Binary Solvent Mixtures Research Articles

Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning

Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperparameters tuned using Bayesian optimization. The resulting top-performing models, both gradient boosted decision trees (light gradient boosting machine and extreme gradient boosting), achieved mean absolute errors (MAE) of 0.33 for LogS (S in g/100 g) on the holdout set. These models were further validated through a prospective study, wherein the solubility of four drug molecules were predicted by the models and then validated with in-house solubility experiments. This prospective study demonstrated that the models accurately predicted the solubility of solutes in specific binary solvent mixtures under different temperatures, especially for drugs whose features closely align within the solutes in the dataset (MAE < 0.5 for LogS). To support future research and facilitate advancements in the field, we have made the dataset and code openly available.Scientific contributionOur research advances the state-of-the-art in predicting solubility for small molecules by leveraging ML and a uniquely comprehensive dataset. Unlike existing ML studies that predominantly focus on solubility in aqueous solvents at fixed temperatures, our work enables prediction of drug solubility in a variety of binary solvent mixtures over a broad temperature range, providing practical insights on the modeling of solubility for realistic pharmaceutical applications. These advancements along with the open access dataset and code support significant steps in the drug development process including new molecule discovery, drug analysis and formulation.Graphical

Exploration of the Solubility Hyperspace of Selected Active Pharmaceutical Ingredients in Choline- and Betaine-Based Deep Eutectic Solvents: Machine Learning Modeling and Experimental Validation.

Deep eutectic solvents (DESs) are popular green media used for various industrial, pharmaceutical, and biomedical applications. However, the possible compositions of eutectic systems are so numerous that it is impossible to study all of them experimentally. To remedy this limitation, the solubility landscape of selected active pharmaceutical ingredients (APIs) in choline chloride- and betaine-based deep eutectic solvents was explored using theoretical models based on machine learning. The available solubility data for the selected APIs, comprising a total of 8014 data points, were collected for the available neat solvents, binary solvent mixtures, and DESs. This set was augmented with new measurements for the popular sulfa drugs in dry DESs. The descriptors used in the machine learning protocol were obtained from the σ-profiles of the considered molecules computed within the COSMO-RS framework. A combination of six sets of descriptors and 36 regressors were tested. Taking into account both accuracy and generalization, it was concluded that the best regressor is nuSVR regressor-based predictive models trained using the relative intermolecular interactions and a twelve-step averaged simplification of the relative σ-profiles.

Solvatochromism, preferential solvation and excited state dipole moments of flufenamic acid in different solvent polarities

The influence of pure solvent and binary solvent mixtures on the optical properties of non-steroidal anti-inflammatory drug (NSAID) molecule, specifically flufenamic acid (FLA), has been studied using absorption and fluorescence spectroscopic techniques. A bathochromic shift is observed in the emission wavelength of FLA with an increase in the solvent polarity of pure solvents, attributed to the highest stability of the excited state. The spectral properties are correlated with Lippert–Mataga, solvent polarity parameter, Kamlet, and Catalan solvent polarity parameters, suggesting that both general and specific solvent effects influence the spectral properties, with general solvent effects dominating over specific solvent effects. Preferential solvation studies have been carried out in tetrahydrofuran (THF) and water solvent mixture, revealing variations in the preference for solvation of FLA as a function of the mole fraction of water. Solvatochromic data are utilized to estimate the excited state dipole moment in pure solvents using different solvatochromic methods, revealing that the excited state dipole moment of FLA is higher than its ground state counterpart. The increase in dipole moment in the excited state compared to the ground state is explained based on intramolecular charge transfer (ICT), with elaboration and discussion on the possible resonance structure corresponding to ICT, inferring the more polar nature of the excited state compared to the ground state. The effect of solute polarizability on the excited state dipole moment and change in dipole moment is also discussed.

