1
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Xu X, Farnós O, Paes BCMF, Nesdoly S, Kamen AA. Multivariate data analysis on multisensor measurement for inline process monitoring of adenovirus production in HEK293 cells. Biotechnol Bioeng 2024; 121:2175-2192. [PMID: 38613199 DOI: 10.1002/bit.28712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/31/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024]
Abstract
In the era of Biopharma 4.0, process digitalization fundamentally requires accurate and timely monitoring of critical process parameters (CPPs) and quality attributes. Bioreactor systems are equipped with a variety of sensors to ensure process robustness and product quality. However, during the biphasic production of viral vectors or replication-competent viruses for gene and cell therapies and vaccination, current monitoring techniques relying on a single working sensor can be affected by the physiological state change of the cells due to infection/transduction/transfection step required to initiate production. To address this limitation, a multisensor (MS) monitoring system, which includes dual-wavelength fluorescence spectroscopy, dielectric signals, and a set of CPPs, such as oxygen uptake rate and pH control outputs, was employed to monitor the upstream process of adenovirus production in HEK293 cells in bioreactor. This system successfully identified characteristic responses to infection by comparing variations in these signals, and the correlation between signals and target critical variables was analyzed mechanistically and statistically. The predictive performance of several target CPPs using different multivariate data analysis (MVDA) methods on data from a single sensor/source or fused from multiple sensors were compared. An MS regression model can accurately predict viable cell density with a relative root mean squared error (rRMSE) as low as 8.3% regardless of the changes occurring over the infection phase. This is a significant improvement over the 12% rRMSE achieved with models based on a single source. The MS models also provide the best predictions for glucose, glutamine, lactate, and ammonium. These results demonstrate the potential of using MVDA on MS systems as a real-time monitoring approach for biphasic bioproduction processes. Yet, models based solely on the multiplicity and timing of infection outperformed both single-sensor and MS models, emphasizing the need for a deeper mechanistic understanding in virus production prediction.
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Affiliation(s)
- Xingge Xu
- Department of Bioengineering, McGill University, Montreal, Canada
| | - Omar Farnós
- Department of Bioengineering, McGill University, Montreal, Canada
| | | | - Sean Nesdoly
- Department of Bioengineering, McGill University, Montreal, Canada
| | - Amine A Kamen
- Department of Bioengineering, McGill University, Montreal, Canada
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2
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Polak J, Huang Z, Sokolov M, von Stosch M, Butté A, Hodgman CE, Borys M, Khetan A. An innovative hybrid modeling approach for simultaneous prediction of cell culture process dynamics and product quality. Biotechnol J 2024; 19:e2300473. [PMID: 38528367 DOI: 10.1002/biot.202300473] [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: 09/12/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/27/2024]
Abstract
The use of hybrid models is extensively described in the literature to predict the process evolution in cell cultures. These models combine mechanistic and machine learning methods, allowing the prediction of complex process behavior, in the presence of many process variables, without the need to collect a large amount of data. Hybrid models cannot be directly used to predict final product critical quality attributes, or CQAs, because they are usually measured only at the end of the process, and more mechanistic knowledge is needed for many classes of CQAs. The historical models can instead predict the CQAs better; however, they cannot directly relate manipulated process parameters to final CQAs, as they require knowledge of the process evolution. In this work, we propose an innovative modeling approach based on combining a hybrid propagation model with a historical data-driven model, that is, the combined hybrid model, for simultaneous prediction of full process dynamics and CQAs. The performance of the combined hybrid model was evaluated on an industrial dataset and compared to classical black-box models, which directly relate manipulated process parameters to CQAs. The proposed combined hybrid model outperforms the black-box model by 33% on average in predicting the CQAs while requiring only around half of the data for model training to match performance. Thus, in terms of model accuracy and experimental costs, the combined hybrid model in this study provides a promising platform for process optimization applications.
