Abraham Solvation Parameter Research Articles

Application of Abraham's solvation parameter model to extractables and leachables studies in pharmaceutical and medical device industries: A tutorial

The Abraham's solvation parameter model is an important tool to model and predict distribution properties of compounds in numerous partitioning systems in chemistry, environmental chemistry, medicine, and biology. The evaluation of the distribution properties in extractables and leachables (E&L) studies in pharmaceutical and medical device industries is another major application of this model. This tutorial is aimed at illustrating the applications of the model in E&L studies. Specific examples of the application illustrated in this tutorial include: (a) establishment of equivalent or similar solvents; (b) determination of polarity of solvents, biological tissues, and materials; (c) development of drug product simulating solvents; (d) understanding solvent extraction power for a material; (e) selection of solvent and standards in pretreatment of extraction samples; and (f) chromatography retention prediction for E&L.

Thermodynamic properties of TPAC-EG deep eutectic solvents: A study based on the IGC method with molecular simulations

The study of thermodynamic properties and intermolecular interactions in deep eutectic solvents (DES) is an important innovative step in the development of DES. This research focuses on the synthesis of DES using a 1:2 M ratio of tetrapropylammonium chloride and ethylene glycol. Inverse gas chromatography (IGC) techniques are used to investigate DES of thermodynamics in the temperature range of 303.15–333.15 K. Additionally, molecular dynamics (MD) simulations and density functional theory (DFT) are used to explore intermolecular interactions in DES at both molecular and atomic levels. The research findings yield crucial insights, including the successful determination of infinite dilution activity coefficients, Flory-Huggin’s interaction parameters, solubility parameters, and Abraham solvation parameters of DES through IGC techniques. A careful study of the temperature-dependent trends in these parameters is provided. Analyses of structural properties, including radial distribution functions, hydrogen bond counts, and thermal fluctuation indices, reveal that, at experiments temperatures, the amount and nature of intermolecular hydrogen bonds in binary mixtures are the main factors influencing the thermodynamic properties of DES. Finally, based on DFT calculations, a quantitative assessment of the molecular interaction mechanisms of DES at the atomic level confirms the predominant role of hydrogen bonding in DES formation, with multicenter hydrogen bonds characterized by stronger hydrogen bond strengths emerging as the primary type.

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

Determination of experimental solute descriptor values for methyl 3-nitrobenzoate by liquid–liquid partitioning and gas chromatography

ABSTRACT In this study, methyl 3-nitrobenzoate, a popular compound used to protect crops and in the textile industry, was studied using liquid–liquid partitioning and gas chromatography and the Abraham solvation parameter descriptor values were determined for potential applications in QSPR. The solver algorithm in Excel was used to determine the descriptor values for methyl 3-nitrobenzoate, resulting in E = 1.0616, S = 1.4170, A = 0.00, L = 6.1354, B = 0.6198, and V = 1.2468 with a standard deviation of 0.076. In the situations when toxicity and exposure assessment data are scarce, the application of QSPR models, such as the Abraham solvation parameter model, can aid in the estimation of the environmental impact of chemicals. With the objective to facilitate physicochemical property estimations of methyl 3-nitrobenzoate widely used in the textile and agricultural industries, this study offers experimental solute descriptor values of the Abrham’s solvation parameter model.

Neural Network Models for Predicting Solubility and Metabolism Class of Drugs in the Biopharmaceutics Drug Disposition Classification System (BDDCS).

The biopharmaceutics drug disposition classification system (BDDCS) categorizes drugs into four classes on the basis of their solubility and metabolism. This framework allows for the study of the pharmacokinetics of transporters and enzymatic metabolization on biopharmaceuticals, as well as drug-drug interactions in the body. The objective of the present study was to develop computational models by neural network models and structural parameters and physicochemical properties to estimate the class of a drug in the BDDCS system. In this study, deep learning methods were utilized to explore the potential of artificial and convolutional neural networks (ANNs and CNNs) in predicting the BDDCS class of 721 substances. The structural parameters and physicochemical properties [Abraham solvation parameters, octanol-water partition (log P) and over the pH range 1-7.5 (log D), number of rotatable bonds, hydrogen bond acceptor numbers, as well as hydrogen bond donor count] are calculated with various software. These compounds were then split into a training set consisting of 602 molecules and a test set of 119 compounds to validate the models. The results of this study showed that neural network models using applied parameters of the drug, i.e., log D and Abraham solvation parameters, are able to predict the class of solubility and metabolism in the BDDCS system with good accuracy. Neural network models are well equipped to deal with the relations between the structural parameters and physicochemical properties of drugs and BDDCS classes. In addition, log D is a more suitable parameter compared with log P in predicting BDDCS.

