Statistical Designs to Improve Downstream Processing.

The efficient extraction and purification of recombinant proteins from leaf and seed tissues is often a challenging task, involving multiple steps that must be optimized by identifying and accommodating complex parameter interactions. Conventional one-factor-at-a-time approaches fail to reveal these complex interactions and often result in sub-optimal processes with unnecessary costs. Here, we describe generic considerations to identify global optima for the extraction and purification of recombinant proteins from complex plant matrices. The corresponding experiments can help to streamline downstream processing by reducing the time, costs, and number of unit operations. The procedure involves the knowledge-based selection of factors for screening, the systematic design and analysis of experiments, and the iterative refinement of suitable conditions. The resulting descriptive models can be used to guide process scale-up and offer scientific justifications for process development decisions in negotiations with regulatory authorities.