University of Hertfordshire

From the same journal

By the same authors

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

Research output: Contribution to journalArticlepeer-review


View graph of relations
Original languageEnglish
Pages (from-to)170-184
Number of pages15
JournalJournal of Pharmacy and Pharmacology
Early online date11 Jan 2016
Publication statusPublished - 16 Feb 2016



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.


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.


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.


This is the peer reviewed version of the following article: Shah, A., Sun, Y., Adams, R. G., Davey, N., Wilkinson, S. C. and Moss, G. P. (2016), Support vector regression to estimate the permeability enhancement of potential transdermal enhancers', Journal of Pharmacy and Pharmacology, Vol. 68 (2): 170–184, which has been published in final form at doi:10.1111/jphp.12508. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. © 2016 Royal Pharmaceutical Society.

ID: 11097672