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Affiliation(s)
| | - Zhuangrong Huang
- Biologics Development, Global Product Development and Supply, Bristol Myers Squibb, Devens, Massachusetts, USA
| | | | | | | | - C Eric Hodgman
- Biologics Development, Global Product Development and Supply, Bristol Myers Squibb, Devens, Massachusetts, USA
| | - Michael Borys
- Biologics Development, Global Product Development and Supply, Bristol Myers Squibb, Devens, Massachusetts, USA
| | - Anurag Khetan
- Biologics Development, Global Product Development and Supply, Bristol Myers Squibb, Devens, Massachusetts, USA
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3
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Sakaki A, Namatame T, Nakaya M, Omasa T. Model-based control system design to manage process parameters in mammalian cell culture for biopharmaceutical manufacturing. Biotechnol Bioeng 2024; 121:605-617. [PMID: 37960996 DOI: 10.1002/bit.28593] [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: 08/22/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 11/15/2023]
Abstract
To enhance the robustness and flexibility of biopharmaceutical manufacturing, a paradigm shift toward methods of continuous processing, such as perfusion, and fundamental technologies for high-throughput process development are being actively investigated. The continuous upstream process must establish an advanced control strategy to ensure a "State of Control" before operation. Specifically, feedforward and feedback control must address the complex fluctuations that occur during the culture process and maintain critical process parameters in appropriate states. However, control system design for industry-standard mammalian cell culture processes is still often performed in a laborious trial-and-error manner. This paper provides a novel control approach in which controller specifications to obtain desired control characteristics can be determined systematically by combining a culture model with control theory. In the proposed scheme, control conditions, such as PID parameters, can be specified mechanistically based on process understanding and control requirements without qualitative decision making or specific preliminary experiments. The effectiveness of the model-based control algorithm was verified by control simulations assuming perfusion Chinese hamster ovary culture. As a tool to assist in the development of control strategies, this study will reduce the high operational workload that is a serious problem in continuous culture and facilitate the digitalization of bioprocesses.
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Affiliation(s)
- Ayumu Sakaki
- Innovation Center, Marketing Headquarters, Yokogawa Electric Corporation, Tokyo, Japan
| | - Tetsushi Namatame
- Innovation Center, Marketing Headquarters, Yokogawa Electric Corporation, Tokyo, Japan
| | - Makoto Nakaya
- Innovation Center, Marketing Headquarters, Yokogawa Electric Corporation, Tokyo, Japan
| | - Takeshi Omasa
- Graduate School of Engineering, Osaka University, Osaka, Japan
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4
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Iglesias CF, Bolic M. How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models. SENSORS (BASEL, SWITZERLAND) 2024; 24:653. [PMID: 38276345 PMCID: PMC11154378 DOI: 10.3390/s24020653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/22/2023] [Accepted: 01/06/2024] [Indexed: 01/27/2024]
Abstract
The unstructured mechanistic model (UMM) allows for modeling the macro-scale of a phenomenon without known mechanisms. This is extremely useful in biomanufacturing because using the UMM for the joint estimation of states and parameters with an extended Kalman filter (JEKF) can enable the real-time monitoring of bioprocesses with unknown mechanisms. However, the UMM commonly used in biomanufacturing contains ordinary differential equations (ODEs) with unshared parameters, weak variables, and weak terms. When such a UMM is coupled with an initial state error covariance matrix P(t=0) and a process error covariance matrix Q with uncorrelated elements, along with just one measured state variable, the joint extended Kalman filter (JEKF) fails to estimate the unshared parameters and state simultaneously. This is because the Kalman gain corresponding to the unshared parameter remains constant and equal to zero. In this work, we formally describe this failure case, present the proof of JEKF failure, and propose an approach called SANTO to side-step this failure case. The SANTO approach consists of adding a quantity to the state error covariance between the measured state variable and unshared parameter in the initial P(t = 0) of the matrix Ricatti differential equation to compute the predicted error covariance matrix of the state and prevent the Kalman gain from being zero. Our empirical evaluations using synthetic and real datasets reveal significant improvements: SANTO achieved a reduction in root-mean-square percentage error (RMSPE) of up to approximately 17% compared to the classical JEKF, indicating a substantial enhancement in estimation accuracy.
