Topical delivery involves the direct application of chemical compounds onto the skin to achieve localised or systemic therapeutic effects. This non-invasive method offers advantages such as targeted treatment, reduced systemic side effects, and bypassing first-pass metabolism, ensuring higher bioavailability. Understanding topical delivery of chemicals is crucial for optimising the efficacy and safety of treatments applied to the skin. This knowledge is vital for developing effective therapies for skin disorders, transdermal drug delivery systems, and cosmetic applications.

Although skin permeation mechanisms have been extensively studied, a critical knowledge gap remains in understanding the spatial-temporal dynamics of how chemicals penetrate the highly heterogeneous structure of skin tissue. Most existing studies focus on bulk permeation, providing limited insights into specific penetration pathways or the local distribution of chemicals within skin layers. This limitation impacts the predictive accuracy of in-silico models, which have become invaluable tools for studying skin permeation due to their cost-effectiveness and efficiency, reducing the reliance on extensive experimental setups.

This thesis addresses the knowledge gap by enhancing in-silico modelling with high-resolution data obtained through stimulated Raman scattering (SRS) imaging for in-vitro skin permeation assessment. SRS microscopy offers (semi) quantitative imaging with submicron spatial resolution with rapid acquisition speeds. These high-resolution datasets are utilised to refine existing in-silico models, resulting in an integrated imaging - modelling framework for a more systematic and precise evaluation of skin permeation. The combined experimental and computational approach elucidates the relative contributions of different penetration pathways and refines in-silico modelling using detailed SRS measurements. The submicron resolution data provides an unprecedented view of chemical distribution across the skin, establishing direct correlations between physicochemical properties and penetration pathways. This refined in-silico model is the first to incorporate such high-resolution data, delivering significantly improved predictive accuracy and reliability.