Suzhou Research Institute

Accurate quantification of drug concentration within the skin's interstitial fluid (ISF) remains a significant analytical challenge due to the limitations of invasive sampling and the inability of bulk measurements to resolve micro-scale distribution. Traditionally, predictive models have treated the skin as a static barrier, ignoring the dynamic matrix effects caused by ISF flow, which leads to substantial errors in estimating deep-tissue analyte concentrations. To address this, this study proposes a computational analytical strategy integrating Finite Element Method (FEM) with Computational Fluid Dynamics (CFD) to quantitatively profile drug transport under varying thermal conditions. By calibrating against HPLC-validated ex vivo permeation data at a reference temperature, diffusion coefficients and ISF flow velocities were extrapolated to predict behavior at other temperatures. This approach effectively decouples the influence of fluid dynamics from passive diffusion, allowing for the precise resolution of temperature-dependent permeation kinetics. The Flow-Field model demonstrated strong correlations with ex vivo skin permeation tests, achieving R 2 values over 0.99 for various drugs and temperature conditions. This work establishes a robust in silico tool for the micro-scale profiling of analytes in complex biological tissues, offering a non-invasive alternative to estimate ISF concentrations where physical sampling is restricted. • Integrated strategy quantifies drug concentration in skin interstitial fluid. • FEM-CFD model decouples fluid dynamics for precise permeation analysis. • Flow-Field model achieves R 2 > 0.99 correlation with ex vivo skin permeation tests. • Temperature-dependent transdermal drug transport accurately predicted. • Non-invasive in silico tool resolves micro-scale analyte distribution.