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Affiliation(s)
- Cristovão Freitas Iglesias
- School of Electrical Engineering and Computer Science (EECS), University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Miodrag Bolic
- School of Electrical Engineering and Computer Science (EECS), University of Ottawa, Ottawa, ON K1N 6N5, Canada
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5
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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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Medeiros Garcia Alcântara J, Iannacci F, Morbidelli M, Sponchioni M. Soft sensor based on Raman spectroscopy for the in-line monitoring of metabolites and polymer quality in the biomanufacturing of polyhydroxyalkanoates. J Biotechnol 2023; 377:23-33. [PMID: 37879569 DOI: 10.1016/j.jbiotec.2023.10.005] [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: 08/05/2023] [Revised: 10/04/2023] [Accepted: 10/16/2023] [Indexed: 10/27/2023]
Abstract
Polyhydroxyalkanoates (PHA) are among the most promising bio-based alternatives to conventional petroleum-based plastics. These biodegradable polyesters can in fact be produced by fermentation from bacteria like Cupriavidus necator, thus reducing the environmental footprint of the manufacturing process. However, ensuring consistent product quality attributes is a major challenge of biomanufacturing. To address this issue, the implementation of real-time monitoring tools is essential to increase process understanding, enable a prompt response to possible process deviations and realize on-line process optimization. In this work, a soft sensor based on in situ Raman spectroscopy was developed and applied to the in-line monitoring of PHA biomanufacturing. This strategy allows the collection of quantitative information directly from the culture broth, without the need for sampling, and at high frequency. In fact, through an optimized multivariate data analysis pipeline, this soft sensor allows monitoring cell dry weight, as well as carbon and nitrogen source concentrations with root mean squared errors (RMSE) equal to 3.71, 7 and 0.03 g/L, respectively. In addition, this tool allows the in-line monitoring of intracellular PHA accumulation, with an RMSE of 14 gPHA/gCells. For the first time, also the number and weight average molecular weights of the polymer produced could be monitored, with RMSE of 8.7E4 and 11.6E4 g/mol, respectively. Overall, this work demonstrates the potential of Raman spectroscopy in the in-line monitoring of biotechnology processes, leading to the simultaneous measurement of several process variables in real time without the need of sampling and labor-intensive sample preparations.
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Affiliation(s)
- João Medeiros Garcia Alcântara
- Dept. of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, via Mancinelli 7, Milano 20131, Italy
| | - Francesco Iannacci
- Dept. of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, via Mancinelli 7, Milano 20131, Italy
| | - Massimo Morbidelli
- Dept. of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, via Mancinelli 7, Milano 20131, Italy
| | - Mattia Sponchioni
- Dept. of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, via Mancinelli 7, Milano 20131, Italy.
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7
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Drobnjakovic M, Hart R, Kulvatunyou BS, Ivezic N, Srinivasan V. Current challenges and recent advances on the path towards continuous biomanufacturing. Biotechnol Prog 2023; 39:e3378. [PMID: 37493037 DOI: 10.1002/btpr.3378] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 05/13/2023] [Accepted: 06/21/2023] [Indexed: 07/27/2023]
Abstract
Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever-evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end-to-end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.
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Affiliation(s)
- Milos Drobnjakovic
- Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Roger Hart
- National Institute for Innovation in Manufacturing Biopharmaceuticals, Newark, New Jersey, USA
| | - Boonserm Serm Kulvatunyou
- Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Nenad Ivezic
- Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Vijay Srinivasan
- Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
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8
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Kemmer A, Fischer N, Wilms T, Cai L, Groß S, King R, Neubauer P, Cruz Bournazou MN. Nonlinear state estimation as tool for online monitoring and adaptive feed in high throughput cultivations. Biotechnol Bioeng 2023; 120:3261-3275. [PMID: 37497592 DOI: 10.1002/bit.28509] [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: 12/23/2022] [Revised: 05/08/2023] [Accepted: 07/11/2023] [Indexed: 07/28/2023]
Abstract
Robotic facilities that can perform advanced cultivations (e.g., fed-batch or continuous) in high throughput have drastically increased the speed and reliability of the bioprocess development pipeline. Still, developing reliable analytical technologies, that can cope with the throughput of the cultivation system, has proven to be very challenging. On the one hand, the analytical accuracy suffers from the low sampling volumes, and on the other hand, the number of samples that must be treated rapidly is very large. These issues have been a major limitation for the implementation of feedback control methods in miniaturized bioreactor systems, where observations of the process states are typically obtained after the experiment has finished. In this work, we implement a Sigma-Point Kalman Filter in a high throughput platform with 24 parallel experiments at the mL-scale to demonstrate its viability and added value in high throughput experiments. The filter exploits the information generated by the ammonia-based pH control to enable the continuous estimation of the biomass concentration, a critical state to monitor the specific rates of production and consumption in the process. The objective in the selected case study is to ensure that the selected specific substrate consumption rate is tightly controlled throughout the complete Escherichia coli cultivations for recombinant production of an antibody fragment.
