Estimation of the environmental properties of compounds from chromatographic measurements and the solvation parameter model

A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database

In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules. In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mechanism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), random forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (rtest > 0.73, order of elution > 60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties. The resulting models will be useful for the prediction of separation and identification of structurally related compounds in pharmaceutical analysis, providing a simultaneous understanding of the interaction mechanisms leading to their retention under RP conditions.

Simulation of gas chromatographic separations and estimation of distribution-centric retention parameters using linear solvation energy relationships

For method development in gas chromatography, suitable computer simulations can be very helpful during the optimization process. For such computer simulations retention parameters are needed, that describe the interaction of the analytes with the stationary phase during the separation process. There are different approaches to describe such an interaction, e.g. thermodynamic models like Blumberg's distribution-centric 3-parameter model (K-centric model) or models using chemical properties like the Linear Solvation Energy Relationships (LSER). In this work LSER models for a Rxi-17Sil MS and a Rxi-5Sil MS GC column are developed for different temperatures. The influences of the temperature to the LSER system coefficients are shown in a range between 40 and 200 °C and can be described with Clark and Glew's ABC model as fit function. A thermodynamic interpretation of the system constants is given and its contribution to enthalpy and entropy is calculated. An estimation method for the retention parameters of the K-centric model via LSER models were presented. The predicted retention parameters for a selection of 172 various compounds, such as FAMEs, PCBs and PAHs are compared to isothermal determined values. 40 measurements of temperature programmed GC separations are compared to computer simulations using the differently determined or estimated K-centric retention parameters. The mean difference (RSME) between the measured and predicted retention time is less than 8 s for both stationary phases using the isothermal retention parameters. With the LSER predicted parameters the difference is 20 s for the Rxi-5Sil MS and 38 s for the Rxi-17Sil MS. Therefore, the presented estimation method can be recommended for first method development in gas chromatography.

Determination of the hydrogen-bond basicity descriptor by reversed-phase liquid chromatography

Except for alkanes, most organic compounds are hydrogen-bond bases. The B° descriptor of the solvation parameter model provides a convenient measure of the effective (or summation) hydrogen-bond basicity of organic compounds. A fast and convenient method to assign the B° descriptor is required to support studies of hydrogen-bonding in separation systems. A two-column system with acetonitrile-water mobile phase compositions and the measurement of up to eleven isocratic retention factors is proposed for this purpose. Several reversed-phase column chemistries and mobile phases were evaluated with the two-column system consisting of a pentafluorophenylpropylsiloxane-bonded and octadecylsiloxane-bonded silica columns recommended for this purpose. To assess the accuracy of the method values for B° were taken from the Wayne State University (WSU) compound descriptor database, which were assigned using conventional multi-technique methods and large datasets. The two-column systems provided an unbiased assignment of B° with an average deviation of 0.008 and an average absolute deviation of 0.021 compared with the target value for 55 varied compounds. The two-column system is unsuitable for assigning the other descriptors used in the solvation parameter model and results in erroneous assignments of B° for nitrogen-containing compounds capable of electrostatic interactions on silica-based reversed-phase columns.

Determination of descriptors for the principal flavor compounds of the cinnamons of commerce by gas chromatography and liquid-liquid partition

Descriptors for fourteen semivolatile organic compounds associated with the authenticity, botanical origin, and flavor potential of the cinnamons of commerce were determined using the Solver method and experimental retention factors determined by gas chromatography at several temperatures on a minimum of seven selectivity-selected, open-tubular columns and liquid-liquid partition constants in up to twenty totally organic biphasic systems. The six descriptors that encode the solvation properties of the compounds were used to predict water-gas, octanol-gas, and octanol-water partition constants commonly employed to assess environmental distribution properties. For octanol-water partition constants, log KOW, the predicted partition constants exhibited an average absolute deviation of 0.12 for log KOW experimental - log KOW predicted (n = 14). Soil-water, soil-air, urban aerosol-air, skin-water permeation, and non-specific toxicity to the fathead minnow were predicted for the same compounds to assess their potential environmental impact. The product terms of the solvation parameter model provide a useful insight into the contribution of individual intermolecular interactions to the distribution properties of the cinnamon compounds and their environmental impact.

Novel Models for Accurate Estimation of Air-Blood Partitioning: Applications to Individual Compounds and Complex Mixtures of Neutral Organic Compounds

The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.

