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

Mathematical modeling and simulation in animal health. Part I: Moving beyond pharmacokinetics

The application of mathematical modeling to problems in animal health has a rich history in the form of pharmacokinetic modeling applied to problems in veterinary medicine. Advances in modeling and simulation beyond pharmacokinetics have the potential to streamline and speed-up drug research and development programs. To foster these goals, a series of manuscripts will be published with the following goals: (i) expand the application of modeling and simulation to issues in veterinary pharmacology; (ii) bridge the gap between the level of modeling and simulation practiced in human and veterinary pharmacology; (iii) explore how modeling and simulation concepts can be used to improve our understanding of common issues not readily addressed in human pharmacology (e.g. breed differences, tissue residue depletion, vast weight ranges among adults within a single species, interspecies differences, small animal species research where data collection is limited to sparse sampling, availability of different sampling matrices); and (iv) describe how quantitative pharmacology approaches could help understanding key pharmacokinetic and pharmacodynamic characteristics of a drug candidate, with the goal of providing explicit, reproducible, and predictive evidence for optimizing drug development plans, enabling critical decision making, and eventually bringing safe and effective medicines to patients. This study introduces these concepts and introduces new approaches to modeling and simulation as well as clearly articulate basic assumptions and good practices. The driving force behind these activities is to create predictive models that are based on solid physiological and pharmacological principles as well as adhering to the limitations that are fundamental to applying mathematical and statistical models to biological systems.

Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest

PURPOSE

The solvent effect on skin permeability is important for assessing the effectiveness and toxicological risk of new dermatological formulations in pharmaceuticals and cosmetics development. The solvent effect occurs by diverse mechanisms, which could be elucidated by efficient and reliable prediction models. However, such prediction models have been hampered by the small variety of permeants and mixture components archived in databases and by low predictive performance. Here, we propose a solution to both problems.

METHODS

We first compiled a novel large database of 412 samples from 261 structurally diverse permeants and 31 solvents reported in the literature. The data were carefully screened to ensure their collection under consistent experimental conditions. To construct a high-performance predictive model, we then applied support vector regression (SVR) and random forest (RF) with greedy stepwise descriptor selection to our database. The models were internally and externally validated.

RESULTS

The SVR achieved higher performance statistics than RF. The (externally validated) determination coefficient, root mean square error, and mean absolute error of SVR were 0.899, 0.351, and 0.268, respectively. Moreover, because all descriptors are fully computational, our method can predict as-yet unsynthesized compounds.

CONCLUSION

Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.

Applications of polyparameter linear free energy relationships in environmental chemistry

Partitioning behavior of organic chemicals has tremendous influences on their environmental distribution, reaction rates, bioaccumulation, and toxic effects. Polyparameter linear free energy relationships (PP-LFERs) have been proven to be useful to characterize the equilibrium partitioning of organic chemicals in various environmental and technical partitioning systems and predict the respective partition coefficients. Over the past decade, PP-LFER solute descriptors for numerous environmentally relevant organic chemicals and system parameters for environmentally important partitioning systems have been determined, extending substantially the applicability of the PP-LFER approaches. However, the information needed for the use of PP-LFERs including descriptors and parameters is scattered over a large number of publications. In this work, we review the state of the art of the PP-LFER approaches in environmental chemical applications. The solute descriptors and system parameters reported in the literature and the availability of their database are summarized, and their calibration and prediction methods are overviewed. We also describe tips and pitfalls associated with the use of the PP-LFER approaches and identify research needs to improve further the usefulness of PP-LFERs for environmental chemistry.

