Determination of Solvation Descriptors for Ionic Species: Hydrogen Bond Acidity and Basicity

Machine learning for skin permeability prediction: random forest and XG boost regression

Background: Machine learning algorithms that can quickly and easily estimate skin permeability (Kp) are increasingly being used in drug delivery research. The linear free energy relationship (LFER) developed by Abraham is a practical technique for predicting Kp. The permeability coefficients and Abraham solute descriptor values for 175 organic compounds have been documented in the scientific literature.Purpose: The purpose of this project was to use a publicly available dataset to make skin permeability predictions using the random forest and XBoost regression techniques.Methods: We employed Pandas-based methods in JupyterLab to predict permeability coefficient (Kp) from solute descriptors (excess molar refraction [E], combined dipolarity/polarizability [S], overall solute hydrogen bond acidity and basicity [A and B], and the McGowan's characteristic molecular volume [V]).Results: The random forest and XG Boost regression models established statistically significant association between the descriptors and the skin permeability coefficient.

Ability of biomimetic chromatography and physicochemical systems to predict the skin permeation of neutral compounds. A comparison study

Five in vitro physicochemical systems have been evaluated in terms of its ability to emulate the skin permeation of neutral compounds: the permeation in two different PAMPA membranes, the classical octanol-water partition coefficient, and two biomimetic chromatography systems, one based in cerasome electrokinetic chromatography and another based in reversed-phase liquid chromatography measurements. The coefficients of the solvation parameter model equation of the mentioned systems have been compared to the ones of the skin permeation process through different comparison parameters. Moreover, a method to predict whether a physicochemical system is able to emulate satisfactorily a biological one, just by the analysis of the equation coefficients has been developed. Results reveal that the two PAMPA systems are a good choice to emulate directly the skin permeation of neutral compounds. Instead, the other three systems need a volume correction term to provide a satisfactory emulation. However, after the correction, all the evaluated systems show a similar ability to emulate well skin permeation, as predicted.

Predicting partitioning from low density polyethylene to blood and adipose tissue by linear solvation energy relationship models

The variety of polymers utilized in medical devices demands for testing of extractables and leachables according to ISO 10993-18:2020 in combination with ISO 10993-1:2018. The extraction of the materials involves the use of organic solvents as well as aqueous buffers to cover a wide range of polarity and pH-values, respectively. To estimate patient exposure to chemicals leaching from a polymer in direct body contact, simulating solvents are applied to best mimic the solubilization and partitioning behavior of the related tissue or body fluid. Here we apply linear solvation energy relationship (LSER) models to predict blood/water and adipose tissue/water partition coefficients. We suggest this predictive approach to project levels of potential leachables, design extraction experiments, and to identify the optimal composition of simulating extraction solvents. We compare our predictions to LSER predictions for commonly applied surrogates like ethanol/water mixtures, butanol, and octanol as well as olive oil, butanone, 1,4-dioxane for blood and adipose tissue, respectively. We therefore selected a set of 26 experimentally determined blood/water partition coefficients and 33 adipose tissue/water partition coefficients, where we demonstrate that based on the root mean squared error rmse the LSER approach performs better than surrogates like octanol or butanol and equally well as 60:40 ethanol/water for blood. For adipose tissue/water partitioning, the experimentally determined octanol/water partition coefficient performs best but the rmse is at the same range as our LSER approach based on experimentally determined descriptors. Further, we applied our approach for 248 extractables where we calculated blood/low density polyethylene (LDPE) and adipose tissue/LDPE partition coefficients. By this approach, we successfully identified chemicals of potential interest to a toxicological evaluation based on the total risk score.

Further Understanding the Roles of Solvent, Brønsted Acids, and Hydrogen Bonding in Iron Porphyrin-Mediated Carbon Dioxide Reduction

