The effect of ionized species on microsomal binding

Adsorptive removal of micropollutants by natural and faujasite zeolites: Structural effect of micropollutants on adsorption

To effectively characterize natural zeolite powder (ZP) and faujasite zeolite (FAU) as adsorbents to remove a wide variety of organic micropollutants, quantitative structure-activity relationship (QSAR) models for the adsorption of zeolites were developed. For this purpose, batch isotherms were performed to measure the adsorption affinity (Kd) between zeolite and organic micropollutants, and the measured Kd values were used as a dependent variable in the QSAR modeling. In the modeling, the concept of a linear free energy relationship (LFER) was employed and used either empirically measured or in silico calculated descriptors. Modeling results based on empirical descriptors showed that log Kd values for ZP could be predicted with R2 = 0.949 and standard error (SE) = 0.137 log units, and for FAU, R2 = 0.895 and SE = 0.144 log units. A test set was used to validate the models developed by the training set. The predictabilities of the models for the test set were R2 = 0.907 and SE = 0.209 log units for ZP and R2 = 0.784 and SE = 0.236 log units for FAU, indicating that the models have reasonable robustness and predictability. Also, we showed that in silico-based descriptors could be applied to the prediction. These findings may help determine the general coverage of ZP and FAU zeolites and identify suitable applications.

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 organic micropollutants on yeast: Batch experiment and modeling

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

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

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

Non-specific binding of compounds in in vitro metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models

Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system.Experimental fraction unbound data (n = 60) were generated in liver microsomes (fu_mic) from five commonly used pre-clinical species (rat, mouse, dog, minipig, monkey) and humans. Unbound fraction in incubations with mouse, rat or human hepatocytes was determined for the same 60 compounds. These data were analysed to determine the relationship between experimentally determined binding in the different matrices and across different species. In hepatocytes there was a good correlation between fraction unbound in human and rat (r²=0.86) or mouse (r²=0.82) hepatocytes. Similar correlations were observed between binding in human liver microsomes and microsomes from rat, mouse, dog, Göttingen minipig or monkey liver microsomes (r² of >0.89, n = 51 - 52 measurements in different species). Physicochemical parameters (logP, pKa and logD) were predicted for all evaluated compounds. In addition, logP and/or logD were measured for a subset of compounds.Binding to human hepatocytes predicted using 5 different methods was compared to the measured data for a set of 59 compounds. The best methods evaluated used measured microsomal binding in human liver microsomes to predict hepatocyte binding. The collated physicochemical data were used to predict the human fu_mic using four different in silico models for a set of 53-60 compounds. The correlation (r²) and root mean square error between predicted and observed microsomal binding was 0.69 & 0.20, 0.47 & 0.23, 0.56 & 0.21 and 0.54 & 0.26 for the Turner-Simcyp, Austin, Hallifax-Houston and Poulin models, respectively. These analyses were extended to include measured literature values for binding in human liver microsomes for a larger set of compounds (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r²=0.67 - 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r²<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro binding should be measured for compounds where more certainty is needed, using appropriately calibrated assays and possibly established weak, moderate, and strong binders as reference compounds to allow comparison across databases.

Adsorption modeling of microcrystalline cellulose for pharmaceutical-based micropollutants

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

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.

Successful and Unsuccessful Prediction of Human Hepatic Clearance for Lead Optimization

Development of new chemical entities is costly, time-consuming, and has a low success rate. Accurate prediction of pharmacokinetic properties is critical to progress compounds with favorable drug-like characteristics in lead optimization. Of particular importance is the prediction of hepatic clearance, which determines drug exposure and contributes to projection of dose, half-life, and bioavailability. The most commonly employed methodology to predict hepatic clearance is termed in vitro to in vivo extrapolation (IVIVE) that involves measuring drug metabolism in vitro, scaling-up this in vitro intrinsic clearance to a prediction of in vivo intrinsic clearance by reconciling the enzymatic content between the incubation and an average human liver, and applying a model of hepatic disposition to account for limitations of protein binding and blood flow to predict in vivo clearance. This manuscript reviews common in vitro techniques used to predict hepatic clearance as well as current challenges and recent theoretical advancements in IVIVE.