Skin absorption of six performance amines used in metalworking fluids

Every year, 10 million workers are exposed to metalworking fluids (MWFs) that may be toxic. There are four types of MWFs: neat oils and three water-based MWFs (soluble oil, semisynthetic and synthetic), which are diluted with water and whose composition varies according to the mineral oils ratio. MWFs also contain various additives. To determine the absorption of six amines used as corrosion inhibitors and biocides in MWFs, porcine skin flow-through diffusion cell experiments were conducted with hydrophilic ethanolamines (mono-, di- and triethanolamine, MEA, DEA and TEA respectively) and a mixture of lipophilic amines (dibutylethanolamine, dicyclohexylamine and diphenylamine). The six amines were dosed in four vehicles (water and three generic water-based MWF formulations) and analyzed using a scintillation counter or gas chromatography/mass spectrometry. These 24 h studies showed that dermal absorption significantly (P < 0.05) increased from water for the six amines (e.g. 1.15 ± 0.29% dose; DEA in water) compared to other formulations (e.g. 0.13 ± 0.01% dose; DEA in semisynthetic MWF) and absorption was greatest for dibutylethanolamine in all the formulations. The soluble oil formulation tended to increase the dermal absorption of the hydrophilic amines. The permeability coefficient was significantly higher (P < 0.05) with TEA relative to the other hydrophilic amines (e.g. 4.22 × 10(-4) ± 0.53 × 10(-4) cm h(-1) [TEA in synthetic MWF] vs. 1.23 × 10(-4) ± 0.10 × 10(-4) cm h(-1) [MEA in synthetic MWF]), except for MEA in soluble oil formulation. Future research will confirm these findings in an in vivo pig model along with dermatotoxicity studies. These results should help MWF industries choose safer additives for their formulations to protect the health of metalworkers.

The prediction of blood-tissue partitions, water-skin partitions and skin permeation for agrochemicals

BACKGROUND

There is considerable interest in the blood-tissue distribution of agrochemicals, and a number of researchers have developed experimental methods for in vitro distribution. These methods involve the determination of saline-blood and saline-tissue partitions; not only are they indirect, but they do not yield the required in vivo distribution.

RESULTS

The authors set out equations for gas-tissue and blood-tissue distribution, for partition from water into skin and for permeation from water through human skin. Together with Abraham descriptors for the agrochemicals, these equations can be used to predict values for all of these processes. The present predictions compare favourably with experimental in vivo blood-tissue distribution where available. The predictions require no more than simple arithmetic.

CONCLUSIONS

The present method represents a much easier and much more economic way of estimating blood-tissue partitions than the method that uses saline-blood and saline-tissue partitions. It has the added advantages of yielding the required in vivo partitions and being easily extended to the prediction of partition of agrochemicals from water into skin and permeation from water through skin.

in vitro and in silico determination of oral, jejunum and Caco-2 human absorption of fatty acids and polyphenols. Micellar liquid chromatography

In this investigation chosen saturated, mono- and polyunsaturated fatty acids as well as polyphenols have been analyzed. The main aim of this study was to determine oral, jejunum and Caco-2 human absorption of chosen fatty acids and polyphenols using in vitro and in silico methods. For in vitro determination of human drug absorption, the usefulness of Micellar Liquid Chromatography (MLC) with mobile phases containing different surfactants (including Brij35-Biopartitioning Micellar Chromatography (BMC)) has been confirmed. On the basis of Foley's equation, 1/k vs. CM correlations for the tested compounds have been done. Satisfactory linearity of the relationships was found over the whole eluents composition range studied with R(2) approximately 0.99 in each case. Moreover, the analyte-micelle association constants (Kma) from Foley's equation have been compared for different micellar environments, containing Brij35, SDS and CTAB as a main component of micellar mobile phases. Completely new models describing human oral as well as Caco-2 and jejunum absorption have been constructed and compared with the cited models. These models are based on the Abraham descriptors and lipophilicity parameters as well as steric descriptors. Furthermore, many different correlations between physicochemical parameters and human intestinal absorption have been done, e.g. the correlation between human jejunum permeability estimated in silico and received using LSER parameters was excellent (R(2) nearly 0.99). Chromatographic parameters have been collated with steric, electronic and physicochemical ones using QRAR (Quantitative Retention - Activity Relationships) and QSAR (Quantitative Structure - Activity Relationships) models. Moreover, retention BMC data have been compared with lipophilicity parameter logPo/w (n-octanol-water partition coefficient). The influence of lipophilicity on oral absorption (%) has been checked. The correlation between predicted oral absorption (%) and logPo/w has been done. Obtained R(2) was 0.82. On the basis of chromatographic, lipophilicity, steric and different physicochemical parameters, the principal components analysis (PCA) has been done.

