Application of RP-18 TLC Retention Data to the Prediction of the Transdermal Absorption of Drugs

Immobilized Keratin HPLC Stationary Phase-A Forgotten Model of Transdermal Absorption: To What Molecular and Biological Properties Is It Relevant?

Chromatographic retention data collected on immobilized keratin (KER) or immobilized artificial membrane (IAM) stationary phases were used to predict skin permeability coefficient (log K_p) and bioconcentration factor (log BCF) of structurally unrelated compounds. Models of both properties contained, apart from chromatographic descriptors, calculated physico-chemical parameters. The log K_p model, containing keratin-based retention factor, has slightly better statistical parameters and is in a better agreement with experimental log K_p data than the model derived from IAM chromatography; both models are applicable primarily to non-ionized compounds.Based on the multiple linear regression (MLR) analyses conducted in this study, it was concluded that immobilized keratin chromatographic support is a moderately useful tool for skin permeability assessment.However, chromatography on immobilized keratin may also be of use for a different purpose-in studies of compounds' bioconcentration in aquatic organisms.

Comparison of supercritical fluid chromatographic methods to predict the skin permeability of pharmaceutical and cosmetic compounds

Supercritical fluid chromatography (SFC) was explored as an alternative for liquid chromatography to predict the skin permeability of pharmaceutical and cosmetic compounds. Nine dissimilar stationary phases were applied to screen a test set of 58 compounds. The experimental retention factors (log k), in addition to two sets of theoretical molecular descriptors, were applied to model the skin permeability coefficient. Different modelling approaches, i.e. multiple linear regression (MLR) and partial least squares (PLS) regression, were used. In general, the MLR models performed better than the PLS models for a given descriptor set. The results obtained on a cyanopropyl (CN) column provided the best correlation with the skin permeability data. The retention factors obtained on this column were included in a simple MLR model, together with the octanol-water partition coefficient and the number of atoms (r² = 0.81, RMSEC = 0.537 or 20.5% and RMSECV = 0.580 or 22.1%). The overall best MLR model included the chromatographic descriptor from a phenyl column and 18 descriptors (r² = 0.98, RMSEC = 0.167 or 6.2% and RMSECV = 0.238 or 8.9%). This model showed a good fit, on top of very good predictive features. However, stepwise MLR models with a reduced complexity could also be determined, with the best performance parameters obtained with the CN-column based retention and eight descriptors (r² = 0.95, RMSEC = 0.282 or 10.7% and RMSECV = 0.353 or 13.4%). SFC thus provides a suitable alternative to the liquid chromatographic techniques previously applied to model the skin permeability.

Immobilized artificial membrane-chromatographic and computational descriptors in studies of soil-water partition of environmentally relevant compounds

Chromatographic retention factor log k_IAM obtained from immobilized artificial membrane (IAM) HPLC with buffered, aqueous mobile phases and calculated molecular descriptors (molecular weight - log M_W; molar volume - V_M; polar surface area - PSA; total count of nitrogen and oxygen atoms -(N + O); count of freely rotable bonds - FRB; H-bond donor count - HD; H-bond acceptor count - HA; energy of the highest occupied molecular orbital - E_HOMO; energy of the lowest unoccupied orbital - E_LUMO; dipole moment - DM; polarizability - α) obtained for a group of 175 structurally unrelated compounds were tested in order to generate useful models of solutes' soil-water partition coefficient normalized to organic carbon log K_oc. It was established that log k_IAM obtained in the conditions described in this study is not sufficient as a sole predictor of the soil-water partition coefficient. Simple, potentially useful models based on log k_IAM and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR) and artificial neural networks (ANN). The models proposed in the study were tested on a group of 50 compounds with known experimental log K_oc values by plotting the calculated vs. experimental values. There is a good close similarity between the calculated and experimental data for both MLR and ANN models for compounds from different chemical families (R² ≥ 0.80, n = 50) which proves the models' reliability.

Theoretical Prediction of Gastrointestinal Absorption of Phytochemicals

The discovery of bioactive compounds for non-invasive therapy has been the goal of research groups focused on pharmacotherapy. Phytonutrients have always been attractive for researchers because they are a significant source of bioactive phytochemicals. Still, it is challenging to determine which components show high biomedical activity and bioavailability after administration. However, based on the chemical structure of these phytochemicals, their physicochemical properties can be calculated to predict the probability of gastrointestinal (GI) absorption after oral administration. Indeed, different researchers have proposed several rules (e.g., Lipinski’s, Veber’s, Ghose’s, and Muegge’s rules) to attain these predictions, but only for synthetic compounds. Most phytochemicals do not fully comply with these rules even though they show high bioactivity and high GI absorption experimentally. Here, we propose a detailed methodology using scientifically validated web-based platforms to determine the physicochemical properties of five phytochemicals found in ginger, echinacea, and tobacco. Furthermore, we analyzed the calculated data and established a protocol based on the integration of these classical rules, plus other extended parameters, that we called the Phytochemical Rule, to obtain a more reliable prediction of the GI absorption of natural compounds. This methodology can help evaluate bioactive phytochemicals as potential drug candidates and predict their oral bioavailability in patients.