Evaluation of a Competitive Equilibrium Dialysis Approach for Assessing the Impact of Protein Binding on Clearance Predictions

Fraction unbound (fu) is an important consideration when characterizing the ADME properties of drug candidates. For highly bound compounds, there can be low confidence in quantifying fu introducing uncertainty in certain parameter estimations. Specifically, predictions of clearance (CL) rely on accurate fu values measured in plasma (fu,p) and microsomes (fu,mic) to scale in vitro intrinsic CL to in vivo CL. However, determining the ratio of fu,p/fu,mic may circumvent the need to measure discrete binding values. The purpose of this study was to evaluate a plasma-to-microsome competitive equilibrium dialysis (cED) method to determine fu,p/fu,mic ratio (fuR) for nine physiochemically-distinct compounds, and to investigate the impact of altering microsomal concentrations on fuR. The values of fuR were comparable to ratios calculated from discretely measured fu,p and fu,mic values. Furthermore, increasing microsomal concentrations increased fuR for basic and neutral compounds. When using fuR values, there was a good in vitro-in vivo correlation (IVIVC) (≤3-fold observed in vivo CL). These results suggest that the cED method used to determine fuR may be an appropriate, alternative IVIVC approach. Application of cED may extend beyond IVIVC of CL to evaluate other parameters such as species differences in protein binding and free tissue to plasma ratios.

Prediction of organic pollutant removal using Corynebacterium glutamicum fermentation waste

The disposal of bio-waste (e.g., Corynebacterium glutamicum) produced by the fermentation industry is a serious problem and has a negative impact on economic returns. Some fermentation waste can be recycled as livestock feed, but much cannot be used. Therefore, other recycling methods must be developed to increase its applications, for example, as an environmentally friendly adsorbent for the removal or recovery of chemicals. To broaden its application as an adsorbent, we carried out comprehensive experimental and theoretical analysis. From the experiments, adsorption affinity values between C. glutamicum and micropollutants were measured, and, based on the experimental values, we developed a predictive model. The experimental results reveal that the degree of adsorption is dependent on the structural properties of the micropollutants. In particular, the adsorbent has remarkable adsorption ability toward cations, whereas anionic and neutral compounds interact weakly with the adsorbent. In addition, we found that adsorption is affected by the sodium chloride concentration. Briefly, an increase in salt concentration increases the adsorption of anions, whereas the opposite behavior is observed for cations. In contrast, the adsorption of neutral compounds was not affected by the presence of salt. The modeling studies revealed that a linear free energy relationship model can be used to predict the adsorption affinity. Based on the developed model, we found that hydrogen-bond basicity, anionic coulombic interactions, and molecular volume are the main contributing factors to the adsorption model. However, to achieve the best predictability (a coefficient of determination (R²) of 0.902), additional parameters, such as the dipolarity/polarizability and dispersive interaction, should be included. This indicates that adsorption is a product of complex interactions.

Consideration of the Unbound Drug Concentration in Enzyme Kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system(s) under investigation. As a consequence, the apparent kinetic parameters, such as K_m or K_i, that are derived can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components which can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

Evidence-based strategies for the characterisation of human drug and chemical glucuronidation in vitro and UDP-glucuronosyltransferase reaction phenotyping

Enzymes of the UDP-glucuronosyltransferase (UGT) superfamily contribute to the elimination of drugs from almost all therapeutic classes. Awareness of the importance of glucuronidation as a drug clearance mechanism along with increased knowledge of the enzymology of drug and chemical metabolism has stimulated interest in the development and application of approaches for the characterisation of human drug glucuronidation in vitro, in particular reaction phenotyping (the fractional contribution of the individual UGT enzymes responsible for the glucuronidation of a given drug), assessment of metabolic stability, and UGT enzyme inhibition by drugs and other xenobiotics. In turn, this has permitted the implementation of in vitro - in vivo extrapolation approaches for the prediction of drug metabolic clearance, intestinal availability, and drug-drug interaction liability, all of which are of considerable importance in pre-clinical drug development. Indeed, regulatory agencies (FDA and EMA) require UGT reaction phenotyping for new chemical entities if glucuronidation accounts for ≥25% of total metabolism. In vitro studies are most commonly performed with recombinant UGT enzymes and human liver microsomes (HLM) as the enzyme sources. Despite the widespread use of in vitro approaches for the characterisation of drug and chemical glucuronidation by HLM and recombinant enzymes, evidence-based guidelines relating to experimental approaches are lacking. Here we present evidence-based strategies for the characterisation of drug and chemical glucuronidation in vitro, and for UGT reaction phenotyping. We anticipate that the strategies will inform practice, encourage development of standardised experimental procedures where feasible, and guide ongoing research in the field.

