Design principles of chemical penetration enhancers for transdermal drug delivery

Abstract

Chemical penetration enhancers (CPEs) are present in a large number of transdermal, dermatological, and cosmetic products to aid dermal absorption of curatives and aesthetics. This wide spectrum of use is based on only a handful of molecules, the majority of which belong to three to four typical chemical functionalities, sporadically introduced as CPEs in the last 50 years. Using >100 CPEs representing several chemical functionalities, we report on the fundamental mechanisms that determine the barrier disruption potential of CPEs and skin safety in their presence. Fourier transform infrared spectroscopy studies revealed that regardless of their chemical make-up, CPEs perturb the skin barrier via extraction or fluidization of lipid bilayers. Irritation response of CPEs, on the other hand, correlated with the denaturation of stratum corneum proteins, making it feasible to use protein conformation changes to map CPE safety in vitro. Most interestingly, the understanding of underlying molecular forces responsible for CPE safety and potency reveals inherent constraints that limit CPE performance. Reengineering this knowledge back into molecular structure, we designed >300 potential CPEs. These molecules were screened in silico and subsequently tested in vitro for molecular delivery. These molecules significantly broaden the repertoire of CPEs that can aid the design of optimized transdermal, dermatological, and cosmetic formulations in the future.

Fig. 1.

ER-IP correlations of CPEs in 10 different categories. (A) ER is proportional to IP for CPEs belonging to NI (blue circles), ZI (red circles), AZ (black circles), and SS (green circles). (B) ER does not show a correlation with IP for CPEs belonging to FM (blue circles), FE (red circles), AI (black circles), FA (green circles), CI (brown circles), or OT (gray circles). (C) Bar graph showing ER/IP for the best CPE in each chemical class (for complete names refer to Table 1, which is published as supporting information on the PNAS web site). Error bars correspond to SD (n = 3).

Fig. 2.

Spectroscopic studies of the SC. (A) Plot of conductivity ER against change in integrated absorbance of methylene stretching, Δ(ν_symCH₂). Extractors show a reduction in absorbance, whereas fluidizers show an increase in absorbance. In each class, ER correlates well with Δ(ν_symCH₂). Error bars correspond to SD (n = 3). (B) Deconvoluted peaks of the amide I band of an SC IR spectrum before (blue curves) and after (red curves) the treatment with a formulation containing 1.5% wt/vol lauric acid in 1:1 EtOD:D₂O. Treatment with lauric acid decreases the relative contribution of the α-helical structures to the amide I band compared to the untreated region of the same sample. Contributions from other secondary structures, β-sheets, random coils, and antiparallel β-sheets and turns, in contrast, increase compared with the corresponding regions of the untreated sample. (C) Plot of IP against change in integrated absorbance of carbonyl stretching mode at 1,650 cm^-1, Δ(νC═O). Δ(νC═O) correlates well with IP. Error bars correspond to SD (n = 3).

Fig. 3.

Molecular descriptors of ER and IP. (A) Plot of Δ(ν_symCH₂) against molecular descriptors for fluidizers (red circles) and extractors (green circles). Change in the absorbance of the methylene stretching peak correlates with logarithm of octanol-water partition coefficient (log P) for fluidizers and the ratio of hydrogen bonding (δ_h) to square root of cohesive energy density (E_C) for extractors. Error bars correspond to SD (n = 3). (B) IP correlates with the ratio of hydrogen bonding interactions (δ_h) to polar interactions (δ_p).

Fig. 4.

Plot of experimental ER/IP vs. ER/IP predicted from molecular descriptors for extractors (A) and fluidizers (B). For extractors and fluidizers, ER/IP is predicted from Eqs. 1 and 2, respectively.

Fig. 5.

Design of CPEs. (A)ER/IP predictions for mutant CPEs calculated using Eqs. 1 and 2. The x axis lists the number of mutant and original CPEs studied and y axis shows the corresponding ER/IP values for mutant extractors (green open circles) and fluidizers (red open circles). Filled green (extractors) and red (fluidizers) circles show ER/IP values for CPEs in the original pool. (B) Pool size of mutant and original fluidizers as a function of ER/IP. Mutant fluidizers clearly outperform the original CPEs. (C) Predicted ER/IP for a mutant CPE, SM, and a commonly used CPE in transdermal literature, OA (gray bars), and comparison of inulin permeability enhancement for SM and OA (black bars).

Fig. 6.

Molecular structures of some of the best mutant fluidizers. I, stearyl methacrylate; II, 1-(2-hydroxy-phenoxy), 1-(4-hydroxy-phenoxy) tetradecane; III, 1-(8-octyl-8-(1,1-dimethylhexyl)heptadecane)-1,3,5-triazine-2,4,6-trione; IV, 1-benzyl-4-(2-((1,1′-biphenyl)-4-yloxy)ethyl)piperazine; V, 1,4-bis-((2-chlorophenyl)-phenyl-methyl)-piperazine; VI, 2,3,6,7-tetrakis(chloromethyl)-1,4,5,8-tetramethylbiphenylene.