(373d) Nonlinear Optimisation of Continuous Artemisinin Crystallisation with Explicit NRTL Model-Based Solubility Prediction

Authors: 
Jolliffe, H. G. - Presenter, University of Edinburgh
Gerogiorgis, D. I., University of Edinburgh
Continuous processing is studied for use in pharmaceutical production to overcome the inherent drawbacks of batch processing, which is the current paradigm in the pharmaceutical and fine chemicals industry. Developments in small-scale flow technologies (and in microreactors in particular) have accelerated this effort; flow chemistry has not only allowed higher efficiencies but also enabled access to reaction pathways that have not been previously available for batch production (Jensen, 2017). This sustained interest in Continuous Pharmaceutical Manufacturing (CPM), along with the development of Process Analytical Technology (PAT) explains the strong interest towards Quality by Design (QbD) for pharmaceutical production processes, which enjoys significant regulatory body encouragement and support (Lee et al., 2015).

One crucial aspect of continuous processing is the continuous separation of Active Pharmaceutical Ingredient (API) products, often at low scales of process throughput and strict purity requirements. Research in product separation, including downstream drug product formulation, is a vibrant area (Ierapetritou et al., 2016; Vural Gürsel et al., 2017; Yabuta et al., 2017). The recent technological development of plug flow crystallisers and Continuous Oscillatory Baffled Crystallisers (COBC, which operate in an oscillatory plug flow regime) offers production advantages, including enhanced heat and mass transfer and improved profile temperature control (Brown and Ni, 2011; McGlone et al., 2015).

Our recent publications explore process modelling, simulation and optimisation of the continuous synthesis and separation of ibuprofen (Jolliffe and Gerogiorgis, 2017a) and artemisinin (Jolliffe and Gerogiorgis, 2017b). By using systematic kinetic parameter estimation, solubility prediction via the UNIFAC method, and comprehensive economic evaluation, optimal total costs have been determined.

Artemisinin is a key antimalarial, and its continuous synthesis has recently been demonstrated (Kopetzki et al., 2013; Gilmore et al., 2014); product recovery and purity enhancement via crystallisation is a viable technique (Malwade et al., 2016). The present paper investigates further the modelling and systematic optimisation of continuous artemisinin crystallisation incorporating explicit solubility predictions via the Non-Random Two-Liquid (NRTL) model. Modelling a plug-flow COBC helps ensure high heat and mass transfer rates, with minimal impact of temperature and concentration gradients on the API product. Toluene, used in a promising CPM process for artemisinin (Horváth et al., 2015), is assumed to be the process solvent; the upstream portion of the flowsheet (reactor) performance is analysed on the basis of previous work (Jolliffe and Gerogiorgis, 2016). The antisolvents considered encompass three different binary mixtures (ethanol and ethyl acetate, ethanol and acetone, or acetone and ethyl acetate) chosen due to to their pharmaceutical processing suitability and the availability of solubility data and NRTL parameters (Nti-Gyabaah et al., 2010). The ratio of antisolvents in the binary mixture ranges continuously between 1:9 to 9:1 by weight. Similarly, the ratio of process solvent (toluene) to binary antisolvent mixture ranges between 1:1 to 1:5 by weight. A continuous crystalliser operating temperature range (between 40 °C and 5 °C) has been considered. Crystal growth and productivity have been modelled on the basis of similar systems (Horváth et al., 2015), and impurities have been assumed to not co-crystallise with the API product.

The nonlinear optimisation (NLP) formulation encompasses the optimal choice of antisolvent, rate of antisolvent addition and the operating temperature which is preferable for continuous artemisinin crystallization. Our results include objective function (minimal cost) surfaces for a range of key variables, such as API recovery; insightful three-dimensional illustrations are obtained for process cost as a function of decision variables, e.g. crystallisation operating temperature and ratio of binary antisolvent mixture addition over incoming reactor effluent. The systematic optimisation of technoeconomic indices under explicit process equilibria and thermodynamic solubility constraints offers unprecedented insight into fine-tuning Continuous Pharmaceutical Manufacturing (CPM) process design and operation.

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