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Digital design strategies for industrial crystallisation development: Considering active pharmaceutical ingredient properties and crystalliser hydrodynamics for process scale up

About This PhD Project

Project Description

Crystallisation is a complex, multi-phase unit operation used in a wide range of manufacturing industries to achieve separation and purification of products. For the pharmaceutical industry, active pharmaceutical ingredient (API) crystallisation may be regarded as the first step in the formulation process with molecules stabilised within the crystal lattice throughout the subsequent processing steps until the crystal dissolves upon administration to the patient allowing the molecular form of the drug to be absorbed. Consequently, crystallisation is a critical process step. The pharmaceutical industry is also placing increasing demands on crystallisation, for example, as drug structures become more complex, they can be more challenging to crystallise; also, advanced formulations require tighter control of API particle attributes.

Transfer of crystallisation processes from the lab to manufacturing is a complex process involving several scale-up and technology transfer steps. As the scale or technology changes, a change in the hydrodynamics can also occur, resulting in an impact on the underlying process mechanisms. However, there are multiple mechanisms that do not all respond in the same manner to a change in scale or technology, nor will the impact be the same for each API being developed. Therefore, process scale-up and/or technology transfer can only be achieved by deeply understanding the interconnection between the API properties, it’s crystallisation behaviour, and the vessel hydrodynamics.

In this 4-year PhD joint PhD programme between the University of Strathclyde, Takeda Pharmaceutical Company Ltd and the National Manufacturing Institute Scotland we will develop a state-of-the-art multiscale modelling framework from the molecule through to the process vessel and provide a scale-up and technology transfer strategy from the lab to manufacturing. To achieve this, the study will combine a range of computational tools (multivariate analysis, machine learning and computational fluid dynamics), equipment design and development, and experimental crystallisations. The impact of this would be the realisation of a digital design for pharmaceutical manufacturing allowing the elimination of unexpected problems and increase process robustness for drug development.

Applicants must have a first or upper second, MEng, MSc degree or equivalent background in Chemical Engineering or similar discipline along with knowledge or interest in computer programming (e.g. python, MATLAB), simulation and appreciation for practical equipment design and experimentation. The PhD student is expected to engage with the wider community by attending the annual Scottish Research Partnership in Engineering conference, each year of their PhD, as well

Applicants should send a CV, contact details of 2 references and a covering letter to Dr Cameron Brown, , by the 31st of January 2020. Interviews will take place in February and the studentship will commence by 31st of March.

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