Implementation of machine learning tool for continued process verification of process chromatography unit operation
•A strategy for achieving CPV for cation exchange chromatography is presented.•SPC charts generated based on real-time measurement of the various CPPs and CQAs.•Python-based program to read these SPC charts and respond to any deviation.•Combination of analyzers, soft sensors, and advanced data analy...
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| Published in: | Journal of Chromatography A Vol. 1742; p. 465642 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
08.02.2025
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| ISSN: | 0021-9673, 1873-3778, 1873-3778 |
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| Abstract | •A strategy for achieving CPV for cation exchange chromatography is presented.•SPC charts generated based on real-time measurement of the various CPPs and CQAs.•Python-based program to read these SPC charts and respond to any deviation.•Combination of analyzers, soft sensors, and advanced data analytics enables Industry 4.0.
Recent advancements in technology, such as the emergence of artificial intelligence (AI) and machine learning (ML), have facilitated the progression of the biopharmaceutical industry toward the implementation of Industry 4.0. As per the guidelines set by the USFDA, process validation for biopharmaceutical production consists of three stages: process design, process qualification, and continuous process verification (CPV). This paper proposes a strategy for achieving CPV for a cation exchange chromatography unit operation, emphasizing the urgent need for such strategies in the biopharmaceutical industry. Statistical process control (SPC) charts were generated based on real-time measurement of the various critical process parameters (CPPs) measured via in-built sensors (pH, conductivity, UV, and delta column pressure) as well as of critical quality attributes (CQAs) like charge variant composition (Raman spectroscopy) and concentration (Near infrared spectroscopy). A Python-based program was created to read these SPC charts and respond to any deviation. The developed models for NIR coupled DNN PAT tool and Raman coupled DNN PAT tool exhibited satisfactory R2 values (> 0.90), highlighting the statistical significance of the proposed model. Further, the control strategy designed based on Raman spectroscopy for charge variant composition in CEX eluate has been demonstrated by intentional perturbations in the CEX load. The resulting CEX eluate output showed a consistent charge variant composition as that of control runs (acidic ∼20 ± 2 %, main ∼62 ± 2 % and basic ∼18 ± 2 %). It has been demonstrated how an appropriate selection of analyzers, soft sensors, and advanced data analytics can be used to execute CPV and enable the biopharmaceutical industry to implement Industry 4.0. |
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| AbstractList | Recent advancements in technology, such as the emergence of artificial intelligence (AI) and machine learning (ML), have facilitated the progression of the biopharmaceutical industry toward the implementation of Industry 4.0. As per the guidelines set by the USFDA, process validation for biopharmaceutical production consists of three stages: process design, process qualification, and continuous process verification (CPV). This paper proposes a strategy for achieving CPV for a cation exchange chromatography unit operation, emphasizing the urgent need for such strategies in the biopharmaceutical industry. Statistical process control (SPC) charts were generated based on real-time measurement of the various critical process parameters (CPPs) measured via in-built sensors (pH, conductivity, UV, and delta column pressure) as well as of critical quality attributes (CQAs) like charge variant composition (Raman spectroscopy) and concentration (Near infrared spectroscopy). A Python-based program was created to read these SPC charts and respond to any deviation. The developed models for NIR coupled DNN PAT tool and Raman coupled DNN PAT tool exhibited satisfactory R
values (> 0.90), highlighting the statistical significance of the proposed model. Further, the control strategy designed based on Raman spectroscopy for charge variant composition in CEX eluate has been demonstrated by intentional perturbations in the CEX load. The resulting CEX eluate output showed a consistent charge variant composition as that of control runs (acidic ∼20 ± 2 %, main ∼62 ± 2 % and basic ∼18 ± 2 %). It has been demonstrated how an appropriate selection of analyzers, soft sensors, and advanced data analytics can be used to execute CPV and enable the biopharmaceutical industry to implement Industry 4.0. Recent advancements in technology, such as the emergence of artificial intelligence (AI) and machine learning (ML), have facilitated the progression of the biopharmaceutical industry toward the implementation of Industry 4.0. As per the guidelines set by the USFDA, process validation for biopharmaceutical production consists of