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Updated: Apr 1, 2026

Visualizing and Quantifying Pharmaceutical Compounds within Skin using Coherent Raman Scattering Imaging
Published on: November 24, 2021
High-throughput PBK modelling for dermal exposure: a pragmatic approach to predict systemic pharmacokinetics
Zeynep Edizcan1, Stephan Schaller2, Lars Kuepfer1
1Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, Germany.
None:
High-throughput physiologically based kinetic (HT-PBK) modelling provides a mechanistic framework for predicting systemic pharmacokinetics (PK) from in vitro and in silico data, supporting non-animal chemical safety assessment within next generation risk assessment (NGRA). Here, we applied HT-PBK modelling to dermal exposure, a key route of human contact with chemicals. Using the skin permeation model of the Open Systems Pharmacology Suite, we simulated systemic PK profiles of 52 compounds based solely on physicochemical properties predicted by quantitative structure-activity relationship (QSAR) models and without any compound-specific in vitro measurements. We systematically compared different QSAR tools for lipophilicity, solubility, and other parameters to identify optimal model parameterisation strategies. Across all compounds, the best-performing HT-PBK strategy predicted 73% of compounds' Cmax and 75% of AUC values within a tenfold range of observed human plasma data extracted from published clinical studies. A systematic tendency toward overprediction of systemic PK was observed, likely due to missing study metadata and the default assumption of fully hydrated skin. Prediction errors were larger for dermal than for oral exposure, reflecting the greater complexity and variability of dermal absorption processes. Nevertheless, key exposure metrics were reproduced within acceptable limits. These results demonstrate the feasibility of fully in silico, non-animal HT-PBK modelling for dermal absorption and support its use as a pragmatic tool for exposure-driven safety assessment within NGRA frameworks.
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