Solubility determination, mathematical modeling, and thermodynamic analysis of naproxen in binary solvent mixtures of (1-propanol/2-propanol) and ethylene glycol at different temperatures

This study investigates the solubility behavior of Naproxen (NAP) in binary solvent mixtures of 1-propanol (1-PrOH) and 2-propanol (2-PrOH) with ethylene glycol (EG) across a range of temperatures. The solubility of NAP was experimentally determined at five different temperatures (293.15 to 313.15 K), and the data were correlated using various thermodynamic models, including the van’t Hoff, Jouyban-Acree, modified Wilson, mixture response surface, Jouyban-Acree-van’t Hoff. The results demonstrated that NAP’s solubility increases with temperature in both solvent systems. Notably, NAP exhibited higher solubility in mixtures with 1-PrOH compared to 2-PrOH, despite the lower polarity of 2-PrOH. This unexpected trend is attributed to the distinct molecular interactions, including hydrogen bonding, influenced by the structural differences between 1-PrOH and 2-PrOH. The X-ray diffraction analysis confirmed that no polymorphic transformation occurred in NAP during dissolution, maintaining its crystalline structure. The solubility data were well-correlated by the applied models, with overall MRDs% (mean relative deviation percentage) below 6.1.

Data-Driven Point Adjusted Jouyban-Acree-Artificial Neural Network Hybrid Model for Predicting Solubility of Active Pharmaceutical Ingredients in Binary Solvent Mixtures

Data-Driven Point Adjusted Jouyban-Acree-Artificial Neural Network Hybrid Model for Predicting Solubility of Active Pharmaceutical Ingredients in Binary Solvent Mixtures

Mg‐Ion Conducting Gel Polymer Electrolyte Based on High Flash Point Solvent Adiponitrile for Magnesium Ion Batteries

ABSTRACTDue to some specific properties of adiponitrile (ADN) including high oxidative stability and high flash point, it is proposed as co‐solvent with an ionic liquid (IL) as a promising electrolyte solvent for application in magnesium batteries. Herein, we report a flexible film of gel polymer electrolyte (GPE) comprising a polymer poly(vinylidene fluoride‐co‐hexafluoropropylene) (PVdF‐HFP) in which a liquid electrolyte of Mg‐trifluoromethane sulfonate (Mg‐triflate) in the mixture of ADN:IL (1‐ethyl‐3‐methylimidazolium triflate, EMITf) is immobilized for use in Mg‐batteries. The structural/morphological properties of the GPE film have been characterized via different physical techniques. The high ionic conductivity (σRT = 5.9 mS cm−1), wide potential range of oxidative stability (~4.18 V vs. Mg/Mg2+), high Mg‐ion transport number (tMg2+ = 0.67) and thermal stability up to ~160°C ascertain the compatibility of electrolyte film in magnesium batteries with high voltage cathode materials. The comparative studies of the interfacial‐stability and Mg‐stripping/plating tests on the two symmetrical cells with Mg and Mg/MWCNTs nanocomposite electrodes show the improved reversibility of the electrolyte film with Mg‐MWCNTs powder as anode material, compared with pure Mg‐powder. The overall results indicate that the GPE based on binary solvent mixture ADN:IL is high performance flexible electrolyte for Mg‐batteries with Mg‐MWCNTs powder as anode material.

Correlating Excess Volumes for Binary Mixtures of Green Solvents with the Help of Density Functional Theory

Correlating Excess Volumes for Binary Mixtures of Green Solvents with the Help of Density Functional Theory

Solubility Reinforcement using Binary Solvent Mixtures and its use in Development and Validation of UV-Spectrophotometric Method for Posaconazole

Objectives: To develop a method that meets the requirements as per ICH guidelines for validation and can be used as simple, specific, economical, and repeatable for routine quality control analysis of formulations containing Posaconazole. Materials and Methods: The maximum absorbance of Posaconazole was successfully recorded using a precise 1:1 ratio of binary solvent system Methanol: Buffersolution pH 7.4 at 262nm. Results: After a preliminary physicochemical investigation the precipitation of the Posaconazole issue was resolved using a precise 1:1 ratio of binary solvent system Methanol: Buffersolution pH 7.4. Development and validation of the analytical method was done using the same binary solvent system. The maximum absorbance with this solvent system was found at 262nm, while the calibration curve shows a Regression coefficient (R2) of about 0.9984 for its linearity in the concentration range of 2.5 to 15µ/ml. The developed method was validated and meets the requirements as per ICH (International Conference for Harmonization) guidelines where the Limit of Detection (LOD) as well as Limit of Quantification (LOQ) were noted as 0.74µ/ml and 2.25µ/ml. Percentage recovery was within the permissible range (with a % relative standard deviation of less than 2.0). Conclusion: The results from validation parameters specifically accuracy or % recovery and robustness study reveal that the developed method meets the requirements and can be used as simple, specific, economical, and repeatable for routine quality control analysis of formulations containing Posaconazole.