Comments on “Solid-liquid equilibrium solubility prediction of sulfanilamide in four binary solvent mixtures by using pure solvents solubility data from 278.15 to 318.15 K with the Abraham solvation parameter model, Yalkowsky Log-Linear and extended log-linear solubility thermodynamic models”

The experimental solubility data of sulfanilamide in binary solvent mixtures at temperature ranging from (278.15 to 318.15) K have carefully been reanalyzed and some additional recommendations were discussed.

Characterization of solute-solvent interactions in liquid chromatography systems: A fast method based on Abraham's linear solvation energy relationships

The Abraham's solvation parameter model, based on linear solvation energy relationships (LSER), allows the accurate characterization of the selectivity of chromatographic systems according to solute-solvent interactions (polarizability, dipolarity, hydrogen bonding, and cavity formation). However, this method, based on multilinear regression analysis, requires the measurement of the retention factors of a considerably high number of compounds, turning it into a time-consuming low throughput method. Simpler methods such as Tanaka's scheme are preferred. In the present work, the Abraham's model is revisited to develop a fast and reliable method, similar to the one proposed by Tanaka, for the characterization of columns employed in reversed-phase liquid chromatography and particularly in hydrophilic interaction liquid chromatography. For this purpose, pairs of compounds are carefully selected in order to have in common all molecular descriptors except for a specific one (for instance, similar molecular volume, dipolarity, polarizability, and hydrogen bonding basicity features, but different hydrogen bonding acidity). Thus, the selectivity factor of a single pair of test compounds can provide information regarding the extent of the dissimilar solute-solvent interactions and their influence on chromatographic retention. The proposed characterization method includes the determination of the column hold-up volume and Abraham's cavity term by means of the injection of four alkyl ketone homologues. Therefore, five chromatographic runs in a reversed-phase column (four pairs of test solutes and a mixture of four homologues) are enough to characterize the selectivity of a chromatographic system. Tanaka's method is also analyzed from the LSER point of view.

Evaluation of the Ability of PAMPA Membranes to Emulate Biological Processes through the Abraham Solvation Parameter Model.

Two parallel artificial membrane permeability assay (PAMPA) systems intended for emulating skin permeability have been characterized through the solvation parameter model of Abraham using multilinear regression analysis. The coefficients of the obtained equations have been compared to the ones already established for other PAMPA membranes using statistical tools. The results indicate that both skin membranes are similar to each other in their physicochemical properties. However, they are different from other PAMPA membranes (e.g., intestinal absorption and blood-brain PAMPAs), mainly in terms of hydrophobicity and hydrogen bonding properties. Next, all PAMPA membranes have been compared to relevant biological processes also characterized through the solvation parameter model. The results highlight that skin-PAMPA membranes are a very good choice to emulate skin permeability.

Surface tension of binary and ternary mixtures mapping with ASP and UNIFAC models based on machine learning

Modeling predictions of surface tension for binary and ternary liquid mixtures is difficult. In this work, we propose a machine learning model to accurately predict the surface tension of binary mixtures of organic solvents-ionic liquids and ternary mixtures of organic solvents-ionic liquids–water and analytically characterize the proposed model. In total, 1593 binary mixture data points and 216 ternary mixture data points were collected to develop the machine learning model. The model was developed by combining machine learning algorithms, UNIFAC (UNIversal quasi-chemical Functional group Activity Coefficient) and ASP (Abraham solvation parameter). UNIFAC parameters are used to describe ionic liquids, and ASP is used to describe organic solvents. The effect of each parameter on the surface tension is characterized by SHAP (SHapley Additive exPlanation). We considered support vector regression, artificial neural network, K nearest neighbor regression, random forest regression, LightGBM (light gradient boosting machine), and CatBoost (categorical boosting) algorithms. The results show that the CatBoost algorithm works best, MAE = 0.3338, RMSE = 0.7565, and R2 = 0.9946. The SHAP results show that the surface tension of the liquid decreases as the volume and surface area of the anion increase. This work not only accurately predicts the surface tension of binary and ternary mixtures, but also provides illuminating insight into the microscopic interactions between physical empirical models and physical and chemical properties.

