Modeling sorption of environmental organic chemicals from water to soils

Reliable estimation of chemical sorption from water to solid phases is an essential prerequisite for reasonable assessments of chemical hazards and risks. However, current fate and exposure models mostly rely on algorithms that lack the capability to quantify chemical sorption resulting from interactions with multiple soil constituents, including amorphous organic matter, carbonaceous organic matter, and mineral matter. Here, we introduce a novel, generic approach that explicitly combines the gravimetric composition of various solid constituents and poly-parameter linear free energy relationships to calculate the solid-water sorption coefficient (Kd) for non-ionizable or predominantly neutral organic chemicals with diverse properties in a neutral environment. Our approach demonstrates an overall statistical uncertainty of approximately 0.9 log units associated with predictions for different types of soil. By applying this approach to estimate the sorption of 70 diverse chemicals from water to two types of soils, we uncover that different chemicals predominantly exhibit sorption onto different soil constituents. Moreover, we provide mechanistic insights into the limitation of relying solely on organic carbon normalized sorption coefficient (KOC) in chemical hazard assessment, as the measured KOC can vary significantly across different soil types, and therefore, a universal cut-off threshold may not be appropriate. This research highlights the importance of considering chemical properties and multiple solid constituents in sorption modeling and offers a valuable theoretical approach for improved chemical hazard and exposure assessments.

Adsorption of organic micropollutants on yeast: Batch experiment and modeling

Yeast is ubiquitous and may act as a solid phase in natural aquatic systems, which may affect the distribution of organic micropollutants (OMs). Therefore, it is important to understand the adsorption of OMs on yeast. Therefore, in this study, a predictive model for the adsorption values of OMs on the yeast was developed. For that, an isotherm experiment was performed to estimate the adsorption affinity of OMs on yeast (i.e., Saccharomyces cerevisiae). Afterwards, quantitative structure-activity relationship (QSAR) modeling was performed for the purpose of developing a prediction model and explaining the adsorption mechanism. For the modeling, empirical and in silico linear free energy relationship (LFER) descriptors were applied. The isotherm results showed that yeast adsorbs a wide range of OMs, but the magnitude of K_d strongly depends on the types of OMs. The measured log K_d values of the tested OMs ranged from -1.91 to 1.1. Additionally, it was confirmed that the K_d measured in distilled water is comparable to that measured in real anaerobic or aerobic wastewater (R² = 0.79). In QSAR modeling, the K_d value could be predicted by the LFER concept with an R² of 0.867 by empirical descriptors and an R² of 0.796 by in silico descriptors. The adsorption mechanisms of yeast for OMs were identified in individual correlations between log K_d and each descriptor: Dispersive interaction, hydrophobicity, hydrogen-bond donor, and cationic Coulombic interaction of OMs attract the adsorption, while the hydrogen-bond acceptor and anionic Coulombic interaction of OMs act as repulsive forces. The developed model can be used as an efficient method to estimate OM adsorption to yeast at a low level of concentration.

Adsorption of ionic and neutral pharmaceuticals and endocrine-disrupting chemicals on activated carbon fiber: batch isotherm and modeling studies

Activated carbon fiber (ACF) has received increasing attention as an adsorbent due to its excellent surface properties. However, the adsorption mechanism of ACF for micropollutants, especially those in ionic forms, has not been sufficiently characterized to date. Therefore, the adsorption property of ACF was characterized using isotherm experiments and linear free energy relationship (LFER). For the experiments, adsorption affinities of thirty-five chemicals, i.e., pharmaceuticals and endocrine-disrupting chemicals, on ACF were estimated. Afterward, the adsorption affinities were used as dependent variables to build the LFER modeling. Finally, three isolated models for each chemical species, i.e., cations, anions, and neutrals, and a comprehensive model for the whole dataset were developed. The LFER results revealed that the models for anionic and neutral compounds have high predictabilities in R² of 0.97 and 0.96, respectively, while that for cations has a slightly lower R² of 0.72. In the comprehensive model including cationic, anionic, and neutral compounds, the accuracy of it is 0.81. From the developed LFER model based on the whole dataset, the adsorption mechanisms of ACF for the selected substances could be interpreted, in which the terms of hydrophobic interaction, hydrogen bonding basicity, and anionic Coulombic force of the compounds were identified as the predominant interactions with ACF.