Revised descriptors for polycyclic aromatic and related hydrocarbons for the prediction of environmental properties using the solvation parameter model

Revised descriptors for twenty-five polycyclic aromatic and related hydrocarbons (PAHs) forming a component of the Wayne State University (WSU) descriptor database are provided for use with the solvation parameter model. The descriptors are determined by the Solver method using experimental data for calibrated gas-liquid and reversed-phase liquid chromatographic retention factors and liquid-liquid partition constants in totally organic biphasic systems. The characteristic solvation properties of the PAHs are accounted for mainly by the additional dispersion interactions (E descriptor) and dipole-type interactions (S descriptor) resulting from the availability of easily polarizable electrons that complement typical dispersion interactions for saturated hydrocarbons. The descriptors afford acceptable prediction of the water-air partition constant (average absolute deviation AAD = 0.17, n = 22), octanol-air partition constant (AAD = 0.12, n = 20), and water-octanol partition constant (AAD = 0.10, n = 23). A two-parameter model containing only the V and B descriptors provides an unbiased prediction of aqueous solubility for the PAHs with an AAD = 0.26 (n = 22). The descriptors estimated by convenient chromatographic and partition constant measurements are demonstrated to be a viable alternative to the experimental determination of environmental properties otherwise only available by tedious, expensive, and low data throughput experimental techniques.

The effect of the assigned descriptors for phthalate esters on the characterization of their separation properties using the solvation parameter model

Revised descriptors are determined for fifteen phthalate esters for use in the solvation parameter model and form part of the Wayne State University (WSU) compound descriptor database. For thirteen phthalate esters a comparison is made with the same compounds in the Abraham descriptor database. Gas chromatographic retention factors on poly(methyloctylsiloxane), SPB-Octyl, and poly(cyanopropylphenyldimethylsiloxane), DB-225, stationary phases are used to facilitate an assessment of the contribution of cavity formation and dispersion interactions, L descriptor, and dipole-type interactions, S descriptor, to the experimental retention factors (log k) for the phthalate esters with minimum interference from competing intermolecular interactions. The results indicate a systematic overprediction of the cavity and dispersion interaction term and underprediction of dipole-type interactions for the Abraham descriptors compared with the WSU descriptors for the phthalate esters. The average absolute deviation (AAD) for 13 phthalate esters on SPB-Octyl is 0.039 (WSU descriptors) compared with 0.252 (Abraham descriptors) and for 9 phthalate esters on DB-225 0.030 (WSU descriptors) compared with 0.167 (Abraham descriptors). The results for dipole-type interactions are confirmed and extended to include the hydrogen-bond basicity of the phthalate esters, B descriptor, by evaluation of partition constants in aqueous biphasic systems and the n-heptane-2,2,2-trifluoroethanol biphasic system. Differences in the contribution of the hydrogen-bond basicity of the phthalate esters to the experimental partition constants are largely random with respect to database selection but important for the accurate prediction of the partition constants. The AAD for the partition constant for 15 phthalate esters is 0.063 (WSU descriptors) compared with 0.320 (Abraham descriptors) for the heptane-2,2,2-trifluoroethanol biphasic system and 0.13 (WSU descriptors) compared with 0.25 (Abraham descriptors) for 9 phthalate esters in the octanol-water biphasic system. The WSU descriptors for the phthalate esters exhibit a better fit with the experimental data for separation systems and are free of the extreme values predicted for the Abraham descriptors for several phthalate esters.

The effect of descriptor database selection on the physicochemical characterization and prediction of water-air, octanol-air and octanol-water partition constants using the solvation parameter model

The distribution of neutral compounds in biphasic separation systems can be described by the solvation parameter model using six solute properties, or descriptors. These descriptors (McGowan's characteristic volume, excess molar refraction, dipolarity/polarizability, hydrogen-bond acidity and basicity, and the gas-liquid partition constant on n-hexadecane at 298.15 K) are curated in two publicly accessible databases for hundreds (WSU compound descriptor database) or thousands (Abraham compound descriptor database). These databases were developed independently using different approaches resulting in descriptor values that vary for many compounds. Previously, it was shown that the two descriptor databases are not interchangeable, and the WSU descriptor database consistently demonstrated improved model performance for chromatographic systems where the uncertainty in the dependent variable was minimized by suitable quality control and calibration procedures. In this report we wish to evaluate whether the same conclusions are true for models with a dependent variable containing significant measurement uncertainty. To evaluate this hypothesis, we assembled databases for water-air, octanol-air, and octanol-water partition constants reported by multiple laboratories using various measurement methods. It was found that database selection has little effect on model quality or model predictive capability but significantly affects the assignment of the contribution of individual intermolecular interactions to the dependent variable. The latter information is database specific, and a quantitative comparison of system constants should be restricted to models using the same compound descriptor database.

