title: Support vector regression to estimate the permeability enhancement of potential transdermal enhancers.
abstract: OBJECTIVE
  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.
  
  
  RESULTS
  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.
authors.affiliation: Department of Software Engineering and IT, Ecole de Technologie Superieure, Montreal, QC, Canada.
authors.forename: Alpa
authors.initials: A
authors.lastname: Shah
authors.affiliation: School of Computer Science, University of Hertfordshire, Hatfield, UK.
authors.forename: Yi
authors.initials: Y
authors.lastname: Sun
authors.affiliation: School of Computer Science, University of Hertfordshire, Hatfield, UK.
authors.forename: Rod G
authors.initials: RG
authors.lastname: Adams
authors.affiliation: School of Computer Science, University of Hertfordshire, Hatfield, UK.
authors.forename: Neil
authors.initials: N
authors.lastname: Davey
authors.affiliation: School of Pharmacy, Keele University, Keele, UK.
authors.forename: Simon C
authors.initials: SC
authors.lastname: Wilkinson
authors.affiliation: Medical Toxicology Centre, Wolfson Unit, Medical School, University of Newcastle-upon-Tyne, Newcastle-upon-Tyne, UK.
authors.forename: Gary P
authors.initials: GP
authors.lastname: Moss
mesh_terms: D000277:Adjuvants, Pharmaceutic / Q000737:chemistry / Q000494:pharmacology; D000279:Administration, Cutaneous; D006854:Hydrocortisone / Q000008:administration & dosage / Q000493:pharmacokinetics; D008954:Models, Biological; D025341:Principal Component Analysis; D012044:Regression Analysis; D012867:Skin / Q000378:metabolism*; D012869:Skin Absorption / Q000187:drug effects*; D013329:Structure-Activity Relationship; D060388:Support Vector Machine
chemical_list: D000277:Adjuvants, Pharmaceutic; D006854:Hydrocortisone
keywords: Gaussian processes; hydrocortisone; support vector machine; support vector regression; transdermal enhancer