(439a) Design of Improved Permeation Enhancers for Transdermal Drug Delivery Part I – Model Development | AIChE

(439a) Design of Improved Permeation Enhancers for Transdermal Drug Delivery Part I – Model Development


Madihally, S. - Presenter, Oklahoma State University
Gasem, K. A. M. - Presenter, Oklahoma State University
Robinson, R. L. Jr. - Presenter, Oklahoma State University

Traditional drug delivery techniques, such as oral or intravenous administration, are often associated with problems relating to over- and under-dosing, interactions with harsh gastro-intestinal environment, and/or the production of toxic by-products through metabolism in the liver. Several alternatives have been developed to overcome these difficulties; one such technique that has rapidly gained in significance has been transdermal drug delivery (TDD). While successful for delivery of small therapeutic molecules (nicotine, hydrocortisone, etc) researchers have not yet succeeded in the delivery of large therapeutic molecules (for example insulin) across human skin. Extensive efforts have been expended to identify possible molecules (chemical permeation enhancers, CPEs), which would facilitate the transdermal delivery of these macromolecules. However, the currently employed CPE developmental techniques have limitations, including the dependence on time-consuming and expensive experimental screening techniques. An attractive alternative is the use of computer aided molecular design (CAMD), in which structure-based models are coupled with powerful search algorithms to identify viable molecules. While several such algorithms have been recently proposed, most have several limitations, including: (a) reliance on rudimentary linear/functional group models for property prediction, (b) lack of a theoretical framework to describe permeation/irritation, (c) use of general-purpose heuristic algorithms for molecular screening, and (e) inadequate data for model development and testing. In this work, we have integrated non-linear, theory-based quantitative structure-property-relationship (QSPR) models and genetic algorithms (GAs) to develop reliable models for skin permeation and skin sensitization. Overall, our new non-linear QSPR model was capable of predicting skin permeation data within 5.1 absolute average percent deviation (%AAD), and skin sensitization probability was classified with an accuracy of 98%.