Evaluation of the Ability of PAMPA Membranes to Emulate Biological Processes through the Abraham Solvation Parameter Model

Two parallel artificial membrane permeability assay (PAMPA) systems intended for emulating skin permeability have been characterized through the solvation parameter model of Abraham using multilinear regression analysis. The coefficients of the obtained equations have been compared to the ones already established for other PAMPA membranes using statistical tools. The results indicate that both skin membranes are similar to each other in their physicochemical properties. However, they are different from other PAMPA membranes (e.g., intestinal absorption and blood-brain PAMPAs), mainly in terms of hydrophobicity and hydrogen bonding properties. Next, all PAMPA membranes have been compared to relevant biological processes also characterized through the solvation parameter model. The results highlight that skin-PAMPA membranes are a very good choice to emulate skin permeability.

Recalibrating polyparameter linear free energy relationships and reanalyzing mechanisms for partition of nonionic organic compounds to low-density polyethylene passive sampler

Equilibrium passive sampling techniques based on the low-density polyethylene (LDPE) film are increasingly used for determining the concentration of contaminants in water and air. Reliable models capable of predicting LDPE-water and LDPE-air partition coefficients (K_iLDPEw and K_iLDPEa) would be very useful. In previous studies, polyparameter linear free energy relationships (PP-LFERs) based on Abraham's solute descriptors were calibrated for LDPE-water and LDPE-air systems. Unfortunately, a portion of unreliable partition coefficients and solute descriptors were included in the calibration sets of these previous studies, leading to unexpected system parameters and predictive performance in the regression results. In this study, more reliable PP-LFERs were recalibrated for LDPE-water and LDPE-air systems (20‒25 °C) using carefully collected reliable partition coefficients and solute descriptors of various polar and nonpolar compounds (over one hundred and with low redundancy) from the literature, as well as the robust regression method. The PP-LFERs performed well with root-mean-square errors of 0.15-0.25 log units and successfully predicted K_iLDPEw and K_iLDPEa values spanning over 10 orders of magnitude for compounds with reliable descriptors. The partitioning mechanisms of compounds to LDPE were also reanalyzed and compared in detail with n-alkanes (C₆-C₁₆). Generally, LDPE is more prone to form dispersion interactions with solutes than n-alkanes, while it is more difficult to form cavities in LDPE. In addition, the crystallinity of LDPE is not the sole reason for the distinct constant terms presenting in PP-LFERs for LDPE-water and n-hexadecane-water systems.

Selectivity evaluation of extraction systems

Extraction is the most common sample preparation technique prior to chromatographic analysis for samples which are too complex, too dilute, or contain matrix components incompatible with the further use of the separation system or interfere in the detection step. The most important extraction techniques are biphasic systems involving the transfer of target compounds from the sample to a different phase ideally accompanied by no more than a tolerable burden of co-extracted matrix compounds. The solvation parameter model affords a general framework to characterize biphasic extraction systems in terms of their relative capability for solute-phase intermolecular interactions (dispersion, dipole-type, hydrogen bonding) and within phase solvent-solvent interactions for cavity formation (cohesion). The approach is general and allows the comparison of liquid and solid extraction phases using the same terms and is used to explain the features important for the selective enrichment of target compounds by a specific extraction phase using solvent extraction, liquid-liquid extraction, and solid-phase extraction for samples in a gas, liquid, or solid phase. Hierarchical cluster analysis with the system constants of the solvation parameter model as variables facilitates the selection of solvents for extraction, the identification of liquid-liquid distribution systems with non-redundant selectivity, and evaluation of different approaches using liquids and solids for the isolation of target compounds from different matrices.

