Modelling the effect of mixture components on permeation through skin

CPE-DB: An Open Database of Chemical Penetration Enhancers

The cutaneous delivery route currently accounts for almost 10% of all administered drugs and it is becoming more common. Chemical penetration enhancers (CPEs) increase the transport of drugs across skin layers by different mechanisms that depend on the chemical nature of the penetration enhancers. In our work, we created a chemical penetration enhancer database (CPE-DB) that is, to the best of our knowledge, the first CPE database. We collected information about known enhancers and their derivatives in a single database, and classified and characterized their molecular diversity in terms of scaffold content, key chemical moieties, molecular descriptors, etc. CPE-DB can be used for virtual screening and similarity search to identify new potent and safe enhancers, building quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models, and other machine-learning (ML) applications for the prediction of biological activity.

Human skin permeation rates ex vivo following exposures to mixtures of glycol ethers

Skin exposure to cleaning products in the general and occupational population are a public health concern. Among the most frequently identified amphiphilic organic solvents in cleaning products are propylene glycol monomethyl ether (PGME) and propylene glycol n-butyl ether (PGBE). Internal dose from skin exposure may be efficiently evaluated using in vitro flow-through diffusion cells with excised human skin. Our aim in this study was two-fold; 1) characterize the permeation rates (J), time lag (T_lag), and permeation coefficients (Kp) of PGME and PGBE in human ex-vivo skin permeation assays, and 2) determine a possible mixture effect on skin permeation characteristics when applied together. Our results showed a short T_lag for PGME and was reduced further depending on the amount of PGBE in the mixture (T_lag was reduced from 2 h to 1-1.7 h) for fresh skin. PGBE T_lag slightly increased when mixed with 50 % or more PGME. Permeation rate decreased to half for both PGME and PGBE in mixture at any concentration. This substantial permeation was greater with previously frozen skin. This mixture effect could favor permeation of other compounds through human skin.

A general linear free energy relationship for predicting partition coefficients of neutral organic compounds

Predicting the effects of organic compounds on environments and biological systems is an important objective for environmental chemistry and human health. The logarithm (to base 10) of the n-octanoll-water partition coefficient has been widely used to predict the mentioned properties. However, the suitability of this parameter for the purpose has been questioned, since the environments relating to the properties may be quite different from that of bulk n-octanol. In this study, we used a theoretical derivation approach to develop a model for predicting the partition coefficients of solutes between water and an organic solvent that may be similar to n-octanol or quite different from it. Our model relies on solute descriptors that can be calculated based on solute structures. It was used to predict the n-octanoll-water, hexadecanel-water and chloroforml-water partition coefficients of solutes. The calculated values of the examined parameters agreed well with their experimental counterparts. The model can find application in the accurate prediction of the effects of organic compounds on environments and the physicochemical properties of organic compounds by a full in-silico approach and can provide useful guidance for improving some properties of organic compounds.

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.

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.

The application of machine learning to the modelling of percutaneous absorption: an overview and guide

Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This paper reviews the application of these methods to the problem domain of skin permeability and addresses critically some of the key issues. Specifically, ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. However, they are beset by perceptions of a lack of transparency and, often, once a ML or related method has been published there is little impetus from other researchers to adopt such methods. This is usually due to the lack of transparency in some methods and the lack of availability of specific coding for running advanced ML methods. This paper reviews critically the application of ML methods to percutaneous absorption and addresses the key issue of transparency by describing in detail - and providing the detailed coding for - the process of running a ML method (in this case, a Gaussian process regression method). Although this method is applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.

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.

Lipophilic chemical exposure as a cause of cardiovascular disease

Environmental chemical exposure has been linked to numerous diseases in humans. These diseases include cancers; neurological and neurodegenerative diseases; metabolic disorders including type 2 diabetes, metabolic syndrome and obesity; reproductive and developmental disorders; and endocrine disorders. Many studies have associated the link between exposures to environmental chemicals and cardiovascular disease (CVD). These chemicals include persistent organic pollutants (POPs); the plastic exudates bisphenol A and phthalates; low molecular weight hydrocarbons (LMWHCs); and poly nuclear aromatic hydrocarbons (PAHs). Here it is reported that though the chemicals reported on differ widely in chemical properties and known points of attack in humans, a common link exists between them. All are lipophilic species that are found in serum. Environmentally induced CVD is related to total lipophilic chemical load in the blood. Lipophiles serve to promote the absorption of otherwise not absorbed toxic hydrophilic species that promote CVD.

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.

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.