In silico models to predict dermal absorption from complex agrochemical formulations

Dermal absorption is a critical part in the risk assessment of complex mixtures such as agrochemical formulations. To reduce the number of in vivo or in vitro absorption experiments, the present study aimed to develop an in silico prediction model that considers mixture-related effects. Therefore, an experimental 'real-world' dataset derived from regulatory in vitro studies with human and rat skin was processed. Overall, 56 test substances applied in more than 150 mixtures were used. Descriptors for the substances as well as the mixtures were generated and used for multiple linear regression analysis. Considering the heterogeneity of the underlying data set, the final model provides a good fit (r² = 0.75) and is able to estimate the influence of a newly composed formulation on dermal absorption of a well-known substance (predictivity Q²Ext = 0.73). Application of this model would reduce animal and non-animal testings when used for the optimization of formulations in early developmental stages, or would simplify the registration process, if accepted for read-across.

Prediction of formulation effects on dermal absorption of topically applied ectoparasiticides dosed in vitro on canine and porcine skin using a mixture-adjusted quantitative structure permeability relationship

Topical application of ectoparasiticides for flea and tick control is a major focus for product development in animal health. The objective of this work was to develop a quantitative structure permeability relationship (QSPeR) model sensitive to formulation effects for predicting absorption and skin deposition of five topically applied drugs administered in six vehicle combinations to porcine and canine skin in vitro. Saturated solutions (20 μL) of (14) C-labeled demiditraz, fipronil, permethrin, imidacloprid, or sisapronil were administered in single or binary (50:50 v/v) combinations of water, ethanol, and transcutol (6 formulations, n = 4-5 replicates per treatment) nonoccluded to 0.64 cm(2) disks of dermatomed pig or dog skin mounted in flow-through diffusion cells. Perfusate flux over 24 h and skin deposition at termination were determined. Permeability (logKp), absorption, and penetration endpoints were modeled using a four-term Abrahams and Martin (hydrogen-bond donor acidity and basicity, dipolarity/polarizability, and excess molar refractivity) linear free energy QSPeR equation with a mixture factor added to compensate for formulation ingredient interactions. Goodness of fit was judged by r(2) , cross-validation coefficient, coefficients (q(2) s), and Williams Plot to visualize the applicability domain. Formulation composition was the primary determinant of permeation. Compounds generally penetrated dog skin better than porcine skin. The vast majority of permeated penetrant was deposited within the dosed skin relative to transdermal flux, an attribute for ectoparasiticides. The best QSPeR logKp model for pig skin permeation (r(2) = 0.86, q(2) s = 0.85) included log octanol/water partition coefficient as the mixture factor, while for dogs (r(2) = 0.91, q(2) s = 0.90), it was log water solubility. These studies clearly showed that the permeation of topical ectoparasiticides could be well predicted using QSPeR models that account for both the physical-chemical properties of the penetrant and formulation components.

