An early decision-making algorithm for accelerating topical drug formulation optimisation

Abstract

Formulated topical drugs (and personal care products) contain diverse and varied mixtures. The experiments for formulation design can be time-consuming, especially those for optimising the delivery of active ingredients into the skin, the so-called in vitro permeation test (IVPT). A single IVPT typically takes 24 hrs and consumes significant resources for sample collection and chemical analysis. In this study, an early decision-making algorithm (EDMA) that can terminate unpromising experiments early, thereby prioritising resources on promising ones and potentially accelerating formulation design is proposed. The algorithm relies on a flexible Gaussian process regression (GPR) model for prediction during the experiments, while the prediction uncertainty is accounted for by a statistical measure, the probability of exceedance (PoE), to guide decision-making. This algorithm was applied to maximise ibuprofen permeation from a gel-like formulation through IVPT. The results show that it is feasible to determine whether a certain formulation has the potential to achieve higher permeation before the end of experiment, leading to significant savings on time and resources.