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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] [MESH Headings] [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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2
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Nikita S, Bhattacharya S, Manocha K, Rathore AS. Deep learning framework for peak detection at the intact level of therapeutic proteins. J Sep Sci 2024; 47:e2400051. [PMID: 38819868 DOI: 10.1002/jssc.202400051] [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: 01/19/2024] [Revised: 05/14/2024] [Accepted: 05/21/2024] [Indexed: 06/01/2024]
Abstract
While automated peak detection functionalities are available in commercially accessible software, achieving optimal true positive rates frequently necessitates visual inspection and manual adjustments. In the initial phase of this study, hetero-variants (glycoforms) of a monoclonal antibody were distinguished using liquid chromatography-mass spectrometry, revealing discernible peaks at the intact level. To comprehensively identify each peak (hetero-variant) in the intact-level analysis, a deep learning approach utilizing convolutional neural networks (CNNs) was employed in the subsequent phase of the study. In the current case study, utilizing conventional software for peak identification, five peaks were detected using a 0.5 threshold, whereas seven peaks were identified using the CNN model. The model exhibited strong performance with a probability area under the curve (AUC) of 0.9949, surpassing that of partial least squares discriminant analysis (PLS-DA) (probability AUC of 0.8041), and locally weighted regression (LWR) (probability AUC of 0.6885) on the data acquired during experimentation in real-time. The AUC of the receiver operating characteristic curve also illustrated the superior performance of the CNN over PLS-DA and LWR.
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Affiliation(s)
- Saxena Nikita
- Department of Chemical Engineering, Indian Institute of Technology, Delhi, India
| | | | - Kriti Manocha
- Department of Chemical Engineering, Indian Institute of Technology, Delhi, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology, Delhi, India
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3
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Sharma V, Mottafegh A, Joo JU, Kang JH, Wang L, Kim DP. Toward microfluidic continuous-flow and intelligent downstream processing of biopharmaceuticals. LAB ON A CHIP 2024; 24:2861-2882. [PMID: 38751338 DOI: 10.1039/d3lc01097j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Biopharmaceuticals have emerged as powerful therapeutic agents, revolutionizing the treatment landscape for various diseases, including cancer, infectious diseases, autoimmune and genetic disorders. These biotherapeutics pave the way for precision medicine with their unique and targeted capabilities. The production of high-quality biologics entails intricate manufacturing processes, including cell culture, fermentation, purification, and formulation, necessitating specialized facilities and expertise. These complex processes are subject to rigorous regulatory oversight to evaluate the safety, efficacy, and quality of biotherapeutics prior to clinical approval. Consequently, these drugs undergo extensive purification unit operations to achieve high purity by effectively removing impurities and contaminants. The field of personalized precision medicine necessitates the development of novel and highly efficient technologies. Microfluidic technology addresses unmet needs by enabling precise and compact separation, allowing rapid, integrated and continuous purification modules. Moreover, the integration of intelligent biomanufacturing systems with miniaturized devices presents an opportunity to significantly enhance the robustness of complex downstream processing of biopharmaceuticals, with the benefits of automation and advanced control. This allows seamless data exchange, real-time monitoring, and synchronization of purification steps, leading to improved process efficiency, data management, and decision-making. Integrating autonomous systems into biopharmaceutical purification ensures adherence to regulatory standards, such as good manufacturing practice (GMP), positioning the industry to effectively address emerging market demands for personalized precision nano-medicines. This perspective review will emphasize on the significance, challenges, and prospects associated with the adoption of continuous, integrated, and intelligent methodologies in small-scale downstream processing for various types of biologics. By utilizing microfluidic technology and intelligent systems, purification processes can be enhanced for increased efficiency, cost-effectiveness, and regulatory compliance, shaping the future of biopharmaceutical production and enabling the development of personalized and targeted therapies.
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Affiliation(s)
- Vikas Sharma
- Center for Intelligent Microprocess of Pharmaceutical Synthesis, Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
| | - Amirreza Mottafegh
- Center for Intelligent Microprocess of Pharmaceutical Synthesis, Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
| | - Jeong-Un Joo
- Center for Intelligent Microprocess of Pharmaceutical Synthesis, Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
| | - Ji-Ho Kang
- Center for Intelligent Microprocess of Pharmaceutical Synthesis, Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
| | - Lei Wang
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, Heilongjiang, P. R. China
| | - Dong-Pyo Kim
- Center for Intelligent Microprocess of Pharmaceutical Synthesis, Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.
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4
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Hashizume T, Ying BW. Challenges in developing cell culture media using machine learning. Biotechnol Adv 2024; 70:108293. [PMID: 37984683 DOI: 10.1016/j.biotechadv.2023.108293] [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: 07/01/2023] [Revised: 10/17/2023] [Accepted: 11/14/2023] [Indexed: 11/22/2023]
Abstract
Microbial and mammalian cells are widely used in the food, pharmaceutical, and medical industries. Developing or optimizing culture media is essential to improve cell culture performance as a critical technology in cell culture engineering. Methodologies for media optimization have been developed to a great extent, such as the approaches of one-factor-at-a-time (OFAT) and response surface methodology (RSM). The present review introduces the emerging machine learning (ML) technology in cell culture engineering by combining high-throughput experimental technologies to develop highly efficient and effective culture media. The commonly used ML algorithms and the successful applications of employing ML in medium optimization are summarized. This review highlights the benefits of ML-assisted medium development and guides the selection of the media optimization method appropriate for various cell culture purposes.
