Solubility parameter of drugs for predicting the solubility profile type within a wide polarity range in solvent mixtures

Evaluation of astaxanthin stability under varying temperatures and ultraviolet irradiation durations based on Raman spectroscopy

As a potent naturally carotenoid, Astaxanthin (AST) is commonly used as a natural coloring agent and antioxidant in food products, and it's stability is of great interest. The stability of AST solution stored in glass bottle under different temperatures and ultraviolet (UV) irradiation durations was analyzed in situ using confocal Raman spectroscopy, and the acceptable depth of focus was optimized. Raman spectra of AST geometrical isomers were determined by density functional theory (DFT) simulation, and characteristic peaks were selected for studying AST degradation and isomerization. Raman spectra and peak-fitting spectra based on gaussian multi-peak fitting analysis combined with Pearson's correlation analysis were conducted to study the effect of temperatures and UV irradiation on AST degradation and isomerization. The peak intensity ratio of I1518/I880 had been selected as the optimal Raman spectral variable for AST degradation based on Pearson's correlation analysis. Finally, degradation kinetic curves and degradation rate prediction equation were established. The results indicated that the isomerization of 9,13-di-cis isomer occurred at a UV irradiation of 288 h. Moreover, high temperatures above 60 °C and prolonged UV exposure exceeds 48 h can cause significant degradation of AST, with a degradation rate above 20 %. This study provided an in-situ, nondestructive potential method for the calculation of AST degradation under different temperatures and UV irradiation durations, which contribute guiding insights into the development and utilization of AST in food industry.

Nanotechnology-Based Strategies for Extended-Release Delivery of Angiotensin Receptor Blockers (ARBs): A Comprehensive Review

There has been a significant shift in the perception of hypertension as an important contributor to the global disease burden. Approximately 6 % and 8 % of pregnancies are affected by hypertension, which can adversely affect the mother and the fetus. Furthermore, a hypertensive individual is at increased risk of developing kidney disease, arterial hardening, eye damage, and strokes. Using angiotensin receptor blockers (ARBs) is widespread in treating hypertension, heart failure, coronary artery disease, and diabetic nephropathy. Despite this, some ARBs have limited use due to their poor oral bioavailability and water solubility. To tackle this, a variety of nanoparticle (NP)-based systems, such as polymeric NPs (i. e., dendrimers), polymeric micelles, polymer-drug conjugates, lipid NPs, nanoemulsions, self-emulsifying drug delivery systems (SEDDS), solid lipid NPs (SLNs), nanostructured lipid carriers (NLCs), carbon-based nanocarriers, inorganic NPs, and nanocrystals, have been recently developed for efficient delivery of losartan, Valsartan (Val), Olmesartan (OLM), Telmisartan (TEL), Candesartan, Eprosartan, Irbesartan, and Azilsartan to target cells. This review article provides a literature-based comparison of the various classes of ARBs, their mechanisms of action, and an overview of the nanoformulations developed for ARB delivery and successfully applied to managing hypertension, diabetic complications, and other conditions.

Solubility of Biocompounds 2,5-Furandicarboxylic Acid and 5-Formylfuran-2-Carboxylic Acid in Binary Solvent Mixtures of Water and 1,4-Dioxane

The solubility of 2,5-furandicarboxylic acid (FDCA) and its synthetic intermediates (e.g., 5-formylfuran-2-carboxylic acid, FFCA) provides fundamental information for the preparation and purification of the value-added biocompound FDCA. We measured the solubility of FDCA and FFCA in binary water + 1,4-dioxane mixtures with different mixing ratios at 303.15 K–342.15 K. The obtained solubility values were correlated with the Jouyban-Acree-van’t Hoff model, and the preferential solvation theory was used to study the microscopic dissolution mechanism. The solubility of FDCA/FFCA increases with increasing temperature, and pure 1,4-dioxane dissolves more solutes than pure water. FFCA shows higher solubility than FDCA. In the binary solvent mixtures, the phenomenon of co-solvency exists for both FDCA and FFCA, i.e., at a 1,4-dioxane mole fraction of about 0.60, FDCA and FFCA dissolve the most. Acceptable mean percentage deviations (MPD) (5.5% and 6.9%) are obtained for FDCA and FFCA (Jouyban-Acree-van’t Hoff model). The calculated preferential solvation parameters show different dissolution mechanisms at different solvent compositions. When the 1,4-dioxane mole fraction is 0.17~0.62/0.63, FDCA/FFCA are preferentially solvated by 1,4-dioxane. Otherwise, they are preferentially solvated by water. A trend similar to the “co-solvency phenomenon” is observed in the differences in solubility of FFCA and FDCA. This study gives important guidance for the use of binary water and 1,4-dioxane solvents in practical FDCA purification.

Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN)

Materials informatics is an emerging field that allows us to predict the properties of materials and has been applied in various research and development fields, such as materials science. In particular, solubility factors such as the Hansen and Hildebrand solubility parameters (HSPs and SP, respectively) and Log P are important values for understanding the physical properties of various substances. In this study, we succeeded at establishing a solubility prediction tool using a unique machine learning method called the in-phase deep neural network (ip-DNN), which starts exclusively from the analytical input data (e.g., NMR information, refractive index, and density) to predict solubility by predicting intermediate elements, such as molecular components and molecular descriptors, in the multiple-step method. For improving the level of accuracy of the prediction, intermediate regression models were employed when performing in-phase machine learning. In addition, we developed a website dedicated to the established solubility prediction method, which is freely available at "http://dmar.riken.jp/matsolca/".

Temperature Dependent Solubility of Benzoic Acid in Aqueous Phase and Aqueous Mixtures of Aliphatic Alcohols

The benzoic acid solubility in aqueous phase and in various aqueous mixtures of methanol, ethanol and 2-propanol was determined at temperatures ranging from 303 to 333 K by an analytical technique. The results showed that the solubility of the acid in alcohols-water binary mixtures is high as compared to pure aqueous phase. The addition of alcohols to water favors the dissolution of benzoic acid which increases further with the increase in alcohols content of water within the investigated temperature range. The benzoic acid solubility in water alone and aqueous mixtures of the selected alcohols was in the order of; 2-propanol in water > ethanol in water > methanol in water > pure water. It is also observed that within the investigated temperature range, the acid solubility increases with rise in temperature in both the aqueous phase and alcohols-water binary solvents. The logarithm of the mole fraction of the acid’s solubility also showed a linear trend against the temperature. The experimental results obtained in the current study were compared with the reported literature for the studied acid and other organic acids in various solvents and showing a good agreement. The study will have implications in the processes involving separation, crystallization and pharmaceutical formulation in various industries.

Solubility prediction, solvate and cocrystal screening as tools for rational crystal engineering

OBJECTIVES

The fact that novel drug candidates are becoming increasingly insoluble is a major problem of current drug development. Computational tools may address this issue by screening for suitable solvents or by identifying potential novel cocrystal formers that increase bioavailability. In contrast to other more specialized methods, the fluid phase thermodynamics approach COSMO-RS (conductor-like screening model for real solvents) allows for a comprehensive treatment of drug solubility, solvate and cocrystal formation and many other thermodynamics properties in liquids. This article gives an overview of recent COSMO-RS developments that are of interest for drug development and contains several new application examples for solubility prediction and solvate/cocrystal screening.

METHODS

For all property predictions COSMO-RS has been used. The basic concept of COSMO-RS consists of using the screening charge density as computed from first principles calculations in combination with fast statistical thermodynamics to compute the chemical potential of a compound in solution.

KEY FINDING

The fast and accurate assessment of drug solubility and the identification of suitable solvents, solvate or cocrystal formers is nowadays possible and may be used to complement modern drug development. Efficiency is increased by avoiding costly quantum-chemical computations using a database of previously computed molecular fragments.

SUMMARY

COSMO-RS theory can be applied to a range of physico-chemical properties, which are of interest in rational crystal engineering. Most notably, in combination with experimental reference data, accurate quantitative solubility predictions in any solvent or solvent mixture are possible. Additionally, COSMO-RS can be extended to the prediction of cocrystal formation, which results in considerable predictive accuracy concerning coformer screening. In a recent variant costly quantum chemical calculations are avoided resulting in a significant speed-up and ease-of-use.

Effect of Co-Solvent on the Solubility of Ginger Bioactive Compounds in Water Using COSMO-RS Calculations

The effects of co-solvent to the solubility of ginger bioactive compounds, 6-gingerol, 6-shogaol, 8-gingerol and 10-gingerol in hot water were calculated using the conductor-like screening model for real solvent (COSMO-RS). In this work, ethanol was used as the co-solvent. The σ-profiles of the molecules were calculated using Gaussian software and the solubility were calculated using the COSMO-RS method. The solubility of these ginger bioactive compounds were calculated at 50 to 150°C.The amounts of ethanol used were 0 mol (binary system), 0.005, 0.010 and 0.015 mol. The results show that when the concentration (mole fraction of ethanol) of co-solvent increases, the solubility of ginger bioactive compounds increase. While the temperature increases, the concentration (mole fraction of ethanol) of co-solvent decreases.

