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

Alpa Shah, Yi Sun, Roderick Adams, Neil Davey, Simon Charles Wilkinson, Gary Patrick Joseph Moss

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
113 Downloads (Pure)



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.
Original languageEnglish
Pages (from-to)170-184
Number of pages15
JournalJournal of Pharmacy and Pharmacology
Issue number2
Early online date11 Jan 2016
Publication statusPublished - 16 Feb 2016


  • Gaussian processes
  • hydrocortisone
  • support vector machine
  • support vector regression
  • transdermal enhancer


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