General model for estimating partition coefficients to organisms and their tissues using the biological compositions and polyparameter linear free energy relationships

Equilibrium partition coefficients of organic chemicals from water to an organism or its tissues are typically estimated by using the total lipid content in combination with the octanol-water partition coefficient (K(ow)). This estimation method can cause systematic errors if (1) different lipid types have different sorptive capacities, (2) nonlipid components such as proteins have a significant contribution, and/or (3) K(ow) is not a suitable descriptor. As an alternative, this study proposes a more general model that uses detailed organism and tissue compositions (i.e., contents of storage lipid, membrane lipid, albumin, other proteins, and water) and polyparameter linear free energy relationships (PP-LFERs). The values calculated by the established PP-LFER-composition-based model agree well with experimental in vitro partition coefficients and in vivo steady-state concentration ratios from the literature with a root mean squared error of 0.32-0.53 log units, without any additional fitting. This model estimates a high contribution of the protein fraction to the overall tissue sorptive capacity in lean tissues (e.g., muscle), in particular for H-bond donor polar compounds. Direct model comparison revealed that the simple lipid-octanol model still calculates many tissue-water partition coefficients within 1 log unit of those calculated by the PP-LFER-composition-based model. Thus, the lipid-octanol model can be used as an order-of-magnitude approximation, for example, for multimedia fate modeling, but may not be suitable for more accurate predictions. Storage lipid-rich phases (e.g., adipose, milk) are prone to particularly large systematic errors. The new model provides useful implications for validity of lipid-normalization of concentrations in organisms, interpretation of biomonitoring results, and assessment of toxicity.

Predicting skin permeability from complex chemical mixtures: incorporation of an expanded QSAR model

Quantitative structure-activity relationship (QSAR) models have been widely used to study the permeability of chemicals or solutes through skin. Among the various QSAR models, Abraham's linear free-energy relationship (LFER) model is often employed. However, when the experimental conditions are complex, it is not always appropriate to use Abraham's LFER model with a single set of regression coefficients. In this paper, we propose an expanded model in which one set of partial slopes is defined for each experimental condition, where conditions are defined according to solvent: water, synthetic oil, semi-synthetic oil, or soluble oil. This model not only accounts for experimental conditions but also improves the ability to conduct rigorous hypothesis testing. To more adequately evaluate the predictive power of the QSAR model, we modified the usual leave-one-out internal validation strategy to employ a leave-one-solute-out strategy and accordingly adjust the Q(2) LOO statistic. Skin permeability was shown to have the rank order: water > synthetic > semi-synthetic > soluble oil. In addition, fitted relationships between permeability and solute characteristics differ according to solvents. We demonstrated that the expanded model (r(2) = 0.70) improved both the model fit and the predictive power when compared with the simple model (r(2) = 0.21).

The use of biopartitioning micellar chromatography and immobilized artificial membrane column for in silico and in vitro determination of blood-brain barrier penetration of phenols

Biopartitioning Micellar Chromatography (BMC) is a mode of micellar liquid chromatography that uses C18 stationary phases and micellar mobile phases of Brij35 under adequate experimental conditions and can be useful to mimic human drug absorption, blood-brain barrier distribution or partitioning processes in biological systems. BMC system can be useful in constructing good predictive models because the characteristics of the BMC system are similar to biological barriers and extracellular fluids. Immobilized Artificial Membrane (IAM) chromatography uses stationary phase which consists of a monolayer of phosphatidylcholine covalently immobilized on an inert silica support. IAM columns are thought to mimic very closely a membrane bilayer and are used in a HPLC system with a physiological buffer as eluent. In this paper the usefulness of BMC and IAM system for in silico and in vitro determination of blood-brain barrier (BBB) penetration of phenols has been demonstrated. The most important pharmacokinetic parameters of brain have been obtained for the determination of BBB penetration, i.e. BBB permeability - surface area product (PS), usually given as a logPS, brain/plasma equilibration rate (log(PS×fu,brain)) and fraction unbound in plasma (Fu). Moreover, the relationships between retention of eighteen phenols and different parameters of molecular size, lipophilicity and BBB penetration were studied. Extrapolated to pure water values of the logarithms of retention factors (logkw) have been compared with the corresponding octanol-water partition coefficient (logPo-w) values of the solutes. In addition, different physicochemical parameters from Foley's equation for BMC system have been collated with the chromatographic data. The Linear Solvation Energy Relationship (LSER) using Abraham model for the describing of phenols penetration across BBB has been used. Four equations were developed as a multiple linear regression using retention data from IAM and BMC system (QRAR models) and molecular volume parameter (Vm) and Abraham descriptors to correlate the logBB values. Moreover, in order to establish the relationships between different variables, the principal components analysis (PCA) has been done. The results of PCA were obtained using chromatographic data from IAM and BMC systems as well as from the structures of tested phenols. The four parameters: logkwIAM(exp), logkwBMC(exp), analyte-micelle association constant (Kma) and logPo-w have been checked.