Correlation of Extractables Transfer from Low-Density Polyethylene into Extraction Solvents with Abraham Solvation Parameter Model and Assessment of Thermodynamic and Kinetic Effects in Achieving Exhaustive Extractions in Chemical Characterization of Medical Devices

Correlation of Extractables Transfer from Low-Density Polyethylene into Extraction Solvents with Abraham Solvation Parameter Model and Assessment of Thermodynamic and Kinetic Effects in Achieving Exhaustive Extractions in Chemical Characterization of Medical Devices

Insights into the Solvation Characteristics of Zwitterionic Deep Eutectic Solvents Using Multiple Polarity Scales

Deep eutectic solvents (DESs) formed by mixing zwitterionic hydrogen bond acceptors (HBAs) with diol- and organic acid-based hydrogen bond donors (HBDs) are popular sustainable solvents. In addition to possessing an amphiphilic character, zwitterionic DESs offer unique solvation properties, unlike those composed of halide-containing HBAs. While all other popular classes of DESs have been investigated, no study has attempted to characterize the solvation characteristics of zwitterionic DESs. In this study, the solvation properties of betaine [Bet]-, sulfobetaine-, and l-carnitine [l-Car]-based DESs were thoroughly investigated for the first time using solvatochromic dye methods and inverse gas chromatography (IGC). The Nile red polarity scale revealed that DESs composed of organic acids were generally more polar than diol-based DESs. Kamlet–Taft solvent parameters revealed that glycerol-based DESs offered exceptionally high hydrogen bond donating ability compared to those composed of 1,4-butane diol and triethylene glycol. The Abraham solvation parameter model revealed hydrogen bond basicity to be the most dominant interaction, and it was generally higher for diol-based HBDs compared to organic acid-based HBDs. Additionally, sulfobetaine HBAs possessing longer alkyl chains generally offered higher dispersive-type interactions and hydrogen bond basicity compared to [Bet] and [l-Car] salts. This is the first study to combine and compare results from solvatochromic dye methods and IGC in an attempt to comprehensively explain and predict the solvation characteristics of zwitterionic DESs.

Abraham Solvation Parameter Model: Revised Predictive Expressions for Solute Transfer into Polydimethylsiloxane Based on Much Larger and Chemically Diverse Datasets

Updated Abraham model correlations are reported for the transfer of organic solutes and inorganic gases to a polydimethylsiloxane coating from both water and the gas phase based on published experimental data for more than 220 different compounds. The derived mathematical expressions back-calculate the observed partitioning behavior to within standard deviations of the residuals of 0.206 and 0.176 log units, respectively.

Linear Free-Energy Relationships and Solvation Thermodynamics: The Thermodynamic Basis of LFER Linearity

The Abraham solvation parameter model, known alternatively as the Linear Solvation Energy Relationships (LSER) model, is widely used in numerous applications in the broader chemical, biochemical, and environmental sector with remarkable success. So far, there is no explanation at the fundamental thermodynamic level for the very linearity of this model. This is, however, essential for the evaluation and exchange of thermodynamic quantities between models and databases. The present work offers such an explanation by combining the equation-of-state solvation thermodynamics with the statistical thermodynamics of hydrogen bonding. It shows what this linearity entails and in what way it is interpreted and handled so far. In addition, it proposes new ways of extending it for estimations over a broad range of external conditions and, eventually, in predicting solvent LFER coefficients from the corresponding molecular descriptors, which are known for thousands of compounds. This would enhance significantly the predictive capacity of the model in practical applications, such as solvent screening, solute partitioning, activity coefficients at infinite dilution, or hydration/solvation energies. Various examples of calculations are reported. A critical discussion of perspectives and challenging emerging issues are also presented.

Linear Solvation–Energy Relationships (LSER) and Equation-of-State Thermodynamics: On the Extraction of Thermodynamic Information from the LSER Database

There is a remarkable wealth of thermodynamic information in freely accessible databases, the LSER database being a classical example. The LSER, or Abraham solvation parameter model, is a very successful predictive tool in a variety of applications in the (bio)chemical and environmental sector. The model and the associated database are very rich in thermodynamic information and information on intermolecular interactions, which, if extracted properly, would be particularly useful in various thermodynamic developments for further applications. Partial Solvation Parameters (PSP), based on equation-of-state thermodynamics, are designed as a versatile tool that would facilitate this extraction of information. The present work explores the possibilities of such an LSER–PSP interconnection and the challenging issues this effort is faced with. The thermodynamic basis of the very linearity of the LSER model is examined, especially, with respect to the contribution of strong specific interactions in the solute/solvent system. This is done by combining the equation-of-state solvation thermodynamics with the statistical thermodynamics of hydrogen bonding. It is verified that there is, indeed, a thermodynamic basis of the LFER linearity. Besides the provenance of the sought linearity, an insight is gained on the thermodynamic character and content of coefficients and terms of the LSER linearity equations. The perspectives from this insight for the further development of LSER and related databases are discussed. The thermodynamic LSER–PSP interconnection is examined as a model for the exchange in information between QSPR-type databases and equation-of-state developments and the associated challenges are examined with representative calculations.

