Measurement of chemical penetration in skin using Stimulated Raman scattering microscopy and multivariate curve resolution - alternating least squares.

The mechanistic understanding of skin penetration underpins the design, efficacy and risk assessment of many high-value products including functional personal care products, topical and transdermal drugs. Stimulated Raman scattering (SRS) microscopy, a label free chemical imaging tool, combines molecular spectroscopy with submicron spatial information to map the distribution of chemicals as they penetrate the skin. However, the quantification of penetration is hampered by significant interference from Raman signals of skin constituents. This study reports a method for disentangling exogeneous contributions and measuring their permeation profile through human skin combining SRS measurements with chemometrics. We investigated the spectral decomposition capability of multivariate curve resolution - alternating least squares (MCR-ALS) using hyperspectral SRS images of skin dosed with 4-cyanophenol. By performing MCR-ALS on the fingerprint region spectral data, the distribution of 4-cyanophenol in skin was estimated in an attempt to quantify the amount permeated at different depths. The reconstructed distribution was compared with the experimental mapping of CN, a strong vibrational peak in 4-cyanophenol where the skin is spectroscopically silent. The similarity between MCR-ALS resolved and experimental distribution in skin dosed for 4 h was 0.79 which improved to 0.91 for skin dosed for 1 h. The correlation was observed to be lower for deeper layers of skin where SRS signal intensity is low which is an indication of low sensitivity of SRS. This work is the first demonstration, to the best of our knowledge, of combining SRS imaging technique with spectral unmixing methods for direct observation and mapping of the chemical penetration and distribution in biological tissues.

In Silico Simulation of Simultaneous Percutaneous Absorption and Xenobiotic Metabolism: Model Development and a Case Study on Aromatic Amines.

PURPOSE: To advance physiologically-based pharmacokinetic modelling of xenobiotic metabolism by integrating metabolic kinetics with percutaneous absorption. METHOD: Kinetic rate equations were proposed to describe the metabolism of a network of reaction pathways following topical exposure and incorporated into the diffusion-partition equations of both xenobiotics and metabolites. The published ex vivo case study of aromatic amines was simulated. Diffusion and partition properties of xenobiotics and subsequent metabolites were determined using physiologically-based quantitative structure property relationships. Kinetic parameters of metabolic reactions were best fitted from published experimental data. RESULTS: For aromatic amines, the integrated transdermal permeation and metabolism model produced data closely matched by experimental results following limited parameter fitting of metabolism rate constants and vehicle:water partition coefficients. The simulation was able to produce dynamic concentration data for all the dermal layers, as well as the vehicle and receptor fluid. CONCLUSION: This mechanistic model advances the dermal in silico functionality. It provides improved quantitative spatial and temporal insight into exposure of xenobiotics, enabling the isolation of governing features of skin. It contributes to accurate modelling of concentrations of xenobiotics reaching systemic circulation and additional metabolite concentrations. This is vital for development of both pharmaceuticals and cosmetics.

Development of a Two-Dimensional Model for Predicting Transdermal Permeation with the Follicular Pathway: Demonstration with a Caffeine Study.

PURPOSE: The 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. METHODS: The 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. RESULTS: The 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. CONCLUSIONS: The 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.

Applying the skin sensitisation adverse outcome pathway (AOP) to quantitative risk assessment.

As documented in the recent OECD report 'the adverse outcome pathway for skin sensitisation initiated by covalent binding to proteins' (OECD, 2012), the chemical and biological events driving the induction of human skin sensitisation have been investigated for many years and are now well understood. Several non-animal test methods have been developed to predict sensitiser potential by measuring the impact of chemical sensitisers on these key events (Adler et al., 2011; Maxwell et al., 2011); however our ability to use these non-animal datasets for risk assessment decision-making (i.e. to establish a safe level of human exposure for a sensitising chemical) remains limited and a more mechanistic approach to data integration is required to address this challenge. Informed by our previous efforts to model the induction of skin sensitisation (Maxwell and MacKay, 2008) we are now developing two mathematical models ('total haptenated protein' model and 'CD8(+) T cell response' model) that will be linked to provide predictions of the human CD8(+) T cell response for a defined skin exposure to a sensitising chemical. Mathematical model development is underpinned by focussed clinical or human-relevant research activities designed to inform/challenge model predictions whilst also increasing our fundamental understanding of human skin sensitisation. With this approach, we aim to quantify the relationship between the dose of sensitiser applied to the skin and the extent of the hapten-specific T cell response that would result. Furthermore, by benchmarking our mathematical model predictions against clinical datasets (e.g. human diagnostic patch test data), instead of animal test data, we propose that this approach could represent a new paradigm for mechanistic toxicology.