A system coefficient approach for quantitative assessment of the solvent effects on membrane absorption from chemical mixtures

Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., K_XAD-A) are indispensable to obtain POPs concentration, and the K_XAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of K_XAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting K_XAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R²_adj and Q²_ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R²_adj = 0.991, Q²_ext = 0.935, RMSE_tra = 0.271 and RMSE_ext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting K_XAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods

Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (K_PDMS-w), PDMS-air (K_PDMS-a) and PDMS-seawater partition coefficients (K_PDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R²_adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.

Prediction of polydimethylsiloxane-water partition coefficients based on the pp-LFER and QSAR models

Obtaining accurate measurements of the partition coefficients between sorbent materials and water is of major importance for the analysis of the adsorption behavior and dissolved concentrations of organic compounds in the environment. In the passive-sampling approach, polydimethylsiloxane (PDMS) has a wide range of applications. Therefore, we established a poly-parameter linear-free energy relationship (pp-LFER) and a quantitative structure-activity relationship (QSAR) model to predict the log K_PDMS-w values for a large dataset of 290 organic chemicals from 11 diverse classes. For the pp-LFER model, E (excess molar refractivity), A (molecular H-bond donor ability), V (McGowan volume), and B (the H-bond acceptor properties) were introduced as the main correlated variables. However, the obtained model is much limited in terms of acquiring available descriptors. For this reason, we developed a QSAR model, and CrippenLogP (Crippen octanol-water partition coefficient), RNCG (Relative negative charge-most negative charge/total negative charge), ATSC4e (Centered Broto-Moreau autocorrelation-lag4/weighted by Sanderson electronegativities) and GATS6p (Geary autocorrelation-lag6/weighted by polarizabilities) were selected as the significant parameters. The predictive power and functional reliability of the presented models were confirmed with validation methods as described in previous studies. The adjusted determination coefficients (R²_adj) of 0.851 and 0.922 and leave-one-out cross-validated (Q²_LOO) of 0.841 and 0.907 revealed that the models have good predictive power and generalizability. Thus, the proposed models are simple yet accurate tools for predicting the log K_PDMS-w values and providing new insights to further understand the adsorption mechanism of organic compounds.

Measurement of the surface hydrophobicity of engineered nanoparticles using an atomic force microscope

A scanning probe method based on atomic force microscopy (AFM) was used to probe the nanoscale hydrophobicity of nanomaterials in liquid environments.

Biological and environmental surface interactions of nanomaterials: characterization, modeling, and prediction

The understanding of nano-bio interactions is deemed essential in the design, application, and safe handling of nanomaterials. Proper characterization of the intrinsic physicochemical properties, including their size, surface charge, shape, and functionalization, is needed to consider the fate or impact of nanomaterials in biological and environmental systems. The characterizations of their interactions with surrounding chemical species are often hindered by the complexity of biological or environmental systems, and the drastically different surface physicochemical properties among a large population of nanomaterials. The complexity of these interactions is also due to the diverse ligands of different chemical properties present in most biomacromolecules, and multiple conformations they can assume at different conditions to minimize their conformational free energy. Often these interactions are collectively determined by multiple physical or chemical forces, including electrostatic forces, hydrogen bonding, and hydrophobic forces, and calls for multidimensional characterization strategies, both experimentally and computationally. Through these characterizations, the understanding of the roles surface physicochemical properties of nanomaterials and their surface interactions with biomacromolecules can play in their applications in biomedical and environmental fields can be obtained. To quantitatively decipher these physicochemical surface interactions, computational methods, including physical, statistical, and pharmacokinetic models, can be used for either analyses of large amounts of experimental characterization data, or theoretical prediction of the interactions, and consequent biological behavior in the body after administration. These computational methods include molecular dynamics simulation, structure-activity relationship models such as biological surface adsorption index, and physiologically-based pharmacokinetic models. WIREs Nanomed Nanobiotechnol 2017, 9:e1440. doi: 10.1002/wnan.1440 For further resources related to this article, please visit the WIREs website.

