Using principal component analysis in studying the transdermal delivery of a lipophilic drug from soft nano-colloidal carriers to develop a quantitative composition effect permeability relationship

Abstract

The aim of principal component analysis is to reduce the dimensionality of the data while retaining its variation. Obtaining a vector component representing the most important variation amongst the data and summarizing the factors are usually needed to achieve a new descriptor for the system. This can be used to elaborate certain properties related to the components used in formulating drug delivery systems. To this end, it is possible to develop what exclusively can be called quantitative composition effect permeability relationship. In this study, fundamental features of the Fourier transform infrared spectroscopy together with the degree of saturation of a model drug, testosterone hormone, were used as initial dimensions and their extent of change were utilized as original variables to generate a correlation matrix. The principal component (PC) with the largest eigen value was selected for regression analysis to provide a quantitative model relating the effects of different compositions with the enhanced penetration of the model lipophilic drug from microemulsions. A strong correlation (r = 0.90) was obtained between the main PC derived data and the observed permeability coefficient results which warrants the use of this analyzing method in optimizing different drug delivery systems.

• Drug permeation
• eigen
• microemulsions
• multivariate
• principal component

Acknowledgements

R. M. Hathout is sincerely grateful to Professor R. H. Guy, Department of Pharmacy and Pharmacology at Bath University in UK and M.O. Darwish, Department of Electrical and Computing Engineering, Carnegie Mellon University, Pennsylvania, USA, for their help, support, useful and instructive discussions.