Enabling Predictive Design of Filtration and Washing Processes

A large majority of small molecular active pharmaceutical ingredients (APIs) undergoes at least one crystallisation step in their respective production process. Crystallisation processes are typically used for purification, but also as a means to establish the downstream processing properties of the generated crystals, for instance their filterability, flowability and ultimately their tabletability. The downstream processing properties result largely from the shape, size, as well as the surface properties of the particles produced in the crystallisation process, which are determined by two main factors: the underlying crystal structure of the API and the environment it has been crystallized. While recent advances in the field of crystal engineering indicate that size, shape and surface properties can be tuned to some degree, there are no quantitative and predictive links between these properties and the downstream processing properties currently available in the literature.
In this project, we will therefore establish a link between the fundamental particle properties and the filtration performance of an API. We will conduct a combined experimental and molecular simulation project aimed at filling this knowledge gap. The properties of the particles will be first quantified, followed by filtration studies and measurements on the cakes formed during them that aim at determining the cakes' structure. The data gained in the quantification of the particles will serve as input to simulation models, while the data gained from the filtration experiments and the quantification of the cake structure will serve as a basis against which the model results will be compared.