Predicting Abraham model solvent coefficients

Modeling the Partitioning of Anionic Carboxylic and Perfluoroalkyl Carboxylic and Sulfonic Acids to Octanol and Membrane Lipid

Perfluoroalkyl carboxylic and sulfonic acids (PFCAs and PFSAs, respectively) have low acid dissociation constant values and are, therefore, deprotonated under most experimental and environmental conditions. Hence, the anionic species dominate their partitioning between water and organic phases, including octanol and phospholipid bilayers which are often used as model systems for environmental and biological matrices. However, data for solvent-water (SW) and membrane-water partition coefficients of the anion species are only available for a few per- and polyfluoroalkyl substances (PFAS). In the present study, an equation is derived using a Born-Haber cycle that relates the partition coefficients of the anions to those of the corresponding neutral species. It is shown via a thermodynamic analysis that for carboxylic acids (CAs), PFCAs, and PFSAs, the log of the solvent-water partition coefficient of the anion, log KSW (A- ), is linearly related to the log of the solvent-water partition coefficient of the neutral acid, log KSW (HA), with a unity slope and a solvent-dependent but solute-independent intercept within a PFAS (or CA) family. This finding provides a method for estimating the partition coefficients of PFCAs and PFSAs anions using the partition coefficients of the neutral species, which can be reliably predicted using quantum chemical methods. In addition, we have found that the neutral octanol-water partition coefficient, log KOW , is linearly correlated to the neutral membrane-water partition coefficient, log KMW ; therefore, log KOW , being a much easier property to estimate and/or measure, can be used to predict the neutral log KMW . Application of this approach to KOW and KMW for PFCAs and PFSAs demonstrates the utility of this methodology for evaluating reported experimental data and extending anion property data for chain lengths that are unavailable. Environ Toxicol Chem 2023;42:2317-2328. © 2023 SETAC.

Isomer-Specific Solvatochromic and Molecular Rotor Properties of ESIPT-Active Push-Pull Fluorescent Chalcone Dyes

Aromatic chromophores possessing intramolecular hydrogen-bonds that can undergo excited-state intramolecular proton transfer (ESIPT) are critical tools for chemosensing/biosensing applications because they create large Stokes-shifted fluorescence with no overlap with the absorption spectrum to limit back-ground interferences. Classic ESIPT-active fluorophores, such as the 2-(2'-hydroxyphenyl) benzazole (HBX) series (X = NH, O, S), favor a ground-state (GS) enol (E) form that undergoes ESIPT to afford an excited-state (ES) keto (K) tautomer that generates red-shifted fluorescence. Herein, we have attached the HBX moiety to 6-methoxy-indanone (6MI) to create isomeric (ortho and para) ESIPT-active chalcone dyes and have characterized their photophysical properties in polar protic solvents (MeOH and glycerol (Gly)/MeOH mixtures) and a nonpolar aprotic (1,4-dioxane) solvent for comparison. The chalcones favor a GS E structure, which undergoes ESIPT in MeOH, Gly/MeOH mixtures, and dioxane to exclusively afford K emission with large Stokes shifts. The o-isomers possess expanded π-conjugation compared to their p-isomer counterparts, which diminishes their tendency to generate twisted intramolecular charge transfer (TICT) states. Consequently, the o-isomers have greater quantum yields and lack molecular rotor (MR) character with little K emission response to increased solvent viscosity. However, they possess strong positive solvatochromism, displaying significant blue wavelength shifts coupled with turn-on K emission in moving from polar protic MeOH to nonpolar dioxane. In contrast, the p-isomers display MR character with turn-on K emission in 75:25 Gly/MeOH compared to their emission in MeOH (up to 14-fold) due to a strong tendency for TICT. Mechanistic insight into the observed isomer-specific photophysical properties of the ESIPT-active chalcones was obtained through density functional theory (DFT) calculations. Implications for DNA biosensing applications are discussed.

Multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) model to predict local and systemic exposure of drug products applied on skin

Physiologically-based pharmacokinetic models combine knowledge about physiology, drug product properties, such as physicochemical parameters, absorption, distribution, metabolism, excretion characteristics, formulation attributes, and trial design or dosing regimen to mechanistically simulate drug pharmacokinetics (PK). The current work describes the development of a multiphase, multilayer mechanistic dermal absorption (MPML MechDermA) model within the Simcyp Simulator capable of simulating uptake and permeation of drugs through human skin following application of drug products to the skin. The model was designed to account for formulation characteristics as well as body site- and sex- population variability to predict local and systemic bioavailability. The present report outlines the structure and assumptions of the MPML MechDermA model and includes results from simulations comparing absorption at multiple body sites for two compounds, caffeine and benzoic acid, formulated as solutions. Finally, a model of the Feldene (piroxicam) topical gel, 0.5% was developed and assessed for its ability to predict both plasma and local skin concentrations when compared to in vivo PK data.

QSPRs for Predicting Equilibrium Partitioning in Solvent–Air Systems from the Chemical Structures of Solutes and Solvents

Poly-parameter Linear Free Energy Relationships (PPLFERs) based on the Abraham solvation model are a useful tool for predicting and interpreting equilibrium partitioning of solutes in solvent systems. The focus of this work is neutral organic solutes partitioning in neutral organic liquid solvent-air systems. This is a follow-up to previous work (Brown, 2021) which developed predictive empirical correlations between solute descriptors and system parameters, allowing system parameters to be predicted from the solute descriptors of the solvent. A database of solute descriptors, and a database of system parameters supplemented by empirical predictions, form the basis for the development of new Quantitative Structure Property Relationships (QSPRs). A total of 11 QSPRs have been developed for the E, S, A, B and L solute descriptors, and the s, a, b, v, l, and c system parameters. The QSPRs were developed using a group-contribution method referred to as Iterative Fragment Selection. The method includes robust internal and external model validation and a well-defined Applicability Domain, including estimates of prediction uncertainty. System parameters can also be predicted by combining the solute descriptor QSPRs and the empirical correlations. The predictive power of PPLFERs applied using different combinations of experimental data, empirical correlations, and QSPRs are externally validated by predicting partition ratios between solvents and air. The uncertainty for predicting the log10KSA of diverse solutes in diverse solvents using only the new QSPRs and empirical correlations is estimated to be one log10 unit or less.

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain while the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20,253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the three models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software packages, and source code.

In silico predictions of absorption of MDI substances after dermal or inhalation exposures to support a category based read-across assessment

Methylenediphenyl diisocyanate (MDI) substances used polyurethane production can range from their simplest monomeric forms (e.g., 4,4'-MDI) to mixtures of the monomers with various homologues, homopolymer, and prepolymer derivatives. The relative dermal or inhalation absorption of 39 constituents of these substances in human were predicted using the GastroPlus® program. Predicted dermal uptake and absorption of the three MDI monomers from an acetone vehicle was 84-86% and 1.4-1.5%, respectively, with lower uptake and absorption predicted for the higher MW analogs. Lower absorption was predicted from exposures in a more lipophilic vehicle (1-octanol). Modeled inhalation exposures afforded the highest pulmonary absorption for the MDI monomers (38-54%), with 3-27% for the MW range of 381-751, and <0.1% for the remaining, higher MW derivatives. Predicted oral absorption, representing mucociliary transport, ranged from 5 to 10% for the MDI monomers, 10-25% for constituents of MW 381-751, and ≤3% for constituents with MW > 900. These in silico evaluations should be useful in category-based, worst-case, Read-Across assessments for MDI monomers and modified MDI substances for potential systemic effects. Predictions of appreciable mucociliary transport may also be useful to address data gaps in oral toxicity testing for this category of compounds.