QSAR modelling for predicting adsorption of neutral, cationic, and anionic pharmaceuticals and other neutral compounds to microalgae Chlorella vulgaris in aquatic environment

Environmental fate or transport of pharmaceutical waste depends on the adsorptive interactions of pharmaceuticals with various environmental phases e.g. soil, sediment, microalgae, and bacteria etc. Therefore, it is important to understand these adsorptive interactions. As part of the study, we studied the adsorptive interaction of 30 chemicals with microalgae, i.e. Chlorella vulgaris, because it is ubiquitous and its surface area occupies a high proportion in aquatic environments. For this study, isotherms between C. vulgaris and 30 micropollutants in neutral and ionic forms (i.e. 15 cations, 5 anions, and 10 neutrals) were experimentally measured, and their adsorptive affinities were then theoretically predicted based on the concept of the linear free energy relationship. For modeling, the dataset was divided into a training set and a test set, where the training set was used for model development and the test set was performed for model validation. This process was repeated ten times. Finally, we suggested one model which has high predictability in R² of 0.96 and standard error (SE) of 0.17 log unit for the training set, R² of 0.818 and SE = 0.217 log unit for the test set, and R² of 0.926 and SE of 0.169 log unit for the total dataset. Moreover, it was found that dispersive force, H-bond basicity, molecular volume, and electrostatic interaction of anion significantly contribute to the model developed based on the entire dataset. Here, dispersive and hydrophobic interactions (proportional to the magnitude of molecular size) are main attractive forces, while the rest cases are repulsive. In addition, it was found that the adsorption property of the surface of C. vulgaris differs from those of Gram negative bacteria Escherichia coli and dissolved organic matters in an aquatic environment.

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

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

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.

Partition of Neutral Molecules and Ions from Water to o-Nitrophenyl Octyl Ether and of Neutral Molecules from the Gas Phase to o-Nitrophenyl Octyl Ether

We have set out an equation for partition of 87 neutral molecules from water to o-nitrophenyl octyl ether, NPOE, an equation for partition of the 87 neutral molecules and 21 ionic species from water to NPOE, and an equation for partition of 87 neutral molecules from the gas phase to NPOE. Comparison with equations for partition into other solvents shows that, as regards partition of neutral (nonelectrolyte) compounds, NPOE would be a good model for 1,2-dichloroethane and for nitrobenzene. In terms of partition of ions and ionic species, NPOE is quite similar to 1,2-dichloroethane and not far away from other aprotic solvents such as nitrobenzene.

Quantitative analysis of adsorptive interactions of ionic and neutral pharmaceuticals and other chemicals with the surface of Escherichia coli cells in aquatic environment

Since Escherichia coli is ubiquitous in nature and has been applied to biological, chemical, and environmental processes, molecular-level understanding of adsorptive interactions between chemicals and the bacterial surface is of great importance. To characterise the adsorption properties of the surface of E. coli cells in aquatic environment, the binding affinities (log K_d) of calibration compounds were experimentally measured, and then based on the values and numerically well-defined molecular interaction forces, i.e. linear free energy relationship (LFER) descriptors, a predictive model was developed. The examined substances are composed of cations, anions, and neutral compounds, and the used LFER descriptors are excess molar refraction (E), dipolarity/polarisability (S), H-bonding acidity (A) and basicity (B), McGowan volume (V), and coulombic interactions of cations (J⁺) and anions (J^-). In experimental results, adsorption of anions on the bacterial surface was not observed, while cations exhibited high affinities. In case of neutral compounds, their low quantities were adsorbed, however whose affinities were mostly lower than those of cations. In a LFER study, it was shown that cationic interaction term has the best correlation in R² of 0.691 and sequential additions of S, A, and V help to increase the prediction accuracy. The LFER model (log K_d = - 0.72-0.79 S + 0.81 A + 0.41 V + 0.85 J⁺) could predict the log K_d in R² of 0.871 and SE of 0.402 log unit, and then to check robustness and predictability of the model, we internally validated it by a leave-one-out cross validation (Q²_LOO) study. As a result, the Q²_LOO value was estimated to be 0.826, which was larger than standard of model acceptability (>0.5).

