Digital design of crystallization process for applications in pharmaceutical manufacturing

About the Project

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

Crystallization of active pharmaceutical ingredients (API) is ubiquitous in the development of various pharmaceutical solid forms as a means of purification and product formulation. These products are required to meet the very strict regulatory requirements as the various enantiomorphs, polymorphs and cocrystals of the same API can affect the solubility, bioavailability, and stability of the drug product. Achieving the desired pharmaceutical crystals, including specific crystal structures and morphologies, demands substantial effort and investment. This involves screening various solid-state forms, selecting the optimal crystalline form for development, and refining crystallization conditions to ensure consistent production of the exact polymorph with ideal crystal morphology. Furthermore, before advancing to human trials, it is crucial to gather comprehensive information about drug candidates, such as potential crystal forms, physical properties, and manufacturing feasibility. The trial-and-error based approach has been widely employed for screening and optimizing crystallization conditions, as well as for developing cocrystals and selecting conformers. However, there is an emergence of digital design over the last two decades through the use of computer modelling and the machine learning based models to complement experimentation for the design of solid forms and their crystallization processes. Utilising a digital design approach allows the assessment of the entire pathway of developing an API into a new solid oral dosage form, while minimizing the risk of downstream failures.

This project aims to develop the digital design tools based on the first-principles-based mechanistic models which will be augmented by machine learning (ML) based models such as physics informed neural network (PINN). The mechanistic models are based on population balance model (PBM) which is supplemented by the mass and energy balances. The rate terms within the PBM are often modelled taking a semi-empirical approach where the kinetic parameters for growth, nucleation, aggregation and breakage phenomena are estimated from experiments. On the contrary, in PINN based models, the population balance equation can be directly embedded in the loss function of a neural network so that the network can be trained efficiently and fulfil physical constraints. These models can be used for the digital design strategy for predicting the final yield and particle size distribution (PSD) in crystallization processes. The proposed strategy is expected to significantly reduce the experimental effort and design complexity required compared with the traditional trial based and mechanistic model development based approach. Therefore, such a digital design approach offers a robust and efficient framework for crystallization process design and optimization in pharmaceutical manufacturing.

Candidates must have a strong academic background in Chemical Engineering or similar disciplines. Knowledge and interest in process design, simulation and experience in computer programming (e.g., MATLAB/Python/C++) are highly desired.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for PhD in Engineering to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project titleon the application form. If you do not include these details, it may not be considered for the studentship.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you do not need to provide a research proposal with this application.

Informal enquiries can be made by contacting Dr A Majumder at a.majumder@abdn.ac.uk. If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at researchadmissions@abdn.ac.uk

Funding Notes

This is a self-funding project open to students worldwide. Our typical start dates for this programme are February or October.

Fees for this programme can be found here Finance and Funding | Study Here | The University of Aberdeen

Additional research costs of £1,200 will be required, in addition to tuition fees and living expenses