(626e) An Industrial Application of Parameter Estimation of Biocatalytic Transaminase Reactions

Han, L., Pfizer Inc.
Magano, J., Pfizer
Damon, D., Pfizer
Wong, J., Pfizer
Guinness, S. M., Pfizer Inc.
Wang, K., Pfizer Inc.
Mustakis, J., Pfizer Inc.

2018 American Institute of Chemical Engineers (AIChE) Annual Meeting

An industrial
application of parameter estimation of biocatalytic transaminase

Javier Magano, David Damon, John Wong, Steven Guinness, Ke
Wang, Jason Mustakis, Lu Han


In the pharmaceutical industry,
there is growing interest to use enzymes to perform highly selective chemical
reactions. Through advancements in protein engineering, the enzyme properties
can be tuned and optimized, to enhance their selectivity and activity for
desired reactions. Biocatalysis presents a valuable tool that complements existing
chemical synthesis approaches. The kinetic models that describe the
enzyme-catalyzed reactions consist of several parameters, which are often
correlated. Typically, many experiments are required in order to effectively estimate
these kinetic parameters [1-3]. Utilizing automation for these types of
experiments at small-scale has the potential to save resources as well as
provide comprehensive data for modeling.

In this work, we demonstrate an
application of a high-throughput reaction screening platform at Pfizer to estimate
the kinetics of a biocatalytic transaminase reaction.
The transaminase experiments were performed at a resource-sparing scale under well-controlled
conditions. The generated data was fitted to a kinetic model, and coupled to a reactor
model to simulate the effects of different reaction and process conditions. The
process model was applied to experimental data generated from lab and plant scale.
The results from the current study reveal that a simple kinetic model can be
developed from a small-scale screening platform and leveraged for predicting
data at the larger scale.


[1] R. H. Ringborg
and J. M. Woodley. React. Chem.
, 2016, 1, 10-22.

[2] N. Al-Haque, P.A. Santacoloma, W. Neto, P. Tufvesson, G. Rafiqul, J.M. Woodley. Biotechnol. Prog., 2012, 28(5), 1186-1196.

[3] B. Chen, E. Hibbert, P. Dalby, J.M. Woodley. AIChE J., 2008, 53(8), 2155-2163.