Support vector regression to estimate the permeability enhancement of potential transdermal enhancers

Moss

Abstract

Objectives
Searching for chemicals that will safely enhance transdermal drug delivery is a significant challenge. This study applies support vector regression (SVR) for the first time to estimating the optimal formulation design of transdermal hydrocortisone formulations.

Methods
The aim of this study was to apply SVR methods with two different kernels in order to estimate the enhancement ratio of chemical enhancers of permeability.

Key findings
A statistically significant regression SVR model was developed. It was found that SVR with a nonlinear kernel provided the best estimate of the enhancement ratio for a chemical enhancer.

Conclusions
Support vector regression is a viable method to develop predictive models of biological processes, demonstrating improvements over other methods. In addition, the results of this study suggest that a global approach to modelling a biological process may not necessarily be the best method and that a ‘mixed-methods’ approach may be best in optimising predictive models.

Citation

Moss. (2016). Support vector regression to estimate the permeability enhancement of potential transdermal enhancers. Journal of Pharmacy and Pharmacology, 170-184. https://doi.org/10.1111/jphp.12508

• Acceptance Date: Nov 19, 2015
• Publication Date: Jan 11, 2016
• Journal: Journal of Pharmacy and Pharmacology
• Publisher: David Publishing
• Pages: 170-184
• DOI: https://doi.org/10.1111/jphp.12508
• Keywords: Gaussian processes, hydrocortisone, support vector machine, support vector regression, transdermal enhancer
• Publisher URL: https://doi.org/10.1111/jphp.12508