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

Abstract

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.

Nanoemulsion containing dapsone for topical administration: a study of in vitro release and epidermal permeation

“…24 These characteristics make n-methyl-pyrrolidone an ideal candidate, and recently they have been exploited in applications testing the efficacy of n-methyl-pyrrolidone in transdermal nanosystems containing granisetron hydrochloride, 25 and in formulations designed for topical use, such as lidocaine, 26 estradiol, 27 bupranolol, 28 and insulin. 29 In formulations intended for topical administration, the aqueous phase and surfactants affect skin permeability by hydration of the stratum corneum and breakdown of the lipoprotein matrix, resulting in decreased resistance to drug diffusion. [30][31][32] As shown in Figure 1, n-methyl-pyrrolidone-containing system shows a large nanoemulsion region.…”

Supramolecular aggregates of oligosaccharides with co-solvents in ternary systems for the solubilizing approach of triamcinolone

“…The presence of NMP and TEA in the ternary complexes can also enhance drug permeability, since both co-solvents were previously studied for this purpose involving NMP (Pappinen et al, 2007;Ren et al, 2008;Yerramsetty, Neely, Madihally, & Gasem, 2010) and TEA (Cheong & Choi, 2002;Feng et al, 2008). In this study, we recognize that the ternary complexes could have different properties related to binaries, with some potential enhancement of TRI absorption or due to the calculated different stability constants that modify drug release from the CD complex.…”

“…The TEA did not present a synergic effect with the CD in the drug solubilization, but stabilized the main conformer of the formed ternary complex, improving the uniformity and the quality of complexation. Considering the similar drug solubility of the binary and ternary complex with TEA, the latter would be the reasonable choice in practical application since previous studies demonstrated that these co-solvents improved drug absorption and bioavailability when used in ternary complexes(Feng et al, 2008;Palma et al, 2009;Yerramsetty et al, 2010). Finally, the large soluble fraction of drug identified for the binary and ternary complexes with TEA was superior to all therapeutic doses commercially available in different dosage forms, mainly in depot suspensions, needed in invasive injections in the eye, joint, or soft tissues…”

Artificial Neural Networks: Theoretical Background and Pharmaceutical Applications: A Review

“…As a consequence, QSPR methods correlate structural or property descriptors of compounds with their chemical or biological activities (113)(114)(115)(116)(117). For example, to take advantage of neural networks, the aqueous solubility of series of structurally related compounds has been assessed (113,114).…”