Leveraging machine learning tools to expedite oral modified release formulation development

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Oral delivery is the most preferred route of administration, accounting for over 90% of the global market share available on the market. The oral delivery market continues to grow, and is expected to reach US 150bn in the coming years. However, behaviour of drugs and delivery systems in the intestine depends on many physiological factors including fluid volume, fluid composition, transit, motility, bacteria and pH, which are further influenced by food, gender and age. These are often considered well understood, but their true variability and idiosyncrasies are not fully appreciated or utilised in intestinal dosage form design or in vitro testing. The project proposes to advance oral delivery through harnessing machine learning (ML) tools to optimise oral product development of modified release biological products. Current research in drug delivery continue to use trial-and-error, which is costly, resource-intensive and time-consuming. Given the economic climate of R&D, empirical approaches are no longer sustainable. There has been a shift in recent years to use simulation and predictive tools to help efficiently accelerate formulation development. Such simulations are performed computational, referred to as in silico modelling, which is helping researchers to minimise the already vast formulation space. ML is one emerging in silico tool that is gaining traction for its ability to outperform humans in decision-making tasks. ML is a subset technology of artificial intelligence (AI), that makes prediction on future outcome from existing data. Increasingly becoming the most important commodity of the 21st century, data can be ubiquitously found throughout pharmaceutics and allied fields. However, compression of vast amount is challenging, and hence, ML is needed. The project will explore different ML strategies to help develop oral dosages with precise control release.The project will involve a mixture of both physical and computational experiments, preparing the student for research in the 21st Century. Aspects of the project will involve:- Assess different data acquisition protocols- Asses the feasibility of ML for small datasets- Develop ML models that can integrate data from different characterisation techniques- Establish an ML pipeline to represent end-2-end drug development- Explore explainable modelling algorithms- Address the gap in the lack of informatics in the pharmaceutics domain- Experimentally validate the ML models