Abstract
Aqueous two-phase extraction (ATPE) is a promising downstream separation technology as an alternative, or addition, to chromatography in the production of biological products. Increasing demand for therapeutic proteins have triggered manufacturers to consider continuous upstream technologies to achieve greater process efficiencies; however, such technologies have an inherent variability, resulting in output streams of varying compositions and properties. It is therefore important to understand how this variability impacts on the downstream separation processes.Exploring all potential sources of variability is challenging due to resource and time constraints, however, the use of targeted mathematical modelling can significantly reduce the need for expensive and time consuming experimentation. In this work, we present a dynamic equilibrium stage process model, and a methodology for prediction of key process parameters from limited experiments, capable of describing ATPE separations under both multi-cycle batch and continuous counter-current modes of operation. The capabilities of the proposed methodology are demonstrated using a case study separation of the enzyme α-amylase from impurities in a PEG 4000–phosphate aqueous two phase system (ATPS) containing NaCl. The model can be used to predict the separation performance of the process, as well as for the investigation of suitable design and operating conditions.
Highlights
The market for therapeutic proteins is currently increasing at a remarkable pace (Ecker et al, 2015)
We present an approach based on a dynamic stage-by-stage equilibrium model that can be used to simulate aqueous two-phase extraction (ATPE) processes under a variety of configurations and operating policies
We present an approach based on a dynamic stage-by-stage equilibrium model that can be used to simulate aqueous two-phase extraction (ATPE) processes under a variety of configurations and operating policies
Summary
The market for therapeutic proteins is currently increasing at a remarkable pace (Ecker et al, 2015). (2015) recently showed that as Chinese hamster ovary (CHO) cells age, the profile of hard to remove host cell proteins (e.g. impurities with similar separation behaviour to the product) changes. In addition to such inherent variability, other sources of disturbances must be evaluated, such as equipment failure, human error, contamination etc. Understanding the impact of process changes on whole bioprocess performance is important as product quality could be compromised if the process is not sufficiently robust It is often costly and time consuming to evaluate all sources of variability experimentally. One solution to this issue is to use predictive process models to simulate the behaviour of systems under varying conditions, such as
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