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Biotechnol Prog. 2017 Nov;33(6):1538-1547. doi: 10.1002/btpr.2523. Epub 2017 Jul 21.

A three plus three parameters mechanistic model for viral filtration.

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Dept. of Chemical Engineering, Indian Institute of Technology, Delhi, 110016, India.
Dept. of Chemical Engineering, NIT, Srinagar, J & K, India.


Viral filtration is an expensive regulatory requirement in downstream processing of monoclonal antibodies (mAbs). This process step is typically operated with an overdesigned filter in order to account for any batch to batch variability in the filter, as well as the feed characteristics. Here, we propose a simple, six-parameter mechanistic model for viral filtration where three parameters are membrane-specific while the other three depend on feed characteristics and membrane-feed interactions. Viruses are considered as passive particles which are retained by the membrane on the basis of size exclusion. The model envisages that the viral filter contains two kind of pores: virus-retentive, small-sized pores and non-retentive, large-sized pores. The small-sized pores get blocked during filtration resulting in decrease in active membrane area, while the large-sized pores get constricted during filtration. The length of constricted part increases during filtration and contributes to increase in hydraulic resistance of the filter. Rate of these processes (blocking and constriction) are assumed to be proportional to the instantaneous rate of retention of the viral particles. The general nature of the model is validated with the experimental data on viral filtration for four different commercial membranes used in biotech industries as well as different model viruses. The proposed model has been demonstrated to describe the behavior of filters with very good accuracy. The best-fit model parameter values indicate about the various phenomena that are responsible for differences in the behavior of the membranes as well as change in retention and flux with feed concentration. The proposed model can be used for improving design of virus filters as well as in appropriate sizing of the filters during processing. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1538-1547, 2017.


LRV; flux decay; modeling; viral filtration

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