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Affiliation(s)
- Annina Kemmer
- Chair of Bioprocess Engineering, Technische Universität Berlin, Berlin, Germany
| | - Nico Fischer
- Chair of Measurement and Control, Technische Universität Berlin, Berlin, Germany
| | - Terrance Wilms
- Chair of Measurement and Control, Technische Universität Berlin, Berlin, Germany
| | - Linda Cai
- Chair of Bioprocess Engineering, Technische Universität Berlin, Berlin, Germany
| | - Sebastian Groß
- Chair of Bioprocess Engineering, Technische Universität Berlin, Berlin, Germany
- wega Informatik (Deutschland) GmbH, Weil am Rhein, Germany
| | - Rudibert King
- Chair of Measurement and Control, Technische Universität Berlin, Berlin, Germany
| | - Peter Neubauer
- Chair of Bioprocess Engineering, Technische Universität Berlin, Berlin, Germany
| | - M Nicolas Cruz Bournazou
- Chair of Bioprocess Engineering, Technische Universität Berlin, Berlin, Germany
- DataHow AG, Dübendorf, Switzerland
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Mahanty B. Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges. Biotechnol Bioeng 2023; 120:2072-2091. [PMID: 37458311 DOI: 10.1002/bit.28503] [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: 05/16/2023] [Revised: 07/09/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.
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Affiliation(s)
- Biswanath Mahanty
- Department of Biotechnology, Krunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
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10
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Schiemer R, Weggen JT, Schmitt KM, Hubbuch J. An adaptive soft-sensor for advanced real-time monitoring of an antibody-drug conjugation reaction. Biotechnol Bioeng 2023. [PMID: 37190793 DOI: 10.1002/bit.28428] [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: 01/20/2023] [Revised: 03/31/2023] [Accepted: 05/01/2023] [Indexed: 05/17/2023]
Abstract
In the production of antibody-drug conjugates (ADCs), the conjugation reaction is a central step defining the final product composition and, hence, directly affecting product safety and efficacy. To enable real-time monitoring, spectroscopic sensors in combination with multivariate regression models have gained popularity in recent years. The extended Kalman filter (EKF) can be used as so-called soft-sensor to fuse sensor predictions with long-horizon forecasts by process models. This enables the dynamic update of the current state and provides increased robustness against experimental noise or model errors. Due to the uncertainty associated with sensor and process models in biopharmaceutical applications, the deployment of such soft-sensors is challenging. In this study, we demonstrate the combination of an uncertainty-aware sensor model with a kinetic reaction model using an EKF to monitor a site-directed ADC conjugation reaction. As the sensor model, a Gaussian process regression model is presented to realize a time-variant determination of the sensor uncertainty. The EKF fuses the time-discrete predictions of the amount of conjugated drug from the sensor model with the time-continuous predictions from the kinetic model. While the ADC species are not distinguishable by on-line recorded UV/Vis spectra, the developed soft-sensor is able to dynamically update all relevant reaction species. It could be shown that the use of time-variant process and sensor noise computation approaches improved the performance of the EKF and achieved a reduction of the prediction error of up to 23% compared with the kinetic model. The developed framework proved to enhance robustness against noisy sensor measurements or wrong model initialization and was successfully transferred from batch to fed-batch mode. In future, this framework could be implemented for model-based process control and be adopted for other ADC conjugation reaction types.
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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, Baden-Württemberg, Germany
| | - Jan Tobias Weggen
- Institute of Process Engineering in Life Sciences-Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Baden-Württemberg, Germany
| | - Katrin Marianne Schmitt
- Institute of Process Engineering in Life Sciences-Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Baden-Württemberg, Germany
| | - Jürgen Hubbuch
- Institute of Process Engineering in Life Sciences-Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Baden-Württemberg, Germany
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11
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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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12
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Iglesias CF, Ristovski M, Bolic M, Cuperlovic-Culf M. rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing. Bioengineering (Basel) 2023; 10:bioengineering10020229. [PMID: 36829723 PMCID: PMC9951952 DOI: 10.3390/bioengineering10020229] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for directly translating the genomic revolution into medicinal therapies. However, the manufacturing of rAAV viral vectors remains challenging in the upstream processing with low rAAV yield in large-scale production and high cost, limiting the generalization of rAAV-based treatments. This situation can be improved by real-time monitoring of critical process parameters (CPP) that affect critical quality attributes (CQA). To achieve this aim, soft sensing combined with predictive modeling is an important strategy that can be used for optimizing the upstream process of rAAV production by monitoring critical process variables in real time. However, the development of soft sensors for rAAV production as a fast and low-cost monitoring approach is not an easy task. This review article describes four challenges and critically discusses the possible solutions that can enable the application of soft sensors for rAAV production monitoring. The challenges from a data scientist's perspective are (i) a predictor variable (soft-sensor inputs) set without AAV viral titer, (ii) multi-step forecasting, (iii) multiple process phases, and (iv) soft-sensor development composed of the mechanistic model.
