correlation coefficients (R2) higher than 0.95 for the solutions. The method was further applied to individual particle pH measurement. The pH value of sulfate aerosol particles was calculated, and the standard error was 0.09 using pH values calculated from the [HSO4-]/[SO42-] as a reference. Furthermore, the applicability of the method was proven by detecting the pH value of chloride particles. Therefore, utilizing water, the most common substance, as the spectroscopic probe to measure [H+] without restriction of the ion system, this method has potential to measure the pH value of atmospheric particles with various compounds, although more work needs to be done to improve the sensitivity of the method.
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Affiliation(s)
- Xiaoyu Cui
- BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Mingjin Tang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
| | - Mingjin Wang
- BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Tong Zhu
- BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China.
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13
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A Gibbons L, Rafferty C, Robinson K, Abad M, Maslanka F, Le N, Mo J, Clark K, Madden F, Hayes R, McCarthy B, Rode C, O'Mahony J, Rea R, O'Mahony Hartnett C. Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process. Biotechnol Prog 2021; 38:e3223. [PMID: 34738336 DOI: 10.1002/btpr.3223] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/07/2021] [Accepted: 11/02/2021] [Indexed: 11/09/2022]
Abstract
The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.
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Affiliation(s)
- Luke A Gibbons
- BioTherapeutics Development, Janssen Sciences Ireland UC, Cork, Ireland.,Department of Biological Sciences, Munster Technological University, Cork, Ireland
| | - Carl Rafferty
- BioTherapeutics Development, Janssen Sciences Ireland UC, Cork, Ireland
| | - Kerry Robinson
- Analytical Development, Janssen Pharmaceutical Companies of Johnson and Johnson, Malvern, Pennsylvania, USA
| | - Marta Abad
- BioTherapeutics Development, Janssen Pharmaceutical Companies of Johnson and Johnson, Malvern, Pennsylvania, USA
| | - Francis Maslanka
- BioTherapeutics Development, Janssen Pharmaceutical Companies of Johnson and Johnson, Malvern, Pennsylvania, USA
| | - Nikky Le
- BioTherapeutics Development, Janssen Pharmaceutical Companies of Johnson and Johnson, Malvern, Pennsylvania, USA
| | - Jingjie Mo
- Analytical Development, Janssen Pharmaceutical Companies of Johnson and Johnson, Malvern, Pennsylvania, USA
| | - Kevin Clark
- BioTherapeutics Development, Janssen Pharmaceutical Companies of Johnson and Johnson, Malvern, Pennsylvania, USA
| | - Fiona Madden
- BioTherapeutics Development, Janssen Sciences Ireland UC, Cork, Ireland
| | - Ronan Hayes
- BioTherapeutics Development, Janssen Sciences Ireland UC, Cork, Ireland
| | - Barry McCarthy
- BioTherapeutics Development, Janssen Sciences Ireland UC, Cork, Ireland
| | - Christopher Rode
- BioTherapeutics Development, Janssen Pharmaceutical Companies of Johnson and Johnson, Malvern, Pennsylvania, USA
| | - Jim O'Mahony
- Department of Biological Sciences, Munster Technological University, Cork, Ireland
| | - Rosemary Rea
- Department of Biological Sciences, Munster Technological University, Cork, Ireland
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14
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The role of Raman spectroscopy in biopharmaceuticals from development to manufacturing. Anal Bioanal Chem 2021; 414:969-991. [PMID: 34668998 PMCID: PMC8724084 DOI: 10.1007/s00216-021-03727-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/08/2021] [Indexed: 12/21/2022]
Abstract
Biopharmaceuticals have revolutionized the field of medicine in the types of active ingredient molecules and treatable indications. Adoption of Quality by Design and Process Analytical Technology (PAT) frameworks has helped the biopharmaceutical field to realize consistent product quality, process intensification, and real-time control. As part of the PAT strategy, Raman spectroscopy offers many benefits and is used successfully in bioprocessing from single-cell analysis to cGMP process control. Since first introduced in 2011 for industrial bioprocessing applications, Raman has become a first-choice PAT for monitoring and controlling upstream bioprocesses because it facilitates advanced process control and enables consistent process quality. This paper will discuss new frontiers in extending these successes in upstream from scale-down to commercial manufacturing. New reports concerning the use of Raman spectroscopy in the basic science of single cells and downstream process monitoring illustrate industrial recognition of Raman’s value throughout a biopharmaceutical product’s lifecycle. Finally, we draw upon a nearly 90-year history in biological Raman spectroscopy to provide the basis for laboratory and in-line measurements of protein quality, including higher-order structure and composition modifications, to support formulation development.
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15
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Park SY, Park CH, Choi DH, Hong JK, Lee DY. Bioprocess digital twins of mammalian cell culture for advanced biomanufacturing. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100702] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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16
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Ricci G, Minsker K, Kapish A, Osborn J, Ha S, Davide J, Califano JP, Sehlin D, Rustandi RR, Dick LW, Vlasak J, Culp TD, Baudy A, Bell E, Mukherjee M. Flow virometry for process monitoring of live virus vaccines-lessons learned from ERVEBO. Sci Rep 2021; 11:7432. [PMID: 33795759 PMCID: PMC8016999 DOI: 10.1038/s41598-021-86688-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 03/04/2021] [Indexed: 12/19/2022] Open
Abstract
Direct at line monitoring of live virus particles in commercial manufacturing of vaccines is challenging due to their small size. Detection of malformed or damaged virions with reduced potency is rate-limited by release potency assays with long turnaround times. Thus, preempting batch failures caused by out of specification potency results is almost impossible. Much needed are in-process tools that can monitor and detect compromised viral particles in live-virus vaccines (LVVs) manufacturing based on changes in their biophysical properties to provide timely measures to rectify process stresses leading to such damage. Using ERVEBO, MSD's Ebola virus vaccine as an example, here we describe a flow virometry assay that can quickly detect damaged virus particles and provide mechanistic insight into process parameters contributing to the damage. Furthermore, we describe a 24-h high throughput infectivity assay that can be used to correlate damaged particles directly to loss in viral infectivity (potency) in-process. Collectively, we provide a set of innovative tools to enable rapid process development, process monitoring, and control strategy implementation in large scale LVV manufacturing.
