Model-based solvent selection for pharmaceutical process development

Solvents play a key role in the manufacturing of pharmaceutical products as they are extensively used to accelerate synthetic reactions, enable separation and purification, and facilitate drug product formulation. The production of active pharmaceutical ingredients (APIs) is a multi-step process involving several reaction and workup steps in which large amounts of solvents are consumed. This makes the pharmaceutical industry a very wasteful chemical sector and highlights the need for systematic tools to enhance the resource efficiency of its processes. Recently, there has been growing interest in incorporating green chemistry principles in product design and development to enhance the sustainability of chemical manufacturing. In particular, solvent selection is a promising research area within the chemistry and engineering communities, given the many solvent-related contributions to process performance, including mass utilisation, energy consumption and process economics. Solvent selection is a difficult and complex design problem that entails molecular-level decisions, such as determining the solvent identities and the compositions of mixtures if mixed solvents are considered, together with process performance objectives, which are often competing. In current practice, most pharmaceutical companies develop in-house solvent selection guides to choose solvents based on physico-chemical properties and safety, health and environment characteristics with the aim to reduce process costs and environmental impact. However, these methods are mostly based on heuristic approaches or time-consuming experimental investigations that often lead to sub-optimal designs and fail to account for the integrated nature of the solvent selection problem. A novel solvent selection approach based on computer-aided mixture/blend design (CAMbD) is proposed to design integrated pharmaceutical processes and evaluate the process performance of pharmaceutical synthesis routes. Predictive thermodynamic models are used to integrate property prediction within process modelling, and advanced optimisation techniques are employed to search the vast design space of potential solvents and process conditions in order to identify the most promising design options. The CAMbD approach is used to optimise the solvent identities, mixture composition and process conditions in: 1) integrated synthesis and crystallisation processes, and 2) end-to-end drug substance manufacturing processes, based on key performance indicators (KPIs) that quantify resource efficiency and product quality. The one-step synthesis of mefenamic acid from 2,3-dimethylaniline and 2-chlorobenzoic acid is used as a case study to illustrate the use of CAMbD in pharmaceutical process design. The CAMbD approach generates different designs by considering a variety of solvent design spaces and performance objectives. Furthermore, multi-objective optimisation CAMbD problems are formulated to explore the trade-offs between competing KPIs, such as solvent utilisation and process safety, or energy consumption and process yield, in order to identify best-compromise solutions. An important feature of the proposed approach is that comprehensive design specifications, such as the miscibility of the chosen reaction and crystallisation solvents with the wash solvent in the end-to-end process, can be embedded in the mathematical formulation, ensuring that only practical designs are obtained. In addition to its use in integrated molecular and process design, the proposed CAMbD approach can be deployed to identify the optimal synthesis route of a pharmaceutical compound based on process performance metrics quantifying resource efficiency, product quality and solvent cost. The two-step synthesis of 4-nitrophenol (NP) via two reaction pathways is used as a case study to illustrate the potential of CAMbD in pharmaceutical process route selection. The work presented in this thesis constitutes a unique scientific contribution to the area of model-based solvent selection for drug substance manufacturing. For the first time, a CAMbD-based approach is developed and deployed to identify promising solvent choices and operating conditions for integrated, end-to-end drug substance manufacturing processes, while focusing on mixture thermodynamics, i.e., species solubility, and considering a range of KPIs that quantify product and process performance within single and multi-objective design formulations. Furthermore, for the first time, CAMbD is deployed to evaluate synthesis routes based on process performance, i.e., process route evaluation, while using simplified thermodynamic models and considering process-related metrics such as process efficiency and product quality. The model-based tool presented in this PhD thesis is relevant to streamline experiments and guide solvent selection and process design during early-stage pharmaceutical process development.Open Acces