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Affiliation(s)
| | - Milica Ristovski
- Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Miodrag Bolic
- Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Miroslava Cuperlovic-Culf
- Digital Technologies Research Center, National Research Council, Ottawa, ON K1A 0R6, Canada
- Department of Biochemistry, Microbiology, and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Correspondence:
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13
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Mondal PP, Galodha A, Verma VK, Singh V, Show PL, Awasthi MK, Lall B, Anees S, Pollmann K, Jain R. Review on machine learning-based bioprocess optimization, monitoring, and control systems. BIORESOURCE TECHNOLOGY 2023; 370:128523. [PMID: 36565820 DOI: 10.1016/j.biortech.2022.128523] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.
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Affiliation(s)
- Partha Pratim Mondal
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Abhinav Galodha
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Vishal Kumar Verma
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, 382715, Gujarat, India
| | - Pau Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India; Department of Chemical and Environmental Engineering, University of Nottingham, Malaysia, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China
| | - Brejesh Lall
- Electrical Engineering Department, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Sanya Anees
- Department of Electronics and Communication Engineering, Indian Institute of Information Technology Guwahati, Bongora, Guwahati 781015, India
| | - Katrin Pollmann
- Helmholtz-Zentrum Dresden-Rossendorf, Helmhholtz Institute Freiberg for Resource Technology, Bautzner Landstrasse 400, 01328 Dresden, Germany
| | - Rohan Jain
- Helmholtz-Zentrum Dresden-Rossendorf, Helmhholtz Institute Freiberg for Resource Technology, Bautzner Landstrasse 400, 01328 Dresden, Germany.
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14
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Okamura K, Badr S, Murakami S, Sugiyama H. Hybrid Modeling of CHO Cell Cultivation in Monoclonal Antibody Production with an Impurity Generation Module. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c00736] [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)
- Kozue Okamura
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Sara Badr
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Sei Murakami
- Manufacturing Technology Association of Biologics, 2-6-16, Shinkawa, Chuo-ku, 104-0033 Tokyo, Japan
| | - Hirokazu Sugiyama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
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15
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Eslami T, Steinberger M, Csizmazia C, Jungbauer A, Lingg N. Online optimization of dynamic binding capacity and productivity by model predictive control. J Chromatogr A 2022; 1680:463420. [PMID: 36007474 DOI: 10.1016/j.chroma.2022.463420] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/03/2022] [Accepted: 08/12/2022] [Indexed: 11/25/2022]
Abstract
In preparative and industrial chromatography, the current viewpoint is that the dynamic binding capacity governs the process economy, and increased dynamic binding capacity and column utilization are achieved at the expense of productivity. The dynamic binding capacity in chromatography increases with residence time until it reaches a plateau, whereas productivity has an optimum. Therefore, the loading step of a chromatographic process is a balancing act between productivity, column utilization, and buffer consumption. This work presents an online optimization approach for capture chromatography that employs a residence time gradient during the loading step to improve the traditional trade-off between productivity and resin utilization. The approach uses the extended Kalman filter as a soft sensor for product concentration in the system and a model predictive controller to accomplish online optimization using the pore diffusion model as a simple mechanistic model. When a soft sensor for the product is placed before and after the column, the model predictive controller can forecast the optimal condition to maximize productivity and resin utilization. The controller can also account for varying feed concentrations. This study examined the robustness as the feed concentration varied within a range of 50%. The online optimization was demonstrated with two model systems: purification of a monoclonal antibody by protein A affinity and lysozyme by cation-exchange chromatography. Using the presented optimization strategy with a controller saves up to 43% of the buffer and increases the productivity together with resin utilization in a similar range as a multi-column continuous counter-current loading process.