Improving our understanding of how molecules and materials mediate the electrochemical reduction of carbon dioxide (CO2) to upgraded products is of great interest as a means to address climate change. A leading class of molecules that can facilitate the electrochemical conversion of CO2 to carbon monoxide (CO) is iron porphyrins. These molecules can have high rate constants for CO2-to-CO conversion; they are robust, and they rely on abundant and inexpensive synthetic building blocks. Important foundational work has been conducted using chloroiron 5,10,15,20-tetraphenylporphyrin (FeTPPCl) in N,N-dimethylformamide (DMF) solvent. A related and recent report points out that the corresponding perchlorate complex, FeTPPClO4, can have superior function due to its solubility in other organic solvents. However, the importance of hydrogen bonding and solvent effects was not discussed. Herein, we present a detailed kinetic study of the triflate (CF3SO3-) complex of FeTPP in DMF and in MeCN using a range of phenol Brønsted acid additives. We also detected the formation of Fe(III)TPP-phenolate complexes using cyclic voltammetry experiments. Importantly, our new analysis of apparent rate constants with different added phenols allows for a modification to the established mechanistic model for CO2-to-CO conversion. Critically, our improved model accounts for hydrogen bonding and solvent effects by using simple hydrogen bond acidity and basicity descriptors. We use this augmented model to rationalize function in other reported porphyrin systems and to make predictions about operational conditions that can enhance the CO2 reduction chemistry.

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.

Aromatic amines leachate from cigarette butts into aquatic environments: Is there risk for water organisms?

There are many toxics, such as aromatic amines (AAs), in cigarette butts (CBs). As CBs are the most abundant litter worldwide, these chemicals may leach into water bodies. In the present work, for the first time, the levels of AAs leachates from CBs in distilled water (DW) and river water (RW) samples were evaluated at different exposure times ranging from 15 min to 30 days. The mean leachate levels of AAs in DW and RW samples were in the range of 0.2-566 and 0.2-596 ng L^-1, respectively, with overall mean values of 569 and 556 ng L^-1. There was no significant difference (p > 0.05) between the total AAs levels as well as the level of each examined AA in DW and RW samples. Aniline (ANL) had the highest leaching rate from CBs into water. The mean leachates of AAs from CBs into water were ranked as: ANL> 1-naphthylamine (1-NA)> 2-naphthylamine (2-NA) > 2,6-dimethylaniline (2, 6-DMA)> ∑toluidine (∑TOL)> o-anisidine (o-ASD)> ∑aminobiphenyl (∑ABP). Ecological risk assessment showed that ∑7AAs, ANL, p-TOL, o-TOL, 2-NA, and ∑ABP had medium risks to sensitive crustaceans and fish. As AAs are not the only hazardous chemicals which may leach from CBs into aquatic environments, restrictions on littering CBs into the environment are required due to the release of different toxics ultimately causing adverse effects on aquatic organisms.

A story of peptides, lipophilicity and chromatography - back and forth in time

Peptides, as part of the beyond the rule of 5 (bRo5) chemical space, represent a unique class of pharmaceutical compounds. Because of their exceptional position in the chemical space between traditional small molecules (molecular weight (MW) < 500 Da) and large therapeutic proteins (MW > 5000 Da), peptides became promising candidates for targeting challenging binding sites, including even targets traditionally considered as undruggable - e.g. intracellular protein-protein interactions. However, basic knowledge about physicochemical properties that are important for a drug to be membrane permeable is missing but would enhance the drug discovery process of bRo5 molecules. Consequently, there is a demand for quick and simple lipophilicity determination methods for peptides. In comparison to the traditional lipophilicity determination methods via shake flask and in silico prediction, chromatography-based methods could have multiple benefits such as the requirement of low analyte amount, insensitivity to impurities and high throughput. Herein we elucidate the role of peptide lipophilicity and different lipophilicity values. Further, we summarize peptide analysis via common chromatographic techniques, in specific reversed phase liquid chromatography, hydrophilic interaction liquid chromatography and supercritical fluid chromatography and their role in drug discovery and development process.

Organic acid-based linear free energy relationship models for green leaching of strategic metals from spent lithium-ion batteries and improvement of leaching performance

Water-soluble organic acids (e.g., acetic acid, acrylic acid, and ascorbic acid), as green leachants, have been applied to leach strategic metals (Ni, Li, Mn, and Co) from spent lithium-ion batteries (LIBs). Organic acid-based linear free energy relationship models were developed and simulated to predict the feasibility of the leaching efficiency for each of the strategic metals based on in silico calculated descriptors. The developed models, with accuracy (R²) of 0.747-0.831, reveal that hydrogen bond acidity of organic acids promotes the leaching efficiency, whereas molecular volume or excess molar refraction inhibits the efficiency. Furthermore, toxicity (lethal dose 50%) of organic acids was discussed along with the predicted leaching efficiency to explore more green and efficient organic acids. Considering both toxicity and leaching efficiency, citric acid was selected as a green and efficient leachant. To more improve the leaching performance (rate and efficiency) of citric acid, glucose as a green reductant and microwave treatment were additionally applied. Under the selected conditions, the leaching efficiencies after 1 h for Ni, Li, Mn, and Co were enhanced up to 98.3%, 99.1%, 98.7%, and 97.7%, respectively.

