Human Skin Permeation and Partition: General Linear Free‐Energy Relationship Analyses

Machine learning for skin permeability prediction: random forest and XG boost regression

Background: Machine learning algorithms that can quickly and easily estimate skin permeability (Kp) are increasingly being used in drug delivery research. The linear free energy relationship (LFER) developed by Abraham is a practical technique for predicting Kp. The permeability coefficients and Abraham solute descriptor values for 175 organic compounds have been documented in the scientific literature.Purpose: The purpose of this project was to use a publicly available dataset to make skin permeability predictions using the random forest and XBoost regression techniques.Methods: We employed Pandas-based methods in JupyterLab to predict permeability coefficient (Kp) from solute descriptors (excess molar refraction [E], combined dipolarity/polarizability [S], overall solute hydrogen bond acidity and basicity [A and B], and the McGowan's characteristic molecular volume [V]).Results: The random forest and XG Boost regression models established statistically significant association between the descriptors and the skin permeability coefficient.

Lipid Biomimetic Models as Simple Yet Complex Tools to Predict Skin Permeation and Drug-Membrane Biophysical Interactions

The barrier function of the skin is primarily determined by its outermost layer, the Stratum Corneum (SC). The SC consists of corneocytes embedded in a lipid matrix composed mainly of ceramides, cholesterol, and free fatty acids in equimolar proportions and is organised in a complex lamellar structure with different periodicities and lateral packings. This matrix provides a diffusion pathway across the SC for bioactive compounds that are administered to the skin. In this regard, and as the skin administration route has grown in popularity, there has been an increase in the use of lipid mixtures that closely resemble the SC lipid matrix, either for a deeper biophysical understanding or for pharmaceutical and cosmetic purposes. This review focuses on a systematic analysis of the main outcomes of using lipid mixtures as SC lipid matrix models for pharmaceutical and cosmetic purposes. Thus, a methodical evaluation of the main outcomes based on the SC structure is performed, as well as the main recent developments in finding suitable new in vitro tools for permeation testing based on lipid models.

Ability of biomimetic chromatography and physicochemical systems to predict the skin permeation of neutral compounds. A comparison study

Five in vitro physicochemical systems have been evaluated in terms of its ability to emulate the skin permeation of neutral compounds: the permeation in two different PAMPA membranes, the classical octanol-water partition coefficient, and two biomimetic chromatography systems, one based in cerasome electrokinetic chromatography and another based in reversed-phase liquid chromatography measurements. The coefficients of the solvation parameter model equation of the mentioned systems have been compared to the ones of the skin permeation process through different comparison parameters. Moreover, a method to predict whether a physicochemical system is able to emulate satisfactorily a biological one, just by the analysis of the equation coefficients has been developed. Results reveal that the two PAMPA systems are a good choice to emulate directly the skin permeation of neutral compounds. Instead, the other three systems need a volume correction term to provide a satisfactory emulation. However, after the correction, all the evaluated systems show a similar ability to emulate well skin permeation, as predicted.

In Silico Study on the Contribution of the Follicular Route to Dermal Permeability of Small Molecules

PURPOSE

This study investigates in silico the contribution of the hair follicle to the overall dermal permeability of small molecules, as published experimental work provides inconclusive information on whether the follicular route favours the permeation of hydrophobic or hydrophilic permeants.

METHOD

A study is conducted varying physico-chemical parameters of permeants such as lipophilicity, molecular weight and protein binding. The simulated data is compared to published experimental data to discuss how those properties can modulate the contribution of the hair follicle to the overall dermal permeation.

RESULTS

The results indicate that the contribution of the follicular route to dermal permeation can range from negligible to notable depending on the combination of lipophilic/hydrophilic properties of the substance filling the follicular route and the permeant.

CONCLUSION

Characterisation of the substance filling the follicular route is required for analysing the experimental data of dermal permeation of small molecules, as changes between in vivo and in vitro due to handling of samples and cessation of vital functions can modify the contribution of the follicular route to overall dermal permeation, hence hindering data interpretation.