Quantum-mechanical parameters for the risk assessment of multi-walled carbon-nanotubes: A study using adsorption of probe compounds and its application to biomolecules

This work forwards new insights into the risk-assessment of multi-walled carbon-nanotubes (MWCNTs) while analysing the role of quantum-mechanical interactions between the electrons in the adsorption of probe compounds and biomolecules by MWCNTs. For this, the quantitative models are developed using quantum-chemical descriptors and their electron-correlation contribution. The major quantum-chemical factors contributing to the adsorption are found to be mean polarizability, electron-correlation energy, and electron-correlation contribution to the absolute electronegativity and LUMO energy. The proposed models, based on only three quantum-chemical factors, are found to be even more robust and predictive than the previously known five or four factors based linear free-energy and solvation-energy relationships. The proposed models are employed to predict the adsorption of biomolecules including steroid hormones and DNA bases. The steroid hormones are predicted to be strongly adsorbed by the MWCNTs, with the order: hydrocortisone > aldosterone > progesterone > ethinyl-oestradiol > testosterone > oestradiol, whereas the DNA bases are found to be relatively less adsorbed but follow the order as: guanine > adenine > thymine > cytosine > uracil. Besides these, the developed electron-correlation based models predict several insecticides, pesticides, herbicides, fungicides, plasticizers and antimicrobial agents in cosmetics, to be strongly adsorbed by the carbon-nanotubes. The present study proposes that the instantaneous inter-electronic interactions may be quite significant in various physico-chemical processes involving MWCNTs, and can be used as a reliable predictor for their risk assessment.

Experimental Solubility Approach to Determine PDMS-Water Partition Constants and PDMS Activity Coefficients

Freely dissolved aqueous concentration and chemical activity are important determinants of contaminant transport, fate, and toxic potential. Both parameters are commonly quantified using Solid Phase Micro-Extraction (SPME) based on a sorptive polymer such as polydimethylsiloxane (PDMS). This method requires the PDMS-water partition constants, KPDMSw, or activity coefficient to be known. For superhydrophobic contaminants (log KOW >6), application of existing methods to measure these parameters is challenging, and independent measures to validate KPDMSw values would be beneficial. We developed a simple, rapid method to directly measure PDMS solubilities of solid contaminants, SPDMS(S), which together with literature thermodynamic properties was then used to estimate KPDMSw and activity coefficients in PDMS. PDMS solubility for the test compounds (log KOW 7.2-8.3) ranged over 3 orders of magnitude (4.1-5700 μM), and was dependent on compound class. For polychlorinated biphenyls (PCBs) and polychlorinated dibenzo-p-dioxins (PCDDs), solubility-derived KPDMSw increased linearly with hydrophobicity, consistent with trends previously reported for less chlorinated congeners. In contrast, subcooled liquid PDMS solubilities, SPDMS(L), were approximately constant within a compound class. SPDMS(S) and KPDMSw can therefore be predicted for a compound class with reasonable robustness based solely on the class-specific SPDMS(L) and a particular congener's entropy of fusion, melting point, and aqueous solubility.

Prediction of partition coefficients of organic compounds between SPME/PDMS and aqueous solution

Polydimethylsiloxane (PDMS) is commonly used as the coated polymer in the solid phase microextraction (SPME) technique. In this study, the partition coefficients of organic compounds between SPME/PDMS and the aqueous solution were compiled from the literature sources. The correlation analysis for partition coefficients was conducted to interpret the effect of their physicochemical properties and descriptors on the partitioning process. The PDMS-water partition coefficients were significantly correlated to the polarizability of organic compounds (r = 0.977, p < 0.05). An empirical model, consisting of the polarizability, the molecular connectivity index, and an indicator variable, was developed to appropriately predict the partition coefficients of 61 organic compounds for the training set. The predictive ability of the empirical model was demonstrated by using it on a test set of 26 chemicals not included in the training set. The empirical model, applying the straightforward calculated molecular descriptors, for estimating the PDMS-water partition coefficient will contribute to the practical applications of the SPME technique.