Experimental and Theoretical Study on Theobromine Solubility Enhancement in Binary Aqueous Solutions and Ternary Designed Solvents

The solubility of theobromine was studied both experimentally and theoretically. The solubility was determined spectrophotometrically at 25 °C in neat organic solvents, aqueous binary mixtures, Natural Deep Eutectic Solvents (NADES) and ternary NADES mixtures with water. It was found that addition of water in unimolar proportions with some organic solvents increases theobromine solubility compared to neat solvents. Additionally, using NADES results in a solubility increase of the studied compound not only in relation to water but also DMSO. The addition of water (0.2 molar fraction) to NADES is responsible for an even larger increase of solubility. The measured solubilities were interpreted in terms of three theoretical frameworks. The first one-belonging to the set of data reduction techniques-proved to be very efficient in quantitative back-computations of excess solubility of theobromine in all studied systems. The default approach utilizing the well-recognized COSMO-RS (Conductor-like Screening Model for Real Solvents) framework offered at most a qualitative solubility description. The extended search for possible contacts provided evidence for the existence of many intermolecular complexes that alter the electron density of the solute molecule, thus influencing solubility computations. Taking into account such intermolecular contacts by using the COSMO-RS-DARE (Conductor-like Screening Model for Realistic Solvation-Dimerization, Aggregation, and Reaction Extension) framework seriously increased the accuracy of solubility computations.

Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning

Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally studied binary systems, the one containing DMSO with water in unimolar proportions was found to be the most effective in theophylline dissolution. Likewise, for NADES, the addition of water (0.2 molar fraction) resulted in increased solubility compared to pure eutectics, with the highest solubilisation potential offered by the composition of choline chloride with glycerol. The ensemble of Statistica Automated Neural Networks (SANNs) developed using intermolecular interactions in pure systems has been found to be a very accurate model for solubility computations. This machine learning protocol was also applied as an extensive screening for potential solvents with higher solubility of theophylline. Such solvents were identified in all three subgroups, including neat solvents, binary mixtures and ternary NADES systems. Some methodological considerations of SANNs applications for future modelling were also provided. Although the developed protocol is focused exclusively on theophylline solubility, it also has general importance and can be used for the development of predictive models adequate for solvent screening of other compounds in a variety of systems. Formulation of such a model offers rational guidance for the selection of proper candidates as solubilisers in the designed solvents screening.

Automated Quantification of Hydroxyl Reactivities: Prediction of Glycosylation Reactions

The stereoselectivity and yield in glycosylation reactions are paramount but unpredictable. We have developed a database of acceptor nucleophilic constants (Aka) to quantify the nucleophilicity of hydroxyl groups in glycosylation influenced by the steric, electronic and structural effects, providing a connection between experiments and computer algorithms. The subtle reactivity differences among the hydroxyl groups on various carbohydrate molecules can be defined by Aka, which is easily accessible by a simple and convenient automation system to assure high reproducibility and accuracy. A diverse range of glycosylation donors and acceptors with well-defined reactivity and promoters were organized and processed by the designed software program "GlycoComputer" for prediction of glycosylation reactions without involving sophisticated computational processing. The importance of Aka was further verified by random forest algorithm, and the applicability was tested by the synthesis of a Lewis A skeleton to show that the stereoselectivity and yield can be accurately estimated.

Structural Relationship Study of Octanol-Water Partition Coefficient of Some Sulfa Drugs Using GA-MLR and GA-ANN Methods

AIM AND OBJECTIVE

Sulfonamides (sulfa drugs) are compounds with a wide range of biological activities and they are the basis of several groups of drugs. Quantitative Structure-Property Relationship (QSPR) models are derived to predict the logarithm of water/ 1-octanol partition coefficients (logP) of sulfa drugs.

MATERIALS AND METHODS

A data set of 43 sulfa drugs was randomly divided into 3 groups: training, test and validation sets consisting of 70%, 15% and 15% of data point, respectively. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm - Multiple Linear Regressions (GA-MLR) and genetic algorithm -artificial neural network (GAANN) were employed to design the QSPR models. The possible molecular geometries of sulfa drugs were optimized at B3LYP/6-31G* level with Gaussian 98 software. The molecular descriptors derived from the Dragon software were used to build a predictive model for prediction logP of mentioned compounds. The Genetic Algorithm (GA) method was applied to select the most relevant molecular descriptors.

