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Visualizing and Quantifying Pharmaceutical Compounds within Skin using Coherent Raman Scattering Imaging
Published on: November 24, 2021
Nonlinear quantitative structure-property relationship modeling of skin permeation coefficient.
Brian J Neely1, Sundararajan V Madihally, Robert L Robinson
1School of Chemical Engineering, Oklahoma State University, Stillwater, Oklahoma 74078, USA.
Journal of Pharmaceutical Sciences
|February 4, 2009
Summary
Researchers developed a new model to predict chemical skin penetration using quantitative structure-property relationships (QSPR) and machine learning. This model accurately predicts permeation coefficients based on molecular structure, aiding in drug delivery and safety assessments.
Area of Science:
- Computational chemistry
- Pharmacokinetics
- Toxicology
Background:
- The permeation coefficient is crucial for understanding chemical transport through the skin.
- Developing accurate predictive models for permeation is essential for drug development and risk assessment.
- Existing models may not fully capture the complex relationship between molecular structure and skin permeation.
Purpose of the Study:
- To develop a reliable, structure-based model for predicting the permeation coefficient.
- To integrate quantitative structure-property relationship (QSPR) models, genetic algorithms (GAs), and neural networks.
- To investigate the influence of molecular structural attributes on skin permeation.
Main Methods:
- Utilized a curated dataset of 160 molecules with permeation coefficient values.
- Employed established and novel molecular descriptors.
- Integrated nonlinear QSPR, genetic algorithms, and neural networks for model development.
Main Results:
- Achieved an absolute-average percentage deviation of 8.0% and a correlation coefficient of 0.93.
- The developed nonlinear QSPR model demonstrated high predictive accuracy.
- Analysis revealed that size/shape and polarity descriptors significantly contribute to predicting permeation.
Conclusions:
- The developed nonlinear QSPR model provides a reliable method for predicting chemical permeation coefficients.
- Molecular size, shape, and polarity are key determinants of skin penetration.
- This approach can enhance the design and safety evaluation of chemical compounds.
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