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A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

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Predicting skin permeability using HuskinDB.

Laura J Waters¹, Xin Ling Quah²

¹School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK. l.waters@hud.ac.uk.

Scientific Data

|September 23, 2022

Summary

This summary is machine-generated.

A new model predicts human skin permeation for novel compounds using data from the Human Skin Database (HuskinDB). This computational approach can replace traditional permeation testing for many chemicals.

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Area of Science:

Pharmacokinetics and Drug Delivery
Computational Chemistry
Dermatology

Background:

The Human Skin Database (HuskinDB) offers valuable human skin permeation data.
Existing databases are limited for assessing novel or unlisted compounds.
Permeation testing is crucial for toxicity and efficacy determination.

Purpose of the Study:

To develop a predictive model for human skin permeation.
To enable assessment of novel compounds not present in existing databases.
To reduce the need for experimental permeation testing.

Main Methods:

Analysis of permeability coefficient (Kp) data from HuskinDB.
Development of predictive models using standard physicochemical properties.
Application of multiple regression analysis to identify key predictors.

Main Results:

A successful model was created to predict Kp through human skin.
The model utilizes only three essential chemical properties.
The predictive model demonstrated high accuracy within its defined property range.

Conclusions:

The developed model transforms HuskinDB from a data repository to a predictive tool.
This model can significantly reduce or replace in vitro permeation testing.
The findings support the use of computational modeling in pharmaceutical and toxicological assessments.