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Affiliation(s)
- Touraj Eslami
- Department of Biotechnology, Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, Vienna A-1190, Austria; Evon GmbH, Wollsdorf 154, A-8181St., Ruprecht an der Raab, Austria
| | - Martin Steinberger
- Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21b, Graz A-8010, Austria
| | - Christian Csizmazia
- Department of Biotechnology, Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, Vienna A-1190, Austria
| | - Alois Jungbauer
- Department of Biotechnology, Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, Vienna A-1190, Austria; Austrian Centre of Industrial Biotechnology, Muthgasse 18, Vienna A-1190, Austria.
| | - Nico Lingg
- Department of Biotechnology, Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, Vienna A-1190, Austria; Austrian Centre of Industrial Biotechnology, Muthgasse 18, Vienna A-1190, Austria.
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16
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A comparative evaluation of machine learning algorithms for predicting syngas fermentation outcomes. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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17
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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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18
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Jiang Q, Seth S, Scharl T, Schroeder T, Jungbauer A, Dimartino S. Prediction of the performance of pre-packed purification columns through machine learning. J Sep Sci 2022; 45:1445-1457. [PMID: 35262290 PMCID: PMC9310636 DOI: 10.1002/jssc.202100864] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/31/2022] [Accepted: 03/01/2022] [Indexed: 11/11/2022]
Abstract
Pre-packed columns have been increasingly used in process development and biomanufacturing thanks to their ease of use and consistency. Traditionally, packing quality is predicted through rate models, which require extensive calibration efforts through independent experiments to determine relevant mass transfer and kinetic rate constants. Here we propose machine learning as a complementary predictive tool for column performance. A machine learning algorithm, extreme gradient boosting, was applied to a large data set of packing quality (plate height and asymmetry) for pre-packed columns as a function of quantitative parameters (column length, column diameter, and particle size) and qualitative attributes (backbone and functional mode). The machine learning model offered excellent predictive capabilities for the plate height and the asymmetry (90 and 93%, respectively), with packing quality strongly influenced by backbone (∼70% relative importance) and functional mode (∼15% relative importance), well above all other quantitative column parameters. The results highlight the ability of machine learning to provide reliable predictions of column performance from simple, generic parameters, including strategic qualitative parameters such as backbone and functionality, usually excluded from quantitative considerations. Our results will guide further efforts in column optimization, for example, by focusing on improvements of backbone and functional mode to obtain optimized packings.
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Affiliation(s)
- Qihao Jiang
- Institute of BioengineeringSchool of EngineeringThe University of EdinburghEdinburghUK
| | - Sohan Seth
- School of InformaticsThe University of EdinburghEdinburghUK
| | - Theresa Scharl
- Austrian Centre of Industrial BiotechnologyViennaAustria
- Institute of StatisticsUniversity of Natural Resources and Life Sciences ViennaViennaAustria
| | | | - Alois Jungbauer
- Austrian Centre of Industrial BiotechnologyViennaAustria
- Department of BiotechnologyUniversity of Natural Resources and Life SciencesViennaAustria
| | - Simone Dimartino
- Institute of BioengineeringSchool of EngineeringThe University of EdinburghEdinburghUK
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19
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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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20
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Bayer B, Duerkop M, Striedner G, Sissolak B. Model Transferability and Reduced Experimental Burden in Cell Culture Process Development Facilitated by Hybrid Modeling and Intensified Design of Experiments. Front Bioeng Biotechnol 2022; 9:740215. [PMID: 35004635 PMCID: PMC8733703 DOI: 10.3389/fbioe.2021.740215] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/22/2021] [Indexed: 12/22/2022] Open
Abstract
Reliable process development is accompanied by intense experimental effort. The utilization of an intensified design of experiments (iDoE) (intra-experimental critical process parameter (CPP) shifts combined) with hybrid modeling potentially reduces process development burden. The iDoE can provide more process response information in less overall process time, whereas hybrid modeling serves as a commodity to describe this behavior the best way. Therefore, a combination of both approaches appears beneficial for faster design screening and is especially of interest at larger scales where the costs per experiment rise significantly. Ideally, profound process knowledge is gathered at a small scale and only complemented with few validation experiments on a larger scale, saving valuable resources. In this work, the transferability of hybrid modeling for Chinese hamster ovary cell bioprocess development along process scales was investigated. A two-dimensional DoE was fully characterized in shake flask duplicates (300 ml), containing three different levels for the cultivation temperature and the glucose concentration in the feed. Based on these data, a hybrid model was developed, and its performance was assessed by estimating the viable cell concentration and product titer in 15 L bioprocesses with the same DoE settings. To challenge the modeling approach, 15 L bioprocesses also comprised iDoE runs with intra-experimental CPP shifts, impacting specific cell rates such as growth, consumption, and formation. Subsequently, the applicability of the iDoE cultivations to estimate static cultivations was also investigated. The shaker-scale hybrid model proved suitable for application to a 15 L scale (1:50), estimating the viable cell concentration and the product titer with an NRMSE of 10.92% and 17.79%, respectively. Additionally, the iDoE hybrid model performed comparably, displaying NRMSE values of 13.75% and 21.13%. The low errors when transferring the models from shaker to reactor and between the DoE and the iDoE approach highlight the suitability of hybrid modeling for mammalian cell culture bioprocess development and the potential of iDoE to accelerate process characterization and to improve process understanding.