The Solubility of Ethyl Candesartan in Mono Solvents and Investigation of Intermolecular Interactions

In this work, the experimental solubility of ethyl candesartan in the selected solvents within the temperature ranging from 278.15 to 318.15 K was studied. It can be easily found that the solubility of ethyl candesartan increases with the rising temperature in all solvents. The maximum solubility value was obtained in N,N-dimethylformamide (DMF, 7.91 × 10−2), followed by cyclohexanone (2.810 × 10−2), 1,4-dioxanone (2.69 × 10−2), acetone (7.04 × 10−3), ethyl acetate (4.20 × 10−3), n-propanol (3.69 × 10−3), isobutanol (3.38 × 10−3), methanol (3.17 × 10−3), n-butanol (3.03 × 10−3), ethanol (2.83 × 10−3), isopropanol (2.69 × 10−3), and acetonitrile (1.15 × 10−2) at the temperature of 318.15 K. Similar results of solubility sequence from large to small were also obtained in other temperatures. The X-ray diffraction analysis illustrates that the crystalline forms of all samples were consistent, and no crystalline transformation occurred during the dissolution process. In aprotic solvents, except for individual solvents, the solubility data decreases with the decreasing values of hydrogen bond basicity (β) and dipolarity/polarizability (π*). The largest average relative deviation (ARD) data in the modified Apelblat equation is 1.9% and observed in isopropanol; the maximum data in λh equation is 4.3% and found in n-butanol. The results of statistical analysis show that the modified Apelblat equation is the more suitable correlation of experimental data for ethyl candesartan in selected mono solvents at all investigated temperatures. In addition, different parameters were used to quantify the solute–solvent interactions that occurred in the dissolution process including Abraham solvation parameters (APi), Hansen solubility parameters (HPi), and Catalan parameters (CPi).

Exploring three-dimensional space of extractables and leachables in volatility, hydrophobicity, and molecular weight and assessment of roles of gas and liquid chromatographic methods in their comprehensive analysis

The three-dimensional (3D) global space of organic extractables and leachables (E&L) in volatility, hydrophobicity, and molecular weight (MW), covering the pharmaceutical container-closure system, biopharmaceutical single use equipment, and tissue-contacting medical devices, has been quantitatively explored by an extensive collection of E&L compounds from the publicly available sources. The total number of compounds collected is 776, including name, CAS number, MW, and boiling point (BP). The hydrophobicity of these compounds in logPo/w are computed by Abraham solvation parameter model, using compounds’ descriptors generated by an online computational program “RMG: Solvation Tools”. A “global” 3D space is built to represent the physicochemical property limits of E&L quantitatively, envisioned to cover a variety of materials extracted by different solvents. This 3D space also represents the scope and capability requirements of general-purpose chromatography-based methods to profile unknown material samples, if a limited number of analytical methods are used in a routine laboratory analysis. It is concluded from the study that the 3D space corresponding to MW of 1000 Da are: BP up to 857 °C and hydrophobicity in logPo/w between − 2.3–19. Additionally, the roles of temperature programmed GC (TPGC) and reversed-phase liquid chromatographic (RPLC) methods are quantitively and critically examined in the comprehensive analysis of the 3D space, particularly for RPLC methods in retention and resolution requirements, and it is also concluded that the current concept of analytical E&L testing strategies based on volatility needs to be challenged. Finally, this study proposes a semi-empirical method in correlation and prediction of the retention time of TPGC using Abraham solvation parameter model.

Correlation of Surface Tension of Mono-Solvents at Various Temperatures

Surface tension is among the most important factors in chemical and pharmaceutical processes. Modeling the surface tension of solvents at different temperatures helps to optimize the type of solvent and temperature. The surface tension of solvents at different temperatures with their solvation parameters was used in this study to develop a model based on the van’t Hoff equation by multiple linear regression. Abraham solvation parameters, Hansen solubility parameters, and Catalan parameters are among the most discriminating descriptors. The overall MPD of the model was 3.48%, with a minimum and maximum MPD of 0.04% and 11.62%, respectively. The model proposed in this study could be useful for predicting the surface tension of mono-solvents at different temperatures.