In Silico Acute Aquatic Hazard Assessment and Prioritization Using a Grouped Target Site Model: A Case Study of Organic Substances Reported in Permian Basin Hydraulic Fracturing Operations

Hydraulic fracturing (HF) is commonly used to enhance onshore recovery of oil and gas during production. This process involves the use of a variety of chemicals to support the physical extraction of oil and gas, maintain appropriate conditions downhole (e.g., redox conditions, pH), and limit microbial growth. The diversity of chemicals used in HF presents a significant challenge for risk assessment. The objective of the present study is to establish a transparent, reproducible procedure for estimating 5th percentile acute aquatic hazard concentrations (e.g., acute hazard concentration 5th percentiles [HC5s]) for these substances and validating against existing toxicity data. A simplified, grouped target site model (gTSM) was developed using a database (n = 1696) of diverse compounds with known mode of action (MoA) information. Statistical significance testing was employed to reduce model complexity by combining 11 discrete MoAs into three general hazard groups. The new model was trained and validated using an 80:20 allocation of the experimental database. The gTSM predicts toxicity using a combination of target site water partition coefficients and hazard group-based critical target site concentrations. Model performance was comparable to the original TSM using 40% fewer parameters. Model predictions were judged to be sufficiently reliable and the gTSM was further used to prioritize a subset of reported Permian Basin HF substances for risk evaluation. The gTSM was applied to predict hazard groups, species acute toxicity, and acute HC5s for 186 organic compounds (neutral and ionic). Toxicity predictions and acute HC5 estimates were validated against measured acute toxicity data compiled for HF substances. This case study supports the gTSM as an efficient, cost-effective computational tool for rapid aquatic hazard assessment of diverse organic chemicals. Environ Toxicol Chem 2024;00:1-12. © 2024 ExxonMobil Petroleum and Chemical BV. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish

Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals.

Multi-task machine learning models for simultaneous prediction of tissue-to-blood partition coefficients of chemicals in mammals

Tissue-to-blood partition coefficients (Ptb) are crucial for assessing the distribution of chemicals in organisms. Given the lack of experimental data and laborious nature of experimental methods, there is an urgent need to develop efficient predictive models. With the help of machine learning algorithms, i,e., random forest (RF), and artificial neural network (ANN), this study developed multi-task (MT) models that can simultaneously predict Ptb values for various mammalian tissues, including liver, muscle, brain, lung, and adipose. Single-task (ST) models using partial least squares regression, RF, and ANN algorithms for each endpoint were established for comparison. Overall, the performances of MT models were superior to those of ST models. The MT model using ANN algorithms showed the highest prediction accuracy with determination coefficients ranging from 0.704 to 0.886, root mean square errors between 0.223 and 0.410, and mean absolute errors ranging from 0.178 to 0.285 log units. Results showed that lipophilicity and polarizability of molecules significantly influence their partition behavior in organisms. Applicability domains (ADs) of the models were characterized by weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes. When constrained by ADs, the models displayed enhanced predictive accuracy, making them valuable tools for the risk assessment and management of chemicals.

Understanding Drug Skin Permeation Enhancers Using Molecular Dynamics Simulations

Our skin constitutes an effective permeability barrier that protects the body from exogenous substances but concomitantly severely limits the number of pharmaceutical drugs that can be delivered transdermally. In topical formulation design, chemical permeation enhancers (PEs) are used to increase drug skin permeability. In vitro skin permeability experiments can measure net effects of PEs on transdermal drug transport, but they cannot explain the molecular mechanisms of interactions between drugs, permeation enhancers, and skin structure, which limits the possibility to rationally design better new drug formulations. Here we investigate the effect of the PEs water, lauric acid, geraniol, stearic acid, thymol, ethanol, oleic acid, and eucalyptol on the transdermal transport of metronidazole, caffeine, and naproxen. We use atomistic molecular dynamics (MD) simulations in combination with developed molecular models to calculate the free energy difference between 11 PE-containing formulations and the skin's barrier structure. We then utilize the results to calculate the final concentration of PEs in skin. We obtain an RMSE of 0.58 log units for calculated partition coefficients from water into the barrier structure. We then use the modified PE-containing barrier structure to calculate the PEs' permeability enhancement ratios (ERs) on transdermal metronidazole, caffeine, and naproxen transport and compare with the results obtained from in vitro experiments. We show that MD simulations are able to reproduce rankings based on ERs. However, strict quantitative correlation with experimental data needs further refinement, which is complicated by significant deviations between different measurements. Finally, we propose a model for how to use calculations of the potential of mean force of drugs across the skin's barrier structure in a topical formulation design.