Insights into the adsorption of simple benzene derivatives on carbon nanotubes

This work characterizes the adsorption characteristics of simple benzene derivatives on carbon nanotubes.

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.

Imaging molecular transport across lipid bilayers

Low-molecular-weight carboxylic acids have many properties common to small molecule drugs. The transport of these acids across cell membranes has been widely studied, but these studies have produced wildly varying permeability values. Recent reports have even claimed that the transport behavior of these drugs is contrary to the rule of thumb called Overton's rule, which holds that more lipophilic molecules transport across lipid membranes more quickly. We used confocal microscopy to image the transport of carboxylic acids with different lipophilicities into a giant unilamellar lipid vesicle (GUV). Fluorescein-dextran, which acts as a pH-sensitive dye, was encapsulated in the GUV to trace the transport of acid. The GUV was immobilized on the surface of a microfluidic channel by biotin-avidin binding. This microchannel allows the rapid and uniform exchange of the solution surrounding the GUV. Using a spinning-disk confocal microscope, the entire concentration field is captured in a short (<100 ms) exposure. Results show that more lipophilic acids cross the bilayer more quickly. A finite difference model was developed to simulate the experimental process and derive permeabilities. The permeabilities change with the same trend as literature oil-water partition coefficients, demonstrating that Overton's rule applies to this class of molecules.

Mapping the surface adsorption forces of nanomaterials in biological systems

The biological surface adsorption index (BSAI) is a novel approach to characterize surface adsorption energy of nanomaterials that is the primary force behind nanoparticle aggregation, protein corona formation, and other complex interactions of nanomaterials within biological systems. Five quantitative nanodescriptors were obtained to represent the surface adsorption forces (hydrophobicity, hydrogen bond, polarity/polarizability, and lone-pair electrons) of the nanomaterial interaction with biological components. We have mapped the surface adsorption forces over 16 different nanomaterials. When the five-dimensional information of the nanodescriptors was reduced to two dimensions, the 16 nanomaterials were classified into distinct clusters according their surface adsorption properties. BSAI nanodescriptors are intrinsic properties of nanomaterials useful for quantitative structure-activity relationship (QSAR) model development. This is the first success in quantitative characterization of the surface adsorption forces of nanomaterials in biological conditions, which could open a quantitative avenue in predictive nanomedicine development, risk assessment, and safety evaluation of nanomaterials.

Confocal imaging to quantify passive transport across biomimetic lipid membranes

The ability of a molecule to pass through the plasma membrane without the aid of any active cellular mechanisms is central to that molecule's pharmaceutical characteristics. Passive transport has been understood in the context of Overton's rule, which states that more lipophilic molecules cross membrane lipid bilayers more readily. Existing techniques for measuring passive transport lack reproducibility and are hampered by the presence of an unstirred layer (USL) that dominates transport across the bilayer. This report describes assays based on spinning-disk confocal microscopy (SDCM) of giant unilamellar vesicles (GUVs) that allow for the detailed investigation of passive transport processes and mechanisms. This approach allows the concentration field to be directly observed, allowing membrane permeability to be determined easily from the transient concentration profile data. A series of molecules of increasing hydrophilicity was constructed, and the transport of these molecules into GUVs was observed. The observed permeability trend is consistent with Overton's rule. However, the values measured depart from the simple partition-diffusion proportionality model of passive transport. This technique is easy to implement and has great promise as an approach to measure membrane transport. It is optimally suited to precise quantitative measurements of the dependence of passive transport on membrane properties.