The influence of descriptor database selection on the solvation parameter model for separation processes

The distribution of neutral compounds in biphasic separation systems can be described by the solvation parameter model using six solute properties, or descriptors. These descriptors characterize the 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. McGowan's characteristic volume and the excess molar refraction for liquids are available by calculation (E requires and experimental refractive index). The other descriptors and excess molar refraction for solids are experimental quantities and subject to greater variation or are estimated using computational or empirical models. Solute descriptors for several thousand compounds are available in the Abraham descriptor database and for several hundred compounds in the WSU descriptor database. These publicly accessible databases were developed independently using different approaches and for many compounds provide different descriptor values. In this report we evaluate the effect of mixing descriptors from the two databases on modeling chromatographic retention factors and liquid-liquid partition constants. It is shown that the two descriptor databases are not interchangeable. The WSU descriptor database consistently demonstrates improved model quality as determined by statistical parameters. Model system constants exhibit a general dependence on database selection with an approximately linear trend as a function of the fraction of compounds assigned descriptors from either database. There is no general model performance advantage to using mixed descriptor datasets and no real cause for concern for relatively large datasets containing < 15 % of compounds with descriptors assigned from the other database. For small datasets, descriptor quality is an important variable for adequate model performance.

Reliable prediction of sensory irritation threshold values of organic compounds using new models based on linear free energy relationships and GC×GC retention parameters

The human sensory irritation threshold (SIT) is an important biochemical parameter for the exposure assessment of organic air pollutants. First, we recalibrated the Abraham solvation models (ASMs) for 9 SIT endpoints by curating 720 individual experimental SIT values to find an accurate and parsimonious ASM variant, which exhibited root mean square error (RMSE) = 0.174-0.473 log unit. Second, we report linear free energy relationships - henceforth called partition models (PMs) - which exploit the correlations of 9 SIT endpoints with the linear combinations of partition coefficients for octanol-water and air-water systems showing RMSE = 0.221-0.591 log unit. These PMs can easily be integrated into widely used EPI-Suite™ screening tool. The explanatory and predictive performance of PMs were like parameter-intensive ASMs. Third, we present GC × GC models that are based on the retention times of the nonpolar analytes on the comprehensive two-dimensional gas chromatography (GC × GC), which successfully described the SIT variance (R²=0.959-0.996) and depicted a strong predictive power (RMSE = 0.359-0.660 log unit) for an independent set of nonpolar analytes. Taken together, PMs allow easy SIT screening of organic chemicals compared to ASMs. Unlike ASMs, our GC × GC models can be applied to estimate SIT of complex nonpolar mixtures.

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.

Immobilized artificial membrane-chromatographic and computational descriptors in studies of soil-water partition of environmentally relevant compounds

Chromatographic retention factor log k_IAM obtained from immobilized artificial membrane (IAM) HPLC with buffered, aqueous mobile phases and calculated molecular descriptors (molecular weight - log M_W; molar volume - V_M; polar surface area - PSA; total count of nitrogen and oxygen atoms -(N + O); count of freely rotable bonds - FRB; H-bond donor count - HD; H-bond acceptor count - HA; energy of the highest occupied molecular orbital - E_HOMO; energy of the lowest unoccupied orbital - E_LUMO; dipole moment - DM; polarizability - α) obtained for a group of 175 structurally unrelated compounds were tested in order to generate useful models of solutes' soil-water partition coefficient normalized to organic carbon log K_oc. It was established that log k_IAM obtained in the conditions described in this study is not sufficient as a sole predictor of the soil-water partition coefficient. Simple, potentially useful models based on log k_IAM and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR) and artificial neural networks (ANN). The models proposed in the study were tested on a group of 50 compounds with known experimental log K_oc values by plotting the calculated vs. experimental values. There is a good close similarity between the calculated and experimental data for both MLR and ANN models for compounds from different chemical families (R² ≥ 0.80, n = 50) which proves the models' reliability.