Evaluation of the Ability of PAMPA Membranes to Emulate Biological Processes through the Abraham Solvation Parameter Model

Two parallel artificial membrane permeability assay (PAMPA) systems intended for emulating skin permeability have been characterized through the solvation parameter model of Abraham using multilinear regression analysis. The coefficients of the obtained equations have been compared to the ones already established for other PAMPA membranes using statistical tools. The results indicate that both skin membranes are similar to each other in their physicochemical properties. However, they are different from other PAMPA membranes (e.g., intestinal absorption and blood-brain PAMPAs), mainly in terms of hydrophobicity and hydrogen bonding properties. Next, all PAMPA membranes have been compared to relevant biological processes also characterized through the solvation parameter model. The results highlight that skin-PAMPA membranes are a very good choice to emulate skin permeability.

Rational Design of Topical Semi-Solid Dosage Forms-How Far Are We?

Specific aspects of semi-solid dosage forms for topical application include the nature of the barrier to be overcome, aspects of susceptibility to physical and chemical instability, and a greater influence of sensory perception. Advances in understanding the driving forces of skin penetration as well as the design principles and inner structure of formulations, provide a good basis for the more rational design of such dosage forms, which still often follow more traditional design approaches. This review analyses the opportunities and constraints of rational formulation design approaches in the industrial development of new topical drugs. As the selection of drug candidates with favorable physicochemical properties increases the speed and probability of success, models for drug selection based on theoretical and experimental approaches are discussed. This paper reviews how progress in the scientific understanding of mechanisms and vehicle-influence of skin penetration can be used for rational formulation design. The characterization of semi-solid formulations is discussed with a special focus on modern rheological approaches and analytical methods for investigating and optimizing the chemical stability of active ingredients in consideration of applicable guidelines. In conclusion, the combination of a good understanding of scientific principles combined with early consideration of regulatory requirements for product quality are enablers for the successful development of innovative and robust semi-solid formulations for topical application.

Recent advances for estimating environmental properties for small molecules from chromatographic measurements and the solvation parameter model

The transfer of neutral compounds between immiscible phases in chromatographic or environmental systems can be described by six solute properties (solute descriptors) using the solvation parameter model. The solute descriptors are size (McGowan's characteristic volume), V, excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity and basicity, A and B, and the gas-liquid partition constant on n-hexadecane at 298.15 K, L. V and E for liquids are accessible by calculation but the other descriptors and E for solids are determined experimentally by chromatographic, liquid-liquid partition, and solubility measurements. These solute descriptors are available for several thousand compounds in the Abraham solute descriptor databases and for several hundred compounds in the WSU experimental solute descriptor database. In the first part of this review, we highlight features important in defining each descriptor, their experimental determination, compare descriptor quality for the two organized descriptor databases, and methods for estimating Abraham solute descriptors. In the second part we focus on recent applications of the solvation parameter model to characterize environmental systems and its use for the identification of surrogate chromatographic models for estimating environmental properties.

Skin permeability prediction with MD simulation sampling spatial and alchemical reaction coordinates

A molecular-level understanding of skin permeation may rationalize and streamline product development, and improve quality and control, of transdermal and topical drug delivery systems. It may also facilitate toxicity and safety assessment of cosmetics and skin care products. Here, we present new molecular dynamics simulation approaches that make it possible to efficiently sample the free energy and local diffusion coefficient across the skin's barrier structure to predict skin permeability and the effects of chemical penetration enhancers. In particular, we introduce a new approach to use two-dimensional reaction coordinates in the accelerated weight histogram method, where we combine sampling along spatial coordinates with an alchemical perturbation virtual coordinate. We present predicted properties for 20 permeants, and demonstrate how our approach improves correlation with ex vivo/in vitro skin permeation data. For the compounds included in this study, the obtained log KPexp-calc mean square difference was 0.9 cm2 h-2.

