Demands within the pharmaceutical sector to cut costs and improve process efficiencies have grown considerably in recent years. Major challenges exist for companies trying to establish financially viable and robust manufacturing processes for increasingly complex therapeutics. These issues have driven the investigation of miniaturised process-design techniques by which to identify suitable operating conditions using small volumes of feed material typical of that available in the early stages of bioprocess development. Such techniques are especially valuable for the optimisation of chromatographic separations, which often represent a significant percentage of manufacturing costs and hence for which there is a pressing need to determine the best operating policies. Several methods employing microlitre volumes of sample and resin have been explored recently, which are aimed at the high-throughput and cost-effective exploration of the design space for chromatographic separations. This methodology paper reviews these microscale approaches and describes how they work, gives examples of their application, discusses their advantages and disadvantages and provides a comparative assessment of the different methods, along with a summary of the challenges that remain to be overcome in relation to these techniques.

BACKGROUND: Production of recombinant virus-like particles (VLPs) in yeast expression systems for use as vaccines requires cell disruption and detergent-mediated steps to liberate the product. Typically, these release high levels of cellular components such as lipids that foul chromatography columns. This study compares the impact of applying lipid-rich and lipid-depleted feedstocks to hydrophobic interaction chromatography columns to quantify the loss of performance caused by the presence of host lipids over a total of 40 operational cycles.

The capacity to locate efficiently a subset of experimental conditions necessary for the identification of an operating envelope is a key objective in many studies. We have shown previously how this can be performed by using the simplex algorithm and this paper now extends the approach by augmenting the established simplex method to form a novel hybrid experimental simplex algorithm (HESA) for identifying 'sweet spots' during scouting development studies. The paper describes the new algorithm and illustrates its use in two bioprocessing case studies conducted in a 96-well filter plate format. The first investigates the effect of pH and salt concentration on the binding of green fluorescent protein, isolated from Escherichia coli homogenate, to a weak anion exchange resin and the second examines the impact of salt concentration, pH and initial feed concentration upon the binding capacities of a FAb', isolated from E. coli lysate, to a strong cation exchange resin. Compared with the established algorithm, HESA was better at delivering valuable information regarding the size, shape and location of operating 'sweet spots' that could then be further investigated and optimized with follow up studies. To test how favorably these features of HESA compared with conventional DoE (design of experiments) methods, HESA results were also compared with approaches including response surface modeling experimental designs. The results show that HESA can return 'sweet spots' that are equivalently or better defined than those obtained from DoE approaches. At the same time the deployment of HESA to identify bioprocess-relevant operating boundaries was accompanied by comparable experimental costs to those of DoE methods. HESA is therefore a viable and valuable alternative route for identifying 'sweet spots' during scouting studies in bioprocess development.