Ionic Liquids: Emerging Antimicrobial Agents

Antimicrobial resistance has become a serious threat to global health. New antimicrobials are thus urgently needed. Ionic liquids (ILs), salts consisting of organic cations and anions with melting points less than 100°C, have been recently found to be promising in antimicrobial field as they may disrupt the bacterial wall and membrane and consequently lead to cell leakage and death. Different types of antimicrobial ILs are introduced in the review, including cationic, polymeric, and anionic ILs. Being the main type of the antimicrobial ILs, the review focuses on the structure and the antimicrobial mechanisms of cationic ILs. The quantitative structure-activity relationship (QSAR) models of the cationic ILs are also included. Increase in alkyl chain length and lipophilicity is beneficial to increase the antimicrobial effects of cationic ILs. Polymeric ILs are homopolymers of monomer ILs or copolymers of ILs and other monomers. They have great potential in the field of antibiotics as they provide stronger antimicrobial effects than the sum of the monomer ILs. Anionic ILs are composed of existing anionic antibiotics and organic cations, being capable to enhance the solubility and bioavailability of the original form. Nonetheless, the medical application of antimicrobial ILs is limited by the toxicity. The structural optimization aided by QSAR model and combination with existing antibiotics may provide a solution to this problem and expand the application range of ILs in antimicrobial field.

Applicability Domain of Polyparameter Linear Free Energy Relationship Models Evaluated by Leverage and Prediction Interval Calculation

Polyparameter linear free energy relationships (PP-LFERs) are accurate and robust models employed to predict equilibrium partition coefficients (K) of organic chemicals. The accuracy of predictions by a PP-LFER depends on the composition of the respective calibration data set. Generally, extrapolation outside the domain defined by the calibration data is likely to be less accurate than interpolation. In this study, the applicability domain (AD) of PP-LFERs was systematically evaluated by calculating the leverage (h) and prediction interval (PI). Repeated simulations with experimental data showed that the root mean squared error of predictions increased with h. However, the analysis also showed that PP-LFERs calibrated with a large number (e.g., 100) of training data were highly robust against extrapolation error. For such PP-LFERs, the common definition of extrapolation (h > 3 h_mean, where h_mean is the mean h of all training compounds) may be excessively strict. Alternatively, the PI is proposed as a metric to define the AD of PP-LFERs, as it provides a concrete estimate of the error range that agrees well with the observed errors, even for extreme extrapolations. Additionally, published PP-LFERs were evaluated in terms of their AD using the new concept of AD probes, which indicated the varying predictive performance of PP-LFERs in the existing literature for environmentally relevant compounds.

Descriptors for some Compounds with Pharmacological Activity; Calculation of Properties

Abraham model solute descriptors have been determined for nisoldipine, nizatidine, loratadine, zonisamide, oxaprozin, rebamipide, domperidone, temozolomide, 'florfenicol', florfenicol A, dapsone, chrysin, benorilate, β-lapachone, and Ipriflavone based on published partition coefficients, molar solubilities and gas chromatographic retention indices. The calculated solute descriptors, combined with our previously published Abraham model correlations, are used to predict several important physicochemical and biological properties, such as air-water, air-blood, air-lung, air-fat, air-skin, water-lipid, water-membrane and water-skin partition coefficients, as well as permeation from water through skin.

Abraham Model Descriptors for Melatonin; Prediction of Solution, Biological and Thermodynamic Properties

Literature solubilities have been used to obtain properties or descriptors of melatonin. These indicate the chemical nature of melatonin: it is dipolar and has moderate hydrogen bond acidity and hydrogen bond basicity. The descriptors can be combined with equations that we have previously constructed to estimate water–solvent partition coefficients and solubilities in a huge number of organic solvents. In the same way, a range of biological properties can be estimated. These include blood–tissue partitions, water–skin partition and permeability through skin.

Combining Experimental Sorption Parameters with QSAR to Predict Neonicotinoid and Transformation Product Sorption to Carbon Nanotubes and Granular Activated Carbon