Evaluation of fatty tissue representative solvents in extraction of medical devices for chromatographic analysis of devices' extractables and leachables based on Abraham general solvation model

Extraction solvents used in chemical characterization (i.e., extractables and leachables testing, E&L) of fatty tissue-contacting medical devices for biocompatibility assessment per ISO 10993 have been studied by Abraham general solvation models. Chemically suitable alternative solvents to fatty tissues in solvation properties (solubility, partition, extraction, etc.) have been proposed based on Abraham's organic solvent system coefficients for water and air to condensed organic solvent phases. This evaluation is built upon the conclusion by Abraham, Acree Jr and Cometto-Muñiz that olive oil is chemically corresponding to fatty tissues. However, olive oil, if used as an extraction solvent to simulate fatty tissues, is in general not analytically expedient (realistic) per ISO 10993-18 (2020) for chromatographic analysis, and it is critical to seek alternative solvents to olive oil to perform the extraction. Although nonpolar solvents such as alkanes have been proposed and used as alternative solvents to vegetable oils, they are not equivalent to olive oil in solvation properties. Due to the practical challenge in chromatographic analysis of oil samples and the difference in migration kinetics of E&L between oil and organic solvents, the computational approach is the only realistic option to evaluate chemically alternative solvents to olive oil to simulate fatty tissue extraction. By comparing Abraham solvent system coefficients for water and air to condensed organic solvent phases distribution, a five-dimensional space distance (D) between solvents and olive oil as a reference solvent is calculated using Abraham and Martin equation to predict alternative or similar solvents to olive oil. The results of the calculation are further evaluated using E&L solubility ratio between solvents and olive oil, taking into consideration of solvent safety and physical properties. It is concluded from the study that butanone and dioxane are chemically the most suitable alternative or representative solvents to olive oil. They can be used as fatty tissue representative solvents in chemical characterization study of medical device. As Abraham solvation model is solvent system specific, not solute specific, the conclusions from this study are considered as universal.

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.

Evaluation of Constrained and Restrained Molecular Dynamics Simulation Methods for Predicting Skin Lipid Permeability

Recently, molecular dynamics (MD) simulations have been utilized to investigate the barrier properties of human skin stratum corneum (SC) lipid bilayers. Different MD methods and force fields have been utilized, with predicted permeabilities varying by few orders of magnitude. In this work, we compare constrained MD simulations with restrained MD simulations to obtain the potential of the mean force and the diffusion coefficient profile for the case of a water molecule permeating across an SC lipid bilayer. Corresponding permeabilities of the simulated lipid bilayer are calculated via the inhomogeneous solubility diffusion model. Results show that both methods perform similarly, but restrained MD simulations have proven to be the more robust approach for predicting the potential of the mean force profile. Critical to both methods are the sampling of the whole trans-bilayer axis and the following symmetrization process. Re-analysis of the previously reported free energy profiles showed that some of the discrepancies in the reported permeability values is due to misquotation of units, while some are due to the inaccurately obtained potential of the mean force. By using the existing microscopic geometrical models via the intercellular lipid pathway, the permeation through the whole SC is predicted from the MD simulation results, and the predicted barrier properties have been compared to experimental data from the literature with good agreement.

Topical drug delivery: History, percutaneous absorption, and product development

Topical products, widely used to manage skin conditions, have evolved from simple potions to sophisticated delivery systems. Their development has been facilitated by advances in percutaneous absorption and product design based on an increasingly mechanistic understanding of drug-product-skin interactions, associated experiments, and a quality-by-design framework. Topical drug delivery involves drug transport from a product on the skin to a local target site and then clearance by diffusion, metabolism, and the dermal circulation to the rest of the body and deeper tissues. Insights have been provided by Quantitative Structure Permeability Relationships (QSPR), molecular dynamics simulations, and dermal Physiologically Based PharmacoKinetics (PBPK). Currently, generic product equivalents of reference-listed products dominate the topical delivery market. There is an increasing regulatory interest in understanding topical product delivery behavior under 'in use' conditions and predicting in vivo response for population variations in skin barrier function and response using in silico and in vitro findings.