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Affiliation(s)
- Takamasa Hashizume
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
| | - Bei-Wen Ying
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan.
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5
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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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6
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Kress J, Nandita E, Jones E, Sanou M, Higgins J, Kosanam H. A targeted liquid chromatography mass spectrometry method for routine monitoring of cell culture media components for bioprocess development. J Chromatogr A 2023; 1706:464281. [PMID: 37566999 DOI: 10.1016/j.chroma.2023.464281] [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: 02/18/2023] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023]
Abstract
The analysis of cell culture media (CCM) components is critical for understanding cell growth kinetics and overall product quality during biomanufacturing. Given the diverse physical and chemical nature of CCM compounds present at a wide range of concentrations, there is an increasing demand for single-platform analytical assays with exceptional specificity and sensitivity. This study presents a targeted LC-MS/MS method for the identification and quantitation of 110 CCM analytes is presented, where target metabolites are monitored over an 20-min gradient. The analyte panel constitutes amino acids, vitamins, organic acids, nucleic acids, carbohydrates, and lipids. The method employs isotopically labeled standards to enable specific and accurate relative quantitation of CCM compounds based on physicochemical properties and retention time. Quantitation is performed on a triple quadrupole mass spectrometer operated in multiple reaction monitoring (MRM) mode. The method demonstrates strong linearity with an R2 of ≥0.99 with three orders of linear dynamic range and inter-day and intra-day precision with a%CV of <10% for spiked-in quality control samples. We also present three case studies to demonstrate method applicability in the bioprocessing space for developing vaccines and biologics.
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Affiliation(s)
- Jared Kress
- Global Vaccines and Biologics Commercialization, Merck & Co., Inc, West Point, PA, USA
| | | | | | - Missa Sanou
- Global Vaccines and Biologics Commercialization, Merck & Co., Inc, West Point, PA, USA
| | - John Higgins
- Global Vaccines and Biologics Commercialization, Merck & Co., Inc, West Point, PA, USA
| | - Hari Kosanam
- Global Vaccines and Biologics Commercialization, Merck & Co., Inc, West Point, PA, USA.
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7
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Park SY, Kim SJ, Park CH, Kim J, Lee DY. Data-driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins. Biotechnol Bioeng 2023; 120:2494-2508. [PMID: 37079452 DOI: 10.1002/bit.28405] [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: 12/01/2022] [Revised: 04/05/2023] [Accepted: 04/10/2023] [Indexed: 04/21/2023]
Abstract
Recently, the advancement in process analytical technology and artificial intelligence (AI) has enabled the generation of enormous culture data sets from biomanufacturing processes that produce various recombinant therapeutic proteins (RTPs), such as monoclonal antibodies (mAbs). Thus, now it is very important to exploit them for the enhanced reliability, efficiency, and consistency of the RTP-producing culture processes and for the reduced incipient or abrupt faults. It is achievable by AI-based data-driven models (DDMs), which allow us to correlate biological and process conditions and cell culture states. In this work, we provide practical guidelines for choosing the best combination of model elements to design and implement successful DDMs for given hypothetical in-line data sets during mAb-producing Chinese hamster ovary cell culture, as such enabling us to forecast dynamic behaviors of culture performance such as viable cell density, mAb titer as well as glucose, lactate and ammonia concentrations. To do so, we created DDMs that balance computational load with model accuracy and reliability by identifying the best combination of multistep ahead forecasting strategies, input features, and AI algorithms, which is potentially applicable to implementation of interactive DDM within bioprocess digital twins. We believe this systematic study can help bioprocess engineers start developing predictive DDMs with their own data sets and learn how their cell cultures behave in near future, thereby rendering proactive decision possible.
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Affiliation(s)
- Seo-Young Park
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Sun-Jong Kim
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Cheol-Hwan Park
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Jiyong Kim
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
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8
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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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Fiedler D, Fink E, Aigner I, Leitinger G, Keller W, Roblegg E, Khinast JG. A multi-step machine learning approach for accelerating QbD-based process development of protein spray drying. Int J Pharm 2023:123133. [PMID: 37315637 DOI: 10.1016/j.ijpharm.2023.123133] [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: 03/31/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/16/2023]
Abstract
This study proposes a new material-efficient multi-step machine learning (ML) approach for the development of a design space (DS) for spray drying proteins. Typically, a DS is developed by performing a design of experiments (DoE) with the spray dryer and the protein of interest, followed by deriving the DoE models via multi-variate regression. This approach was followed as a benchmark to the ML approach. The more complex the process and required accuracy of the final model is, the more experiments are necessary. However, most biologics are expensive and thus experiments should be kept to a minimum. Therefore, the suitability of using a surrogate material and ML for the development of a DS was investigated. To this end, a DoE was performed with the surrogate and the data used for training the ML approach. The ML and DoE model predictions were compared to measurements of three protein-based validation runs. The suitability of using lactose as surrogate was investigated and advantages of the proposed approach were demonstrated. Limitations were identified at protein concentrations >35mg/ml and particle sizes of x50>6µm. Within the investigated DS protein secondary structure was preserved, and most process settings, resulted in yields >75% and residual moisture <10wt.%.