We recently discovered that transformation of the neonicotinoid insecticidal pharmacophore alters sorption propensity to activated carbon, with products adsorbing less than parent compounds. To assess the environmental fate of novel transformation products that lack commercially available standards, researchers must rely on predictive approaches. In this study, we combined computationally derived quantitative structure-activity relationship (QSAR) parameters for neonicotinoids and neonicotinoid transformation products with experimentally determined Freundlich partition constants (log K F for sorption to carbon nanotubes [CNTs] and granular activated carbon [GAC]) to model neonicotinoid and transformation product sorption. QSAR models based on neonicotinoid sorption to functionalized/nonfunctionalized CNTs (used to generalize/simplify neonicotinoid-GAC interactions) were iteratively generated to obtain a multiple linear regression that could accurately predict neonicotinoid sorption to CNTs using internal and external validation (within 0.5 log units of the experimentally determined value). The log K F,CNT values were subsequently related to log K F,GAC where neonicotinoid sorption to GAC was predicted within 0.3 log-units of experimentally determined values. We applied our neonicotinoid-specific model to predict log K F,GAC for a suite of novel neonicotinoid transformation products (i.e., formed via hydrolysis, biotransformation, and chlorination) that do not have commercially available standards. We present this modeling approach as an innovative yet relatively simple technique to predict fate of highly specialized/unique polar emerging contaminants and/or transformation products that cannot be accurately predicted via traditional methods (e.g., pp-LFER), and highlights molecular properties that drive interactions of emerging contaminants.

In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches

Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (K_PE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (K_PE-W), LDPE-air (K_PE-A) and LDPE-seawater (K_PE-SW) partition coefficients. These developed models have satisfying predictability (R²_adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSE_tra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q²_ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSE_ext: 0.308-0.514, 0.299-0.426 and 0.407-0.462) in three datasets (water, air and seawater), respectively. In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown K_PE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.

Solvation parameter model: Tutorial on its application to separation systems for neutral compounds

The solvation parameter model affords a useful tool to model distribution properties of neutral compounds in biphasic separation systems. Common applications include column characterization and method development in gas chromatography; reversed-phase, micellar and hydrophilic interaction liquid chromatography; supercritical fluid chromatography; and micellar electrokinetic chromatography. The characterization of the distribution properties of liquid-liquid partition systems is another major application of this model. This tutorial is aimed at establishing good practices for the application of the model to separation systems. Suitable experimental protocols to determine system constants by multiple linear regression analysis and descriptors by the Solver method are presented; statistical tools to evaluate model quality are discussed; and model-specific data analysis tools based on system maps and correlation diagrams are described.

Development and evaluation of two-parameter linear free energy models for the prediction of human skin permeability coefficient of neutral organic chemicals

The experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or require many parameters that are not readily available. Furthermore, current estimation methods are not easy to apply to complex environmental mixtures. We present two models to estimate the skin permeability coefficients of neutral organic chemicals. The first model, referred to here as the 2-parameter partitioning model (PPM), exploits a linear free energy relationship (LFER) of skin permeability coefficient with a linear combination of partition coefficients for octanol–water and air–water systems. The second model is based on the retention time information of nonpolar analytes on comprehensive two-dimensional gas chromatography (GC × GC). The PPM successfully explained variability in the skin permeability data (n = 175) with R² = 0.82 and root mean square error (RMSE) = 0.47 log unit. In comparison, the US-EPA’s model DERMWIN™ exhibited an RMSE of 0.78 log unit. The Zhang model—a 5-parameter LFER equation based on experimental Abraham solute descriptors (ASDs)—performed slightly better with an RMSE value of 0.44 log unit. However, the Zhang model is limited by the scarcity of experimental ASDs. The GC × GC model successfully explained the variance in skin permeability data of nonpolar chemicals (n = 79) with R² = 0.90 and RMSE = 0.23 log unit. The PPM can easily be implemented in US-EPA’s Estimation Program Interface Suite (EPI Suite™). The GC × GC model can be applied to the complex mixtures of nonpolar chemicals.

Relook on the Linear Free Energy Relationships Describing the Partitioning Behavior of Diverse Chemicals for Polyethylene Water Passive Samplers

Over the past 3 decades, low-density polyethylene (PE) passive sampling devices have been widely used to scout organic chemicals in air, water, sediments, and biotic phases. Experimental partition coefficient data, required to calculate the concentrations in environmental compartments, are not widely available. In this study, we developed and rigorously evaluated linear free energy relationships (LFERs) to predict the partition coefficient between the PE and the water phase (log K _pe-w). Poly-parameter (pp) LFERs based on Abraham solute parameters performed better (root-mean-square error, rmse = 0.333-0.350 log unit) in predicting log K _pe-w compared to the two one-parameter (op) LFERs built on n-hexadecane-water and octanol-water partition coefficients (rmse = 0.41-0.42 log unit), indicating that one parameter is not able to account for all types of interactions experienced by a chemical during PE-water exchange. Dimensionality analyses show that the calibration dataset used to train pp-LFERs fulfills all the requirements to obtain a robust model for log K _pe-w. Van der Waals interactions of the molecule tend to favor the PE phase, and polar interactions of the molecule favor the water phase. The PE phase is the most sensitive to polarizable chemicals compared to other commonly used passive sampling polymeric phases such as polydimethylsiloxane, polyoxymethylene, and polyacrylate. For op-LFERs, the PE phase is better represented by the hexadecane phase than by the octanol phase. A computational method based on the conductor-like screening model for real solvents theory did good job in estimating log K _pe-w for chemicals that were neither very hydrophobic nor very hydrophilic in nature. Our models can be used to reliably predict the log K _pe-w values of simple neutral organic chemicals. This study provides insights into the partitioning behavior of PE samplers compared to other commonly used passive samplers.