Adsorption of polar and ionic organic compounds on activated carbon: Surface chemistry matters

Persistent and mobile organic compounds (PMOCs) are often detected micropollutants in the water cycle, thereby challenging the conventional wastewater and drinking water treatment techniques. Carbon-based adsorbents are often less effective or even unable to remove this class of pollutants. Understanding of PMOC adsorption mechanisms is urgently needed for advanced treatment of PMOC-contaminated water. Here, we investigated the effect of surface modifications of activated carbon felts (ACFs) on the adsorption of six selected PMOCs carrying polar or ionic groups. Among three ACFs, defunctionalized ACF bearing net positive surface charge at neutral pH provides the most versatile sorption efficiency for all studied PMOC types representing neutral, anionic and cationic compounds. Ion exchange capacity giving quantitative information of sorbent surface charges at specified pH is recognized as a frequently underestimated key property for evaluating adsorbents aiming at PMOC adsorption. A most recently developed prediction tool for Freundlich parameters in PMOC adsorption was applied and the prediction results are compared to the experimental data. The comparison demonstrates the so far underestimated importance of the sorbent surface chemistry for PMOC adsorption affinity and capacity. PMOC adsorption mechanisms were additionally investigated by adsorption experiments at various temperatures, pH values and electrolyte concentrations. Exothermic sorption was observed for all sorbate-sorbent pairs. Adsorption is improved for ionic PMOCs on AC carrying sites of the same charge (positive or negative) at increased electrolyte concentration, while not affected for neutral PMOCs unless strong electron donor-acceptor yet weak non-Coulombic interactions exist. Our findings will allow for better design and targeted application of activated carbon-based sorbents in water treatment facilities.

Predicting adsorption of micropollutants on non-functionalized and functionalized multi-walled carbon nanotubes: Experimental study and LFER modeling

It is of great importance to predict the adsorption of micropollutants onto CNTs, which is not only useful for exploring their potential adsorbent applications, but also helpful for better understanding their fate and risks in aquatic environments. This study experimentally examined the adsorption affinities of thirty-one micropollutants on four multi-walled CNTs (MWCNTs) with different functional groups (non-functionalized, -COOH, -OH, and -NH₂). The properties of each adsorbent were predicted based on the linear free energy relationship (LFER) model. The experimental results showed that MWCNTs-COOH has remarkable adsorption affinities for positively charged compounds (1.996-3.203 log unit), whereas MWCNTs-NH₂ has high adsorption affinities for negatively charged compounds (1.360-3.073 log unit). Regarding neutral compounds, there was no significant difference in adsorption affinities of all types of CNTs. According to modeling results, the adsorption affinity can be accurately predicted using LFER models with R² in the range of 0.81-0.91. Based on the developed models, the adsorption mechanism and contribution of individual intermolecular interactions to the overall adsorption were interpreted. For non-functionalized MWCNTs, molecular interactions induced by molecular volume and H-bonding basicity predominantly contribute to adsorption, whereas for functionalized MWCNTs, the Coulombic interaction due to the charges is an important factor.

Linear free energy relationship models for the retention of partially ionized acid-base compounds in reversed-phase liquid chromatography

The LFER model of Abraham is applied to the retention of the neutral and ionic forms of 94 solutes in a C18 column and 40% v/v acetonitrile/water mobile phase. The results show that polarizability and cavity formation interactions increase retention, whereas dipole and hydrogen bonding interactions favours partition to the mobile phase and thus, they decrease retention. The coefficients of the ionic descriptors measure the effect of the electrostatic interactions and their contribution to partition of the cation or anion between the two mobile and stationary chromatographic phases. A new LFER model for application to the retention of partially dissociated acids and bases is derived averaging the descriptors of the neutral and ionic forms according to their degrees of ionization in the mobile phase. This new LFER model is satisfactorily compared to other literature modified Abraham models for a set of 498 retention data of partially dissociated acids and bases. All tested models require the calculation of the ionization degrees of the compounds at the measuring pH. Calculation of the ionization degrees in the chromatographic mobile phase (i.e. from pH and pK_a in the eluent) give good correlations for all tested models. However, estimation of these ionization degrees from pH - pK_a data in pure water gives biased estimations of the retention of the partially ionized solutes.