Intrinsic Clearance Assay Incubational Binding: A Method Comparison

The fraction of unbound drug (fu_inc) in in vitro intrinsic clearance (CL_int) incubation is an important parameter in the pursuit of accurate clearance predictions and is often predicted using algorithms based on drug lipophilicity measures. However, analysis of an AstraZeneca database suggests that simple lipophilicity alone is a relatively poor predictor of fu_inc measured using equilibrium dialysis. He fu_inc value can also be measured directly in CL_int assays using multiple concentrations of hepatocytes or microsomal protein. Since this approach informs of the unbound drug concentration in the assay used to predict in vivo clearance, it should be considered the gold standard method. As a starting point for building better predictive algorithms we aimed to determine if equilibrium dialysis really is an appropriate assay for assessing fu_inc Employing a large number of compounds with a wide range of lipophilicities, experiments were performed to measure fu_inc using rat hepatocytes (RH) and human liver microsomes (HLM) in both assay formats. A high percentage (94% and 93% for HLM and RH, respectively) of the fu_inc values were within 2-fold when the compound distribution coefficient describing the ratio of compound concentration in octanol and pH 7.4 buffer when the test system is at equilibrium (lipophilicity measure) (logD_7.4) values were less than 3.5. However, with logD_7.4 values greater than these, the agreement was considerably worse. Additional experimental data generated indicated that this discrepancy was likely due to failings in the direct method when drug binding is high. Thus, we conclude that unbound CL_int can be indeed calculated indirectly by incorporating equilibrium dialysis data with measured CL_int but that simple lipophilicity descriptors alone may be inadequate for predicting fu_inc.

Solvation descriptors for porphyrins (porphines)

Descriptors for porphyrin show that it is dipolar, a weak hydrogen-bond acid, a moderately strong hydrogen-bond base, and very hydrophobic.

Response to "comment on 'structural determinants of drug partitioning in surrogates of phosphatidylcholine bilayer strata'"

We used the solvatochromic correlation to explain the influence of characteristics of studied compounds on the partition coefficients (P) measured using n-hexadecane (C16) and the novel headgroup surrogate (diacetyl phosphatidylcholine, DAcPC), and compared them with those in other systems, including the C16/water (W) system. The comment analyzes why our correlation for the C16/W system has the standard deviation (SD) higher than that published previously. The main reason is that in our, much smaller, data set the measured P values are complemented by the P values predicted by a reliable, unrelated method. We believe that this approach is acceptable for the aforementioned comparison. We did not use just experimental values, as suggested in the comment, because the solvatochromic correlation, although exhibiting 35% reduction in the SD, was accompanied by a sign change of one of the regression coefficients. The recommended use of special solvatochromic solute characteristics for a few compounds and replacement of a predicted PC16/W value by the experimental value resulted in improved correlations. The observed differences between our correlation and those published in the comment and in a previous article do not affect our main conclusions regarding the solvation of solutes in the surrogates (DAcPC and C16) of intrabilayer strata.

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.

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.

Consideration of the unbound drug concentration in enzyme kinetics

The study of enzyme kinetics in drug metabolism involves assessment of rates of metabolism and inhibitory potencies over a suitable concentration range. In all but the very simplest in vitro system, these drug concentrations can be influenced by a variety of nonspecific binding reservoirs that can reduce the available concentration to the enzyme system under investigation. As a consequence, the apparent kinetic parameters that are derived, such as K m or K i, can deviate from the true values. There are a number of sources of these nonspecific binding depots or barriers, including membrane permeation and partitioning, plasma or serum protein binding, and incubational binding. In the latter case, this includes binding to the assay apparatus, as well as biological depots, depending on the characteristics of the in vitro matrix being used. Given the wide array of subcellular, cellular, and recombinant enzyme systems utilized in drug metabolism, each of these has different components that can influence the free drug concentration. The physicochemical properties of the test compound are also paramount in determining the influential factors in any deviation between true and apparent kinetic behavior. This chapter describes the underlying mechanisms determining the free drug concentration in vitro and how these factors can be accounted for in drug metabolism studies, illustrated with case studies from the literature.

In silico physicochemical parameter predictions

Drug discovery is a complex process with the aim of discovering efficacious molecules where their potency and selectivity are balanced against ADMET properties to set the appropriate dose and dosing interval. The link between physicochemical properties and molecular structure are well established. The subsequent connections between physicochemical properties and a drug's biological behavior provide an indirect link back to structure, facilitating the prediction of a biological property as a consequence of a particular molecular manipulation. Due to this understanding, during early drug discovery in vitro physicochemical property assays are commonly performed to eliminate compounds with properties commensurate with high attrition risks. However, the goal is to accurately predict physicochemical properties to prevent the synthesis of high risk compounds and hence minimize wasted drug discovery efforts. This paper will review the relevance to ADMET behaviors of key physicochemical properties, such as ionization, aqueous solubility, hydrogen bonding strength and hydrophobicity, and the in silico methodology for predicting them.