Lujia Han

Recent advances in predicting skin permeability of hydrophilic solutes

Understanding the permeation of hydrophilic molecules is of relevance to many applications including transdermal drug delivery, skin care as well as risk assessment of occupational, environmental, or consumer exposure. This paper reviews recent advances in modeling skin permeability of hydrophilic solutes, including quantitative structure-permeability relationships (QSPR) and mechanistic models. A dataset of measured human skin permeability of hydrophilic and low hydrophobic solutes has been compiled. Generally statistically derived QSPR models under-estimate skin permeability of hydrophilic solutes. On the other hand, including additional aqueous pathway is necessary for mechanistic models to improve the prediction of skin permeability of hydrophilic solutes, especially for highly hydrophilic solutes. A consensus yet has to be reached as to how the aqueous pathway should be modeled. Nevertheless it is shown that the contribution of aqueous pathway can constitute to more than 95% of the overall skin permeability. Finally, future prospects and needs in improving the prediction of skin permeability of hydrophilic solutes are discussed.

Prediction of human skin permeability using artificial neural network (ANN) modeling.

AbstractAim:To develop an artificial neural network (ANN) model for predicting skin permeability (log Kp) of new chemical entities.Methods:A large dataset of 215 experimental data points was compiled from the literature. The dataset was subdivided into 5 subsets and 4 of them were used to train and validate an ANN model. The same 4 datasets were also used to build a multiple linear regression (MLR) model. The remaining dataset was then used to test the 2 models. Abraham descriptors were employed as inputs into the 2 models. Model predictions were compared with the experimental results. In addition, the relationship between log Kp and Abraham descriptors were investigated.Results:The regression results of the MLR model were n=215, determination coefficient (R2)=0.699, mean square error (MSE)=0.243, and F=493.556. The ANN model gave improved results with n=215, R2=0.832, MSE=0.136, and F=1050.653. The ANN model suggests that the relationship between log Kp and Abraham descriptors is non-linear.Conclusion:The study suggests that Abraham descriptors may be used to predict skin permeability, and the ANN model gives improved prediction of skin permeability.

Determination of partition and binding properties of solutes to stratum corneum

The binding property of a number of relatively hydrophilic solutes to native and delipidized stratum corneum (SC) and their partition coefficients to extracted lipid have been measured by equilibration experiments to expand the current database which consisted of mostly hydrophobic solutes. Using the extended database, quantitative structure property relationships (QSPR) have been proposed for predicting the partition and binding coefficients of both hydrophobic and hydrophilic solutes to the SC protein, and lipid. Solute partition to the SC lipid is best fitted by PC(lip/w)=K(ow)(0.69) and solute binding to the SC protein is best described by PC(pro/w)=4.2K(ow)(0.31). The two QSPR models of solute partition to the SC lipid and binding to the SC protein have been further combined into a two-phase model to predict the overall partition coefficient of solutes to the stratum corneum (K(sc/w)). Our study not only extends the database of solute partition and binding properties of the SC to include hydrophilic solutes, but also demonstrates that the thermodynamic equilibrium properties of the SC partition and binding can be fitted with good accuracy by combining QSPR models with the multiphase and heterogeneous structures of the SC.

Experimental and theoretical studies on the binding of epigallocatechin gallate to purified porcine gastric mucin.

