Digital Design for Crystallisation in Advanced Pharmaceutical Manufacturing: Uncertainty of information and how to find data

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Advanced pharmaceutical manufacturing approaches including continuous pharmaceutical manufacturing (C-PM) involve complex operations requiring high degrees of process control, understanding and experience. We are innovating the development of smart, AI-driven and robotically enabled development platforms, or 'DataFactories' to accelerate product and process development and enable the development and application of digital twins. CMAC's vision is to deliver the digital transformation of Chemistry, Manufacturing and Control (CMC) operations as part of the approval of new medicines and a key aim of this is the development of a Crystallisation Classification System (CCS) that will allow rapid prediction of crystallisation outcomes from targeted, material sparing experimental approaches. Significant investment by CMAC has established new DataFactories for accelerated crystallisation development using model driven experimental design and collaborative robotics to drive initial experiments to estimate thermodynamic (solubility) and kinetic (nucleation, growth) parameters as well as key particle attributes (solid form, size and shape) that will direct process design. The platform's comprise multiple sensors with offline measurements and data analysis to achieve this. Currently, the measurement of uncertainties and their propagation throughout the digital design process are not well understood. Evaluation of these at all measurement points in the CCS DataFactory will improve process understanding, identify routes for improvement and build confidence in digital tools. Presently, there are no common standards for the storage of (meta)data of instruments or the handling of such large heterogenous data. While control software tools attempt to capture the information required to regulate the processes ad-hoc, they do not facilitate capturing analytical results from process-related instruments. Thus, there is a need for improved data storage, governance and access to the myriad of information about complex mulit-phase processes to enhance the quality and efficiency of medicines development and manufacturing and realise the full potential of digital transformation. The PhD will focus on the acquisition of comprehensive data using the DataFactory and develop novel methods for the propagation of information and uncertainty delivered through the novel Crystallisation Parameter database being established as part of CMAC's vision to become the data centre for pharmaceutical products and processes and deliver the CCS.