Characterization of the retention behavior of organic and pharmaceutical drug molecules on an immobilized artificial membrane column with the Abraham model

Investigation on the adsorption affinity of organic micropollutants on seaweed and its QSAR study

Seaweed, one of the most abundant biomaterials, can be used as a biosorbent to remove organic micropollutants. In order to effectively use seaweed to remove a variety of micropollutants, it is vital to rapidly estimate the adsorption affinity according to the types of micropollutants. Thus, the isothermal adsorption affinities of 31 organic micropollutants in neutral or ionic form on seaweed were measured, and a predictive model using quantitative structure-adsorption relationship (QSAR) modeling was developed. As a result, it was found that the types of micropollutants had a significant effect on the adsorption of seaweed, as expected, and QSAR modeling with a predictability (R²) of 0.854 and a standard error (SE) of 0.27 log units using a training set could be developed. The model's predictability was internally and externally validated using leave-one-out cross validation and a test set. Its predictability for the external validation set was R² = 0.864, SE = 0.171 log units. Using the developed model, we identified the most important driving forces of the adsorption at the molecular level: Coulomb interaction of the anion, molecular volume, and H-bond acceptor and donor, which significantly affect the basic momentum of molecules on the surface of seaweed. Moreover, in silico calculated descriptors were applied to the prediction, and the results revealed reasonable predictability (R² of 0.944 and SE of 0.17 log units). Our approach provides an understanding of the adsorption process of seaweed for organic micropollutants and an efficient prediction method to estimate the adsorption affinities of seaweed and micropollutants in neutral and ionic forms.

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.

Using membrane-water partition coefficients in a critical membrane burden approach to aid the identification of neutral and ionizable chemicals that induce acute toxicity below narcosis levels

The risk assessment of thousands of chemicals used in our society benefits from adequate grouping of chemicals based on the mode and mechanism of toxic action (MoA). We measure the phospholipid membrane-water distribution ratio (D_MLW) using a chromatographic assay (IAM-HPLC) for 121 neutral and ionized organic chemicals and screen other methods to derive D_MLW. We use IAM-HPLC based D_MLW as a chemical property to distinguish between baseline narcosis and specific MoA, for reported acute toxicity endpoints on two separate sets of chemicals. The first set comprised 94 chemicals of US EPA's acute fish toxicity database: 47 categorized as narcosis MoA, 27 with specific MoA, and 20 predominantly ionic chemicals with mostly unknown MoA. The narcosis MoA chemicals clustered around the median narcosis critical membrane burden (CMB_narc) of 140 mmol kg^-1 lipid, with a lower limit of 14 mmol kg^-1 lipid, including all chemicals labelled Narcosis_I and Narcosis_II. This maximum 'toxic ratio' (TR) between CMB_narc and the lower limit narcosis endpoint is thus 10. For 23/28 specific MoA chemicals a TR >10 was derived, indicative of a specific adverse effect pathway related to acute toxicity. For 10/12 cations categorized as "unsure amines", the TR <10 suggests that these affect fish via narcosis MoA. The second set comprised 29 herbicides, including 17 dissociated acids, and evaluated the TR for acute toxic effect concentrations to likely sensitive aquatic plant species (green algae and macrophytes Lemna and Myriophyllum), and non-target animal species (invertebrates and fish). For 21/29 herbicides, a TR >10 indicated a specific toxic mode of action other than narcosis for at least one of these aquatic primary producers. Fish and invertebrate TRs were mostly <10, particularly for neutral herbicides, but for acidic herbicides a TR >10 indicated specific adverse effects in non-target animals. The established critical membrane approach to derive the TR provides for useful contribution to the weight of evidence to bin a chemical as having a narcosis MoA or less likely to have acute toxicity caused by a more specific adverse effect pathway. After proper calibration, the chromatographic assay provides consistent and efficient experimental input for both neutral and ionizable chemicals to this approach.

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.

