QSAR Analyses of Skin Penetration Enhancers

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

An Integrated Biophysical Model for Predicting the Clinical Pharmacokinetics of Transdermally Delivered Compounds

The delivery of therapeutic drugs through the skin is a promising alternative to oral or parenteral delivery routes because dermal drug delivery systems (D³S) offer unique advantages such as controlled drug release over sustained periods and a significant reduction in first-pass effects, thus reducing the required dosing frequency and level of patient noncompliance. Furthermore, D³S find applications in multiple therapeutic areas, including drug repurposing. This article presents an integrated biophysical model of dermal absorption for simulating the permeation and absorption of compounds delivered transdermally. The biophysical model is physiologically/biologically inspired and combines a holistic model of healthy skin with whole-body physiology-based pharmacokinetics through dermis microcirculation. The model also includes the effects of chemical penetration enhancers and hair follicles on transdermal transport. The model-predicted permeation and pharmacokinetics of select compounds were validated using in vivo data reported in the literature. We conjecture that the integrated model can be used to gather insights into the permeation and systemic absorption of transdermal formulations (including cosmetic products) released from novel depots and optimize delivery systems. Furthermore, the model can be adapted to diseased skin with parametrization and structural adjustments specific to skin diseases.

Permeation-enhancing effects and mechanisms of O-acylterpineol on isosorbide dinitrate: mechanistic insights based on ATR-FTIR spectroscopy, molecular modeling, and CLSM images

The present study aimed to evaluate the penetration activity of O-acylterpineol derivatives both in vitro and in vivo, and to investigate the enhancing mechanism of O-acylterpineol derivatives which were synthesized by α-terpineol and fatty acid. The promoting activities on the isosorbide dinitrate patch were tested across full thickness rabbit skin both in vitro and in vivo. In order to elucidate the permeation mechanism, attenuated total reflection Fourier transform infrared spectroscopy, molecular modeling, and confocal laser scanning microscopy were introduced to investigate the regulation of enhancers in the skin permeability and biophysical properties. With in vitro cytotoxicity test and in vivo erythema model, the skin irritation of enhancers was also evaluated. Permeation studies showed 2-(4-methylcyclohex-3-en-l-yl) propan-2-yl tetradecanoate produced the obvious enhancement activity for ISDN both in vitro and in vivo from patches. These results were supported by ATR-FTIR, molecular modeling, and CLSM studies which revealed that O-acylterpineol could decrease the order of the alkyl chains in the skin lipids. Additionally, it was found that TER-C14 produced a relatively low skin irritation, compared with the TER which was assumed to be a safe compound. The present research suggested that some newly designed acylterpineol derivatives are shown to be suitable permeation enhancers for transdermal drug delivery, and the chain length of C14 seem to be safe and more favorable for the penetration of ISDN from DIA patches.

Design of skin penetration enhancers using replacement methods for the selection of the molecular descriptors

Transdermal delivery of certain drugs is challenging because of skin barrier resistance. This study focuses on the implementation of feature-selection algorithms to design chemical penetration enhancers. A database, consisting of 145 polar and nonpolar chemicals, was chosen for the investigation. Replacement, enhanced replacement and stepwise algorithms were applied to identify relevant structural properties of these compounds. The descriptors were calculated using Molecular Modeling Pro™ Plus. Based on the coefficient of determination, the replacement methods outperformed the stepwise approach in selecting the features that best correlated with the flux enhancement ratio. An artificial neural network model was built to map a subset of descriptors from sixty-one nonpolar enhancers onto the output vector. The R² value improved from 0.68, for a linear model, to 0.74, which shows that the improved framework might be effective in the design of compounds with user-defined properties.

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.

3D pharmacophore mapping using 4D QSAR analysis for the cytotoxicity of lamellarins against human hormone-dependent T47D breast cancer cells

4D quantitative structure-activity relationship (QSAR) and 3D pharmacophore models were built and investigated for cytotoxicity using a training set of 25 lamellarins against human hormone dependent T47D breast cancer cells. Receptor-independent (RI) 4D QSAR models were first constructed from the exploration of eight possible receptor-binding alignments for the entire training set. Since the training set is small (25 compounds), the generality of the 4D QSAR paradigm was then exploited to devise a strategy to maximize the extraction of binding information from the training set and to also permit virtual screening of diverse lamellarin chemistry. 4D QSAR models were sought for only six of the most potent lamellarins of the training set as well as another subset composed of lamellarins with constrained ranges in molecular weight and lipophilicity. This overall modeling strategy has permitted maximizing 3D pharmacophore information from this small set of structurally complex lamellarins that can be used to drive future analog synthesis and the selection of alternate scaffolds. Overall, it was found that the formation of an intermolecular hydrogen bond and the hydrophobic interactions for substituents on the E ring most modulate the cytotoxicity against T47D breast cancer cells. Hydrophobic substitutions on the F-ring can also enhance cytotoxic potency. A complementary high-throughput virtual screen to the 3D pharmacophore models, a 4D fingerprint QSAR model, was constructed using absolute molecular similarity. This 4D fingerprint virtual high-throughput screen permits a larger range of chemistry diversity to be assayed than with the 4D QSAR models. The optimized 4D QSAR 3D pharmacophore model has a leave-one-out cross-correlation value of xv-r2 = 0.947, while the optimized 4D fingerprint virtual screening model has a value of xv-r2 = 0.719. This work reveals that it is possible to develop significant QSAR, 3D pharmacophore, and virtual screening models for a small set of lamellarins showing cytotoxic behavior in breast cancer screens that can guide future drug development based upon lamellarin chemistry.