An Accurate Quantum-Based Approach to Explicit Solvent Effects: Interfacing the General Effective Fragment Potential Method with Ab Initio Electronic Structure Theory

An interface between ab initio quantum mechanics (QM) methods and the general effective fragment potential (EFP2) method, QM-EFP2, is implemented in which the intermolecular interactions between a QM molecule and EFP fragments consist of Coulomb, polarization, exchange repulsion (exrep), and dispersion components. In order to ensure accuracy in the QM-EFP2 exrep interaction energy, the EFP2-EFP2 spherical Gaussian overlap (SGO) approximation is abandoned and replaced with the exact electron repulsion integrals (ERI) that are evaluated with a direct method to reduce disk usage. A Gaussian damping function for the QM-EFP2 Coulomb component damps both the EFP nuclear and electronic charges. A new overlap damping function has been implemented for the QM-EFP2 dispersion component. The current QM-EFP2 implementation has been benchmarked with the S22 and S66 data sets and demonstrates excellent agreement with symmetry-adapted perturbation theory (SAPT) for component energies and with coupled cluster theory [CCSD(T)] for the total interaction energies. Water clusters of various sizes (up to 256 water molecules) have been tested; it is shown that the QM-EFP2 method has an accuracy that is comparable to that of EFP2-EFP2. It has been shown previously that the accuracy of EFP2-EFP2 intermolecular interactions is comparable to that of second-order perturbation theory (MP2) or better. The implementation of the distributed data interface (DDI) parallelization scheme significantly improves the efficiency of QM-EFP2 calculations. The time to form the QM-EFP2 Fock operator per SCF iteration for water clusters scales linearly with the number EFP basis functions.

H-Bond donor parameters for cations

UV/Vis absorption and NMR spectroscopy titrations have been used to investigate the formation of complexes between cations and neutral H-bond acceptors in organic solvents. Complexes formed by two different H-bond acceptors with fifteen different cations were studied in acetone and in acetonitrile. The effects of water and ion pairing with the counter-anion were found to be negligible in the two polar solvents employed for this study. The data were used to determine self-consistent H-bond donor parameters (α) for a series of organic and inorganic cations; guanidinium, primary, tertiary and quaternary ammonium, imidazolium, methylpyridinium, lithium, sodium, potassium, rubidium and caesium. The results demonstrate the transferability of α parameters for cations between different solvents and different H-bond acceptor partners, allowing reliable prediction of cation recognition properties in different environments. Lithium and protonated nitrogen cations form the most stable complexes, but the α parameter is only 5.0, which is similar to the neutral H-bond donor 3-trifluoromethyl,4-nitrophenol (α = 5.1). Quaternary ammonium is the weakest H-bond donor investigated with an α value of 2.7, which is comparable to an alcohol. The α parameters for alkali metal cations decrease down the group from 5.0 (Li+) to 3.5 (Cs+).

Column Characterization and Selection Systems in Reversed-Phase High-Performance Liquid Chromatography

Reversed-phase high-performance liquid chromatography (RP-HPLC) is the most popular chromatographic mode, accounting for more than 90% of all separations. HPLC itself owes its immense popularity to it being relatively simple and inexpensive, with the equipment being reliable and easy to operate. Due to extensive automation, it can be run virtually unattended with multiple samples at various separation conditions, even by relatively low-skilled personnel. Currently, there are >600 RP-HPLC columns available to end users for purchase, some of which exhibit very large differences in selectivity and production quality. Often, two similar RP-HPLC columns are not equally suitable for the requisite separation, and to date, there is no universal RP-HPLC column covering a variety of analytes. This forces analytical laboratories to keep a multitude of diverse columns. Therefore, column selection is a crucial segment of RP-HPLC method development, especially since sample complexity is constantly increasing. Rationally choosing an appropriate column is complicated. In addition to the differences in the primary intermolecular interactions with analytes of the dispersive (London) type, individual columns can also exhibit a unique character owing to specific polar, hydrogen bond, and electron pair donor-acceptor interactions. They can also vary depending on the type of packing, amount and type of residual silanols, "end-capping", bonding density of ligands, and pore size, among others. Consequently, the chromatographic performance of RP-HPLC systems is often considerably altered depending on the selected column. Although a wide spectrum of knowledge is available on this important subject, there is still a lack of a comprehensive review for an objective comparison and/or selection of chromatographic columns. We aim for this review to be a comprehensive, authoritative, critical, and easily readable monograph of the most relevant publications regarding column selection and characterization in RP-HPLC covering the past four decades. Future perspectives, which involve the integration of state-of-the-art molecular simulations (molecular dynamics or Monte Carlo) with minimal experiments, aimed at nearly "experiment-free" column selection methodology, are proposed.

