Model-Based Methods in the Biopharmaceutical Process Lifecycle

Kröll, Paul; Hofer, Alexandra; Ulonska, Sophia; Kager, Julian; Herwig, Christoph

doi:10.1007/s11095-017-2308-y

Cited by 64 publications

(41 citation statements)

References 117 publications

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“…The process variability represented in the training set defines the model applicability (Esmonde‐White et al, ). Therefore transferability of the models to other processes relies on measured data in the training set (Craven, Shirsat, Whelan, & Glennon, ; Kroll, Hofer, Ulonska, Kager, & Herwig, ; Pernot, ). The established methodology allows simultaneous real‐time prediction of quantity, HCP, and dsDNA.…”

Section: Discussionmentioning

confidence: 99%

Real‐time monitoring and model‐based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2

Sauer

Melcher

Mosor

et al. 2019

Biotech & Bioengineering

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Process analytical technology combines understanding and control of the process with real‐time monitoring of critical quality and performance attributes. The goal is to ensure the quality of the final product. Currently, chromatographic processes in biopharmaceutical production are predominantly monitored with UV/Vis absorbance and a direct correlation with purity and quantity is limited. In this study, a chromatographic workstation was equipped with additional online sensors, such as multi‐angle light scattering, refractive index, attenuated total reflection Fourier‐transform infrared, and fluorescence spectroscopy. Models to predict quantity, host cell proteins (HCP), and double‐stranded DNA (dsDNA) content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in Escherichia coliOnline data and corresponding offline data for product quantity and co‐eluting impurities, such as dsDNA and HCP, were analyzed using boosted structured additive regression. Different sensor combinations were used to achieve the best prediction performance for each quality attribute. Quantity can be adequately predicted by applying a small predictor set of the typical chromatographic workstation sensor signals with a test error of 0.85 mg/ml (range in training data: 0.1–28 mg/ml). For HCP and dsDNA additional fluorescence and/or attenuated total reflection Fourier‐transform infrared spectral information was important to achieve prediction errors of 200 (2–6579 ppm) and 340 ppm (8–3773 ppm), respectively.

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Section: Discussionmentioning

confidence: 99%

Real‐time monitoring and model‐based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2

Sauer

Melcher

Mosor

et al. 2019

Biotech & Bioengineering

View full text Add to dashboard Cite

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“…For this processing step model-based methods should be applied as they facilitate process understanding and thus the implementation of QbD. Modelling is a tool for the detection and characterization of the relationship between critical process parameters (CPP), key process parameters (kPP) and the generation of process knowledge (Kroll et al 2017). CPPs define product quality, whereas kPPs also influence the productivity and economical viability (Rathore and Winkle 2009).…”

Section: Recommendations and Outlookmentioning

confidence: 99%

Wanted: more monitoring and control during inclusion body processing

Humer

Spadiut

2018

World J Microbiol Biotechnol

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Inclusion bodies (IBs) are insoluble aggregates of misfolded protein in Escherichia coli. Against the outdated belief that the production of IBs should be avoided during recombinant protein production, quite a number of recombinant products are currently produced as IBs, which are then processed to give correctly folded and soluble product. However, this processing is quite cumbersome comprising IB wash, IB solubilization and refolding. To date, IB processing often happens rather uncontrolled and relies on empiricism rather than sound process understanding. In this mini review we describe current efforts to introduce more monitoring and control in IB processes, focusing on the refolding step, and thus generate process understanding and knowledge.

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“…Alternatively, atline analytical devices, such as HPLC systems, can give important process insight along the sequence of unit operations in the manufacturing platform (Karst et al, 2017). In cases where CQAs cannot be directly assessed, soft‐sensor‐based technologies can provide relevant substitutes (Kroll, Hofer, Ulonska, Kager, & Herwig, 2017; Mandenius & Gustavsson, 2015; Narayanan et al, 2019; Sokolov, Feidl, Morbidelli, & Butte, 2018; Solle et al, 2017; Sommeregger et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Process‐wide control and automation of an integrated continuous manufacturing platform for antibodies

Feidl

Vogg

Wolf

et al. 2020

Biotech & Bioengineering

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Integrated continuous manufacturing is entering the biopharmaceutical industry. The main drivers range from improved economics, manufacturing flexibility, and more consistent product quality. However, studies on fully integrated production platforms have been limited due to the higher degree of system complexity, limited process information, disturbance, and drift sensitivity, as well as difficulties in digital process integration. In this study, we present an automated end‐to‐end integrated process consisting of a perfusion bioreactor, CaptureSMB, virus inactivation (VI), and two polishing steps to produce an antibody from an instable cell line. A supervisory control and data acquisition (SCADA) system was developed, which digitally integrates unit operations and analyzers, collects and centrally stores all process data, and allows process‐wide monitoring and control. The integrated system consisting of bioreactor and capture step was operated initially for 4 days, after which the full end‐to‐end integrated run with no interruption lasted for 10 days. In response to decreasing cell‐specific productivity, the supervisory control adjusted the loading duration of the capture step to obtain high capacity utilization without yield loss and constant antibody quantity for subsequent operations. Moreover, the SCADA system coordinated VI neutralization and discharge to enable constant loading conditions on the polishing unit. Lastly, the polishing was sufficiently robust to cope with significantly increased aggregate levels induced on purpose during virus inactivation. It is demonstrated that despite significant process disturbances and drifts, a robust process design and the supervisory control enabled constant (optimum) process performance and consistent product quality.

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Model-Based Methods in the Biopharmaceutical Process Lifecycle

Cited by 64 publications

References 117 publications

Real‐time monitoring and model‐based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2

Real‐time monitoring and model‐based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2

Wanted: more monitoring and control during inclusion body processing

Process‐wide control and automation of an integrated continuous manufacturing platform for antibodies

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