Most biotechnology unit operations are complex in nature with numerous process variables that can have significant impact on the performance of the process. Design of experiments (DOE)-based approach offers a solution to this conundrum with minimal number of experiments. This works aims to examine the suitability of the different designs for biotech applications. Three case studies involving bioprocess unit operations have been used to elucidate the underlying concepts viz. anion exchange chromatography, cation exchange chromatography and refolding of GCSF, a bio-therapeutic protein. We have compared the I-optimal and D-optimal designs to the commonly used central composite and Box–Behnken designs. Use of Akaike information criterion for model selection has been examined and found to be suitable for the applications under consideration. It was observed that for the applications examined in this article, I-optimal designs are better than either D-optimal or Box–Behnken designs in making predictions as the criteria are based on the least possible average variance of prediction for a given set of runs. Box–Behnken designs are better than other D-optimal designs if the numbers of runs are of same order. Approach of fitting model with higher order term gave more accurate results. The two statistical selection criteria AICc along with R2 proved sufficient for the datasets that were analyzed in this study. Model selection based only on model p-value and R2 may not lead to accurate prediction. Based on this dataset, model design, and fitting, we can say that it is possible to obtain precise prediction of set points and response with as many as half the number of runs than what is typically performed. The practical use of DOE may prove to be more valuable for prediction if the proposed approach is used.

Systematic approach for evaluation of the different DOE designs for biotech applications

249th National Meeting (2015)

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