Affinity of Compounds for Phosphatydylcholine-Based Immobilized Artificial Membrane-A Measure of Their Bioconcentration in Aquatic Organisms

The BCF (bioconcentration factor) of solutes in aquatic organisms is an important parameter because many undesired chemicals enter the ecosystem and affect the wildlife. Chromatographic retention factor log k_w^IAM obtained from immobilized artificial membrane (IAM) HPLC chromatography with buffered, aqueous mobile phases and calculated molecular descriptors obtained for a group of 120 structurally unrelated compounds were used to generate useful models of log BCF. It was established that log k_w^IAM obtained in the conditions described in this study is not sufficient as a sole predictor of bioconcentration. Simple, potentially useful models based on log k_w^IAM and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR), partial least squares (PLS) regression and artificial neural networks (ANN). The models proposed in the study were tested on an external group of 120 compounds and on a group of 40 compounds with known experimental log BCF values. It was established that a relatively simple MLR model containing four independent variables leads to satisfying BCF predictions and is more intuitive than PLS or ANN models.

IAM Chromatographic Models of Skin Permeation

Chromatographic retention factor log k_IAM obtained from IAM HPLC chromatography with buffered aqueous mobile phases and calculated molecular descriptors (surface area—S_a; molar volume—V_M; polar surface area—PSA; count of freely rotable bonds—FRB; H-bond acceptor count—HA; energy of the highest occupied molecular orbital—E_HOMO; energy of the lowest unoccupied orbital—E_LUMO; and polarizability—α) obtained for a group of 160 structurally unrelated compounds were tested in order to generate useful models of solutes’ skin permeability coefficient log K_p. It was established that log k_IAM obtained in the conditions described in this study is not sufficient as a sole predictor of the skin permeability coefficient. Simple put, potentially useful models based on log k_IAM and readily available calculated descriptors, accounting for 85 to 91% of the total variability, were generated using Multiple Linear Regression (MLR).The models proposed in the study were tested on a group of 20 compounds with known experimental log K_p values.

Preparation of covalently bonded liposome capillary column and its application in evaluation of drug membrane permeability

Two liposomes, including 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC) + 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (PE) + cholesterol (Chol) (DSPC/PE/Chol liposomes) and soybean lecithin (SPC) + PE + Chol (SPC/PE/Chol liposomes), were prepared and fixed on the inner wall of capillary column by using the adhesion of polydopamine (PDA) membrane and the cross-linking property of glutaraldehyde (GA). The immobilized liposome capillary column (ILCC) has good repeatability and stability based on the electrophoretic mobility of analyte. A CE method based on the immobilized liposome capillary column chromatography (ILCCC) was successfully developed to study the retention behavior of drugs on ILCC, and the logarithm of retention factor (log k) of neutral and ionic drugs were determined. The results show that the log k measured by the ILCCC based on two liposomes have a good linear fitting (R² = 0.86). Moreover, the linear relationship between ILCCC system and other related research systems (octanol-water system and immobilized artificial membrane (IAM)) was analyzed, and the results indicate that SPC/PE/Chol ILCCC, DSPC/PE/Chol ILCCC and IAM systems have good fitting results, R² values are 0.86 and 0.78, respectively. In addition, the normalization coefficients of ILCCC and IAM systems obtained by the linear free energy relationship (LFER) analysis are close and the d value is small. In short, the ILCCC is a simple and feasible method for studying drug membrane permeability.

Determination of lipid-water partition coefficient of neutral and ionic drugs by liposome electrokinetic chromatography

Profiling of lipid-water partition coefficients (K_L/W ) of drugs is an essential issue during the early stage of drug development. In this study, two liposomes, including 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC) + cholesterol (Chol) (DSPC/Chol liposomes) and soybean lecithin (SPC) + Chol (SPC/Chol liposomes), were prepared for the liposome electrokinetic chromatography (LEKC) analysis, and the logarithm of lipid-water partition coefficients (log K_L/W ) of neutral and ionic drugs were determined based on an iterative method. The log K_L/W values determined by the SPC/Chol or DSPC/Chol liposomes LEKC were linearly fitted, which showed a good fitting coefficient (R² = 0.89). Furthermore, the linear relationship between the data obtained from LEKC system and octanol-water system, immobilized artificial membrane, Caco-2 cell model, and software prediction was analyzed, respectively. Results illustrated that DSPC/Chol liposomes or SPC/Chol liposomes had a good linear relationship with Caco-2 cell model, and R² was 0.81 and 0.72, respectively. Moreover, the linear free energy relationship analysis suggested that the solute volume, hydrogen bond basicity, and J^- were the main descriptors that drove the partition process of solutes in the SPC/Chol or DSPC/Chol LEKC system. In addition, the normalized properties of the SPC/Chol and DSPC/Chol LEKC systems through linear free energy relationship analysis were very close. In short, DSPC/Chol liposomes are more suitable for simulating cell membranes than SPC/Chol liposomes, and the developed LEKC is an effective partitioning model for measuring the log K_L/W of drugs.

