10142729DigiCCAMMS: Digital Crystallisation Control for Advanced Medicines Manufacturing SustainabilityActiveCollaborative R&DInnovate UKCrystallisation is the process of forming a solid crystal from a solution of molecules. It is an important process in the development and manufacturing of small molecule new medicines. The crystalline particles that go into "solid dosage forms" of medicines like tablets and inhalation drugs (where clinical performance is directly correlated to particle behaviour) need to be well understood so that pharmaceutical scientists can design the processes and workflows that will allow them to manufacture new medicines quickly and safely. Currently, a significant body of experimental work is required to understand the crystallisation behaviour of a new pharmaceutical molecule. Getting this wrong can lead to significant problems during medicines manufacturing, requiring time and resource intensive experimental trials by industrial scientists to ensure effective control strategies for crystallisation and particle design. By developing new software to simulate crystallisation processes, scientists could do a lot of this exploratory work on a computer, eliminating the need for experiments that can generate significant harmful waste (such as solvents) in addition to considerable raw material and energy costs. This could make the development of new medicines much greener and help the pharmaceutical industry to meet its Net-Zero goals. These digital approaches could also increase efficiency in the way that pharmaceuticals are manufactured, meaning that new medicines can be delivered to patients faster. This project will take some software models that have been developed by chemical engineers at the University of California Santa Barbara and embed it into an existing computer programme that industrial scientists are familiar with. State of the art crystallisation experiments will be performed to validate, inform and improve the models. This will deliver a crystallisation prediction tool able to minimise the experiments required, resulting enabling greener solvent selection and reduced material and energy waste during pharmaceutical R&D and manufacturing.