Unexpected Solvent Effect in Electrocatalytic CO2 to CO Conversion Revealed Using Asymmetric Metalloporphyrins

Rapid and efficient electrochemical CO₂ reduction is an ongoing challenge for the production of sustainable fuels and chemicals. In this work, electrochemical CO₂ reduction is investigated using metalloporphyrin catalysts (metal = Mn, Fe, Co, Ni, Cu) that feature one hydroxyphenyl group, and three other phenyl groups, in the porphyrin heterocycle (5-(2-hydroxyphenyl)-10,15,20-triphenylporphyrin, TPOH). These complexes, which are minimal versions of related complexes bearing up to eight proton relays, were designed to allow more straightforward determination of the role of the 2-hydroxylphenyl functional group. The iron-substituted version of TPOH supports robust reduction of CO₂ in acetonitrile solvent, where carbon monoxide is the only detected product. Addition of weak Brønsted acids (1 M water or 8 mM phenol) gives rise to almost 100-fold enhancement in turnover frequency. Surprisingly, the iron analogue is a poor catalyst when the solvent is changed to dimethylformamide. These results lead to the proposal of a model where the hydroxyphenyl group behaves as a local proton source, a hydrogen bond donor to CO₂-bound intermediates, and a hydrogen bonding partner to Brønsted acids. The observations from this model suggest improvements for existing electrocatalytic CO₂ reduction systems.

Linear solvation energy relationship (LSER) characterization of the normal phase retention mechanism on hypercrosslinked polystyrenes

Linear solvation energy relationships (LSERs) were applied to retention on hypercrosslinked polystyrene on silica (HC-Tol) to elucidate the type and relative importance of molecular interactions between model solutes and the HC-Tol stationary phase. Classical amino phase and another hypercrosslinked phase (5-HGN) were used as reference columns. On both the HC-Tol and amino, polar interactions predominate and contribute to retention. Solute volume V has no impact on retention on the amino column, while V has a slightly negative influence on retention for the HC-Tol column. The differences in coefficient v between the amino and the HC-Tol columns might explain why the HC-Tol is capable of group-type separations. 5-HGN phase has smaller a and b values compared to HC-Tol, which means that 5-HGN is not as basic or acidic in terms of hydrogen bonds as is HC-Tol. This suggests that the hydrogen bonding character of the HC-Tol phase arises from its silica substrate.

Widely Applicable Hydrofluorination of Alkenes via Bifunctional Activation of Hydrogen Fluoride

Expanding the use of fluorine in pharmaceuticals, agrochemicals and materials requires a widely applicable and more efficient protocol for the preparation of fluorinated compounds. We have developed a new generation nucleophilic fluorination reagent, KHSO4-13HF, HF 68 wt/wt %, that is not only easily handled and inexpensive but also capable of hydrofluorinating diverse, highly functionalized alkenes, including natural products. The high efficiency observed in this reaction hinges on the activation of HF using a highly "acidic" hydrogen bond acceptor.

H-Bond Acceptor Parameters for Anions

UV/vis absorption titrations have been used to investigate the formation of H-bonded complexes between anionic H-bond acceptors (HBAs) and neutral H-bond donors (HBDs) in organic solvents. Complexes formed by three different HBDs with 15 different anions were studied in chloroform and in acetonitrile. The data were used to determine self-consistent HBA parameters (β) for chloride, bromide, iodide, phosphate diester, acetate, benzoate, perrhenate, nitrate, triflimide, perchlorate, hexafluorophosphate, hydrogen sulfate, methyl sulfonate, triflate, and perfluorobutyl sulfonate. The results demonstrate the transferability of H-bond parameters for anions between different solvents and different HBD partners, allowing reliable prediction of anion recognition properties in other scenarios. Carboxylates are the strongest HBAs studied, with β parameters (≈ 15) that are significantly higher than those of neutral organic HBAs, and the non-coordinating anion hexafluorophosphate is the weakest acceptor, with a β parameter comparable to that of pyridine. The effects of ion pairing with the counter-cation were found to be negligible, provided small polar cations were avoided in the less polar solvent (chloroform). There is no correlation between the H-bonding properties of the anions and the pK_a values of the conjugate acids.