Experimental and QSAR studies on adsorptive interaction of anionic nonsteroidal anti-inflammatory drugs with activated charcoal

Adsorptive interactions, namely adsorption capacity (q_m) and affinity (b), between nonsteroidal anti-inflammatory drugs (NSAIDs) in anionic forms and commercial activated charcoal (AC), were estimated by isotherm experiment in a batch, and the properties were modeled based on the concept of quantitative structure-activity relationship (QSAR). Experimental results showed that AC had a high q_m (0.38-0.67 mmol g^-1) and b (14.03-930.8 L mmol^-1) for the selected NSAIDs. In QSAR modeling, linear free energy relationship (LFER) descriptors of excess molar refraction (E), dipolarity/polarizability (S), and Coulombic interactions of anions (J^-) were highly related to log q_m, and the combination of the three terms could predict log q_m in R² of 0.97 and SE of 0.015 log unit. In the case of b, only single B term showed a good correlation with log b in R² of 0.81. Additionally, the combination of hydrogen-bonding acceptors (HBAs) and molar volume (MV), which are easily calculable parameters, could also derive good predictability in R² = 0.81 and SE = 0.26 log unit. Afterwards, validation of the QSAR models based on the leave-one-out cross-validation (Q²_LOO) method showed that the models were acceptable.

Applications of polyparameter linear free energy relationships in environmental chemistry

Partitioning behavior of organic chemicals has tremendous influences on their environmental distribution, reaction rates, bioaccumulation, and toxic effects. Polyparameter linear free energy relationships (PP-LFERs) have been proven to be useful to characterize the equilibrium partitioning of organic chemicals in various environmental and technical partitioning systems and predict the respective partition coefficients. Over the past decade, PP-LFER solute descriptors for numerous environmentally relevant organic chemicals and system parameters for environmentally important partitioning systems have been determined, extending substantially the applicability of the PP-LFER approaches. However, the information needed for the use of PP-LFERs including descriptors and parameters is scattered over a large number of publications. In this work, we review the state of the art of the PP-LFER approaches in environmental chemical applications. The solute descriptors and system parameters reported in the literature and the availability of their database are summarized, and their calibration and prediction methods are overviewed. We also describe tips and pitfalls associated with the use of the PP-LFER approaches and identify research needs to improve further the usefulness of PP-LFERs for environmental chemistry.

Reversed-phase liquid chromatography with octadecylsilyl, immobilized artificial membrane and cholesterol columns in correlation studies with in silico biological descriptors of newly synthesized antiproliferative and analgesic active compounds

Reversed-phase liquid chromatography (RPLC) with different stationary phases, i.e., octadecylsilyl, immobilized artificial membrane and immobilized cholesterol, was used to study lipophilicity of 56 newly-designed 7,8-dihydroimidazo[2,1-c][1,2,4]triazin-4(6H)-ones and 2,6,7,8-tetrahydroimidazo[2,1-c][1,2,4]triazine-3,4-diones with potential anti-proliferative, anti-metastatic and analgesic activities. Extrapolated retention parameters that correspond to pure buffer as the mobile phase, i.e., logkw values are used as chromatographic lipophilicities. The lipophilic properties of compounds also are characterized by computed logP values and basic pharmacokinetic descriptors calculated in silico with the use of ACD/Percepta software according to Abraham's linear solvation energy relationship. Chromatographic and partitioning parameters are compared with biological descriptors using principal component analysis (PCA), and similarities and dissimilarities between variables and compounds are described. Highly significant, predictive relationships between biological descriptors and chromatographic parameters are obtained. Reversed parabolic relationships, which have very good statistical quality between various biological descriptors, i.e., logKsc, logKp, logBB, and logKhsa, and the logkw values, indicate the advantages of a cholesterol column in comparison with immobilized artificial membrane and octadecylsilyl stationary phase.

