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

Correlation between the structure and skin permeability of compounds

A three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R² of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R² of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

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.

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.

chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models

The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of these models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, all of the methods in chemmodlab have broad utility for the machine learning community. chemmodlab contains several assessment utilities, including a plotting function that constructs accumulation curves and a function that computes many performance measures. The most novel feature of chemmodlab is the ease with which statistically significant performance differences for many machine learning models is presented by means of the multiple comparisons similarity plot. Differences are assessed using repeated k-fold cross validation, where blocking increases precision and multiplicity adjustments are applied. chemmodlab is freely available on CRAN at https://cran.r-project.org/web/packages/chemmodlab/index.html.

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.