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Predicting skin permeability from complex vehicles

Authors: Daniela, Karadzovska; James D, Brooks; Nancy A, Monteiro-Riviere; Jim E, Riviere;

Predicting skin permeability from complex vehicles

Abstract

It is now widely accepted that vehicle and formulation components influence the rate and extent of passive chemical absorption through skin. Significant progress, over the last decades, has been made in predicting dermal absorption from a single vehicle; however the effect of a complex, realistic mixture has not received its due attention. Recent studies have aimed to bridge this gap by extending the use of quantitative structure-permeation relationship (QSPR) models based on linear free energy relationships (LFER) to predict dermal absorption from complex mixtures with the inclusion of significant molecular descriptors such as a mixture factor that accounts for the physicochemical properties of the vehicle/mixture components. These models have been compiled and statistically validated using the data generated from in vitro or ex vivo experimental techniques. This review highlights the progress made in predicting skin permeability from complex vehicles.

Keywords

Skin Absorption, Quantitative Structure-Activity Relationship, Models, Theoretical, Permeability, Drug Delivery Systems, Pharmaceutical Preparations, Animals, Humans, Linear Energy Transfer, Pharmaceutical Vehicles, Skin

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
72
Top 10%
Top 10%
Top 10%
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