Thermodynamics-Based Model Construction for the Accurate Prediction of Molecular Properties From Partition Coefficients

Developing models for predicting molecular properties of organic compounds is imperative for drug development and environmental safety; however, development of such models that have high predictive power and are independent of the compounds used is challenging. To overcome the challenges, we used a thermodynamics-based theoretical derivation to construct models for accurately predicting molecular properties. The free energy change that determines a property equals the sum of the free energy changes (ΔG_Fs) caused by the factors affecting the property. By developing or selecting molecular descriptors that are directly proportional to ΔG_Fs, we built a general linear free energy relationship (LFER) for predicting the property with the molecular descriptors as predictive variables. The LFER can be used to construct models for predicting various specific properties from partition coefficients. Validations show that the models constructed according to the LFER have high predictive power and their performance is independent of the compounds used, including the models for the properties having little correlation with partition coefficients. The findings in this study are highly useful for applications in drug development and environmental safety.

Predicting the Membrane Permeability of Fentanyl and Its Analogues by Molecular Dynamics Simulations

The lipid membrane is considered a crucial component of opioid general anesthesia. The main drug used for the induction and maintenance of opioid anesthesia is fentanyl and its various analogues. However, these drugs have different clinical effects, and detailed atomic-level insight into the drug–membrane interactions could lead to a better understanding how these drugs exert their anesthetic properties. In this study, we have used extensive umbrella sampling molecular dynamics simulations to study the permeation process of fentanyl and three of its analogues into a variety of simple phospholipid membrane models. Our simulations show that we can accurately predict the permeability coefficients of these drug molecules, which is an important process in understanding how pharmaceuticals reach their molecular targets. We were also able to show that one phospholipid provides more accurate predictions than other lipids commonly used in these types of permeation studies, which will aid future studies of these types of processes.

Comparison of in-silico modelling and reversed-phase liquid chromatographic retention on an octadecyl silica column to predict skin permeability of pharmaceutical and cosmetic compounds

This study focuses on the in-silico modelling of the skin permeability using a test set of pharmaceutical and cosmetic compounds. Two sets of theoretical molecular descriptors, obtained from the E-Dragon and Vega ZZ software programs, were used in the models. Different linear regression methods, i.e. Multiple Linear Regression (MLR) and Partial Least Squares (PLS) regression, were applied for modelling and estimating the skin permeability. The best model was obtained using a stepwise MLR approach on the E-Dragon descriptor set. In a second step, the retention of the test set compounds was measured on a C18 column at two pH levels: pH 5.5 and pH 7. Different organic-modifier fractions were applied in the mobile phase to be able to extrapolate the retention factors to a log k_w value, with k_w the estimated retention factor in an aqueous mobile phase without organic modifier. Thereafter it was examined whether combining this chromatographic descriptor with the theoretical descriptors could improve the modelling of the skin permeability. The chromatographic descriptor often did not show an added value compared to the models containing only theoretical descriptors. Therefore, the in-silico models were preferred, and these models could be useful to predict the skin permeability of pharmaceutical and cosmetic compounds.

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

Several chromatographic parameters (R_M⁰ and S obtained from RP-18 TLC with methanol—pH 7.4 phosphate buffer mobile phases by extrapolation to zero concentration of methanol; R_f and R_M obtained from RP-18 TLC with acetonitrile—pH 7.4 phosphate buffer 70:30 v/v as a mobile phase) and calculated molecular descriptors (molecular weight—M_W; molar volume—V_M; polar surface area—PSA; total count of nitrogen and oxygen atoms—(N+O); H-bond donor count—HD; H-bond acceptor count—HA; distribution coefficient—log D; total energy—E_T; binding energy—E_b; hydration energy—E_h; energy of the highest occupied molecular orbital—E_HOMO; energy of the lowest unoccupied orbital—E_LUMO; electronic energy—E_e; surface area—S_a; octanol-water partition coefficient—log P; dipole moment—DM; refractivity—R, polarizability—α) and their combinations (R_f/PSA, R_M/M_W, R_M/V_M) were tested in order to generate useful models of solutes’ skin permeability coefficient log K_p. It was established that neither R_M⁰ nor S obtained in the conditions used in this study is a good predictor of the skin permeability coefficient. The chromatographic parameters R_f and R_f/PSA were also unsuitable for this purpose. A simple and potentially useful, purely computational model based on (N+O), log D and HD as independent variables and accounting for ca. 83% of total variability was obtained. The evaluation of parameters derived from R_M (R_M, R_M/M_W, R_M/V_M) as independent variables in log K_p models proved that R_M/V_M is the most suitable descriptor belonging to this group. In a search for a reliable log K_p model based on this descriptor two possibilities were considered: a relatively simple model based on 5 independent variables: (N+O), log D, R_M/V_M, E_T and E_h and a more complex one, involving also E_b, M_W and PSA.