Recent advances for estimating environmental properties for small molecules from chromatographic measurements and the solvation parameter model

The transfer of neutral compounds between immiscible phases in chromatographic or environmental systems can be described by six solute properties (solute descriptors) using the solvation parameter model. The solute descriptors are size (McGowan's characteristic volume), V, excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity and basicity, A and B, and the gas-liquid partition constant on n-hexadecane at 298.15 K, L. V and E for liquids are accessible by calculation but the other descriptors and E for solids are determined experimentally by chromatographic, liquid-liquid partition, and solubility measurements. These solute descriptors are available for several thousand compounds in the Abraham solute descriptor databases and for several hundred compounds in the WSU experimental solute descriptor database. In the first part of this review, we highlight features important in defining each descriptor, their experimental determination, compare descriptor quality for the two organized descriptor databases, and methods for estimating Abraham solute descriptors. In the second part we focus on recent applications of the solvation parameter model to characterize environmental systems and its use for the identification of surrogate chromatographic models for estimating environmental properties.

Exploring the role of octanol-water partition coefficient and Henry's law constant in predicting the lipid-water partition coefficients of organic chemicals

Partition coefficients for storage lipid-water (logKlw) and phospholipid-water (logKpw) phases are key parameters to understand the bioaccumulation and toxicity of organic contaminants. However, the published experimental databases of these properties are dwarfs and current estimation approaches are cumbersome. Here, we present partition models that exploit the correlations of logKlw, and of logKpw with the linear combinations of the octanol-water partition coefficient (logKow) and the dimensionless Henry's law constant (air-water partition coefficient, logKaw). The calibrated partition models successfully describe the variations in logKlw data (n = 305, R2 = 0.971, root-mean-square-error (rmse) = 0.375), and in logKpw data (n = 131, R2 = 0.953, rmse = 0.413). With the inputs of logKow and logKaw estimated from the U.S. EPA's EPI Suite, our models of logKlw and logKpw have exhibited rmse = 0.52 with respect to experimental values indicating suitability of these models for inclusion in the EPI Suite. Our models perform similar to or better than the previously reported models such as one parameter partition models, Abraham solvation models, and models based on quantum-chemical calculations. Taken together, our models are robust, easy-to-use, and provide insight into variations of logKlw and logKpw in terms of hydrophobicity and volatility trait of chemicals.

Adsorption modeling of microcrystalline cellulose for pharmaceutical-based micropollutants

Cellulose can be considered as a raw material for the production of filters and adsorbents for the removal of micropollutants, particularly in pharmaceutical-based products. To study its applications, it is important to estimate the adsorptive interaction of cellulose with the targeted chemicals, and develop predictive models for the expandable estimation into various types of micropollutants. Therefore, the adsorption affinity between cellulose and micropollutants was measured through isotherm experiments, and a quantitative structure-adsorption relationship model was developed using the linear free energy relationship (LFER) equation. The results indicate that microcrystalline cellulose has a remarkably high adsorption affinity with cationic micropollutants. Moreover, it has interactions with neutral and anionic micropollutants, although they have relatively lower affinities than those of cations. Through a modeling study, an LFER model - comprising of excess molar refraction, polar interaction, molecular volume, and charge-related terms - was developed, which could be used to predict the adsorption affinity values with an R² of 0.895. To verify the robustness and predictability of the model, internal and external validation studies were performed. The results proved that the model was reasonable and acceptable, with an SE = 0.207 log unit.

Environmental Sorption Behavior of Ionic and Ionizable Organic Chemicals

Traditionally our tools for environmental risk assessment of organic chemicals have been developed for neutral chemicals. However, many commercial chemicals are ionic or ionizable and require different tools and approaches for their assessment. In recent years this task starts to obtain increasing attention but our understanding for their environmental fate is still far behind that for neutral chemicals. This review first gives an overview on the principles that govern ionic partitioning in environmental systems which are more complex than the simple partition processes of neutral chemicals. Second, a summary of our current knowledge on various topics such as bioaccumulation, sorption in soils, and nonspecific-toxicity reveals that ionic species can actually be quite hydrophobic contrary to commonly held beliefs. Eventually, we discuss existing models for the quantitative prediction of organic ions' sorption in soils and biota. We have to assert that the available model tools are quite restricted in their application range compared to neutral chemicals which is due to the higher complexity of the various ionic sorption processes. In order to further advance our understanding more high-quality sorption data are needed with a focus on multivalent and zwitterionic ions in all partition systems as well as cations in biological matrices.