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.

Predicting skin permeability from complex vehicles

It is now widely accepted that vehicle and formulation components influence the rate and extent of passive chemical absorption through skin. Significant progress, over the last decades, has been made in predicting dermal absorption from a single vehicle; however the effect of a complex, realistic mixture has not received its due attention. Recent studies have aimed to bridge this gap by extending the use of quantitative structure-permeation relationship (QSPR) models based on linear free energy relationships (LFER) to predict dermal absorption from complex mixtures with the inclusion of significant molecular descriptors such as a mixture factor that accounts for the physicochemical properties of the vehicle/mixture components. These models have been compiled and statistically validated using the data generated from in vitro or ex vivo experimental techniques. This review highlights the progress made in predicting skin permeability from complex vehicles.

Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design

Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].

A generic emission model to predict release of organic substances from materials in consumer goods

Organic chemicals may be released when consumer goods are used, contributing to environmental and human levels of potentially hazardous chemicals. A generic model was developed to predict emissions of organic chemicals from various materials in consumer products. The model involved three modules, which each predict a key parameter needed to calculate the mass of individual chemicals emitted. Partition coefficients between a material and the surrounding air were predicted using Abraham solvation parameters, diffusion coefficients in materials were calculated using the Piringer equation, and convective mass transfer coefficients were evaluated by applying the Chilton-Colburn analogy. The calculated emission rates from predicted parameters were evaluated and agreed well with literature data. The release of plasticizers from vinyl flooring used in Sweden was calculated to demonstrate the utility of the generic model. The estimated emitted masses of di(2-ethylhexyl)phthalate (DEHP), di-iso-nonylphthalate (DINP), and 1,2-cyclohexanedicarboxylic acid di-iso-nonyl ester (DINCH) in 2012 were 210 kg, 40 kg, and 3.6 kg respectively. Emissions from vinyl flooring were estimated for the period 1990 to 2035 and it was shown that the recent substitution of DEHP with DINP will help to reduce plasticizer emissions. Model calculations for alternative plasticizers revealed that DINCH would yield similar emissions to DINP, whereas use of diethyl hexyl-iso-sorbide or diethyl hexyl adipate would result in higher emissions.

Quantitative risk assessment methods for cancer and noncancer effects

Human health risk assessments have evolved from the more qualitative approaches to more quantitative approaches in the past decade. This has been facilitated by the improvement in computer hardware and software capability and novel computational approaches being slowly recognized by regulatory agencies. These events have helped reduce the reliance on experimental animals as well as better utilization of published animal toxicology data in deriving quantitative toxicity indices that may be useful for risk management purposes. This chapter briefly describes some of the approaches as described in the guidance documents from several of the regulatory agencies as it pertains to hazard identification and dose-response assessment of a chemical. These approaches are contrasted with more novel computational approaches that provide a better grasp of the uncertainty often associated with chemical risk assessments.

