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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Bibliographic Details
Published in:Journal of Chromatography A Vol. 1742; p. 465642
Main Authors: Anupa, Anupa, Jesubalan, Naveen G., Trivedi, Rishika, Nitika, Nitika, Buddhiraju, Venkata Sudheendra, Runkana, Venkataramana, Rathore, Anurag S
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 08.02.2025
Subjects:
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
Monoclonal antibodies
Machine learning (ML)
Continued process verification (CPV)
Artificial intelligence (AI)
Cation exchange chromatography
Deep neural network (DNN)
ISSN:0021-9673, 1873-3778, 1873-3778
Online Access:Get full text
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Summary:•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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ISSN:0021-9673
1873-3778
1873-3778
DOI:10.1016/j.chroma.2024.465642