(426f) Comparing Tendency Modeling and Design of Dynamic Experiments for the Optimization of Batch Pharmaceutical Processes: The Case of Asymmetric Catalytic Hydrogenation

This research work compares the effectiveness of two approaches for the identification of the best operating conditions of a batch catalytic asymmetric hydrogenation involved in the manufacture of an active pharmaceutical ingredient. The first a approach is the Tendency Modeling and Optimization and Control (TeMOC) approach (Fotopoulos, Georgakis, & Stenger, 1994; Makrydaki & Georgakis, 2007) where an approximate stoichiometric and kinetic model of the reaction is developed. The second one is the Design of Dynamic Experiments (DoDE) methodology recently introduced (Georgakis, 2008, 2009) which generalizes the classical Design of Experiments (DoE) approach for the case one or more time-varying decision variables that can have a significant impact on the process performance. In this case the effective model is a very simple interpolative input-output response surface model (RSM). Clearly the TeMOC approach requires a larger investment than the DoDE in the development of the corresponding models. We are interested in examining whether TeMOC provided a better process operating profile than DoDE approach.

Catalytic asymmetric hydrogenation is an important subset of reactions frequently encountered in the pharmaceutical industry, with interest in maximizing the yield and diastereoselectivity of the desired product. The DoDE approach is used to design the set of experiments, with the reactor temperature being the time-varying decision variable, and with the initial amount of catalyst and reactants the time-independent decision variables (factors). Online monitoring of the Raman spectra and a PLS model, relating spectra to the reaction mixture compositions, are used to develop an approximate understanding of the inner workings of the reaction necessary for the development of the TeMOC model. The DoDE approach utilizes only composition measurements at the end of each batch. The DoDE enables us to rapidly optimize the process; however if the kinetic model is desired, the TeMOC approach is suggested. The experimental work for this project is performed at the laboratories of Sepracor, Inc.

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Fotopoulos, J., Georgakis, C., & Stenger, H. G. (1994, June 28 - July 1). Structured target factor analysis for the stoichiometric modeling of batch reactors. Paper presented at the American Control Conference, Baltimore, MD.

Georgakis, C. (2008). Dynamic Design of Experiments for the Modeling and Optimization of Batch Process.

Georgakis, C. (2009, July 12-15). A Model-Free Methodology for the Optimization of Batch Processes: Design of Dynamic Experiments. Paper presented at the IFAC Symposium on Advanced Control of Chemical Processes 2009, Istanbul, Turkey.

Makrydaki, F., & Georgakis, C. (2007). Multivariate Linear Regression as a Tool in Modeling the Kinetics of Complex Chemical Reactions, Annual AIChE meeting, Salt Lake City, UT.

*Corresponding Author: Room 273, Science and Technology Center, 4 Colby Street, Medford, MA 02155

Phone: 617-627-2573; Fax: 617-627-3991; E-mail: Christos.Georgakis@Tufts.edu