Pat Tools for Biologics: Considerations and Challenges

Molony, Michael; Ündey, Cenk

doi:10.1002/9780470466315.ch12

Cited by 8 publications

(6 citation statements)

References 45 publications

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“…Thus, a clear need exists to transfer the monitoring of these traditionally offline measured parameters toward an in situ application which will create the potential for the real‐time control. Driving this adaptation of PAT while also meeting the guidance provided in the regulatory framework poses specific challenges for the development, manufacturing, and quality assurance of biologics due to the multivariate nature of the environments required for synthesizing these complex protein structures …”

Section: Introductionmentioning

confidence: 99%

Cross‐scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis

Berry

Moretto

Matthews

et al. 2014

Biotechnology Progress

120

125

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Multi-component, multi-scale Raman spectroscopy modeling results from a monoclonal antibody producing CHO cell culture process including data from two development scales (3 L, 200 L) and a clinical manufacturing scale environment (2,000 L) are presented. Multivariate analysis principles are a critical component to partial least squares (PLS) modeling but can quickly turn into an overly iterative process, thus a simplified protocol is proposed for addressing necessary steps including spectral preprocessing, spectral region selection, and outlier removal to create models exclusively from cell culture process data without the inclusion of spectral data from chemically defined nutrient solutions or targeted component spiking studies. An array of single-scale and combination-scale modeling iterations were generated to evaluate technology capabilities and model scalability. Analysis of prediction errors across models suggests that glucose, lactate, and osmolality are well modeled. Model strength was confirmed via predictive validation and by examining performance similarity across single-scale and combination-scale models. Additionally, accurate predictive models were attained in most cases for viable cell density and total cell density; however, these components exhibited some scale-dependencies that hindered model quality in cross-scale predictions where only development data was used in calibration. Glutamate and ammonium models were also able to achieve accurate predictions in most cases. However, there are differences in the absolute concentration ranges of these components across the datasets of individual bioreactor scales. Thus, glutamate and ammonium PLS models were forced to extrapolate in cases where models were derived from small scale data only but used in cross-scale applications predicting against manufacturing scale batches.

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Section: Introductionmentioning

confidence: 99%

Cross‐scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis

Berry

Moretto

Matthews

et al. 2014

Biotechnology Progress

120

125

View full text Add to dashboard Cite

show abstract

“…The suitability of several spectroscopic techniques coupled with chemometric methods to improve the monitoring and control of mammalian cell processes ensuring predefined final product quality is the subject of a recent review . The challenges of PAT for biologics and the benefits derived from using PLS and PCA to analyze process control data is reviewed by Molony . The use of Raman spectroscopy on food systems with emphasis on the use of multivariate data analysis methods for the interpretation and quantification of Raman signals has been the subject of a review .…”

Section: Process Chemometricsmentioning

confidence: 99%

Process Analytical Chemistry

et al. 2011

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“…With the potential to generate large quantities of analytical data, systems are needed to collect and archive the data for future use, e.g. for long‐term process monitoring, or for building control models by converting the large quantities of raw data into meaningful libraries of information, such as by principal component analysis or partial least squares 17. Such chemometric techniques are also useful for reducing the dimensionality of the data set by identifying trends in key input variables alone, thereby eliminating unimportant parameters.…”

Section: Analytical Developmentsmentioning

confidence: 99%

How implementation of Quality by Design and advances in Biochemical Engineering are enabling efficient bioprocess development and manufacture

Chhatre

Farid

Coffman

et al. 2011

J. Chem. Technol. Biotechnol.

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In recent years, Quality by Design (QbD) has gained significant prominence in the pharmaceutical industry as an efficient way of designing and controlling processes used to make therapeutic products. At its heart, QbD seeks to identify an operating envelope within which production consistently satisfies a target product quality profile and thereby achieves the desired level of safety and efficacy. Such an approach is assisted by a range of Biochemical Engineering techniques which increase process and product understanding. This perspective describes how the principles of Quality by Design and developments in the field of Biochemical Engineering are providing the pharmaceutical sector with a toolbox of methods that enable efficient bioprocess development and manufacture.

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Pat Tools for Biologics: Considerations and Challenges

Cited by 8 publications

References 45 publications

Cross‐scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis

Cross‐scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis

Process Analytical Chemistry

How implementation of Quality by Design and advances in Biochemical Engineering are enabling efficient bioprocess development and manufacture

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