Slowdown of solvent structural dynamics in aqueous DMF solutions

This study presents a comprehensive investigation into the molecular dynamics of solvation environments through an integrated approach combining Fourier-transform infrared (FTIR) spectroscopy, molecular dynamics (MD) simulations, and two-dimensional infrared (2D IR) spectroscopy. We explore the solvation of an ionic solute (ammonium thiocyanate) in various solvent systems, including N,N-dimethylformamide (DMF), water, and a 0.5 mole fraction of DMF in water, aiming to unravel the intricate interplay between solute-solvent interactions and solvent dynamics across diverse solvation environments. By integrating FTIR spectral analysis with radial distribution functions and coordination numbers obtained from MD simulations, we decipher the solvent composition around the solute molecule. Analysis of 2D IR spectra and hydrogen bond, as well as dipolar autocorrelation function from MD simulations, further elucidates the nuances of solute-solvent interactions, highlighting the impact of solvent dynamics on solvation structures. Our results reveal a significant slowdown of the solvent structural dynamics in the equimolar binary solvent mixture compared to the neat solvents. This slowdown underscores the complex relationship between solute-solvent interactions and solvent dynamics. The integration of FTIR, MD simulations, and 2D IR spectroscopy provides a unified framework for obtaining a holistic understanding of solvation dynamics, offering valuable insights into the underlying molecular mechanisms governing solute-solvent interactions in complex systems. These results pave the way for future studies to delve deeper into the molecular intricacies of solvation phenomena.

Densities and Apparent Molar Volume of Benzimidazole in Pure and Binary Solvent Mixtures of Ethanol + Water

Densities and Apparent Molar Volume of Benzimidazole in Pure and Binary Solvent Mixtures of Ethanol + Water

Porous nanofiber/microparticles-structured membrane of polymyxin B modified poly(styrene-random-glycidyl methacrylate) by electrospray for endotoxin removal in blood purification

Inspired by the kidney's renal glomerulus, a binary solvent mixture system of tetrahydrofuran (THF) and methanol was used to form porous nanofiber/microparticles (PNfMp) during electrospray technology. Poly(styrene-random-glycidyl methacrylate) (PSGM) was synthesized by atom-transfer radical polymerization as a supporting material for the electrospray process. During the electrospray process, THF, with a lower boiling point, evaporated first, forming the PNfMp of PSGM containing the nanoemulsified methanol with a higher boiling point. After the solidification of the PSGM, the nanoemulsified methanol evaporated, creating highly porous microparticles/nanofibers of PSGM. The Brunauer-Emmett-Teller (BET) surface area of the PNfMp-structured PSGM membranes reached 986.6 m2/g. We further investigated modifying the surfaces of the electrospun membranes with polymyxin B (PMB) through the epoxy groups of PSGM for endotoxin adsorption. The maximum adsorption capacity of endotoxin on the PMB-modified PSGM membrane with a PNfMp structure is approximately 3.1 EU/mg, which is much higher than that without a PNfMp structure, at 0.7 EU/mg. A filter packed with the PMB-modified PSGM membrane was constructed to remove endotoxin from human plasma. The removal efficiency reaches almost 100 % for 14 mL using 3.2 mg of the membrane at a flow rate of 1 mL/min. Our proposed method provides an ideal strategy to prepare PNfMp membranes for endotoxin removal in blood purification.

Studies of Viscosity and Thermodynamic Parameters of Substituted Benzimidazolyl Derivatives in Binary Solvent Mixture

Studies of Viscosity and Thermodynamic Parameters of Substituted Benzimidazolyl Derivatives in Binary Solvent Mixture

Comparative Study of Two Spectral Methods for Estimating the Excited State Dipole Moment of Non-Fluorescent Molecules.

The electronic absorption spectral characteristics of cycloimmonium ylids with a zwitterionic structure have been analyzed in forty-three solvents with different hydrogen bonding abilities. The two ylids lack fluorescence emission but are very dynamic in electronic absorption spectra. Using the maximum of the ICT band, the goal was to establish an accurate relationship between the shift of the ICT visible band and the solvent parameters and to estimate two of the descriptors of the first (the) excited state: the dipole moment and the polarizability. Two procedures were involved: the variational method and the relationships of the Abe model. The results indicate that the excited state dipole moment of the two methylids decreases in the absorption process in comparison with the ground state. The introduction of a correction term in the Abe model that neglects the intermolecular H-bonding interactions leads to a more accurate determination of the two descriptors. The strong solvatochromic response of both ylids has been further applied in distinguishing the solvents as a function of their specific parameters. Principal component analysis was applied to five selected properties, including the maximum of the charge transfer band. The results were further applied to discriminate several binary solvent mixtures.