Solvation Descriptors for Zwitterionic α-Aminoacids; Estimation of Water-Solvent Partition Coefficients, Solubilities, and Hydrogen-Bond Acidity and Hydrogen-Bond Basicity

The literature data on solubilities and water-solvent partition coefficients have been used to obtain properties or "Absolv descriptors" for zwitterionic α-aminoacids: glycine, α-alanine (α-aminopropanoic acid), α-aminobutanoic acid, norvaline (α-aminopentanoic acid), norleucine (α-aminohexanoic acid), valine (α-amino-3-methylbutanoic acid), leucine (α-amino-4-methylpentanoic acid), and α-phenylalanine. Together with equations that we have previously constructed, these descriptors can be used to estimate further solubilities and partition coefficients in a variety of organic solvents and in water-methanol and water-ethanol mixtures. It is shown that equations for neutral solutes are inadequate for the description of solubilities and partition coefficients for these α-aminoacids, and our equations developed for use with both neutral and ionic solutes must be used. The Absolv descriptors include those for hydrogen-bond acidity, A, and hydrogen-bond basicity, B. We find that both of these descriptors are far smaller in value than those for compounds that contain the corresponding ionic groups. Thus, A for α-alanine is 0.28, but A for the ethylammonium cation is 1.31; B for α-alanine is 0.83, and yet B for the acetate anion is no less than 2.93. The additional descriptors that we developed for equations that involve ions, J ⁺ and J ^-, are very significant for the α-aminoacids, although numerically smaller than for ionic species such as EtNH₃ ⁺ and CH₃CO₂ ^-.

Generalized molecular solvation in non-aqueous solutions by a single parameter implicit solvation scheme

In computer simulations of solvation effects on chemical reactions, continuum modeling techniques regain popularity as a way to efficiently circumvent an otherwise costly sampling of solvent degrees of freedom. As effective techniques, such implicit solvation models always depend on a number of parameters that need to be determined earlier. In the past, the focus lay mostly on an accurate parametrization of water models. Yet, non-aqueous solvents have recently attracted increasing attention, in particular, for the design of battery materials. To this end, we present a systematic parametrization protocol for the Self-Consistent Continuum Solvation (SCCS) model resulting in optimized parameters for 67 non-aqueous solvents. Our parametrization is based on a collection of ≈6000 experimentally measured partition coefficients, which we collected in the Solv@TUM database presented here. The accuracy of our optimized SCCS model is comparable to the well-known universal continuum solvation model (SMx) family of methods, while relying on only a single fit parameter and thereby largely reducing statistical noise. Furthermore, slightly modifying the non-electrostatic terms of the model, we present the SCCS-P solvation model as a more accurate alternative, in particular, for aromatic solutes. Finally, we show that SCCS parameters can, to a good degree of accuracy, also be predicted for solvents outside the database using merely the dielectric bulk permittivity of the solvent of choice.

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.

Descriptors for Cyclooctasulfur: Estimation of Water-Solvent Partition Coefficients, Solubilities in Solvents, and Physicochemical Properties

We have used literature data on the solubility of cyclooctasulfur in a number of solvents to drive Abraham descriptors for cyclooctasulfur. These can then be used in linear free-energy relationships that we have already constructed to predict partition coefficients and solubilities in a very large number of additional solvents. Cyclooctasulfur is very hydrophobic, has zero hydrogen bond acidity and zero hydrogen bond basicity, and dissolves best in nonpolar or only moderately polar solvents. We have also obtained enthalpies of solvation of cyclooctasulfur in solvents; again our linear free-energy relationships can be used to predict enthalpies of solvation in further solvents.

Structural properties governing drug-plasma protein binding determined by high-performance liquid chromatography method

The high-performance liquid chromatography (HPLC) method employing stationary phases immobilized with plasma proteins was used for this study to investigate the structural properties governing drug-plasma protein binding. A set of 65 compounds with a broad range of structural diversity (in terms of volume, hydrogen-bonding, polarity and electrostatic force) were selected for this purpose. The Abraham linear free energy relationship (LFER) analyses of the retention factors on the immobilized HSA (human serum albumin) and AGP (α₁-acid glycoprotein) stationary phases showed that McGowan's characteristic molecular volume (V), dipolarity/polarizability (S) and hydrogen bond basicity (B) are the three significant molecular descriptors of solutes determining the interaction with immobilized plasma proteins, whereas excess molar refraction (E) is less important and hydrogen bond acidity (A) is not of statistical significance in both systems, for electrically neutral compounds. It was shown that ionised acids, as carboxylate anions, bind very strongly to the immobilized HSA stationary phase and that ionised bases, as cations bind strongly to the AGP stationary phase. This is the first time that the effect of ionised species on plasma protein binding has been determined quantitatively; the increased binding of acids to HSA is due almost entirely to acids in their ionised form.