Solid-liquid equilibrium solubility prediction of sulfanilamide in four binary solvent mixtures by using pure solvents solubility data from 278.15 to 318.15 K with the Abraham solvation parameter model, Yalkowsky Log-Linear and extended log-linear solubility thermodynamic models

The industrial application of sulfanilamide solubility in binary solvent mixtures is a critical factor in drug development, assessment, as well as future improvement. The current work eliminates the substantial experimental work, as well as the ability to apply improved and upgraded computed models that include solute physicochemical properties and activity coefficients. The predicted solid–liquid equilibrium data of sulfanilamide in acetone containing binary solvent mixtures at temperatures ranging from 278.15 to 318.15 K were calculated by applying the combined version of Jouyban-Acree model with Abraham solute parameter model, combined version of Jouyban-Acree model with Abraham solvation parameter model, combined version of Jouyban-Acree model with Hansen solubility parameter model, the Yalkowsky Log-Linear model, Extended log-linear model. In comparison to previously published binary solvent solubility data, this paper provides predicted binary solubility data using only mono-solvent solubility data as well as from the computed models. From the solubility trained model the %ARD can be ranked as the combined version of combined version of the Jouyban-Acree model with Abraham Solute parameter model (19.47 %) < the Jouyban-Acree model with Hansen solubility parameter model (23.76 %) < the Yalkowsky model (50.54 %) < the combined version of Jouyban-Acree model with Abraham solvation parameter model (68.83 %) < the Extended log-linear model (615.78 %). The combined version of Jouyban-Acree model with Abraham Solute parameter model giving most reliable predicted solubility results compared to other computed models. The most important outcome of this research will be immensely beneficial in calculating binary solubility data utilizing only mono-solvent solubility data as well as from computed models. The binary solubility predictions were calculated from already reported mono-solvent solubility data from experimentally determined, Hildebrand-Scatchard equation-1 and Hildebrand-Scatchard equation-2.

Determination of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids and Deep Eutectic Solvents by Factorization-Machine-Based Neural Networks

Widely known as “green solvents,” ionic liquids (ILs) and deep eutectic solvents (DESs) have been used as substitutes for traditional organic solvents in separation science. To achieve better separations using ILs and DESs, this work aimed to predict the infinite dilution activity coefficients (IDACs) of molecular solutes in these solvents with the state-of-the-art factorization-machine-based neural network (DeepFM). DeepFM combines the benefits of factorization machines and deep neural networks to learn both low-order and high-order interactions among features. The IDAC prediction model was established with 52,372 experimental IDAC datapoints including 260 solvents (252 ILs and 8 DESs) and 112 molecular solutes collected at various temperatures from 288.15 to 428.15 K. Chemical information describing the ILs, DESs, and molecular solutes was included in the IDAC prediction model, including chemical functional groups, molecular weights, and Abraham solvation parameters. The IDAC prediction model showed an improved accuracy compared with alternative models; additionally, DESs were included in the IDAC prediction model for the first time. The model will reduce the energy and resources needed to optimize the selection of ILs and DESs for specific separations, which will promote the development of these green solvents for sustainable chemical processes.

Dielectric constant prediction of pure organic liquids and their mixtures with water based on interpretable machine learning

The thermodynamic properties of mixed-solvent electrolytes are functions of pressure, temperature, and composition (PTC), and are generally considered to be characterized by their dielectric constant. In this work, an interpretable dielectric constant model is proposed based on a machine learning algorithm. The model combines machine learning algorithms, Abraham Solvation Parameters (ASP) and SHapley Additive exPlanations (SHAP) methods to accurately predict the dielectric constants of pure organic liquids and their mixtures with water, the significance of each feature and its impact on the results are explained. The predictions demonstrate extremely low mean square errors, and the effect of each feature on the dielectric constant is clearly characterized. This model provides the ability to accurately predict the dielectric constant for any pure organic liquids and their mixtures with water, and can analyze the sensitivity and influence of each feature to the dielectric constant. Furthermore, this work extends the application of the Abraham solvation parameter to prediction of solution properties. The interpretability of the model will make this a great resource to direct the prediction of physical and chemical properties of materials.