Exploring pNIPAM lyogels: Experimental study on swelling equilibria in various organic solvents and mixtures, supported by COSMO-RS analysis

Stimuli-responsive lyogels are considered to be smart materials due to their capability of undergoing significant macroscopic changes in response to external triggers. Due to their versatile and unique properties, smart lyogels exhibit great potential in various applications such as drug delivery or actuation processes. While Poly-N-isopropylacrylamide (pNIPAM) is widely known as a thermo-responsive material, it also shows significant solvent-responsive swelling behavior. For the systems tested, polar solvents induce strong swelling due to hydrogen bonding with the amide group, nonpolar solvents lead to significant shrinkage of the lyogels. The aim of this study is to investigate and to model this behavior for future application in chemical or biochemical reactors. As a current area of research, incorporating smart lyogel technology into (bio-)chemical reactors facilitates the development of smart reactor systems. Applying thermodynamic modelling with the gE model COSMO-RS on the monomer or oligomers of pNIPAM, the correlation between solvent-polymer interactions and the degree of swelling can be observed. pNIPAM derivatives exhibit low infinite dilution activity coefficients (IDACs) in polar solvents with large degrees of swelling, while displaying an increase of IDACs in nonpolar solvents. Hydrogen bonds dominate the swelling behavior of lyogels not only in pure solvents but also in mixtures of solvents with varying polarity. Even in mixtures containing high amounts of nonpolar solvents, large degrees of swelling were observed due to the uptake of the polar solvent in the lyogel matrix. This effect can be observed in binary solvent mixtures but also in representative mixtures along an esterification reaction with varying carboxylic chain length of alcohol and carboxylic acids.

Determination and Correlation of the Solubility of Potassium Fluoride in Eight Single Solvents and Four Binary Solvent Mixtures at Different Temperatures

Determination and Correlation of the Solubility of Potassium Fluoride in Eight Single Solvents and Four Binary Solvent Mixtures at Different Temperatures

The solvation thermodynamics of rose acetate in four mixed solvents: Molecular dynamics simulation, molecular conformation analysis, machine learning and solubility prediction

The solubility of rose acetate (alpha-(trichloromethyl) benzyl acetate) in four binary solvent mixtures was measured by static gravimetric method at the temperature ranging from 288.15 K to 328.15 K under atmosphere pressure. The dissolution behavior and solubility prediction were performed through simulations and calculations, using Materials Studio software and Back Propagation Neural Network. The modified Apelblat equation, λh model, Van’t Hoff equation, and NRTL model were employed to correlate the solubility data respectively. And the modified Apelblat equation provided the best fitting performance, corresponding to the lowest average value of average relative deviation and root mean square deviation. After analyzing the solvent properties, it was obsersved that the solubility of rose acetate in selected solvents was influenced by the polarity of the solvent. The mixing thermodynamic properties including ΔmixH, ΔmixS and ΔmixG indicated that the dissolution process of rose acetate was exothermic and spontaneous.Molecular dynamics (MD) simulations were utilized to analyze the interaction mechanism between rose acetate and binary solvent mixtures. The results revealed that the hydrogen bonding interaction played an insignificant role in the dissolution process. Moreover, the solute molecules were characterized in both crystal and solution state based on their conformation and molecular energies. A back-propagation (BP) neural network was constructed to predict the solubility of rose acetate, and the predicted data exhibited good agreement with the measured valuses.

Co-Solvent effect and Kinetics of Alkaline Hydrolysis of Butyle Salicylate

The base catalised hydrolysis of buyl salicylate has been carred out in water-DMF binary solvent mixture covering range of 30 to 70% (v/v) of solvent composition at different Temperature (20 to 400 C). The reaction followed second order rate constant which decreases with increase of solvent composition. The salvation and desolvation concept are used to explain the solvent influence on rate and mechanism is the reaction. The relation between changes in dielectric constant due to variation of reaction mixture and change in specific rate constant has been explained on the basis of electrostatic and non-electrostatic contribution of solvent mixture. Calculated value of Iso-composition activation energy is found to be increases with increase of solvent composition. The thermodynamic parameters (ΔG*, ΔH* &amp; ΔS*) has been determined with help of Wynne Jone and Eyring equation which show the great dependency on solvent composition.

