Journal of Solution Chemistry最新文献

About 40% of new chemical entities (NCEs) manufactured by pharmaceutical companies are practically insoluble or poorly soluble in water. Solubility is the main challenge for researchers and formulation scientists. Class II and IV medications are classified as low solubility by the biopharmaceutical classification system (BCS). The solubility of these classes of drugs can be increased to enhance their bioavailability. Different methods are used to improve the solubility of low soluble products, including chemical and physical drug modifications and other techniques such as crystallization, particle size reduction, salt formation, nanonization, micronization, surfactant use, etc. The selection of solubility enhancement techniques depends on the characteristics of the drug, the site of absorption, and the necessary features of the dosage form. An outline of the effects of low water solubility and the primary methods used to improve the solubility of medications with low water solubility are given in this review. How the drug’s solubilization procedure and the biopharmaceutical classification system relate is also considered. This review article’s highlights provide information on several prospective and present modern technologies created to improve the solubility of poorly soluble drugs.

Apixaban (APX) is an oral anticoagulant that selectively inhibits Factor Xa, effectively preventing and treating thromboembolic disorders. However, its low water solubility and limited oral bioavailability restrict its therapeutic efficacy. Encapsulating APX in a host such as cyclodextrins (CD) might enhance its solubility and subsequently improve its bioavailability. Complexation with methylated βCDs (methyl, dimethyl, trimethyl), as studied via phase solubility diagram (PSDs) indicates a noticeable enhancement in the solubility (~ 3–4fold). The APX–DMβCD (Heptakis(2,6-di-O-methyl)-βCD) complex was prepared with freeze-drying, and its formation was confirmed by differential scanning calorimetry and IR spectroscopy, with the latter indicating the involvement of the amide group of APX in interactions with the host. The ¹H and 2D NMR spectra of APX with DMβCD suggest the formation of an inclusion complex characterized by more than one geometry. Molecular dynamics simulations demonstrate the formation of a stable APX-DMβCD complex in various binding modes. Molecular mechanics Poisson–Boltzmann surface area analysis indicates a preference of the binding mode where the methoxy phenyl is included in the host’s cavity. Furthermore, van der Waals interactions are found to be the predominant forces in stabilizing the complex.

Hypervalent iodine (HVI) reagents are widely used in organic synthesis due to their oxidative versatility, tunable reactivity, and environmentally friendly profile. However, accurately predicting their reactivity, typically quantified by bond dissociation energy (BDE), remains computationally intensive and experimentally demanding. In this work, we propose Inter-HVI, a transparent and high-performing machine learning framework for BDE prediction. Inter-HVI, a well-designed framework, combines molecular 2,809 descriptors from RDKit, Mordred, PyBioMed, CDK, and Avalon/Morgan/MACCS fingerprints. Descriptors with more than 5% missing values were removed to reduce computational cost and prevent potential redundancy that could arise from imputation using mean or median values. After a generous feature selection process, the Inter-HVI model was trained using RuleFit, which remains robust even with a large number of descriptors due to its tree-derived rule structure. Such a structure of Inter-HVI enables the model to focus on the most informative feature interactions while naturally filtering out irrelevant or redundant variables, thus maintaining both accuracy and interoperability. As a result, Inter-HVI achieved top-tier performance in predicting bond dissociation energy, matching the test (text{R}^2) of the benchmark ANN model at 0.960, while improving cross-validation (text{R}^2) (0.931 vs. 0.887), reducing RMSE (3.033 vs. 3.030 kcal·mol⁻¹ (1 kcal = 4.184 kJ) on test, 3.836 vs. 4.690 kcal·mol⁻¹ on cross-validation), and lowering MAE (2.230 vs. 2.276 kcal·mol⁻¹ on test). This demonstrates that Inter-HVI maintains comparable predictive accuracy to advanced deep learning models while offering enhanced interpretability. To enhance interpretability, besides high prediction performance, seven complementary model explanation techniques were employed to uncover the relationships between molecular features and HVI reactivity. In particular, the interpretable rules extracted by RuleFit offer human-readable insights and can guide rational optimization of HVI compounds by modifying key descriptors to achieve desired bond dissociation properties.

This study experimentally measured the liquid–liquid equilibrium (LLE) data for the cyclohexanol + phenol + water system at three specific temperatures (298.15 K, 308.15 K, and 318.15 K) under standard atmospheric conditions, and these data were subsequently utilized to compute the distribution coefficients (D) and separation factors (S), which served as critical indicators for evaluating cyclohexanol's performance in extracting phenol. Quantitative analysis indicates that for systems with phenol concentrations above 0.0015, cyclohexanol is not only a significantly better extractant than cyclohexanone, but also has stronger temperature adaptability. Additionally, the non-random two liquid model and the universal quasi-chemical model were employed for correlation analysis of the experimental data, yielding binary interaction parameters and root-mean-square deviation (RMSD) values, with the RMSD consistently remaining below 0.32 across all tested temperatures, thereby validating the strong agreement between the model predictions and the experimental data.

A highly concentrated borate solution stable at room temperature is successfully prepared by mixing methylamine and boric acid. The boron concentrations of saturated solutions are strongly dependent on B/N molar ratios, and the highest concentration, 33.4 mol⋅kg⁻¹ as to boron, is achieved at a B/N ratio of 1.55 at 25 °C. A new stable methylammonium borate, whose chemical formula is estimated as CH₃NH₃B₅O₈⋅6H₂O, is precipitated by drying the solution at a B/N ratio of 5.0. The saturation concentration of the salt at the B/N ratio of 5.0 is 1.94 mol⋅kg⁻¹, much lower than that of the solution at the B/N ratio of 1.55. Interestingly, the highly concentrated solutions at the B/N ratio less than 5.0 form the same solid salt as that obtained at the B/N ratio of 5.0 by drying. The Raman spectra of the solutions suggest that the structures of borate ions change significantly depending on the B/N ratios, which can be related to the high solubility. The methylammonium borate solutions are expected to be widely used as new, highly concentrated ones containing much boron for industrial applications, such as flame retardants, termiticides, and neutron absorbers.

