Development of polyparameter linear free energy relationship models for octanol-air partition coefficients of diverse chemicals

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.

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.

Predicting the Temperature Dependence of the Octanol–Air Partition Ratio: A New Model for Estimating $$\Delta {U^{ \circ}_{\text{OA}}}$$

The octanol–air partition ratio (KOA) describes the partitioning of a chemical between air and octanol and is often used to approximate other partitioning phenomena in environmental chemistry (e.g., blood–air, atmospheric particulate matter–air, polyurethane foam-air). Such partitioning processes often occur at environmental temperatures other than 25 °C. Enthalpies $$\Delta {H^{ \circ}_{\text{OA}}}$$ Δ H OA ∘ or internal energies $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ of phase transfer are used to express the temperature dependence of the KOA. Existing poly-parameter linear free energy relationships (ppLFERs) for predicting $$\Delta {H^{ \circ}_{\text{OA}}}$$ Δ H OA ∘ were developed using a relatively small dataset. In this work we utilize a recently developed comprehensive KOA database to create and curate a $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ dataset containing 195 chemicals and use this dataset in the development of new predictive equations. Using the QSAR development platform QSARINS we evaluate the use of Abraham descriptors, other molecular descriptors, and the log10KOA at 25 °C as variables in different multilinear regression equations for $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ . The $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ of neutral organic chemicals can be reliably predicted using only the log10KOA (RMSEEXT = 6.86 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2  = 0.94), only the solute’s hydrogen acidity A and the logarithm of the hexadecane–air partition ratio L (RMSEEXT = 7.23 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2  = 0.93), or A and log10KOA (RMSEEXT = 6.76 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2  = 0.95).

Reliable Prediction of the Octanol-Air Partition Ratio

The octanol-air equilibrium partition ratio (K_OA ) is frequently used to describe the volatility of organic chemicals, whereby n-octanol serves as a substitute for a variety of organic phases ranging from organic matter in atmospheric particles and soils, to biological tissues such as plant foliage, fat, blood, and milk, and to polymeric sorbents. Because measured K_OA values exist for just over 500 compounds, most of which are nonpolar halogenated aromatics, there is a need for tools that can reliably predict this parameter for a wide range of organic molecules, ideally at different temperatures. The ability of five techniques, specifically polyparameter linear free energy relationships (ppLFERs) with either experimental or predicted solute descriptors, EPISuite's KOAWIN, COSMOtherm, and OPERA, to predict the K_OA of organic substances, either at 25 °C or at any temperature, was assessed by comparison with all K_OA values measured to date. In addition, three different ppLFER equations for K_OA were evaluated, and a new modified equation is proposed. A technique's performance was quantified with the mean absolute error (MAE), the root mean square error (RMSE), and the estimated uncertainty of future predicted values, that is, the prediction interval. We also considered each model's applicability domain and accessibility. With an RMSE of 0.37 and a MAE of 0.23 for predictions of log K_OA at 25 °C and RMSE of 0.32 and MAE of 0.21 for predictions made at any temperature, the ppLFER equation using experimental solute descriptors predicted the K_OA the best. Even if solute descriptors must be predicted in the absence of experimental values, ppLFERs are the preferred method, also because they are easy to use and freely available. Environ Toxicol Chem 2021;40:3166-3180. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Pesticide distribution and depletion kinetic determination in honey and beeswax: Model for pesticide occurrence and distribution in beehive products

Beehive products such as honey, beeswax and recently pollen have been regarded for many years as appropriate sentinels for environmental pesticide pollutions. However, despite yearly application of hundreds of approved pesticides in agricultural fields, only a minor fraction of these organic compounds were actually detected in honey and beeswax samples. This observation has led us to question the general suitability of beehive products as a sentinel for synthetic organic pesticides applied in the field. The aim of the present study was to experimentally determine the distribution (logarithmic ratio of beeswax to honey pesticide concentration, LogD) and depletion kinetics (half-life) of selected pesticides in honey and beeswax as a measure of the latter matrixes to serve as a pesticide sentinel. The obtained parameters were used to extrapolate to pesticide burden in honey and beeswax samples collected from German and Israeli apiaries. In addition, we aimed to establish a mathematical model, enabling us to predict distribution of selected pesticides between honey to beeswax, by utilizing simple substance descriptors, namely, octanol/water partitioning coefficient, molar weight and Henry coefficient. Based on the present results, it appears that pesticides with LogD values > 1 and half-life in beeswax > 1 day, were likely to accumulate and detected in beeswax samples, and less likely to be found in honey. On the other hand, pesticides with negative LogD values were highly likely to be found in honey and less so in beeswax samples. Finally, pesticides with LogD values between 0–1 were expected to be found in both matrixes. The developed model was successfully applied to predict LogD values, thereby identifying octanol/water partitioning and molar weight as the most prominent substance descriptors, which affect pesticide distribution between honey and beeswax.

History of EPI Suite™ and future perspectives on chemical property estimation in US Toxic Substances Control Act new chemical risk assessments

Chemical property estimation is a key component in many industrial, academic, and regulatory activities, including in the risk assessment associated with the approximately 1000 new chemical pre-manufacture notices the United States Environmental Protection Agency (US EPA) receives annually. The US EPA evaluates fate, exposure and toxicity under the 1976 Toxic Substances Control Act (amended by the 2016 Frank R. Lautenberg Chemical Safety for the 21^st Century Act), which does not require test data with new chemical applications. Though the submission of data is not required, the US EPA has, over the past 40 years, occasionally received chemical-specific data with pre-manufacture notices. The US EPA has been actively using this and publicly available data to develop and refine predictive computerized models, most of which are housed in EPI Suite™, to estimate chemical properties used in the risk assessment of new chemicals. The US EPA develops and uses models based on (quantitative) structure-activity relationships ([Q]SARs) to estimate critical parameters. As in any evolving field, (Q)SARs have experienced successes, suffered failures, and responded to emerging trends. Correlations of a chemical structure with its properties or biological activity were first demonstrated in the late 19^th century and today have been encapsulated in a myriad of quantitative and qualitative SARs. The development and proliferation of the personal computer in the late 20^th century gave rise to a quickly increasing number of property estimation models, and continually improved computing power and connectivity among researchers via the internet are enabling the development of increasingly complex models.