In Silico Studies on Triterpenoid Saponins Permeation through the Blood-Brain Barrier Combined with Postmortem Research on the Brain Tissues of Mice Affected by Astragaloside IV Administration

As the number of central nervous system (CNS) drug candidates is constantly growing, there is a strong need for precise a priori prediction of whether an administered compound is able to cross the blood–brain barrier (BBB). The aim of this study was to evaluate the ability to cross the BBB of triterpenoid saponins occurring in Astragalus mongholicus roots. The research was carried out using in silico methods combined with postmortem studies on the brain tissues of mice treated with isolated astragaloside IV (AIV). Firstly, to estimate the ability to cross the BBB by the tested saponins, new quantitative structure–activity relationship (QSAR) models were established. The reliability and predictability of the model based on the values of the blood–brain barrier penetration descriptor (logBB), the difference between the n-octanol/water and cyclohexane/water logP (ΔlogP), the logarithm of n-octanol/water partition coefficient (logP_ow), and the excess molar refraction (E) were both confirmed using the applicability domain (AD). The critical leverage value h* was found to be 0.128. The relationships between the standardized residuals and the leverages were investigated here. The application of an in vitro acetylcholinesterase-inhibition test showed that AIV can be recognized as the strongest inhibitor among the tested compounds. Therefore, it was isolated for the postmortem studies on brain tissues and blood using semi-preparative HPLC with the mobile phase composed of water, methanol, and ethyl acetate (1.7:2.1:16.2 v/v/v). The results of the postmortem studies on the brain tissues show a regular dependence of the final concentration of AIV in the analyzed brain samples of animals treated with 12.5 and 25 mg/kg b.w. of AIV (0.00012299 and 0.0002306 mg, respectively, per one brain). Moreover, the AIV logBB value was experimentally determined and found to be equal to 0.49 ± 0.03.

Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies

The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.

Target site model: Predicting mode of action and aquatic organism acute toxicity using Abraham parameters and feature-weighted k-nearest neighbors classification

A database of 1480 chemicals with 47 associated modes of action compiled from the literature encompasses a wide range of chemical classes (alkanes, polycyclic aromatic hydrocarbons, pesticides, and polar compounds) and includes toxicity data for 79 different aquatic genera. The data were split into a calibration group and a validation group (80/20) to apply k-nearest neighbors (k-NN) methodology to predict the toxic mode of action for the compound. Other approaches were tested (support vector machines and linear discriminant analysis) as well as variations in the k-NN technique (distance weighting, feature weighting). Best-prediction results were found with k = 3, in a voting platform with optimized feature weighting. Using the predicted mode of action, the appropriate polyparameter target site model for that mode of action is applied to calculate the 50% lethal concentration (LC50). Predicted LC50s for the validation database resulted in a root-mean squared error (RMSE) of 0.752. This can be compared to an RMSE of 0.655 for the same validation set using the reference mode of action labels. The complete database resulted in an RMSE of 0.793 for reference mode of action labels. This confirms that the classification model has sufficient accuracy for predicting the mode of action and for determining toxicity using the target site model. Environ Toxicol Chem 2019;38:375-386. © 2018 SETAC.

