Daniel Markl CMAC,
Reader in Pharmaceutical Product Engineering at University of Strathclyde

Traditional methods of developing drug products for a new active pharmaceutical ingredient are time-consuming, costly and often inflexible. The selection of the right excipients in tablets and process conditions are crucially important as they can impact manufacturability, performance and stability of the drug product. Formulation optimisation studies are conducted to identify a robust formulation that can meet manufacturability criteria (e.g. flowability, tensile strength) while fulfilling the desired performance targets, e.g. release of > 80% of the drug in less than 30 min. This is a multidimensional optimisation problem with a high degree of interdependence between raw material attributes, process parameters, and drug product properties. These complex relationships cannot be fully captured by first principle models and it is not feasible, in a reasonable time, to experimentally optimise these multidimensional formulation (type of excipient, concentration, drug loading) and process parameter (e.g. compression force, dwell time) spaces following traditional experimental planning and methods. This talk will present a self-driving, high-throughput, data-intensive micro-scale tablet development system – a tableting DataFactory – that can automatically prepare and measure powder, and produce and test single tablets. By employing robots, the system combines an automated dosing unit, a dedicated powder transportation unit, near-infrared spectroscopy for evaluating powder blend homogeneity, a compaction simulator, and an automated testing system for measuring tablet properties. The data is automatically structured and fed into a database for the development of a hybrid system of models, including mechanistic and data-driven (AI) approaches, to predict critical powder blend (e.g. flowability) and tablet attributes (tensile strength, porosity) from raw material properties. This talk will further discuss the combination of hybrid modelling approaches with model-based optimisation and the micro-scale tablet development system. This approach significantly reduces hands-on-lab time (> 80%), material, and waste, offering significant potential for accelerated and sustainable drug product development.