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Affiliation(s)
- Benjamin Bayer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.,Novasign GmbH, Vienna, 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.,Novasign GmbH, Vienna, Austria
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21
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A hybrid model for multipoint real time potency observation in continuous direct compression manufacturing operations. Int J Pharm 2021; 613:121385. [PMID: 34919995 DOI: 10.1016/j.ijpharm.2021.121385] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/22/2021] [Accepted: 12/10/2021] [Indexed: 11/23/2022]
Abstract
The ongoing transition from batch to continuous manufacturing offers both challenges and opportunities in the field of oral solid dosage form production. In turn, Process Analytical Technology (PAT) offers a path towards the successful deployment of continuous tablet manufacturing in rotary tablet presses. One promising PAT tool for this endeavour is the NIR-derived potency measurement. However, the high degree of noise in the data may hamper the extraction of useful information. For this reason, this work focused on the implementation of an adaptive Kalman filter algorithm that incorporates and reconciles the potency prediction given by one or more NIR probes with those of a semi-mechanistic compartmental model developed for the application at hand. This approach allowed for more robust concentration estimations. Furthermore, it was observed that potency levels in multiple locations in the studied tablet press (including those in the finished tablets) could be appropriately inferred using a single in-line measurement data stream. This methodology thus opens the door to advanced process control applications.
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22
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23
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Sinner P, Stiegler M, Goldbeck O, Seibold GM, Herwig C, Kager J. Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture. Biotechnol Bioeng 2021; 119:575-590. [PMID: 34821377 PMCID: PMC9299845 DOI: 10.1002/bit.28001] [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: 05/12/2021] [Revised: 10/06/2021] [Accepted: 11/12/2021] [Indexed: 01/16/2023]
Abstract
Model‐based state estimators enable online monitoring of bioprocesses and, thereby, quantitative process understanding during running operations. During prolonged continuous bioprocesses strain physiology is affected by selection pressure. This can cause time‐variable metabolic capacities that lead to a considerable model‐plant mismatch reducing monitoring performance if model parameters are not adapted accordingly. Variability of metabolic capacities therefore needs to be integrated in the in silico representation of a process using model‐based monitoring approaches. To enable online monitoring of multiple concentrations as well as metabolic capacities during continuous bioprocessing of spent sulfite liquor with Corynebacterium glutamicum, this study presents a particle filtering framework that takes account of parametric variability. Physiological parameters are continuously adapted by Bayesian inference, using noninvasive off‐gas measurements. Additional information on current parameter importance is derived from time‐resolved sensitivity analysis. Experimental results show that the presented framework enables accurate online monitoring of long‐term culture dynamics, whereas state estimation without parameter adaption failed to quantify substrate metabolization and growth capacities under conditions of high selection pressure. Online estimated metabolic capacities are further deployed for multiobjective optimization to identify time‐variable optimal operating points. Thereby, the presented monitoring system forms a basis for adaptive control during continuous bioprocessing of lignocellulosic by‐product streams.