Determination of physicochemical properties of ionic liquids by gas chromatography

Over the years room temperature ionic liquids have gained attention as solvents with favorable environmental and technical features. Both chromatographic and conventional methods afford suitable tools for the study of their physicochemical properties. Use of gas chromatography compared to conventional methods for the measurement of physicochemical properties of ionic liquids have several advantages; very low sample concentrations, high accuracy, faster measurements, use of wider temperature range and the possibility to determine physicochemical properties of impure samples. Also, general purpose gas chromatography instruments are widely available in most laboratories thus alleviating the need to purchase more specific instruments for less common physiochemical measurements. Some of the main types of physicochemical properties of ionic liquids accessible using gas chromatography include gas-liquid partition constants, infinite dilution activity coefficients, partial molar quantities, solubility parameters, system constants of the solvation parameter model, thermal stability, transport properties, and catalytic and other surface properties.

BTEX compounds leachates from cigarette butts into water environment: A primary study

Cigarette butts (CBs) are the most abundant types of litter in the environment and may contain toxic chemicals such as BTEX that pose serious risks to the water bodies and health of aquatic organisms. So far there is no systematic study on BTEX compounds (benzene, toluene, ethylbenzene, o-xylene, and p-xylene) leaching from CBs into water environments. In this work, the leaching concentrations of BTEX compounds in deionized water (DW) and river water (RW) samples were studied for the first time. The mean concentrations of benzene, toluene, ethylbenzene, p-xylene, and o-xylene at contact times of 15 min to 1 day in water samples ranged from 0.13 to 0.18, 0.39-0.9, 0.11-0.25, 0.12-0.38, and 0.09-0.19 μgL^-1 respectively. Benzene, toluene, ethylbenzene, o-xylene and p-xylene were detected at all contact times in both DW and RW samples. There were no significant differences of the leachate levels of BTEX compounds between DW and RW samples. The highest and lowest mean concentration levels in both DW and RW samples were determined for toluene and o-xylene respectively. The time after smoking had a significant effect on BTEX levels in leachates. The concentration levels of benzene, toluene, ethylbenzene, o-xylene and p-xylene leachates in water samples, after only 15 min, were reduced by 100, 93, 70, 68, and 59 percent respectively. Our data revealed that leached concentrations of benzene did not exceed the Water Framework Directive (WFD) guidelines, but with regard to the amount of CBs littered each year and other toxic chemicals contents of CBs this can still be a threat for aquatic creatures and possibly humans as well. Further studies are needed to cover the knowledge gap on the toxic leachates from CBs into water systems.

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.

Descriptors for terpene esters from chromatographic and partition measurements: Estimation of human odor detection thresholds

We have used gas chromatographic retention data together with other data to obtain Abraham descriptors for 30 terpene esters. These include the air-water partition coefficient, as log Kw, for which no experimental values are available for any terpene ester. The other descriptors are the ester dipolarity, S, the hydrogen bond basicity, B, (the ester hydrogen bond acidity is zero for the esters studied), and L the logarithm of the air-hexadecane partition coefficient. Both S and B are larger than those for simple aliphatic esters, as expected from the terpene ester structures that include ring systems and ethylenic double bonds. These descriptors can then be used to obtain a large number of physicochemical and environmental properties of terpene esters. We have analyzed experimental results on human odor detection thresholds and have constructed another equation for the calculation of these thresholds, to go with a previous equation that we have reported. Then the descriptors for terpene esters can be used to estimate the important odor detection thresholds.