IAM Chromatographic Models of Skin Permeation

Chromatographic retention factor log k_IAM obtained from IAM HPLC chromatography with buffered aqueous mobile phases and calculated molecular descriptors (surface area—S_a; molar volume—V_M; polar surface area—PSA; count of freely rotable bonds—FRB; H-bond acceptor count—HA; energy of the highest occupied molecular orbital—E_HOMO; energy of the lowest unoccupied orbital—E_LUMO; and polarizability—α) obtained for a group of 160 structurally unrelated compounds were tested in order to generate useful models of solutes’ skin permeability coefficient log K_p. It was established that log k_IAM obtained in the conditions described in this study is not sufficient as a sole predictor of the skin permeability coefficient. Simple put, potentially useful models based on log k_IAM and readily available calculated descriptors, accounting for 85 to 91% of the total variability, were generated using Multiple Linear Regression (MLR).The models proposed in the study were tested on a group of 20 compounds with known experimental log K_p values.

RP-18 TLC and Computational Descriptors of Skin Permeability of Sunscreens

Abstract The relationships between the reversed-phase thin layer chromatographic retention parameters obtained on octadecyl-modified silica (RP-18) sorbent for mobile phases containing water and one of six water-miscible organic modifiers (acetone, methanol, acetonitrile, tetrahydrofurane, N,N-dimethylformamide, 1,4-dioxane) and skin permeability coefficients were studied for a group of 21 cosmetic raw materials, mainly organic sunscreens and preservatives. The correlations between the skin permeability coefficients log Kd calculated in silico using EpiSuite software and the RP-18 thin layer chromatographic retention parameters are mostly linear, especially for compounds of lower-to-medium lipophilicity.. It was established that skin permeability coefficient models based on retention parameters collected for mobile phases containing acetone or dioxane (75% v/v), proposed for structurally unrelated cosmetic raw materials are also applicable to other actives, as shown using a test set of compounds whose in vivo log Kd data are available. Skin permeability models developed in this study have the benefit of being based on easily obtained, chromatographic descriptors and their applicability extends beyond cosmetic chemistry.

Evaluating micellar liquid chromatographic methods on octadecyl particle-based and monolithic columns to predict the skin permeation of drug and cosmetic molecules

A micellar liquid chromatographic method was developed to assist in the modeling of the skin permeability of pharmaceutical and cosmetic compounds. The composition of the mobile phase was determined by means of a two-factor central composite design, after which it was tested on both a particle-based and monolithic column. The latter provided the opportunity to increase the flow rate from 1 to 8 mL/min without reaching too high backpressures. The micellar conditions allowed analyzing a large test set of compounds with diverse characteristics with just one mobile-phase composition. The obtained experimental chromatographic descriptors besides two sets of theoretical molecular descriptors were used to model the skin permeability coefficient log K_p, applying multiple linear regression and partial least squares regression approaches. The micellar method on the monolithic column provided useful models with similar or even slightly better performance parameters than the method on the particle-based column. Furthermore, a much faster analysis can be achieved when applying a flow rate of 8 mL/min, making the micellar monolithic method ideal to estimate skin permeability.

Phenylalanine Butyramide Is a New Cosmetic Ingredient with Soothing and Anti-Reddening Potential

Human skin is colonized by diverse commensal microbes, making up the skin microbiota (SM), contributing to skin integrity and homeostasis. Many of the beneficial effects aroused by the SM are exerted by microbial metabolites such as short-chain fatty acids (SCFAs), including butyric acid. The SCFAs can be used in cosmetic formulations against skin diseases to protect SM by preserving and/or restoring their natural balance. Unpleasant sensorial properties and unfavorable physico-chemical properties of butyrate strongly limit its cosmetic use. In contrast, some butyrate derivatives, including phenylalanine butyramide (C13H18N2O2, FBA), a solid form of butyric acid, are odorless while retaining the pharmacokinetic properties and safety profile of butyric acid. This study assessed the FBA's permeation across the skin and its soothing and anti-reddening potential to estimate its cosmetic application. The dosage method used to estimate FBA's levels was validated to be sure of analytical results. The FBA diffusion tests were estimated in vitro using a Franz-type vertical diffusion cell. The soothing action was evaluated in vivo by Colorimeter CL400, measuring the erythema index. The results suggest that the FBA represents an innovative way to exploit the benefits of butyric acid in the cosmetic fields since it cannot reach the bloodstream, is odorless, and has a significative soothing action (decrease the erythema index -15.7% after 30', and -17.8% after 60').

RP-18 TLC Chromatographic and Computational Study of Skin Permeability of Steroids

The skin permeability of steroids, as investigated in this study, is important because some of these compounds are, or could, be used in preparations applied topically. Several models of skin permeability, involving thin layer chromatographic and calculated descriptors, were generated and validated using K_p reference values obtained in silico and then tested on a group of solutes whose experimental K_p values could be found (log K_p^exp). The study established that the most applicable log K_p model is based on RP-18 thin layer chromatographic data (R_M) and the calculated descriptors V_M (molar volume) and PSA (polar surface area). Two less efficient, yet simple, equations based on PSA or V_M combined with HD (H-donor count) can be used with caution for rapid, rough estimations of compounds’ skin permeability prior to their chemical synthesis.