Pharmacokinetic Relevance of In Vitro Skin Models: Absorption, Cutaneous Distribution, Metabolism, and Clearance-like...

Background: In vitro skin models are widely used in dermal and transdermal drug development, yet their pharmacokinetic interpretation often remains endpoint-dependent and does not fully capture cutaneous disposition, including partitioning, local metabolism, or clearance-like removal. This review evaluates the utility of these models through a comprehensive pharmacokinetic process framework. Methods: Using a structured literature search, we critically compared data from studies utilizing a range of in vitro skin models, including synthetic membranes, ex vivo human skin, reconstructed human epidermis, full-thickness equivalents, and advanced bioprinted or microfluidic platforms. The assessment focused on model performance across the key processes of absorption, distribution, metabolism, and elimination. Results: Our analysis reveals that alternative barriers exhibit no uniform direction of bias relative to human skin; model rank order varies with compound, formulation, and dose, so that the available evidence does not currently support a single universal scaling relationship, although calibration within a defined applicability domain may remain possible. Synthetic membranes provide reproducible platforms for formulation ranking but lack biological retention and metabolism. Ex vivo human skin remains the closest reference for permeation and mass balance, though donor variability and storage sensitivity limit standardization. Reconstructed epidermis supports standardized permeation and epidermal targeting, while full-thickness equivalents permit dermal retention analysis. Advanced perfused constructs extend experimental capabilities, but their predictive performance remains largely uncharacterized. Conclusions: No single model universally replicates human pharmacokinetics. Model selection must therefore be carefully aligned with the specific pharmacokinetic process of interest, the model's biological capacity, and the intended context of use to ensure clinically relevant data interpretation.