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Affiliation(s)
- Peter Sinner
- Research Unit of Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria
| | - Marlene Stiegler
- Research Unit of Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria
| | - Oliver Goldbeck
- Institute of Microbiology and Biotechnology, University of Ulm, Ulm, Germany
| | - Gerd M Seibold
- Section for Synthetic Biology, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Christoph Herwig
- Research Unit of Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria
| | - Julian Kager
- Research Unit of Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria.,Competence Center CHASE GmbH, Linz, Austria
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24
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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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25
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Luo Y, Kurian V, Ogunnaike BA. Bioprocess systems analysis, modeling, estimation, and control. Curr Opin Chem Eng 2021. [DOI: 10.1016/j.coche.2021.100705] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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26
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Hutter C, von Stosch M, Cruz Bournazou MN, Butté A. Knowledge transfer across cell lines using hybrid Gaussian process models with entity embedding vectors. Biotechnol Bioeng 2021; 118:4389-4401. [PMID: 34383309 DOI: 10.1002/bit.27907] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 05/06/2021] [Accepted: 07/23/2021] [Indexed: 11/06/2022]
Abstract
To date, a large number of experiments are performed to develop a biochemical process. The generated data is used only once, to take decisions for development. Could we exploit data of already developed processes to make predictions for a novel process, we could significantly reduce the number of experiments needed. Processes for different products exhibit differences in behaviour, typically only a subset behave similar. Therefore, effective learning on multiple product spanning process data requires a sensible representation of the product identity. We propose to represent the product identity (a categorical feature) by embedding vectors that serve as input to a Gaussian process regression model. We demonstrate how the embedding vectors can be learned from process data and show that they capture an interpretable notion of product similarity. The improvement in performance is compared to traditional one-hot encoding on a simulated cross product learning task. All in all, the proposed method could render possible significant reductions in wet-lab experiments.
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Affiliation(s)
- Clemens Hutter
- DataHow AG, Zurich, Switzerland.,Chair for Mathematical Information Science, ETH Zurich
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27
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Ghosh D, Mhaskar P, MacGregor JF. Hybrid Partial Least Squares Models for Batch Processes: Integrating Data with Process Knowledge. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c00865] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Debanjan Ghosh
- Department of Chemical Engineering, McMaster University, Hamilton, Ontario L8S 4L7, Canada
| | - Prashant Mhaskar
- Department of Chemical Engineering, McMaster University, Hamilton, Ontario L8S 4L7, Canada
| | - John F. MacGregor
- Department of Chemical Engineering, McMaster University, Hamilton, Ontario L8S 4L7, Canada
- ProSensus Inc., Burlington, Ontario L7L 5M4, Canada
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28
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Abstract
The monitoring of the main variables and parameters of biotechnological processes is of key importance for the research and control of the processes, especially in industrial installations, where there is a limited number of measurements. For this reason, many researchers are focusing their efforts on developing appropriate algorithms (software sensors (SS)) to provide reliable information on unmeasurable variables and parameters, based on the available on-line information. In the literature, a large number of developments related to this topic that concern data-based and model-based sensors are presented. Up-to-date reviews of data-driven SS for biotechnological processes have already been presented in the scientific literature. Hybrid software sensors as a combination between the abovementioned ones are under development. This gives a reason for the article to be focused on a review of model-based software sensors for biotechnological processes. The most applied model-based methods for monitoring the kinetics and state variables of these processes are analyzed and compared. The following software sensors are considered: Kalman filters, methods based on estimators and observers of a deterministic type, probability observers, high-gain observers, sliding mode observers, adaptive observers, etc. The comparison is made in terms of their stability and number of tuning parameters. Particular attention is paid to the approach of the general dynamic model. The main characteristics of the classic variant proposed by D. Dochain are summarized. Results related to the development of this approach are analyzed. A key point is the presentation of new formalizations of kinetics and the design of new algorithms for its estimation in cases of uncertainty. The efficiency and applicability of the considered software sensors are discussed.
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29
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Sokolov M. Decision Making and Risk Management in Biopharmaceutical Engineering-Opportunities in the Age of Covid-19 and Digitalization. Ind Eng Chem Res 2020; 59:17587-17592. [PMID: 37556286 PMCID: PMC7507805 DOI: 10.1021/acs.iecr.0c02994] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In 2020, the Covid-19 pandemic resulted in a worldwide challenge without an evident solution. Many persons and authorities involved befriended the value of available data and established expertise to make decisions under time pressure. This omnipresent example is used to illustrate the decision-making procedure in biopharmaceutical manufacturing. This commentary addresses important challenges and opportunities to support risk management in biomanufacturing through a process-centered digitalization approach combining two vital worlds-formalized engineering fundamentals and data empowerment through customized machine learning. With many enabling technologies already available and first success stories reported, it will depend on the interaction of different groups of stakeholders how and when the huge potential of the discussed technologies will be broadly and systematically realized.
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Affiliation(s)
- Michael Sokolov
- DataHow, c/o ETH Zurich,
Vladimir-Prelog-Weg 1, Zurich, 8093, Switzerland
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