Development of in situ product removal strategies in biocatalysis applying scaled-down unit operations.

An experimental platform based on scaled-down unit operations combined in a plug-and-play manner enables easy and highly flexible testing of advanced biocatalytic process options such as in situ product removal (ISPR) process strategies. In such a platform, it is possible to compartmentalize different process steps while operating it as a combined system, giving the possibility to test and characterize the performance of novel process concepts and biocatalysts with minimal influence of inhibitory products. Here the capabilities of performing process development by applying scaled-down unit operations are highlighted through a case study investigating the asymmetric synthesis of 1-methyl-3-phenylpropylamine (MPPA) using ω-transaminase, an enzyme in the sub-family of amino transferases (ATAs). An on-line HPLC system was applied to avoid manual sample handling and to semi-automatically characterize ω-transaminases in a scaled-down packed-bed reactor (PBR) module, showing MPPA as a strong inhibitor. To overcome the inhibition, a two-step liquid-liquid extraction (LLE) ISPR concept was tested using scaled-down unit operations combined in a plug-and-play manner. Through the tested ISPR concept, it was possible to continuously feed the main substrate benzylacetone (BA) and extract the main product MPPA throughout the reaction, thereby overcoming the challenges of low substrate solubility and product inhibition. The tested ISPR concept achieved a product concentration of 26.5 gMPPA · L-1 , a purity up to 70% gMPPA · gtot-1 and a recovery in the range of 80% mol · mol-1 of MPPA in 20 h, with the possibility to increase the concentration, purity, and recovery further. Biotechnol. Bioeng. 2017;114: 600-609. © 2016 Wiley Periodicals, Inc.

Screening of organic solvents for bioprocesses using aqueous-organic two-phase systems.

The application of conventional organic solvents has been essential in several steps of bioprocesses in order to achieve sufficient economic efficiency. The use of organic solvents is frequently used either to partly or fully replace water in the reaction medium or as a process aid for downstream separation. Nowadays, manufacturers are increasingly requested to avoid and substitute solvents with hazardous potential. Therefore, the solvent selection must account for potential environmental hazards, health and safety problems, in addition to fulfilling the ideal characteristics for application in a process. For the first time, criteria including Environment, Health and Safety (EHS), as well as the technical requirements for reaction and separation have been reviewed, collected and integrated in a single organic solvent screening strategy to be used as a guideline for narrowing down the list of solvents to test experimentally. Additionally, we have also included a solvent selection guide based on the methodology developed in the Innovative Medicines Initiative CHEM21 (IMI CHEM21) project and applied specifically to water-immiscible solvents commonly used in bioprocesses.

Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models.

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1278-1293, 2017.