Binding of epigallocatechin gallate (EGCG) to highly purified short side-chain porcine gastric mucin similar to human MUC6 type has been studied by ultraviolet-visible absorption spectroscopy (UV-vis), ultrafiltration isothermal titration microcalorimetry (ITC) and transmission electron microscopy (TEM). The thermodynamic equilibrium of EGCG binding to mucin has been quantitatively determined using ultrafiltration and high-performance liquid chromatography (HPLC)-UV/vis. The relationship suggests multilayer binding rather than simple Langmuir monolayer binding of EGCG. By combining the ultrafiltration and ITC data, the thermodynamic parameters of EGCG binding to mucin have been obtained. The binding constant for the first layer is about an order of magnitude higher than that of the consecutive multilayers. Negative entropy indicates multilayer of EGCG formed. Hydrogen bonding may be responsible for the multilayer formation. Increasing temperature resulted in a decrease in the binding affinity, further suggesting that hydrogen bonds dominated the interaction energy. A TEM micrograph of the EGCG-mucin complex revealed a monodispersion of blobs similar to pure mucin solution but with relatively bigger size (about twice). It is proposed that the EGCG-mucin binding process occurs by single and/or cluster of EGCG molecules driven to the surface of the two hydrophobic globules of mucin by hydrophobic interaction followed by hydrogen bond interaction between EGCG and mucin. Further adsorption of EGCG molecules onto bound EGCG molecules to form multilayers can also occur. This fits well with the observations that EGCG-mucin interaction followed a multilayer adsorption isotherm, the energy released is dominated by hydrogen bonds, and no large aggregates were formed.

In Silico Prediction of Percutaneous Absorption and Disposition Kinetics of Chemicals

PurposeTo develop in-silico model for predicting percutaneous absorption and disposition kinetics of chemicals in skin layers so as to facilitate the design of transdermal drug delivery systems and skin care products, and risk assessment of occupational or consumer exposure.MethodsA general-purpose computer model for simulating skin permeation, absorption and disposition kinetics in the stratum corneum, viable dermis and dermis has been developed. Equations have been proposed for determining the partition and diffusion properties of chemicals by considering molecular partition, binding and mobility in skin layers. In vitro skin penetration data of 12 chemicals was used to validate the model.ResultsThe observed and simulated permeation and disposition in skin layers were compared for 12 tested chemicals. For most tested chemicals, the experimental and model results are in good agreement with the coefficient of determination >0.80 and relative root mean squared error <1.20. The disposition kinetic parameters of the maximum concentration and the area under the curve in the viable epidermis and dermis initially increased with hydrophobicity, but reached maxima and then decreased with further increase of hydrophobicity.ConclusionsBy considering skin physiological structure and composition, the partition and diffusion properties of chemicals in skin layers are determined. This allows in-silico simulation of percutaneous permeation, absorption and disposition kinetics of wide chemical space. The model produced results in good agreement with experimental data of 12 chemicals, suggesting a much improved framework to support transdermal delivery of drug and cosmetic actives as well as integrated risk assessment.

Free energy predictions of ligand binding to an α-helix using steered molecular dynamics and umbrella sampling simulations.

Free energy prediction of ligand binding to macromolecules using explicit solvent molecular dynamics (MD) simulations is computationally very expensive. Recently, we reported a linear correlation between the binding free energy obtained via umbrella sampling (US) versus the rupture force from steered molecular dynamics (SMD) simulations for epigallocatechin-3-gallate (EGCG) binding to α-helical-rich keratin. This linear correlation suggests a potential route for fast free energy predictions using SMD alone. In this work, the generality of the linear correlation is further tested for several ligands interacting with the α-helical motif of keratin. These molecules have significantly varying properties, i.e., octanol/water partition coefficient (log P), and/or overall charges (oleic acid, catechin, Fe(2+), citric acid, hydrogen citrate, dihydrogen citrate, and citrate). Using the constant loading rate of our previous study of the keratin-EGCG system, we observe that the linear correlation for keratin-EGCG can be extended to other uncharged molecules where interactions are governed by hydrogen bonds and/or a combination of hydrogen bonds and hydrophobic forces. For molecules where interactions with the keratin helix are governed primarily by electrostatics between charged molecules, a second, alternative linear correlation model is derived. While further investigations are needed to expand the molecular space and build a fully predictive model, the current approach represents a promising methodology for fast free energy predictions based on short SMD simulations (requiring picoseconds to nanoseconds of sampling) for defined biomolecular systems.

Kinetics and equilibrium of solute diffusion into human hair.