Fluorescent and colorimetric molecular recognition probe for hydrogen bond acceptors

We report on the development of a dual molecular recognition probe for hydrogen bond acceptors.

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).

Partitioning of Organic Ions to Muscle Protein: Experimental Data, Modeling, and Implications for in Vivo Distribution of Organic Ions

The in vivo partitioning behavior of ionogenic organic chemicals (IOCs) is of paramount importance for their toxicokinetics and bioaccumulation. Among other proteins, structural proteins including muscle proteins could be an important sorption phase for IOCs, because of their high quantity in the human and other animals' body and their polar nature. Binding data for IOCs to structural proteins are, however, severely limited. Therefore, in this study muscle protein-water partition coefficients (KMP/w) of 51 systematically selected organic anions and cations were determined experimentally. A comparison of the measured KMP/w with bovine serum albumin (BSA)-water partition coefficients showed that anionic chemicals sorb more strongly to BSA than to muscle protein (by up to 3.5 orders of magnitude), while cations sorb similarly to both proteins. Sorption isotherms of selected IOCs to muscle protein are linear (i.e., KMP/w is concentration independent), and KMP/w is only marginally influenced by pH value and salt concentration. Using the obtained data set of KMP/w a polyparameter linear free energy relationship (PP-LFER) model was established. The derived equation fits the data well (R(2) = 0.89, RMSE = 0.29). Finally, it was demonstrated that the in vitro measured KMP/w values of this study have the potential to be used to evaluate tissue-plasma partitioning of IOCs in vivo.

Equilibrium Sorption of Structurally Diverse Organic Ions to Bovine Serum Albumin

Reliable partitioning data are essential for assessing the bioaccumulation potential and the toxicity of chemicals. In contrast to neutral organic chemicals, the partitioning behavior of ionogenic organic chemicals (IOCs) is still a black box for environmental scientists. Partitioning to serum albumin, the major protein in blood plasma, strongly influences the freely dissolved concentration of many chemicals (including IOCs), which affects their transport and distribution in the body. Because consistent data sets for partitioning of IOCs are rarely available, bovine serum albumin-water partition coefficients (KBSA/w) were measured in this study for 45 anionic and 4 cationic organic chemicals, including various substituted benzoic and naphthoic acids, sulfonates and several pesticides and pharmaceuticals. The results of this study suggest that binding to BSA is substantially influenced by the three-dimensional structure of the chemicals and the position of substitutions on the sorbing molecules. For example, we found a difference of >1.5 log units between isomeric chemicals such as 3,4-dichlorobenzoic acid and 2,6-dichlorobenzoic acid, and 1-naphthoic acid and 2-naphthoic acid. Conventional modeling approaches (e.g., based on octanol-water partition coefficients) poorly predict log KBSA/w of organic ions (R(2) ≤ 0.5), partially because they do not capture the observed steric effects. Hence, alternative modeling strategies will be required for accurate prediction of serum albumin-water partition coefficients of organic ions.

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.

Sorption mechanism and predictive models for removal of cationic organic contaminants by cation exchange resins

Understanding the sorption mechanism of organic contaminants on cation exchange resins (CXRs) will enable application of these resins for the removal of cationic organic compounds from contaminated water. In this study, sorption of a diverse set of 12 organic cations and 8 neutral aromatic solutes on two polystyrene CXRs, MN500 and Amberlite 200, was examined. MN500 showed higher sorbed concentrations due to its microporous structure. The sorbed concentrations followed the same trend of aromatic cations > aliphatic cations > neutral solutes for both resins. Generally, solute-solvent interactions, nonpolar moiety of the solutes, and resin matrix can affect selectivity of the cations. Sorbed concentrations of the neutral compounds were significantly less than those of the cations, indicating a combined effect of electrostatic and nonelectrostatic interactions. By conducting multiple linear regression between Gibbs free energy of sorption and Abraham descriptors for all 20 compounds, polarity/polarizability (S), H-bond acidity (A), induced dipole (E), and electrostatic (J(+)) interactions were found to be involved in the sorption of the cations by the resins. After converting the aqueous sorption isotherms to sorption from the ideal gas-phase by water-wet resins, a more significant effect of J(+) was observed. Predictive models were then developed based on the linear regressions and validated by accurately estimating the sorption of different test set compounds with a root-mean-square error range of 0.91-1.1 and 0.76-0.85 for MN500 and Amberlite 200, respectively. The models also accurately predicted sorption behavior of aniline and imidazole between pH 3 and 10.