Salt water and skin interactions: new lines of evidence

In Health Resort Medicine, both balneotherapy and thalassotherapy, salt waters and their peloids, or mud products are mainly used to treat rheumatic and skin disorders. These therapeutic agents act jointly via numerous mechanical, thermal, and chemical mechanisms. In this review, we examine a new mechanism of action specific to saline waters. When topically administered, this water rich in sodium and chloride penetrates the skin where it is able to modify cellular osmotic pressure and stimulate nerve receptors in the skin via cell membrane ion channels known as "Piezo" proteins. We describe several models of cutaneous adsorption/desorption and penetration of dissolved ions in mineral waters through the skin (osmosis and cell volume mechanisms in keratinocytes) and examine the role of these resources in stimulating cutaneous nerve receptors. The actions of salt mineral waters are mediated by a mechanism conditioned by the concentration and quality of their salts involving cellular osmosis-mediated activation/inhibition of cell apoptotic or necrotic processes. In turn, this osmotic mechanism modulates the recently described mechanosensitive piezoelectric channels.

Predicting drug permeability through skin using molecular dynamics simulation

Understanding and predicting permeability of compounds through skin is of interest for transdermal delivery of drugs and for toxicity predictions of chemicals. We show, using a new atomistic molecular dynamics model of the skin's barrier structure, itself validated against near-native cryo-electron microscopy data from human skin, that skin permeability to the reference compounds benzene, DMSO (dimethyl sulfoxide), ethanol, codeine, naproxen, nicotine, testosterone and water can be predicted. The permeability results were validated against skin permeability data in the literature. We have investigated the relation between skin barrier molecular organization and permeability using atomistic molecular dynamics simulation. Furthermore, it is shown that the calculated mechanism of action differs between the five skin penetration enhancers Azone, DMSO, oleic acid, stearic acid and water. The permeability enhancing effect of a given penetration enhancer depends on the permeating compound and on the concentration of penetration enhancer inside the skin's barrier structure. The presented method may open the door for computer based screening of the permeation of drugs and toxic compounds through skin.

Human skin barrier structure and function analyzed by cryo-EM and molecular dynamics simulation

In the present study we have analyzed the molecular structure and function of the human skin's permeability barrier using molecular dynamics simulation validated against cryo-electron microscopy data from near native skin. The skin's barrier capacity is located to an intercellular lipid structure embedding the cells of the superficial most layer of skin - the stratum corneum. According to the splayed bilayer model (Iwai et al., 2012) the lipid structure is organized as stacked bilayers of ceramides in a splayed chain conformation with cholesterol associated with the ceramide sphingoid moiety and free fatty acids associated with the ceramide fatty acid moiety. However, knowledge about the lipid structure's detailed molecular organization, and the roles of its different lipid constituents, remains circumstantial. Starting from a molecular dynamics model based on the splayed bilayer model, we have, by stepwise structural and compositional modifications, arrived at a thermodynamically stable molecular dynamics model expressing simulated electron microscopy patterns matching original cryo-electron microscopy patterns from skin extremely closely. Strikingly, the closer the individual molecular dynamics models' lipid composition was to that reported in human stratum corneum, the better was the match between the models' simulated electron microscopy patterns and the original cryo-electron microscopy patterns. Moreover, the closest-matching model's calculated water permeability and thermotropic behaviour were found compatible with that of human skin. The new model may facilitate more advanced physics-based skin permeability predictions of drugs and toxicants. The proposed procedure for molecular dynamics based analysis of cellular cryo-electron microscopy data might be applied to other biomolecular systems.

Partition of Neutral Molecules and Ions from Water to o-Nitrophenyl Octyl Ether and of Neutral Molecules from the Gas Phase to o-Nitrophenyl Octyl Ether

We have set out an equation for partition of 87 neutral molecules from water to o-nitrophenyl octyl ether, NPOE, an equation for partition of the 87 neutral molecules and 21 ionic species from water to NPOE, and an equation for partition of 87 neutral molecules from the gas phase to NPOE. Comparison with equations for partition into other solvents shows that, as regards partition of neutral (nonelectrolyte) compounds, NPOE would be a good model for 1,2-dichloroethane and for nitrobenzene. In terms of partition of ions and ionic species, NPOE is quite similar to 1,2-dichloroethane and not far away from other aprotic solvents such as nitrobenzene.