An index for characterization of nanomaterials in biological systems

In a physiological environment, nanoparticles selectively absorb proteins to form 'nanoparticle-protein coronas', a process governed by molecular interactions between chemical groups on the nanoparticle surfaces and the amino-acid residues of the proteins. Here, we propose a biological surface adsorption index to characterize these interactions by quantifying the competitive adsorption of a set of small molecule probes onto the nanoparticles. The adsorption properties of nanomaterials are assumed to be governed by Coulomb forces, London dispersion, hydrogen-bond acidity and basicity, polarizability and lone-pair electrons. Adsorption coefficients of the probe compounds were measured and used to create a set of nanodescriptors representing the contributions and relative strengths of each molecular interaction. The method successfully predicted the adsorption of various small molecules onto carbon nanotubes, and the nanodescriptors were also measured for 12 other nanomaterials. The biological surface adsorption index nanodescriptors can be used to develop pharmacokinetic and safety assessment models for nanomaterials.

Quantification of chemical mixture interactions modulating dermal absorption using a multiple membrane fiber array

Dermal exposures to chemical mixtures can potentially increase or decrease systemic bioavailability of toxicants in the mixture. Changes in dermal permeability can be attributed to changes in physicochemical interactions between the mixture, the skin, and the solute of interest. These physicochemical interactions can be described as changes in system coefficients associated with molecular descriptors described by Abraham's linear solvation energy relationship (LSER). This study evaluated the effects of chemical mixtures containing either a solvent (ethanol) or a surfactant (sodium lauryl sulfate, SLS) on solute permeability and partitioning by quantifying changes in system coefficients in skin and a three-membrane-coated fiber (MCF) system, respectively. Regression analysis demonstrated that changes in system coefficients in skin were strongly correlated ( R2 = 0.89-0.98) to changes in system coefficients in the three-membrane MCF array with mixtures containing either 1% SLS or 50% ethanol. The PDMS fiber appeared to play a significant role (R2 = 0.84-0.85) in the MCF array in predicting changes in solute permeability, while the WAX fiber appeared to contribute less (R2 = 0.59-0.77) to the array than the other two fibers. On the basis of changes in system coefficients that are part of a LSER, these experiments were able to link physicochemical interactions in the MCF with those interactions in skin when either system is exposed to 1% SLS or 50% ethanol. These experiments further demonstrated the utility of a MCF array to adequately predict changes in dermal permeability when skin is exposed to mixtures containing either a surfactant or a solvent and provide some insight into the nature of the physiochemical interactions that modulate dermal absorptions.

A solvatochromatic approach to quantifying formulation effects on dermal permeability

Dermal risk assessments are most often concerned with the occupational and environmental exposure to a single chemical and then determining solute permeability through in vitro or in vivo experimentation with various animal models and/or computational approaches. Oftentimes, the skin is exposed to more than one chemical that could potentially modulate dermal permeability of the chemical that could cause adverse health effects. The focus of this article is to demonstrate that these formulation effects on dermal permeability can occur with simple solvent formulations or complex industrial formulations and that these effects can be modeled within the context of a linear solvation energy relationship (LSER). This research demonstrated that formulation-specific strength coefficients (r p a b v) predicted (r(2) = 0.75-0.83) changes in the dermal permeability of phenolic compounds when formulated with commercial metal-working fluid (MWF) formulations or 50% ethanol. Further experimentation demonstrated that chemical-induced changes in skin permeability with 50% ethanol are strongly correlated (r(2) = 0.91) to similar changes in an inert membrane-coated fiber (MCF) array system consisting of three chemically diverse membranes. Changes in specific strength coefficients pertaining to changes in hydrogen donating ability (Deltab) and hydrophobicity (Deltav) across membrane systems were identified as important quantitative interactions associated with ethanol mixtures. This solvatochromatic approach along with the use of a MCF array system holds promise for predicting dermal permeability of complex chemical formulations in occupational exposures where performance additives can potentially modulate permeability of potential toxicants.