Understanding and modeling removal of anionic organic contaminants (AOCs) by anion exchange resins

Ionic organic contaminants (OCs) are a growing concern for water treatment and the environment and are removed inefficiently by many existing technologies. This study examined removal of anionic OCs by anion exchange resins (AXRs) as a promising alternative. Results indicate that two polystyrene AXRs (IRA910 and IRA96) have higher sorption capacities and selectivity than a polyacrylate resin (A860). For the polystyrene resins, selectivity follows: phenolates ≥ aromatic dicarboxylates > aromatic monocarboxylates > benzenesulfonate > aliphatic carboxylates. This trend can be explained based on hydration energy, the number of exchange groups, and aromaticity and hydrophobicity of the nonpolar moiety (NPM) of the anions. For A860, selectivity only varies within a narrow range (0.13-1.64). Despite the importance of the NPM of the anions, neutral solutes were sorbed much less, indicating synergistic combinations of electrostatic and nonelectrostatic interactions in the overall sorption. By conducting multiple linear regression between Abraham's descriptors and nature log of selectivity, induced dipole-related interactions and electrostatic interactions were found to be the most important interaction forces for sorption of the anions, while solute H-bond basicity has a negative effect. A predictive model was then developed for carboxylates and phenolates based on the poly parameter linear free energy relationships established for a diverse range of 16 anions and 5 neutral solutes, and was validated by accurate prediction of sorption of five test solutes within a wide range of equilibrium concentrations and that of benzoate at different pH.

Structural adaptations of proteins to different biological membranes

To gain insight into adaptations of proteins to their membranes, intrinsic hydrophobic thicknesses, distributions of different chemical groups and profiles of hydrogen-bonding capacities (α and β) and the dipolarity/polarizability parameter (π*) were calculated for lipid-facing surfaces of 460 integral α-helical, β-barrel and peripheral proteins from eight types of biomembranes. For comparison, polarity profiles were also calculated for ten artificial lipid bilayers that have been previously studied by neutron and X-ray scattering. Estimated hydrophobic thicknesses are 30-31Å for proteins from endoplasmic reticulum, thylakoid, and various bacterial plasma membranes, but differ for proteins from outer bacterial, inner mitochondrial and eukaryotic plasma membranes (23.9, 28.6 and 33.5Å, respectively). Protein and lipid polarity parameters abruptly change in the lipid carbonyl zone that matches the calculated hydrophobic boundaries. Maxima of positively charged protein groups correspond to the location of lipid phosphates at 20-22Å distances from the membrane center. Locations of Tyr atoms coincide with hydrophobic boundaries, while distributions maxima of Trp rings are shifted by 3-4Å toward the membrane center. Distributions of Trp atoms indicate the presence of two 5-8Å-wide midpolar regions with intermediate π* values within the hydrocarbon core, whose size and symmetry depend on the lipid composition of membrane leaflets. Midpolar regions are especially asymmetric in outer bacterial membranes and cell membranes of mesophilic but not hyperthermophilic archaebacteria, indicating the larger width of the central nonpolar region in the later case. In artificial lipid bilayers, midpolar regions are observed up to the level of acyl chain double bonds.

Solvation models and computational prediction of orientations of peptides and proteins in membranes

Membrane-associated peptides and proteins function in the highly heterogeneous environment of the lipid bilayer whose physico-chemical properties change non-monotonically along the bilayer normal. To simulate insertion of peptides and proteins into membranes and correctly reproduce the energetics of this process, an adequate solvation model and physically realistic representation of the lipid bilayer should be employed. We present a brief overview of the existing solvation models and their application for prediction of binding affinities and orientations of proteins in membranes. Particular emphasis is placed on the recently proposed PPM method, the corresponding web server, and the OPM database that were designed for positioning in membranes of integral and peripheral proteins with known three-dimensional structures.