three stages: process design, process qualification, and continuous process verification (CPV). This paper proposes a strategy for achieving CPV for a cation exchange chromatography unit operation, emphasizing the urgent need for such strategies in the biopharmaceutical industry. Statistical process control (SPC) charts were generated based on real-time measurement of the various critical process parameters (CPPs) measured via in-built sensors (pH, conductivity, UV, and delta column pressure) as well as of critical quality attributes (CQAs) like charge variant composition (Raman spectroscopy) and concentration (Near infrared spectroscopy). A Python-based program was created to read these SPC charts and respond to any deviation. The developed models for NIR coupled DNN PAT tool and Raman coupled DNN PAT tool exhibited satisfactory R² values (> 0.90), highlighting the statistical significance of the proposed model. Further, the control strategy designed based on Raman spectroscopy for charge variant composition in CEX eluate has been demonstrated by intentional perturbations in the CEX load. The resulting CEX eluate output showed a consistent charge variant composition as that of control runs (acidic ∼20 ± 2 %, main ∼62 ± 2 % and basic ∼18 ± 2 %). It has been demonstrated how an appropriate selection of analyzers, soft sensors, and advanced data analytics can be used to execute CPV and enable the biopharmaceutical industry to implement Industry 4.0. Recent advancements in technology, such as the emergence of artificial intelligence (AI) and machine learning (ML), have facilitated the progression of the biopharmaceutical industry toward the implementation of Industry 4.0. As per the guidelines set by the USFDA, process validation for biopharmaceutical production consists of three stages: process design, process qualification, and continuous process verification (CPV). This paper proposes a strategy for achieving CPV for a cation exchange chromatography unit operation, emphasizing the urgent need for such strategies in the biopharmaceutical industry. Statistical process control (SPC) charts were generated based on real-time measurement of the various critical process parameters (CPPs) measured via in-built sensors (pH, conductivity, UV, and delta column pressure) as well as of critical quality attributes (CQAs) like charge variant composition (Raman spectroscopy) and concentration (Near infrared spectroscopy). A Python-based program was created to read these SPC charts and respond to any deviation. The developed models for NIR coupled DNN PAT tool and Raman coupled DNN PAT tool exhibited satisfactory R2 values (> 0.90), highlighting the statistical significance of the proposed model. Further, the control strategy designed based on Raman spectroscopy for charge variant composition in CEX eluate has been demonstrated by intentional perturbations in the CEX load. The resulting CEX eluate output showed a consistent charge variant composition as that of control runs (acidic ∼20 ± 2 %, main ∼62 ± 2 % and basic ∼18 ± 2 %). It has been demonstrated how an appropriate selection of analyzers, soft sensors, and advanced data analytics can be used to execute CPV and enable the biopharmaceutical industry to implement Industry 4.0.Recent advancements in technology, such as the emergence of artificial intelligence (AI) and machine learning (ML), have facilitated the progression of the biopharmaceutical industry toward the implementation of Industry 4.0. As per the guidelines set by the USFDA, process validation for biopharmaceutical production consists of three stages: process design, process qualification, and continuous process verification (CPV). This paper proposes a strategy for achieving CPV for a cation exchange chromatography unit operation, emphasizing the urgent need for such strategies in the biopharmaceutical industry. Statistical process control (SPC) charts were generated based on real-time measurement of the various critical process parameters (CPPs) measured via in-built sensors (pH, conductivity, UV, and delta column pressure) as well as of critical quality attributes (CQAs) like charge variant composition (Raman spectroscopy) and concentration (Near infrared spectroscopy). A Python-based program was created to read these SPC charts and respond to any deviation. The developed models for NIR coupled DNN PAT tool and Raman coupled DNN PAT tool exhibited satisfactory R2 values (> 0.90), highlighting the statistical significance of the proposed model. Further, the control strategy designed based on Raman spectroscopy for charge variant composition in CEX eluate has been demonstrated by intentional perturbations in the CEX load. The resulting CEX eluate output showed a consistent charge variant composition as that of control runs (acidic ∼20 ± 2 %, main ∼62 ± 2 % and basic ∼18 ± 2 %). It has been demonstrated how an appropriate selection of analyzers, soft sensors, and advanced data analytics can be used to execute CPV and enable the biopharmaceutical industry to implement Industry 4.0. •A strategy for achieving CPV for cation exchange chromatography is presented.