Linear Solvation Energy Relationships in the Determination of Specificity and Selectivity of Stationary Phases

The retention of fifty structurally different compounds has been studied using linear solvation energy relationships. Investigations were performed with the use of six various stationary phases with two mobile phases (50/50 % v/v methanol/water and 50/50 % v/v acetonitrile/water). Packing materials were home-made and functionalized with octadecyl, alkylamide, cholesterol, alkyl-phosphate and phenyl molecules. This is the first attempt to compare all of these stationary phases synthesized on the same silica gel batch. Therefore, all of them may be compared in more complex and believable way, than it was performed earlier in former investigations. The phase properties (based on Abraham model) were used to the classification of stationary phases according to their interaction properties. The hydrophilic system properties s, a, b indicate stronger interactions between solute and mobile phase for most of the columns. Both e and v cause greater retention as a consequence of preferable interactions with stationary phase by electron pairs and cavity formation as well as hydrophobic bonds. However, alkyl-phosphate phase has different retention properties, as it was expressed by positive sign of s coefficient. It may be concluded that most important parameters influencing the retention of compounds are volume and hydrogen bond acceptor basicity. The LSER coefficients showed also the dependency on the type of organic modifier used as a mobile phase component.

Database of published retention factors for immobilized artificial membrane HPLC and an assessment of the effect of experimental variability

A database was collated of published experimental logarithmic values for the relative retention factors (log k(IAM)) measured using an immobilized artificial membrane column and high-performance liquid chromatography (IAM HPLC). Log k(IAM) is an alternative measure of hydrophobicity to the octanol/water partition coefficient (log K(OW)). While there are several accepted methods to measure log K(OW), no standardized method exists to determine log k(IAM). The database of collated log k(IAM) values includes 13 key experimental parameters and contains 1,686 values for 555 compounds, which are predominantly polar organic compounds and include drug molecules and surfactants. These compounds are acidic, basic, and neutral and both ionized and un-ionized under the conditions of analysis. The data compiled demonstrated experimental variability for each experimental parameter considered, including column stationary phase, pH, temperature, and mobile phase. Reducing the experimental variability allowed for greater consistency in the datasets.

Capacities of membrane lipids to accumulate neutral organic chemicals

Lipids have been considered as the predominant components for bioaccumulation of organic chemicals. However, differences in accumulation properties between different types of lipid (e.g., storage and membrane lipids) have rarely been considered. Moreover, in view of toxic effects on organisms, chemical accumulation specifically in biological membranes is of particular importance. In this review article, partition coefficients of 240 neutral organic compounds between liposomes (phospholipid membrane vesicles) and water (K(lipw)), reported in the literature or measured additionally for this work, were evaluated. Values of log K(lipw) and log K(ow) (octanol-water partition coefficients) differ by 0.4 on average. Polyparameter linear free energy relationships (PP-LFERs) can describe the log K(lipw) data even better (standard deviations = 0.28-0.31) than the log K(ow) model. Recent experimental data for highly hydrophobic compounds fit well to the PP-LFERs and do not indicate the existence of a previously postulated "hydrophobicity cutoff". Predictive approaches based only on the molecular structure (KOWWIN, SPARC, COSMOthermX, COSMOmic) were also evaluated for K(lipw) prediction. The PP-LFERs revealed that partition coefficients into membrane lipids can be two log units higher than those into storage lipids for H-bond donor compounds, suggesting that distinguishing between the two lipids is necessary to account for the bioaccumulation of these compounds, and that tissues rich in membrane lipids (e.g., kidneys, liver) instead of fat tissue can be the primary phase for accumulation.