Predicting solvent-water partitioning of charged organic species using quantum-chemically estimated Abraham pp-LFER solute parameters

Methods for obtaining accurate predictions of solvent-water partitioning for neutral organic chemicals (e.g., K_ow) are well-established. However, methods that provide comparable accuracy are not available for predicting the solvent-water partitioning of ionic species. Previous methods for addressing charge contributions to solvent-water partitioning rely on charged solute descriptors which are obtained from regressions to neutral species descriptors as well as charged descriptors which are specific to unique charge-functionalities and structural moieties. This paper presents a method for obtaining Abraham poly-parameter linear free energy relationship (pp-LFER) descriptors using quantum chemical calculations and molecular structure, only. The method utilizes a large number of solvent-water systems to overcome large errors in individual quantum chemical computations of ionic solvent-water partition coefficients. The result is a single set of quantum-chemically estimated Abraham solute parameters (QCAP) which are solvent-independent, and can be used to predict the solvent-water partitioning of ionic species. Predictions of solvent-water partition coefficients for ionic species using quantum-chemically estimated Abraham parameters (QCAPs) are shown to provide improved accuracy compared over both existing Absolv-estimated Abraham solute parameters (AAP) as well as direct a priori quantum chemical (QC) calculations for partitioning of anionic solutes in 4 organic solvent-water systems (RMS = 0.740, 2.48 and 0.426 for the Absolv, QC and QCAP methods, respectively). For quaternary amine cations in the octanol-water system the RMS errors of the solvent-water partition coefficients were larger and similar between the two Abraham models (RMSE = 0.997 and 1.16, for the AAP and QCAP methods, respectively). Both methods showed significant improvement over direct QC calculations (RMSE = 2.82).

Modelling for antimicrobial activities of ionic liquids towards Escherichia coli, Staphylococcus aureus and Candida albicans using linear free energy relationship descriptors

To predict antimicrobial activities i.e., minimal inhibitory concentration (MIC) and minimal biocidal concentration (MBC) for ionic liquids (ILs) against Escherichia coli, Staphylococcus aureus and Candida albicans, six quantitative structure-activity relationship (QSAR) models were developed using linear free energy relationship (LFER) descriptors calculated by density functional theory and conductor screening model. The LFER descriptors are excess molar refraction, dipolarity/polarizability, H-bonding acidity, H-bonding basicity, McGowan volume, cationic interaction, and anionic interaction. By excluding some descriptors with ignorable contributions to training set, components of the QSAR models were simplified. Their estimated predictabilities were in R(2)=0.900, standard error (SE; in log unit of μM)=0.430 for log 1/MIC of E. coli, R(2)=0.934, SE=0.370 for log 1/MBC of E. coli, R(2)=0.910, SE=0.470 for log 1/MIC of S. aureus, R(2)=0.947, SE=0.350 for log 1/MBC of S. aureus, R(2)=0.892, SE=0.362 for log 1/MIC of C. albicans and R(2)=0.803, SE=0.233 for log 1/MBC of C. albicans. Then, except for log 1/MBC of C. albicans due to lack of data points, the models were validated by comparing between observed and calculated values of test set; its checked correlations were all within R(2) of 0.921.

Comparison of different models predicting the phospholipid-membrane water partition coefficients of charged compounds

A large fraction of commercially used chemicals is ionizable. This results in the need for mechanistic models to describe the physicochemical properties of ions, like the membrane-water partition coefficient (K(mw)), which is related to toxicity and bioaccumulation. In this work we compare 3 different and already existing modelling approaches to describe the liposome-water partition coefficient (K(lipw)) of organic ions, including 36 cations, 56 anions, 2 divalent cations and 2 zwitterions (plus 207 neutral compounds for ensuring model consistency). 1) The empirical correlation with the octanol-water partition coefficient of the corresponding neutral species yielded better results for the prediction of anions (RMSE = 0.79) than for cations (RMSE = 1.14). Though describing most anions reasonably well, the lack of mechanistic basis and the poor performance for cations constrain the usage of this model. 2) The polyparameter linear free energy relationship (pp-LFER) model performs worse (RMSE = 1.26/1.12 for anions/cations). The different physicochemical environments, due to different sorption depths into the membrane of the different species, cannot be described with a single pp-LFER model. 3) COSMOmic is based on quantum chemistry and fluid phase thermodynamics and has the widest applicability domain. It was the only model applicable for multiply charged ions and gave the best results for anions (RMSE = 0.66) and cations (RMSE = 0.71). We expect COSMOmic to contribute to a better estimation of the environmental risk of ionizable emerging pollutants.

In silico prediction of linear free energy relationship descriptors of neutral and ionic compounds

Prediction models for LFER descriptors – excess molar refraction (E), dipolarity/polarizability (S), H-bonding acidity (A) & basicity (B), McGowan volume (V), and interaction of cations (J⁺) and anions (J⁻) – of both ionic and neutral compounds.