Research Associate University of Waterloo Kitchener , Ontario, Canada
This work highlights important factors to consider when designing experiments for training PBPK dermal models, aiming to improve the robust estimation of drug delivery and chemical risk associated with dermatological products. Such models account for numerous factors including permeant properties, the choice of vehicle, experimental setups, and skin conditions.
A learning algorithm is employed to derive statistical distributions of key model quantities based on in vitro experimental permeability measurements. The updated model is used to simulate different scenarios based on parameters sampled from the learned distributions.
The algorithm is applied multiple times, using distinct combinations of permeability measurements collected under various experimental conditions in each instance. Experiments differed in terms of permeating compounds, vehicle pH levels and the anatomical sites of skin samples. The most accurate and precise extrapolated permeability results were obtained when the measurements encompassed multiple vehicle pH levels and tested skin samples using multiple compounds.
Learning Objectives:
Design experiments to build, train, and validate versatile dermal absorption models for improving the robust estimation of drug delivery and chemical risk associated with dermatological products.