Enabling Predictive Design of Filtration and Washing Processes

Lead Research Organisation: University of Manchester
Department Name: Chem Eng and Analytical Science

Abstract

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.

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/P510579/1 01/10/2016 30/09/2021
1805866 Studentship EP/P510579/1 18/09/2016 30/09/2020 Guilio Perini
 
Description AstraZeneca industrial collaboration 
Organisation AstraZeneca
Department Pharmaceutical Technology & Development
Country United Kingdom 
Sector Private 
PI Contribution This partnership aims at improving the filtration processes of AstraZeneca through the research done in this PhD Project. So far we have contributed by sharing the outcome of the plant design for a new automated laboratory setup to carry out reproducible and meaningful filterability analysis. We are in the process of applying the knowledge gathered on modeled compounds to systems that are of interest for the company.
Collaborator Contribution They provided us with knowledge and know-how about this process and the way that it is carried out in industry. In the very near future they will also contribute to the project by hosting the PhD student for a 3 months industrial placement, plus letting him use some specialty equipment.
Impact No public outcome yet
Start Year 2016
 
Description Crystal characterization collaboration with ETH Zurich 
Organisation ETH Zurich
Country Switzerland 
Sector Academic/University 
PI Contribution We worked together with the research group at ETH to carry out both an experimental and a modeling work. The PhD student contributed by carrying out all the lab work and most of the analysis and modeling work.
Collaborator Contribution The group at ETH contributed by letting the student use their facility, equipment and materials, as well as sharing know-how and and insight in the world of particle characterization for needle-like crystals.
Impact Paper - DOI: 10.1016/j.seppur.2018.10.042
Start Year 2016