PurposeThe development of a new two-dimensional (2D) model to predict follicular permeation, with integration into a recently reported multi-scale model of transdermal permeation is presented.MethodsThe follicular pathway is modelled by diffusion in sebum. The mass transfer and partition properties of solutes in lipid, corneocytes, viable dermis, dermis and systemic circulation are calculated as reported previously [Pharm Res 33 (2016) 1602]. The mass transfer and partition properties in sebum are collected from existing literature. None of the model input parameters was fit to the clinical data with which the model prediction is compared.ResultsThe integrated model has been applied to predict the published clinical data of transdermal permeation of caffeine. The relative importance of the follicular pathway is analysed. Good agreement of the model prediction with the clinical data has been obtained. The simulation confirms that for caffeine the follicular route is important; the maximum bioavailable concentration of caffeine in systemic circulation with open hair follicles is predicted to be 20% higher than that when hair follicles are blocked.ConclusionsThe follicular pathway contributes to not only short time fast penetration, but also the overall systemic bioavailability. With such in silico model, useful information can be obtained for caffeine disposition and localised delivery in lipid, corneocytes, viable dermis, dermis and the hair follicle. Such detailed information is difficult to obtain experimentally.
Abstract. The volatility in a CPG market is modelled using a bottom up simulation approach and validated against disaggregated supermarket transactions data. The simulation uses independent agents, each agent representing unique households in the data. A simple behavioural model incorporates household preferences for product attributes, prices and promotions. Our validation strategy tests the model predictions at both macro and micro levels. At the macro level, the model is validated against out of sample evolution of market shares while at the micro level, household level choice of individual products and product attribute combinations are used. The model captures the volatility in terms of market share of brands and flavours -with the direction of change being more accurately predicted than the magnitude. At the micro level, we achieve a reasonable degree of prediction accuracy of household level SKU choice and a substantially higher accuracy for attribute choice. We found that product size to be the most difficult to predict among all attributes.
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