Postgraduate research opportunities Autonomous MicroScale Modelling of Active Pharmaceutical Ingredient Crystallisation

Control of a drug substance’s physical and chemical properties relies on several manufacturing unit operations within the drug substance or primary space. Likewise, final product performance relies on several unit operations within the drug product or secondary space. With obvious interaction between the two. In primary manufacturing crystallisation is used in over 80% of all drug substance manufacturing processes for purification and particle engineering. One of the tools to aid in rapid development of crystallisations is the development of process models and their subsequent application in analysis, optimisation, design, and control. In conjunction with the application of process analytical technology (PAT) for crystalliser analysis, in silico design of crystallisations systems is a major trend in the community. However, the application of crystallisation modelling in pharmaceutical process development is still limited due to challenges in:

1. limited material resource in early development
2. development time pressures to produce material for clinical trials
3. model inefficiencies for complexity processes

In contrast, recently the chemical reaction and synthesis space has seen an update in the use of models in the identification of reaction mechanisms (3,4) and design of experiments to maximise data quality to aid in this (5). Coupled with automation (6), these tools have become powerful in the identification and development of new chemical processes.

This studentship will focus on developing an agile, small-scale production facility via creation of a unique autonomous microscale API manufacturing and testing system. This system will enable the automated design of experiments to undertake and perform the best experiments to inform process model development. The fundamental research will resolve the complex challenges of:

• automating with IDTs the performing of multiphase crystallisations
• collection of high quality data
• parameterisation of crystallisation process model for multi-mechanism processes
• design of experiments to maximise data but minimise material and time

Further information

• O’Grady, et al., 2006, Pharma Manufacturing
• Nagy and Braatz, 2012, Annu. Rev. Chem. Biomol. Eng., 3(1)
• Taylor, et al., 2021, React. Chem. Eng., 6, 1404
• Sen, et al., 2021, React. Chem. Eng., 6, 2092
• Florit, et al. 2021, React. Chem. Eng., 6, 2306
• Reed, 2021, Materials 4.0, Henry Royce Institute