Salting-out effect in aqueous NaCl solutions: trends with size and polarity of solute molecules

Salting-out in aqueous NaCl solutions is relevant for the environmental behavior of organic contaminants. In this study, Setschenow (or salting-out) coefficients (K(s) [M(-1)]) for 43 diverse neutral compounds in NaCl solutions were measured using a shared headspace passive dosing method and a negligible depletion solid phase microextraction technique. The results were used to calibrate and evaluate estimation models for K(s). The molar volume of the solute correlated only moderately with K(s) (R(2) = 0.49, SD = 0.052). The polyparameter linear free energy relationship (pp-LFER) model that uses five compound descriptors resulted in a more accurate fit to our data (R(2) = 0.83, SD = 0.031). The pp-LFER analysis revealed that Na(+) and Cl(-) in aqueous solutions increase the cavity formation energy cost and the polar interaction energies toward neutral organic solutes. Accordingly, the salting-out effect increases with the size and decreases with the polarity of the solute molecule. COSMO-RS, a quantum mechanics-based fully predictive model, generally overpredicted the experimental K(s), but the predicted values were moderately correlated with the experimental values (R(2) = 0.66, SD = 0.042). Literature data (n = 93) were predicted by the calibrated pp-LFER and COSMO-RS models with root mean squared errors of 0.047 and 0.050, respectively. This study offers prediction models to estimate K(s), allowing implementation of the salting-out effect in contaminant fate models, linkage of various partition coefficients (such as air-water, sediment-water, and extraction phase-water partition coefficients) measured for fresh water and seawater, and estimation of enhancement of extraction efficiency in analytical procedures.

Equilibrium partition coefficients of diverse polar and nonpolar organic compounds to polyoxymethylene (POM) passive sampling devices

Equilibrium passive samplers (EPS) based on polyoxymethylene (POM) are increasingly used for determining freely dissolved water and pore water concentrations of hydrophobic organic compounds in the environment. Unlike other polymeric materials commonly used as EPS, namely poly(dimethylsiloxane) (PDMS) and low-density polyethylene (PE), POM is a polar polymer, containing repeating H-bond accepting ether units. Thus, POM is expected to be a more sensitive EPS than PDMS and PE for polar, H-bond donating compounds, such as many hormones, pharmaceuticals, and biocides. To better characterize the sorption capacity of POM for diverse polar and apolar compounds, equilibrium POM-water partition coefficients, K(POM/w), were measured for 56 compounds, including several classes of polar compounds and organochlorine pesticides. Using this data set and literature data, various POM-partitioning models were calibrated and validated for their ability to predict K(POM/w). The best performing models tested were an Abraham descriptor based polyparameter linear free energy relationship (PP-LFER) (SD = 0.24 log units) and COSMOthermX (SD = 0.37 log units). The performance of SPARC (SD = 0.61 log units) and log-log correlations with K(ow) (SD = 0.49 log units) were lower. A comparison with PDMS and PE confirmed expectations that POM exhibits a higher sensitivity for H-bond donating polar compounds than PDMS and PE do for these compounds. These findings expand the domain of chemicals for which POM can be used as an EPS sampler, and demonstrate that POM is as suitable a passive sampler for many polar organic compounds as it is for hydrophobic organic compounds.

Capacities of membrane lipids to accumulate neutral organic chemicals

Lipids have been considered as the predominant components for bioaccumulation of organic chemicals. However, differences in accumulation properties between different types of lipid (e.g., storage and membrane lipids) have rarely been considered. Moreover, in view of toxic effects on organisms, chemical accumulation specifically in biological membranes is of particular importance. In this review article, partition coefficients of 240 neutral organic compounds between liposomes (phospholipid membrane vesicles) and water (K(lipw)), reported in the literature or measured additionally for this work, were evaluated. Values of log K(lipw) and log K(ow) (octanol-water partition coefficients) differ by 0.4 on average. Polyparameter linear free energy relationships (PP-LFERs) can describe the log K(lipw) data even better (standard deviations = 0.28-0.31) than the log K(ow) model. Recent experimental data for highly hydrophobic compounds fit well to the PP-LFERs and do not indicate the existence of a previously postulated "hydrophobicity cutoff". Predictive approaches based only on the molecular structure (KOWWIN, SPARC, COSMOthermX, COSMOmic) were also evaluated for K(lipw) prediction. The PP-LFERs revealed that partition coefficients into membrane lipids can be two log units higher than those into storage lipids for H-bond donor compounds, suggesting that distinguishing between the two lipids is necessary to account for the bioaccumulation of these compounds, and that tissues rich in membrane lipids (e.g., kidneys, liver) instead of fat tissue can be the primary phase for accumulation.

