A Predictive Self-Organizing Multicellular Computational Model of Infant Skin Permeability to Topically Applied Substances

Abstract

Computational models of skin permeability are typically based on assumptions of fixed geometry and homogeneity of the whole epidermis or of epidermal strata and are often limited to adult skin. Infant skin differs quantitatively from that of the adult in its structure and its functional properties, including its barrier function to permeation. To address this problem, we developed a self-organizing multicellular epidermis model of barrier formation with realistic cell morphology. By modulating the parameters relating to cell turnover reflecting those in adult or infant epidermis, we were able to generate accordingly two distinct models. Emerging properties of these models reflect the corresponding experimentally measured values of epidermal and stratum corneum thickness. Diffusion of an externally applied substance (e.g., caffeine) was simulated by a molecular exchange between the model agents, defined by the individual cells and their surrounding extracellular space. By adjusting the surface concentration and the intercellular exchange rate, the model can recapitulate experimental permeability data after topical exposure. By applying these parameters to an infant model, we were able to predict the caffeine concentration profile in infant skin, closely matching experimental results. This work paves the way for a better understanding of skin physiology and function during the first years of life.