Continuous pharmaceutical manufacturing (CPM): process modelling and economic optimisation

Abstract

Continuous Pharmaceutical Manufacturing (CPM) is a recent field of research that shows promise towards overcoming some of the technical and economic difficulties faced by a batch-production-dominated pharmaceutical sector. This PhD Thesis explores potential benefits of CPM by bringing together and comprehensively studying kinetic data analysis, process modelling and simulation, and technoeconomic evaluation via nonlinear optimisation. The CPM of two Active Pharmaceutical Ingredients (APIs) are studied in this Thesis: ibuprofen (a widely used over-the-counter analgaesic) and artemisinin (a key antimalarial substance). The continuous synthesis routes of these APIs have been published in the literature, and the kinetic data is analysed to allow the creation of process models. These are simulated with the aid of a variety of software packages (Excel, MATLAB). Continuous separation technologies, key to realising the full potential of CPM, are also studied. These include continuous liquid-liquid-extraction (LLE) operations and continuous crystallisation. Published performance data for these operations allows continuous API recovery to be simulated. Where necessary, solubility estimation methods (UNIFAC, NRTL) are employed for APIs in single, binary and ternary solutions. Established cost estimation methods are used to evaluate the economic merit of the processes and process options. These include capacity-cost correlations, cost factors to account for equipment design options, and widely used, reliable assumptions for a working plant. Material and equipment prices are drawn from a wide range of sources. The technical and economic aspects of the work are also brought together in nonlinear optimisation formulations to comprehensively explore the design spaces and determine optimal total costs. The MATLAB software package with the OPTI Toolbox plugin is used, which allows the use of a great variety of nonlinear optimisation algorithms, including both local and global solvers, and those for derivative-free optimisation. The CPM models are given multiple decision variables (such as cooling temperature, quantity of antisolvent use, number of separation unit operations) and are optimised for minimum total cost. The work in this PhD represents the first time that open-source kinetic and economic data is combined with explicit thermodynamic property modelling in a nonlinear optimisation framework to determine optimum design solutions, toward showing firstly how CPM can be beneficial and secondly how Process Systems Engineering and optimisation tools can be used in this regard. The use of systematic frameworks and design methods will be of paramount importance in continuing to build the case for CPM.