Journal Highlight: Chemometrics application in biotech processes: assessing comparability across processes and scales

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  • Published: Oct 20, 2014
  • Author: spectroscopyNOW
  • Channels: Chemometrics & Informatics
thumbnail image: Journal Highlight: Chemometrics application in biotech processes: assessing comparability across processes and scales
Chemometrics has been applied to compare two versions of a biotech process and identify process steps where significant differences exist as well as the parameters that cause these differences.



Chemometrics application in biotech processes: assessing comparability across processes and scales

Journal of Chemical Technology and Biotechnology, 2014, 89, 1311-1316
Anurag S. Rathore, Shachi Mittal, Mili Pathak and Velu Mahalingam

Abstract: Assessment of process comparability is often required across different phases of manufacturing (Phase I vs Phase II vs Phase III vs Commercial) as well as other key activities during product commercialization (process scale-up, technology transfer, process improvement). In this work, chemometrics was applied to compare two versions of a biotech process and identify process steps where significant differences exist as well as the parameters that cause these differences. The dataset used in this analysis consists of data from 229 manufacturing batches. Partial least squares has been used for modeling the data. Scatter plots and variable importance plots have been used for evaluating comparability. Process parameters identified as significant by using chemometrics have been compared against those identified from process characterization using traditional lab scale experimental studies. The comparison has been followed by discussion on the pros and cons of the two approaches. To our knowledge this is the first time that such a comparison has been published for biotech processing. The study further demonstrates the usefulness of chemometrics in defining process comparability and in gathering process understanding from analysis of manufacturing data to supplement traditional lab-scale experimentation.

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