•SPC charts generated based on real-time measurement of the various CPPs and CQAs.•Python-based program to read these SPC charts and respond to any deviation.•Combination of analyzers, soft sensors, and advanced data analytics enables Industry 4.0. Recent advancements in technology, such as the emergence of artificial intelligence (AI) and machine learning (ML), have facilitated the progression of the biopharmaceutical industry toward the implementation of Industry 4.0. As per the guidelines set by the USFDA, process validation for biopharmaceutical production consists of three stages: process design, process qualification, and continuous process verification (CPV). This paper proposes a strategy for achieving CPV for a cation exchange chromatography unit operation, emphasizing the urgent need for such strategies in the biopharmaceutical industry. Statistical process control (SPC) charts were generated based on real-time measurement of the various critical process parameters (CPPs) measured via in-built sensors (pH, conductivity, UV, and delta column pressure) as well as of critical quality attributes (CQAs) like charge variant composition (Raman spectroscopy) and concentration (Near infrared spectroscopy). A Python-based program was created to read these SPC charts and respond to any deviation. The developed models for NIR coupled DNN PAT tool and Raman coupled DNN PAT tool exhibited satisfactory R2 values (> 0.90), highlighting the statistical significance of the proposed model. Further, the control strategy designed based on Raman spectroscopy for charge variant composition in CEX eluate has been demonstrated by intentional perturbations in the CEX load. The resulting CEX eluate output showed a consistent charge variant composition as that of control runs (acidic ∼20 ± 2 %, main ∼62 ± 2 % and basic ∼18 ± 2 %). It has been demonstrated how an appropriate selection of analyzers, soft sensors, and advanced data analytics can be used to execute CPV and enable the biopharmaceutical industry to implement Industry 4.0. |
| ArticleNumber | 465642 |
| Author | Nitika, Nitika Buddhiraju, Venkata Sudheendra Anupa, Anupa Trivedi, Rishika Rathore, Anurag S Runkana, Venkataramana Jesubalan, Naveen G. |
| Author_xml | – sequence: 1 givenname: Anupa orcidid: 0000-0003-2242-8764 surname: Anupa fullname: Anupa, Anupa organization: School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, India – sequence: 2 givenname: Naveen G. surname: Jesubalan fullname: Jesubalan, Naveen G. organization: School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, India – sequence: 3 givenname: Rishika surname: Trivedi fullname: Trivedi, Rishika organization: Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India – sequence: 4 givenname: Nitika surname: Nitika fullname: Nitika, Nitika organization: Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India – sequence: 5 givenname: Venkata Sudheendra orcidid: 0000-0002-2671-4023 surname: Buddhiraju fullname: Buddhiraju, Venkata Sudheendra organization: Tata Research Development and Design Centre, TCS Research, Tata Consultancy Services, Pune, India – sequence: 6 givenname: Venkataramana orcidid: 0000-0002-3609-5529 surname: Runkana fullname: Runkana, Venkataramana organization: Tata Research Development and Design Centre, TCS Research, Tata Consultancy Services, Pune, India – sequence: 7 givenname: Anurag S orcidid: 0000-0002-5913-4244 surname: Rathore fullname: Rathore, Anurag S email: asrathore@biotechcmz.com organization: Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39778281$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1021/acs.analchem.1c03250 10.1016/j.tibtech.2022.08.007 10.1021/acs.analchem.8b02386 10.1002/jctb.6798 10.1007/s11095-024-03663-9 10.1002/bit.27236 10.1002/biot.202000524 10.1016/j.memsci.2020.118492 10.1002/btpr.2435 10.1002/bit.27454 10.1002/biot.201700286 10.1002/biot.202000121 10.1016/j.chroma.2017.01.068 10.1002/btpr.3252 10.1016/j.tibtech.2021.12.003 10.1002/biot.201800061 10.1021/ac0205154 10.1002/bit.28307 10.1002/bit.25695 10.5731/pdajpst.2021.012665 10.3390/bioengineering4010021 |
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| Keywords | Monoclonal antibodies Machine learning (ML) Continued process verification (CPV) Artificial intelligence (AI) Cation exchange chromatography Deep neural network (DNN) |
| Language | English |
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| SubjectTerms | artificial intelligence Artificial intelligence (AI) biopharmaceutical industry biopharmaceuticals Cation exchange chromatography Chromatography, Ion Exchange - methods Continued process verification (CPV) data analysis Deep neural network (DNN) Machine Learning Machine learning (ML) Monoclonal antibodies near-infrared spectroscopy process control process design Raman spectroscopy Spectrum Analysis, Raman |
| Title | Implementation of machine learning tool for continued process verification of process chromatography unit operation |
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