A skin permeability model of insulin in the presence of chemical penetration enhancer.

Enhancing transdermal delivery of insulin using chemical penetration enhancers (CPEs) has several advantages over other non-traditional methods; however, lack of suitable predictive models, make experimentation the only alternative for discovering new CPEs. To address this limitation, a quantitative structure-property relationship (QSPR) model was developed, for predicting insulin permeation in the presence of CPEs. A virtual design algorithm that incorporates QSPR models for predicting CPE properties was used to identify 48 potential CPEs. Permeation experiments using Franz diffusion cells and resistance experiments were performed to quantify the effect of CPEs on insulin permeability and skin structure, respectively. Of the 48 CPEs, 35 were used for training and 13 were used for validation. In addition, 12 CPEs reported in literature were also included in the validation set. Differential evolution (DE) was coupled with artificial neural networks (ANNs) to develop the non-linear QSPR models. The six-descriptor model had a 16% absolute average deviation (%AAD) in the training set and 4 misclassifications in the validation set. Five of the six descriptors were found to be statistically significant after sensitivity analyses. The results suggest, molecules with low dipoles that are capable of forming intermolecular bonds with skin lipid bi-layers show promise as effective insulin-specific CPEs.