Differential solute gas response in ionic-liquid-based QCM arrays: elucidating design factors responsible for discriminative explosive gas sensing

An eight-sensor array coupling a chemoselective room-temperature ionic liquid (RTIL) with quartz crystal microbalance (QCM) transduction is presented in this work in order to demonstrate the power of this approach in differentiating closely related analytes in sensory devices. The underlying mechanism behind the specific sensory response was explored by (i) studying mass loading and viscoelasticity effects of the sensing layers, predominantly through variation in damping impedance, the combination of which determines the sensitivity; (ii) creation of a solvation model based on Abraham's solvation descriptors which reveals the fact that polarizability and lipophilicity are the main factors influencing the dissolution of gas analytes into the RTILs; and (iii) determination of enthalpy and entropy values for the studied interactions and comparison via a simulation model, which is also effective for pattern discrimination, in order to establish a foundation for the analytical scientist as well as inspiration for synthetic pathways and innovative research into next-generation sensory approaches. The reported sensors displayed an excellent sensitivity with detection limit of <0.2%, fast response and recovery, and a workable temperature range of 27-55 °C and even higher. Linear discriminant analysis (LDA) showed a discrimination accuracy of 86-92% for nitromethane and 1-ethyl-2-nitrobenzene, 71% for different mixtures of nitromethane, and 100% for these analytes when thermodynamic parameters were used as input data. We envisage applications to detecting other nitroaromatics and security-related gas targets, and high-temperature or real-time situations where manual access is restricted, opening up new horizons in chemical sensing.

Anisotropic solvent model of the lipid bilayer. 1. Parameterization of long-range electrostatics and first solvation shell effects

A new implicit solvation model was developed for calculating free energies of transfer of molecules from water to any solvent with defined bulk properties. The transfer energy was calculated as a sum of the first solvation shell energy and the long-range electrostatic contribution. The first term was proportional to solvent accessible surface area and solvation parameters (σ(i)) for different atom types. The electrostatic term was computed as a product of group dipole moments and dipolar solvation parameter (η) for neutral molecules or using a modified Born equation for ions. The regression coefficients in linear dependencies of solvation parameters σ(i) and η on dielectric constant, solvatochromic polarizability parameter π*, and hydrogen-bonding donor and acceptor capacities of solvents were optimized using 1269 experimental transfer energies from 19 organic solvents to water. The root-mean-square errors for neutral compounds and ions were 0.82 and 1.61 kcal/mol, respectively. Quantification of energy components demonstrates the dominant roles of hydrophobic effect for nonpolar atoms and of hydrogen-bonding for polar atoms. The estimated first solvation shell energy outweighs the long-range electrostatics for most compounds including ions. The simplicity and computational efficiency of the model allows its application for modeling of macromolecules in anisotropic environments, such as biological membranes.

Anisotropic solvent model of the lipid bilayer. 2. Energetics of insertion of small molecules, peptides, and proteins in membranes

A new computational approach to calculating binding energies and spatial positions of small molecules, peptides, and proteins in the lipid bilayer has been developed. The method combines an anisotropic solvent representation of the lipid bilayer and universal solvation model, which predicts transfer energies of molecules from water to an arbitrary medium with defined polarity properties. The universal solvation model accounts for hydrophobic, van der Waals, hydrogen-bonding, and electrostatic solute-solvent interactions. The lipid bilayer is represented as a fluid anisotropic environment described by profiles of dielectric constant (ε), solvatochromic dipolarity parameter (π*), and hydrogen bonding acidity and basicity parameters (α and β). The polarity profiles were calculated using published distributions of quasi-molecular segments of lipids determined by neutron and X-ray scattering for DOPC bilayer and spin-labeling data that define concentration of water in the lipid acyl chain region. The model also accounts for the preferential solvation of charges and polar groups by water and includes the effect of the hydrophobic mismatch for transmembrane proteins. The method was tested on calculations of binding energies and preferential positions in membranes for small-molecules, peptides and peripheral membrane proteins that have been experimentally studied. The new theoretical approach was implemented in a new version (2.0) of our PPM program and applied for the large-scale calculations of spatial positions in membranes of more than 1000 peripheral and integral proteins. The results of calculations are deposited in the updated OPM database ( http://opm.phar.umich.edu ).