4.7 Article

High throughput process development (HTPD) platform for membrane chromatography

Journal

JOURNAL OF MEMBRANE SCIENCE
Volume 442, Issue -, Pages 245-253

Publisher

ELSEVIER
DOI: 10.1016/j.memsci.2013.04.021

Keywords

High throughput process development HTPD; Membrane chromatography; Design of Experiments (DOE); Granulocyte Colony Stimulating Factor (GCSF)

Funding

  1. Pall Life Sciences (Bangalore, India)
  2. IIT Delhi

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Membrane chromatography has emerged as a potential alternative to conventional packed bed chromatography with advantages including reduced hardware requirement, operational ease, and shorter processing time. Transport of molecules in such membranes is primarily driven by convection with very limited pore diffusion as compared to packed bed column chromatography resulting in increased mass transfer and quicker capture of the product and also allowing us to work at very high flow rates. While development of such a step is relatively simpler when compared to packed bed chromatography, it still involves optimization of the various parameters experimentally as a first step towards commercialization. This is often a time and resource intensive exercise. It is expected that companies have better understanding and control of their manufacturing processes. Biotech manufacturers, however, are under ever-increasing pressure to reduce the cost of development and commercialization of a biotech therapeutic. Use of miniaturization and automation in the form of high throughput process development (HTPD) offers a potential solution. In this paper, we address development of such a platform for membrane chromatography. The platform proposed in this paper has been successfully applied towards process development for purification of a biotech therapeutic, Granulocyte Colony Stimulating Factor (GCSF). Further, we have also validated the platform by comparing the results obtained with the HTPD platform (7 mu l membrane volume) against those obtained at the traditional laboratory scale (0.18 mL membrane volume). Statistical analysis of the data has been performed to assess the strengths and limitations of the platform. (C) 2013 Elsevier B.V. All rights reserved.

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