The uptake kinetics of five molecules by hair has been measured and the effects of pH and physical chemical properties of molecules were investigated. A theoretical model is proposed to analyze the experimental data. The results indicate that the binding affinity of solute to hair, as characterized by hair–water partition coefficient, scales to the hydrophobicity of the solute and decreases dramatically as the pH increases to the dissociation constant. The effective diffusion coefficient of solute depended not only on the molecular size as most previous studies suggested, but also on the binding affinity as well as solute dissociation. It appears that the uptake of molecules by hair is due to both hydrophobic interaction and ionic charge interaction. Based on theoretical considerations of the cellular structure, composition and physical chemical properties of hair, quantitative–structure–property-relationships (QSPR) have been proposed to predict the hair–water partition coefficient (PC) and the effective diffusion coefficient (De) of solute. The proposed QSPR models fit well with the experimental data. This paper could be taken as a reference for investigating the adsorption properties for polymeric materials, fibres, and biomaterials.

A Study on Fe2+ – α-Helical-Rich Keratin Complex Formation Using Isothermal Titration Calorimetry and Molecular Dynamics Simulation

Iron binding to protein is common in biological processes of dioxygen transport, electron transfer as well as in stabilizing drug-protein complexes. α-Helix is the most prevalent secondary structure of proteins. In this study, Fe(2+) binding to α-helix has been studied by isothermal titration calorimetry (ITC) and explicitly solvated molecular dynamics (MD) simulation. Ferrous gluconate and α-helix-rich keratin are used for the ITC study and the results revealed followed one set of identical sites binding model. The MD simulations further revealed that only the acidic side-chain functional groups and η(2) (O,O) coordination modes are involved in the binding of Fe(2+) to α-helix. The ITC results also showed that the binding of ferrous gluconate to keratin was entropy driven and the higher the temperature, the stronger the binding free energy. The favorable entropy of Fe(2+) binding to keratin was attributed to the displacement of water molecules on the α-helix surface, and was confirmed via MD simulations. The most stable coordination states of Fe(2+) and α-helix were identified via simulation: Fe(2+) stacks between two glutamic acid side chain carboxylate groups, displacing water molecules. The binding free energies calculated using MD simulation and the theoretical values were in excellent agreement with the ITC results.

Determining the Effect of pH on the Partitioning of Neutral, Cationic and Anionic Chemicals to Artificial Sebum: New Physicochemical Insight and QSPR Model

ABSTRACTPurposeSebum is an important shunt pathway for transdermal permeation and targeted delivery, but there have been limited studies on its permeation properties. Here we report a measurement and modelling study of solute partition to artificial sebum.MethodsEquilibrium experiments were carried out for the sebum-water partition coefficients of 23 neutral, cationic and anionic compounds at different pH.ResultsSebum-water partition coefficients not only depend on the hydrophobicity of the chemical but also on pH. As pH increases from 4.2 to 7.4, the partition of cationic chemicals to sebum increased rapidly. This appears to be due to increased electrostatic attraction between the cationic chemical and the fatty acids in sebum. Whereas for anionic chemicals, their sebum partition coefficients are negligibly small, which might result from their electrostatic repulsion to fatty acids. Increase in pH also resulted in a slight decrease of sebum partition of neutral chemicals.ConclusionsBased on the observed pH impact on the sebum-water partition of neutral, cationic and anionic compounds, a new quantitative structure-property relationship (QSPR) model has been proposed. This mathematical model considers the hydrophobic interaction and electrostatic interaction as the main mechanisms for the partition of neutral, cationic and anionic chemicals to sebum.

Investigation of pH effect on cationic solute binding to keratin and partition to hair

In the process of hair treatment, various cationic actives contained in hair care products can be absorbed into hair fibre to modulate the physicochemical properties of hair such as colour, strength, style and volume. There have been very limited studies on the binding and partition properties of hair care actives to hair. This study aimed to investigate the pH effects on cationic solute absorption into hair and binding to keratin.