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Affiliation(s)
- Geoffri Ricci
- Vaccines Process Development and Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, WP 42-3, West Point, PA, 19486, USA
| | - Kevin Minsker
- Vaccines Process Development and Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, WP 42-3, West Point, PA, 19486, USA
| | - Austin Kapish
- Vaccines Process Development and Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, WP 42-3, West Point, PA, 19486, USA
| | - James Osborn
- Vaccines Process Development and Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, WP 42-3, West Point, PA, 19486, USA
| | - Sha Ha
- Vaccines Process Development and Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, WP 42-3, West Point, PA, 19486, USA
| | - Joseph Davide
- Vaccines Process Development and Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, WP 42-3, West Point, PA, 19486, USA
| | - Joseph P Califano
- Vaccines Process Development and Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, WP 42-3, West Point, PA, 19486, USA
| | - Darrell Sehlin
- Vaccines Process Development and Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, WP 42-3, West Point, PA, 19486, USA
| | - Richard R Rustandi
- Vaccines Analytical Research and Development, Merck & Co., Inc., West Point, PA, USA
| | - Lawrence W Dick
- Vaccines Analytical Research and Development, Merck & Co., Inc., West Point, PA, USA
| | - Josef Vlasak
- Vaccines Analytical Research and Development, Merck & Co., Inc., West Point, PA, USA
| | - Timothy D Culp
- Vaccines Process Development, Merck & Co., Inc., West Point, PA, USA
| | - Andreas Baudy
- Safety Assessment and Laboratory Animal Resources, Merck & Co., Inc., West Point, PA, USA
| | - Edward Bell
- Vaccines Process Development and Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, WP 42-3, West Point, PA, 19486, USA
| | - Malini Mukherjee
- Vaccines Process Development and Commercialization, Merck & Co., Inc., 770 Sumneytown Pike, WP 42-3, West Point, PA, 19486, USA.
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17
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Rathore AS, Nikita S, Thakur G, Deore N. Challenges in process control for continuous processing for production of monoclonal antibody products. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100671] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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18
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High-Throughput Raman Spectroscopy Combined with Innovate Data Analysis Workflow to Enhance Biopharmaceutical Process Development. Processes (Basel) 2020. [DOI: 10.3390/pr8091179] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Raman spectroscopy has the potential to revolutionise many aspects of biopharmaceutical process development. The widespread adoption of this promising technology has been hindered by the high cost associated with individual probes and the challenge of measuring low sample volumes. To address these issues, this paper investigates the potential of an emerging new high-throughput (HT) Raman spectroscopy microscope combined with a novel data analysis workflow to replace off-line analytics for upstream and downstream operations. On the upstream front, the case study involved the at-line monitoring of an HT micro-bioreactor system cultivating two mammalian cell cultures expressing two different therapeutic proteins. The spectra generated were analysed using a partial least squares (PLS) model. This enabled the successful prediction of the glucose, lactate, antibody, and viable cell density concentrations directly from the Raman spectra without reliance on multiple off-line analytical devices and using only a single low-volume sample (50–300 μL). However, upon the subsequent investigation of these models, only the glucose and lactate models appeared to be robust based upon their model coefficients containing the expected Raman vibrational signatures. On the downstream front, the HT Raman device was incorporated into the development of a cation exchange chromatography step for an Fc-fusion protein to compare different elution conditions. PLS models were derived from the spectra and were found to predict accurately monomer purity and concentration. The low molecular weight (LMW) and high molecular weight (HMW) species concentrations were found to be too low to be predicted accurately by the Raman device. However, the method enabled the classification of samples based on protein concentration and monomer purity, allowing a prioritisation and reduction in samples analysed using A280 UV absorbance and high-performance liquid chromatography (HPLC). The flexibility and highly configurable nature of this HT Raman spectroscopy microscope makes it an ideal tool for bioprocess research and development, and is a cost-effective solution based on its ability to support a large range of unit operations in both upstream and downstream process operations.
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19
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Wasalathanthri DP, Rehmann MS, Song Y, Gu Y, Mi L, Shao C, Chemmalil L, Lee J, Ghose S, Borys MC, Ding J, Li ZJ. Technology outlook for real‐time quality attribute and process parameter monitoring in biopharmaceutical development—A review. Biotechnol Bioeng 2020; 117:3182-3198. [DOI: 10.1002/bit.27461] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 05/30/2020] [Accepted: 06/11/2020] [Indexed: 12/11/2022]
Affiliation(s)
| | - Matthew S. Rehmann
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
| | - Yuanli Song
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
| | - Yan Gu
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
| | - Luo Mi
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
| | - Chun Shao
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
| | - Letha Chemmalil
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
| | - Jongchan Lee
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
| | - Sanchayita Ghose
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
| | - Michael C. Borys
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
| | - Julia Ding
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
| | - Zheng Jian Li
- Biologics Process Development Bristol‐Myers Squibb Company Devens Massachusetts
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