Skin-mimetic chromatography for prediction of human percutaneous absorption of biologically active compounds occurring in medicinal plant extracts

The main aim of this study was to predict quantitatively human percutaneous absorption of chosen compounds commonly occurring in plants which can be used as medicinal extracts in the drug and beauty industries. The most important human percutaneous descriptors, i.e. logK_p (logarithm of the water/skin partition coefficient) and logJ_max (logarithm of the maximum flux of solutes penetrating the skin), of fatty acids and polyphenols were determined using both in vitro and in silico methods. For in vitro determination of human percutaneous absorption, micellar liquid chromatography based on hexadecyltrimethylammonium bromide, sodium dodecyl sulfate and polyoxyethylene (23) lauryl ether (Brij35) was used. Human percutaneous absorption was characterized by entirely new QSAR/QRAR models based on retention, lipophilic, steric and electronic data as well as on the linear free energy relationship parameters. Many different correlations between human skin absorption and different physicochemical parameters were performed, e.g. the in silico estimated logK_p value was correlated with the retention parameter logk_w (logarithm of the retention factor extrapolated to pure water) from the systems imitating a cutaneous environment (R²  = 0.92). Moreover, the influence of lipophilicity on percutaneous absorption was examined. The obtained correlation was excellent (R²  = 0.95).

Dermal permeation data and models for the prioritization and screening-level exposure assessment of organic chemicals

High-throughput screening (HTS) models are being developed and applied to prioritize chemicals for more comprehensive exposure and risk assessment. Dermal pathways are possible exposure routes to humans for thousands of chemicals found in personal care products and the indoor environment. HTS exposure models rely on skin permeability coefficient (KP; cm/h) models for exposure predictions. An initial database of approximately 1000 entries for empirically-based KP data was compiled from the literature and a subset of 480 data points for 245 organic chemicals derived from testing with human skin only and using only water as a vehicle was selected. The selected dataset includes chemicals with log octanol-water partition coefficients (KOW) ranging from -6.8 to 7.6 (median=1.8; 95% of the data range from -2.5 to 4.6) and molecular weight (MW) ranging from 18 to 765g/mol (median=180); only 3% >500g/mol. Approximately 53% of the chemicals in the database have functional groups which are ionizable in the pH range of 6 to 7.4, with 31% being appreciably ionized. The compiled log KP values ranged from -5.8 to 0.1cm/h (median=-2.6). The selected subset of the KP data was then used to evaluate eight representative KP models that can be readily applied for HTS assessments, i.e., parameterized with KOW and MW. The analysis indicates that a version of the SKINPERM model performs the best against the selected dataset. Comparisons of representative KP models against model input parameter property ranges (sensitivity analysis) and against chemical datasets requiring human health assessment were conducted to identify regions of chemical properties that should be tested to address uncertainty in KP models and HTS exposure assessments.

Molecular Dynamics Simulation Study of Permeation of Molecules through Skin Lipid Bilayer

Stratum Corneum (SC), the outermost layer of skin, is mainly responsible for skin's barrier function. The complex lipid matrix of SC determines these barrier properties. In this study, the lipid matrix is modeled as an equimolar mixture of ceramide (CER), cholesterol (CHOL), and free fatty acid (FFA). The permeation of water, oxygen, ethanol, acetic acid, urea, butanol, benzene, dimethyl sulfoxide (DMSO), toluene, phenol, styrene, and ethylbenzene across this layer is studied using a constrained MD simulations technique. Several long constrained simulations are performed at a skin temperature of 310 K under NPT conditions. The free energy profiles and diffusion coefficients along the bilayer normal have been calculated for each molecule. Permeability coefficients are also calculated and compared with experimental data. The main resistance for the permeation of hydrophilic and hydrophobic permeants has been found to be in the interior of the lipid bilayer and near the lipid-water interface, respectively. The obtained permeability is found to be a few orders of magnitude higher than experimental values for hydrophilic molecules while for hydrophobic molecules more discrepancy was observed. Overall, the qualitative ranking is consistent with the experiments.