Application of a novel particle coating technology for paediatric formulation development

"Background: The lack of suitable medicines for the paediatric population means that unlicensed formulations are often prepared and administered by healthcare professionals, which entails exposing this vulnerable population to risks in terms of safety and clinical efficacy. The multitude of challenges restricting progress in paediatric medicine development include lack of understanding of dosage forms that offer dose flexibility, safety and commercial feasibility. The translation of paediatric formulations has been limited, due to the limited availability of technologies to formulate commercially viable products that would be applicable for a wide range of high dose drugs[1],[2],[3]. In addition, a limited choice of excipients for safe inclusion in paediatric medicines, together with taste acceptability issues, presents an additional layer of formulation difficulty.

Work leading to this proposal: Dry particle coating developed at Aston is a novel particle engineering technology that, for the first time, will permit layering of fine particles over coarse particles through fluidisation and meticulous precision control of processing parameters. This method allows high drug loading, minimal excipient load and acceptable taste and can be tailored to handle small molecules and biologicals. In data analytics, we have established new approaches for data visualisation, data abstractions, probabilistic modelling and prediction techniques, and analysis and control of plant dynamics[4],[5]. Explicitly, our approach for using machine learning methods, such as radial basis function networks[6] for automated response surface methodology, has never been developed before in formulation development. It is superior to existing machine learning approaches of response surface methodology and swarm optimisation in terms of speed of convergence and accuracy of solution, and has the advantage of not being limited to locally quadratic surfaces. The overarching hypothesis of the proposal is that application of machine learning will produce predictive tools for the manufacture of high dose, fragile dosage forms including biologicals. This project will involve understanding the impact of processing parameters during dry coating on the stability and performance of small drug molecules and biologics such as peptides and proteins. Following the optimisation of formulations, work in this project will involve cell culture based transport and toxicity studies to develop a mechanistic understanding of formulation performance in vivo. Additionally, characterisation studies including HPLC, protein/peptide stability indicating assays, laser diffraction studies to study particle size and charge will be carried out to characterise powder blends. "