RESULTS

The R2 and MSE values of the MLR model were calculated to be 0.312 and 5.074 respectively. R2 coefficients were 0.9869, 0.9944 and 0.9601for the training, test and validation sets of the ANN model, respectively.

CONCLUSION

Comparison of the results revealed that the application the GA-ANN method gave better results than GA-MLR method.

Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors

The present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, different molecular symmetry, and spatial orientation. Methods of chemometrics can usefully be used to extract and explore accurately the information contained in such data. In this order, advanced fuzzy divisive hierarchical-clustering methods were efficiently applied in the present study of a large group of solvents using specific descriptors. The fuzzy divisive hierarchical associative-clustering algorithm provides not only a fuzzy partition of the solvents investigated, but also a fuzzy partition of descriptors considered. In this way, it is possible to identify the most specific descriptors (in terms of higher, smallest, or intermediate values) to each fuzzy partition (group) of solvents. Additionally, the partitioning performed could be interpreted with respect to the molecular symmetry. The chemometric approach used for this goal is fuzzy c-means method being a semi-supervised clustering procedure. The advantage of such a clustering process is the opportunity to achieve separation of the solvents into similarity patterns with a certain degree of membership of each solvent to a certain pattern, as well as to consider possible membership of the same object (solvent) in another cluster. Partitioning based on a hybrid approach of the theoretical molecular descriptors and experimentally obtained ones permits a more straightforward separation into groups of similarity and acceptable interpretation. It was shown that an important link between objects’ groups of similarity and similarity groups of variables is achieved. Ten classes of solvents are interpreted depending on their specific descriptors, as one of the classes includes a single object and could be interpreted as an outlier. Setting the results of this research into broader perspective, it has been shown that the fuzzy clustering approach provides a useful tool for partitioning by the variables related to the main physicochemical properties of the solvents. It gets possible to offer a simple guide for solvents recognition based on theoretically calculated or experimentally found descriptors related to the physicochemical properties of the solvents.

Prediction of Skin Permeation of Flurbiprofen from Neat Ester Oils and Their O/W Emulsions

Although many in silico models were reported to predict the skin permeation of drugs from aqueous solutions, few studies were founded on the in silico estimation models for the skin permeation of drugs from neat oil formulations and o/w emulsions. In the present study, the cumulative amount of a model lipophilic drug, flurbiprofen (FP), that permeated through skin was determined from 12 different kinds of ester oils (Q_oil) and an in silico model was developed for predicting the skin permeation of FP from these ester oils. Thus, the obtained Q_oil values were well predicted with the FP solubility in the oils (S_oil), the amount of FP uptake into the stratum corneum (SC_oil) and molecular descriptors of dipolarity/polarizability (π₂^H) and molecular density. This model suggests that the thermodynamic activities of FP both in the formulations and skin are the key factors for predicting the skin permeation of FP from the ester oils. In addition, a high linear relationship was observed in the double-logarithm plots between the Q_oil and the cumulative amount of FP permeated through skin from 20% ester oil in water emulsion (Q_emul20%). Furthermore, the skin permeations of FP from 5 and 10% ester oil in water emulsions, Q_emul5% and Q_emul10%, respectively, were also predicted by the horizontal translation of the y-axis intercept of the liner equation for the relation between the Q_oil and Q_emul20%. These prediction methods must be helpful for designing topical oily and/or o/w emulsion formulations having suitable and high skin permeation rate of lipophilic drugs.

Solubility of organic compounds in octanol: Improved predictions based on the geometrical fragment approach

Two new models are introduced to predict the solubility of chemicals in octanol (S_oct), taking advantage of the extensive character of log(S_oct) through a decomposition of molecules into so-called geometrical fragments (GF). They are extensively validated and their compliance with regulatory requirements is demonstrated. The first model requires just a molecular formula as input. Despite an extreme simplicity, it performs as well as an advanced random forest model involving 86 descriptors, with a root mean square error (RMSE) of 0.64 log units for an external test set of 100 molecules. For the second one, which requires the melting point T_m as input, introducing GF descriptors reduces the RMSE from about 0.7 to <0.5 log units, a performance that could previously be obtained only through the use of Abraham descriptors. A script is provided for easy application of the models, taking into account the limits of their applicability domains.