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Affiliation(s)
- Daniela Fiedler
- Graz University of Technology, Institute of Process and Particle Engineering, Inffeldgasse 13/III, 8010 Graz, Austria
| | - Elisabeth Fink
- Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/II, 8010 Graz, Austria
| | - Isabella Aigner
- Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/II, 8010 Graz, Austria
| | - Gerd Leitinger
- Medical University of Graz, Division of Cell Biology, Histology, and Embryology, Gottfried Schatz Research Center, Neue Stiftingtalstraße 6/II, 8010 Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria
| | - Walter Keller
- University of Graz, Institute of Molecular Biosciences, Department of Structural Biology, Humboldstraße 50/III, 8010 Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria
| | - Eva Roblegg
- Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/II, 8010 Graz, Austria; University of Graz, Institute of Pharmaceutical Sciences, Pharmaceutical Technology & Biopharmacy, Universitätsplatz 1, 8010 Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria
| | - Johannes G Khinast
- Graz University of Technology, Institute of Process and Particle Engineering, Inffeldgasse 13/III, 8010 Graz, Austria; Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13/II, 8010 Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria
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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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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: 14] [Impact Index Per Article: 14.0] [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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12
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Richter J, Lange F, Scheper T, Solle D, Beutel S. Digitale Zwillinge in der Bioprozesstechnik – Chancen und Möglichkeiten. CHEM-ING-TECH 2022. [DOI: 10.1002/cite.202200166] [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]
Affiliation(s)
- Jannik Richter
- Leibniz Universität Hannover Institut für Technische Chemie Callinstraße 5 30167 Hannover Deutschland
| | - Ferdinand Lange
- Leibniz Universität Hannover Institut für Technische Chemie Callinstraße 5 30167 Hannover Deutschland
| | - Thomas Scheper
- Leibniz Universität Hannover Institut für Technische Chemie Callinstraße 5 30167 Hannover Deutschland
| | - Dörte Solle
- Leibniz Universität Hannover Institut für Technische Chemie Callinstraße 5 30167 Hannover Deutschland
| | - Sascha Beutel
- Leibniz Universität Hannover Institut für Technische Chemie Callinstraße 5 30167 Hannover Deutschland
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13
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Doyle K, Tsopanoglou A, Fejér A, Glennon B, del Val IJ. Automated assembly of hybrid dynamic models for CHO cell culture processes. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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14
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Puranik A, Dandekar P, Jain R. Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals. Biotechnol Prog 2022; 38:e3291. [PMID: 35918873 DOI: 10.1002/btpr.3291] [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: 03/26/2022] [Revised: 06/20/2022] [Accepted: 07/31/2022] [Indexed: 11/10/2022]
Abstract
Principles of Industry 4.0 direct us to predict how pharmaceutical operations and regulations may exist with automation, digitization, artificial intelligence (AI), and real time data acquisition. Machine learning (ML), a sub-discipline of AI, involves the use of statistical tools to extract the desired information either through understanding the underlying patterns in the information or by development of mathematical relationships among the critical process parameters (CPPs) and critical quality attributes (CQAs) of biopharmaceuticals. ML is still in its infancy for directly supporting the quality-by-design based development and manufacturing of biopharmaceuticals. However, adoption of ML-based models in place of conventional multi-variate-data-analysis (MVDA) is increasing with the accumulation of large-scale data. This has been majorly contributed by the real-time monitoring of process variables and quality attributes of products through the implementation of process analytical technology in biopharmaceutical manufacturing. All aspects of healthcare, from drug design to product distribution, are complex and multidimensional. Thus, ML-based approaches are being applied to achieve sophistication, accuracy, flexibility and agility in all these areas. This review discusses the potential of ML for addressing the complex issues in diverse areas of biopharmaceutical development, such as biopharmaceuticals design and assessment of early stage development, upstream and downstream process development, analysis, characterization and prediction of post translational modifications (PTMs), formulation and stability studies. Moreover, the challenges in acquisition, cleaning and structuring the bioprocess data, which is one of the major hurdles in implementation of ML in biopharma industry, have also been discussed. Regulatory perspectives on implementation of AI/ML in the biopharma sector have also been briefly discussed. This article is a bird's eye view on the recent developments and applications of ML in overcoming the challenges for adopting "Industry - 4.0" in the biopharma industry.
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Affiliation(s)
- Amita Puranik
- Department of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai, India
| | - Prajakta Dandekar
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Matunga, Mumbai, India
| | - Ratnesh Jain
- Department of Chemical Engineering, Institute of Chemical Technology, Matunga, Mumbai, India
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