Solvents play a critical role in separation processes by selectively dissolving or extracting specific components from a mixture, enabling their effective separation. The choice of solvent influences the efficiency, selectivity, and energy consumption of the process, making it a key factor in optimizing separation techniques such as distillation, extraction, and crystallization. The present study highlights a development of several machine learning (ML) models to predict the solubility of a solute in a solvent by using their SMILES as inputs. Molecular descriptors of solutes and solvents are obtained from SMILES of the original dataset. The top 5 descriptors are selected based on Pearson’s coefficient for solutes and solvents and are considered as inputs along with temperature. Different ML models are used for solubility prediction including linear models (linear, lasso, and ridge regression models), tree-based models (decision tree, random forest regressor, gradient boost, xgboost models and AdaBoost models) and other models (support vector regressor, k-nearest neighbor). The random forest model performed well with R² = 0.98, RMSE = 0.0121, and MSE = 0.0001 using training dataset, R² = 0.95, RMSE = 0.0266, and MSE = 0.0007 using testing dataset, and R² = 0.97, RMSE = 0.0161, and MSE = 0.0003 with the overall data. The prediction capability of the model is analyzed with respect to different descriptors and with respect to solutes and solvents, and with respect to temperature dependency. The model selected in the present study can be directly used for solvent design in various separation processes.

The present work presents a critical review on the ionization degree of the micelles (α), analyzing its derivation from electrostatics, as well as the influence of different factors and their interpretation. We have considered the effect of the hydrocarbon chain length, the aggregation number, the polar group’s size and hydrolysis, the counterions´ charge and cases when non-ionic surfactants or alcohol molecules are included into ionic micelles. The appropriate interpretation of α depends not only on the nature of the system studied but also on the methodology employed for its determination. We have concluded that the most appropriate denomination for this property is degree of counterion release.

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This study explores the potential use of sodium bentonite (SB) as a carrier for the broad-spectrum antibiotic levofloxacin (LVO), known for its efficacy against bacterial infections such as those caused by Staphylococcus epidermidis (S. epidermidis). Adsorption experiments were conducted under varying conditions of pH, LVO concentration, temperature, SB dosage, and contact time, with UV spectroscopy employed for quantification. The results demonstrated that LVO adsorption onto SB followed pseudo-second-order kinetics and conformed to the Langmuir isotherm model, suggesting monolayer adsorption. Acidic pH conditions significantly enhanced adsorption efficiency. The SB–LVO composites were extensively characterized using FTIR, SEM, XRD, BET surface area analysis, and TGA, confirming successful drug loading and structural integrity. In vitro release studies under different pH conditions indicated sustained-release behavior, particularly under basic conditions. Antibacterial assays confirmed the efficacy of the SB–LVO composites against S. epidermidis, highlighting their potential as a controlled-release drug delivery system. Given that stomach level acidity significantly reduces LVO release from SB–LVO, strategies to bypass or protect against the gastric environment are warranted. Overall, the findings suggest that SB–LVO composites offer a promising platform for targeted and extended antibiotic delivery, presenting an innovative approach for pharmaceutical formulations in clinical settings.

Hydrazinium cyclo-pentazolate (N₂H₅N₅) is a promising high-energy-density ionic salt characterized by high enthalpy of formation and detonation velocity. To investigate its crystallization thermodynamics, the solubility of N₂H₅N₅ was determined in eleven pure solvents via the static method over the temperature range 278.15–308.15 K. Experimental results demonstrated that the solubility of N₂H₅N₅ is positively correlated with increasing temperature in the selected solvents and the solubility data is well correlated by the modified Apelblat equation, λh equation, Wilson model, and non-random two-liquid (NRTL) model. Furthermore, the mixing thermodynamic properties of N₂H₅N₅ in selected solvents were analyzed by the NRTL model, which indicated that the dissolution process of N₂H₅N₅ in organic solvents is spontaneous by entropy- or enthalpy-driven forces. Solute–solvent interactions were further elucidated through solvation free energy and radial distribution function (RDF) analysis. Crystal morphology predictions for N₂H₅N₅ in vacuum and various solvents revealed that dichloromethane, cyclohexane, and n-hexane solvents have important regulatory effects on the morphology of N₂H₅N₅.

Using a Parr 1455 solution calorimeter, we determined the excess molar enthalpies, (H_{text m}^{text E}) of binary mixtures of ethylene glycol diacetate (EGDA) with propan-1-ol, butan-1-ol, pentan-1-ol, hexan-1-ol, heptan-1-ol, and octan-1-ol at 298.15 K and 81.5 kPa. All mixtures showed positive excess molar enthalpies, suggesting repulsive forces and weaker interactions compared to the pure components. These positive values indicate weaker interactions within the mixtures compared to the pure components. Furthermore, the partial molar enthalpies, (overline{H}_{text m,i}) and the partial molar enthalpies at infinite dilution, (overline{H}_{text m,i}^{infty }) were calculated. The Redlich–Kister polynomial equation was used to correlate the experimental data and Wilson, UNIQUAC, and NRTL models were applied to analyze the excess molar enthalpies.