Solvatochromism in Mixtures of Hydrogen Bond Acceptor (HBA) Solvents with Water.

The UV/Vis absorption energies νmax of Reichardt's dye B30 with respect to ET(30) and 4-nitroaniline (NA) are investigated as a function of the solvent composition Nav,z. in co-solvent/water mixtures. Nav,z. is the average molar concentration of the solvent mixture at a given solvent fraction z. The z can be the mole, the volume or the mass fraction. The co-solvents considered were acetonitrile, acetone, tetrahydrofuran, pyridine, piperidine and 2-(diethylamino)-ethanol. Acetone and acetonitrile can be expected to slightly enhance the water structure at low co-solvent concentrations. This interpretation is supported by the analysis of the refractive index as a function of the solvent composition. In general, it can be stated that the structural complexity of the binary solvent mixtures is mainly responsible for the evolution of the absorption energies ET(30) or νmax(NA) as a function of the mixture composition. In particular, the endothermic solvation of NA in co-solvent/water mixtures and its effect on the νmax(NA) is highlighted.

Correlating Substrate Reactivity at Electrified Interfaces with the Electrolyte Structure in Synthetically Relevant Organic Solvent/Water Mixtures.

Optimizing electrosynthetic reactions requires fine tuning of a vast chemical space, including charge transfer at electrocatalyst/electrode surfaces, engineering of mass transport limitations, and complex interactions of reactants and products with their environment. Hybrid electrolytes, in which supporting salt ions and substrates are dissolved in a binary mixture of organic solvent and water, represent a new piece of this complex puzzle as they offer a unique opportunity to harness water as the oxygen or proton source in electrosynthesis. In this work, we demonstrate that modulating water-organic solvent interactions drastically impacts the solvation properties of hybrid electrolytes. Combining various spectroscopies with synchrotron small-angle X-ray scattering (SAXS) and force field-based molecular dynamics (MD) simulations, we show that the size and composition of aqueous domains forming in hybrid electrolytes can be controlled. We demonstrate that water is more reactive for the hydrogen evolution reaction (HER) in aqueous domains than when strongly interacting with solvent molecules, which originates from a change in reaction kinetics rather than a thermodynamic effect. We exemplify novel opportunities arising from this new knowledge for optimizing electrosynthetic reactions in hybrid electrolytes. For reactions proceeding first via the activation of water, fine tuning of aqueous domains impacts the kinetics and potentially the selectivity of the reaction. Instead, for organic substrates reacting prior to water, aqueous domains have no impact on the reaction kinetics, while selectivity may be affected. We believe that such a fine comprehension of solvation properties of hybrid electrolytes can be transposed to numerous electrosynthetic reactions.

Experimental and model-based approach to evaluate solvent effects on the solubility of the pharmaceutical artemisinin

The separation and purification of plant-based Active Pharmaceutical Ingredients (API) from extracts is a crucial part in pharmaceutical process development. For the purification of the antimalarial drug component artemisinin (ARTE) from an Artemisia anna L. toluene extract, antisolvent crystallization is considered. Solubilities of ARTE in binary solvent mixtures of toluene and two potential antisolvents, n-heptane and ethanol, were determined at temperatures from 278.15 K to 313.15 K. The experimental work was supported by the application of various models, utilizing varying amounts of experimental input data. The goal was the identification of models that are able to predict solubilities in binary solvent mixtures sufficiently accurate and, thus, can help to reduce the experimental effort for future solvent screenings. In this study, we applied the PC-SAFT model both with and without fitting the binary interaction parameter kij between ARTE and the respective solvent, as well as the empirical Jouyban-Acree model. From the experiments, n-heptane demonstrated to be a promising antisolvent, while ethanol acted more as a cosolvent. All models tested were capable of distinguishing between effective and ineffective antisolvents. The purely predictive PC-SAFT model applied with kij = 0 exhibited the largest deviation from the experimental data. This was followed by the PC-SAFT model including fitted kij values, based on at least four experimental data points. The Jouyban-Acree model fitted the data most accurately. Its parametrization required a minimum of ten experimental data points.