Predicting skin permeability from complex chemical mixtures: dependency of quantitative structure permeation relationships on biology of skin model used

Dermal absorption of topically applied chemicals usually occurs from complex chemical mixtures; yet, most attempts to quantitate dermal permeability use data collected from single chemical exposure in aqueous solutions. The focus of this research was to develop quantitative structure permeation relationships (QSPR) for predicting chemical absorption from mixtures through skin using two levels of in vitro porcine skin biological systems. A total of 16 diverse chemicals were applied in 384 treatment mixture combinations in flow-through diffusion cells and 20 chemicals in 119 treatment combinations in isolated perfused porcine skin. Penetrating chemical flux into perfusate from diffusion cells was analyzed to estimate a normalized dermal absorptive flux, operationally an apparent permeability coefficient, and total perfusate area under the curve from perfused skin studies. These data were then fit to a modified dermal QSPR model of Abraham and Martin including a sixth term to account for mixture interactions based on physical chemical properties of the mixture components. Goodness of fit was assessed using correlation coefficients (r²), internal and external validation metrics (q²L00, q²L25%, q²EXT), and applicable chemical domain determinations. The best QSPR equations selected for each experimental biological system had r² values of 0.69-0.73, improving fits over the base equation without the mixture effects. Different mixture factors were needed for each model system. Significantly, the model of Abraham and Martin could also be reduced to four terms in each system; however, different terms could be deleted for each of the two biological systems. These findings suggest that a QSPR model for estimating percutaneous absorption as a function of chemical mixture composition is possible and that the nature of the QSPR model selected is dependent upon the biological level of the in vitro test system used, both findings having significant implications when dermal absorption data are used for in vivo risk assessments.

Calculating the dermal flux of chemicals with OELs based on their molecular structure: An attempt to assign the skin notation

Our objectives included calculating the permeability coefficient and dermal penetration rates (flux value) for 112 chemicals with occupational exposure limits (OELs) according to the LFER (linear free-energy relationship) model developed using published methods. We also attempted to assign skin notations based on each chemical's molecular structure. There are many studies available where formulae for coefficients of permeability from saturated aqueous solutions (K(p)) have been related to physicochemical characteristics of chemicals. The LFER model is based on the solvation equation, which contains five main descriptors predicted from chemical structure: solute excess molar refractivity, dipolarity/polarisability, summation hydrogen bond acidity and basicity, and the McGowan characteristic volume. Descriptor values, available for about 5000 compounds in the Pharma Algorithms Database were used to calculate permeability coefficients. Dermal penetration rate was estimated as a ratio of permeability coefficient and concentration of chemical in saturated aqueous solution. Finally, estimated dermal penetration rates were used to assign the skin notation to chemicals. Defined critical fluxes defined from the literature were recommended as reference values for skin notation. The application of Abraham descriptors predicted from chemical structure and LFER analysis in calculation of permeability coefficients and flux values for chemicals with OELs was successful. Comparison of calculated K(p) values with data obtained earlier from other models showed that LFER predictions were comparable to those obtained by some previously published models, but the differences were much more significant for others. It seems reasonable to conclude that skin should not be characterised as a simple lipophilic barrier alone. Both lipophilic and polar pathways of permeation exist across the stratum corneum. It is feasible to predict skin notation on the basis of the LFER and other published models; from among 112 chemicals 94 (84%) should have the skin notation in the OEL list based on the LFER calculations. The skin notation had been estimated by other published models for almost 94% of the chemicals. Twenty-nine (25.8%) chemicals were identified to have significant absorption and 65 (58%) the potential for dermal toxicity. We found major differences between alternative published analytical models and their ability to determine whether particular chemicals were potentially dermotoxic.