Computational modelling predicts impaired barrier function and higher sensitivity to skin inflammation following pH elevation.

Skin surface pH has been identified as a key regulator of the epidermal homeostasis through its action on serine protease activity. These enzymes, like kallikreins (KLK), are responsible for the degradation of corneodesmosomes, the protein structures linking together corneocytes, and are regulated by Lympho-Epithelial Kazal-Type-related Inhibitor (LEKTI). KLK activity increases at pH levels higher than physiological. An increase in skin surface pH has been observed in patients suffering from skin diseases characterized by impaired barrier function, like atopic dermatitis. In this work, we introduce an agent-based model of the epidermis to study the impact of a change in skin surface pH on the structural and physiological properties of the epidermis, through the LEKTI-KLK mechanism. We demonstrate that a less acidic pH, compared to the slightly acidic pH observed in healthy skin, is sufficient to significantly affect the water loss at the surface and the amount of irritant permeating through the epidermis. This weakening of the skin barrier function eventually results in a more intense skin inflammation following exposure to an external irritant. This work provides additional evidence that skin surface pH and serine proteases can be therapeutic targets to improve skin barrier integrity.

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

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.