Using water-solvent systems to estimate in vivo blood-tissue partition coefficients

BACKGROUND

Blood-tissue partition coefficients indicate how a chemical will distribute throughout the body and are an important part of any pharmacokinetic study. They can be used to assess potential toxicological effects from exposure to chemicals and the efficacy of potential novel drugs designed to target certain organs or the central nervous system. In vivo measurement of blood-tissue partition coefficients is often complicated, time-consuming, and relatively expensive, so developing in vitro systems that approximate in vivo ones is desirable. We have determined such systems for tissues such as brain, muscle, liver, lung, kidney, heart, skin, and fat.

RESULTS

Several good (p < 0.05) blood-tissue partition coefficient models were developed using a single water-solvent system. These include blood-brain, blood-lung, blood-heart, blood-fat, blood-skin, water-skin, and skin permeation. Many of these partition coefficients have multiple water-solvent systems that can be used as models. Several solvents-methylcyclohexane, 1,9-decadiene, and 2,2,2-trifluoroethanol-were common to multiple models and thus a single measurement can be used to estimate multiple blood-tissue partition coefficients. A few blood-tissue systems require a combination of two water-solvent partition coefficient measurements to model well (p < 0.01), namely: blood-muscle: chloroform and dibutyl ether, blood-liver: N-methyl-2-piperidone and ethanol/water (60:40) volume, and blood-kidney: DMSO and ethanol/water (20:80) volume.

CONCLUSION

In vivo blood-tissue partition coefficients can be easily estimated through water-solvent partition coefficient measurements.Graphical abstract:Predicted blood-brain barrier partition coefficients coloured by measured log BB value.

Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge

Background

1-Octanol solubility is important in a variety of applications involving pharmacology and environmental chemistry. Current models are linear in nature and often require foreknowledge of either melting point or aqueous solubility. Here we extend the range of applicability of 1-octanol solubility models by creating a random forest model that can predict 1-octanol solubilities directly from structure.

Results

We created a random forest model using CDK descriptors that has an out-of-bag (OOB) R² value of 0.66 and an OOB mean squared error of 0.34. The model has been deployed for general use as a Shiny application.

Conclusion

The 1-octanol solubility model provides reasonably accurate predictions of the 1-octanol solubility of organic solutes directly from structure. The model was developed under Open Notebook Science conditions which makes it open, reproducible, and as useful as possible.Graphical abstract

Analysis of partitioning of organic compounds and proteins in aqueous polyethylene glycol-sodium sulfate aqueous two-phase systems in terms of solute-solvent interactions

Partition behavior of nine small organic compounds and six proteins was examined in poly(ethylene glycol)-8000-sodium sulfate aqueous two-phase systems containing 0.5M osmolyte (sorbitol, sucrose, trehalose, TMAO) and poly(ethylene glycol)-10000-sodium sulfate system, all in 0.01M sodium phosphate buffer, pH 6.8. The differences between the solvent properties of the coexisting phases (solvent dipolarity/polarizability, hydrogen bond donor acidity, and hydrogen bond acceptor basicity) were characterized with solvatochromic dyes using the solvatochromic comparison method. Differences between the electrostatic properties of the phases were determined by analysis of partitioning of sodium salts of dinitrophenylated (DNP-) amino acids with aliphatic alkyl side-chain. It was found out that the partition coefficient of all compounds examined (including proteins) may be described in terms of solute-solvent interactions. The results obtained in the study show that solute-solvent interactions of nonionic organic compounds and proteins in polyethylene glycol-sodium sulfate aqueous two-phase system differ from those in polyethylene glycol-dextran system.