Discriminant analysis as a tool to identify compounds with potential as transdermal enhancers

Area of Science:

• Pharmaceutical Sciences
• Dermal Drug Delivery
• Physical Chemistry

Background:

• Understanding structure-activity relationships is crucial for designing effective drug delivery systems.
• Propylene glycol is a common vehicle for topical drug delivery, but its permeation enhancement properties require optimization.
• Identifying reliable predictors of enhancer efficacy can streamline the development of novel transdermal formulations.

Purpose of the Study:

• To investigate the structure-activity relationships of chemical enhancers for hydrocortisone permeation across hairless mouse skin.
• To identify readily available physicochemical properties that predict enhancer performance.
• To develop simple guidelines for predicting potent enhancers.

Main Methods:

• Synthesized and tested 73 enhancers with varying chain lengths (CC) and hydrogen-bonding atoms (HB).
• Measured hydrocortisone permeation enhancement ratio (ER) over 24 hours.
• Employed multiple regression, discriminant analysis, and multivariate analysis to correlate physicochemical properties (CC, HB, molecular weight, log P, log S) with ER.

Main Results:

• Multiple regression analysis failed to accurately predict high ER values.
• Discriminant analysis using CC, HB, and molecular weight correctly classified 92% of potent enhancers (ER > 10).
• Simple guidelines (CC > 12, HB 2-5) showed potential, refined by multivariate analysis.

Conclusions:

• Physicochemical properties like chain length and hydrogen-bonding atoms are significant predictors of enhancer activity.
• Discriminant analysis provides a successful method for identifying potent enhancers.
• This approach holds potential for predicting novel enhancer activity in transdermal drug delivery.