Solubility prediction of drugs in mixed solvents using partial solubility parameters

Solubility of drugs in binary and ternary solvent mixtures composed of water and pharmaceutical cosolvents at different temperatures were predicted using the Jouyban-Acree model and a combination of partial solubility parameters as interaction descriptors in the solution. The generally trained version of the model produced the overall mean percentage deviation values for the back-calculated solubility of drugs in binary solvents of 34.3% and the predicted solubilities in ternary solvent mixtures of 38.0%. In addition, the applicability of the trained model for predicting the solvent composition providing the maximum solubility of a drug was investigated. The results of collected solubility data of drugs in various mixed solvents and the newly measured solubility data of five drugs in ethanol + propylene glycol + water mixtures at 25°C showed that the model provided acceptable predictions and could be used in the pharmaceutical industry.

Solubility behavior and prediction for antihelmintics at several temperatures in aqueous and nonaqueous mixtures

A model based on solubility parameters is proposed to predict the solubility curves of antihelmintic drugs at several temperatures, including aqueous and non-aqueous mixtures. The solubility of the drugs was measured in ethanol-water and ethanol-ethyl acetate mixtures at 15-35 degrees C (mebendazole) and at 25 degrees C (thiabendazole and metronidazole). The solid phases were analyzed by differential scanning calorimerty. The polymorphic form A of mebendazole was also characterized from infrared spectroscopy. Markedly different solubility profile shapes were obtained against the solubility parameter of the mixtures: two symmetrical peaks (metronidazole), two maxima of different height (mebendazole) and a single peak (thiabendazole). The solubility parameter of the drugs was related to the co-solvent action of both mixtures and to the solubility peaks. The single equation proposed was able to predict solubility profiles of different shape, including both mixtures and all temperatures, providing reasonable physical meaning for the regression coefficients. The model was successfully tested for its predictive capability using a limited number of experimental data. More than 100 solubilities were predicted at several temperatures using 20 data point for each drug.

Thermodynamics of cosolvent action: phenacetin, salicylic acid and probenecid

The solubility of phenacetin, salicylic acid, and probenecid in ethanol-water and ethanol-ethyl acetate mixtures at several temperatures (15-40 degrees C) was measured. The solubility profiles are related to medium polarity changes. The apparent thermodynamic magnitudes and enthalpy-entropy relationships are related to the cosolvent action. Salicylic acid and probenecid show a single peak against the solubility parameter delta(1) of both solvent mixtures, at 40% (delta(1) = 21.70 MPa(1/2)) and 30% (delta(1) = 20.91 MPa(1/2)) ethanol in ethyl acetate, respectively. Phenacetin displays two peaks at 60% ethanol in ethyl acetate (23.30 MPa(1/2)) and 90% ethanol in water (delta(1) = 28.64 MPa(1/2)). The apparent enthalpies of solution display a maximum at 30% (phenacetin and salicylic acid) and 40% (probenecid) ethanol in water, respectively. Two different mechanisms, entropy at low ethanol ratios, and enthalpy at high ethanol ratios control the solubility enhancement in the aqueous mixture. In the nonaqueous mixture (ethanol-ethyl acetate) enthalpy is the driving force throughout the whole solvent composition for salicylic acid and phenacetin. For probenecid, the dominant mechanism shifts from entropy to enthalpy as the ethanol in ethyl acetate concentration increases. The enthalpy-entropy compensation plots corroborate the different mechanisms involved in the solubility enhancement by cosolvents.

Use of solubility parameter to design dry suspension of cefaclor as a dual pack system

One of the important methods to improve the solubility of a less water-soluble drug is by the use of co solvents. The solubility enhancement produced by two binary blends with a common co solvent (water-propylene glycol and propylene glycol-ethyl acetate) was studied against the solubility parameter of solvent blends (δ₁) to evaluate the solubility parameter of drug (δ₂). The binary blend water:propylene glycol (20:80) gave maximum solubility with an experimental δ₂ value of 16.52 (Cal/cm³)^0.5 that was comparable to the theoretical value of 16.52 (Cal/cm³)^0.5 determined by molar volume method and 16.35 (Cal/cm³)^0.5 when determined by method proposed by Lin and Nash. The solvent blend water:propylene glycol (20:80) in which the drug exhibited maximum solubility was used as the reconstituting medium for formulation of dry suspension of cefaclor. The percentage cumulative drug release of cefaclor from the formulation F7 was compared to the marketed formulation by calculating the f1 (dissimilarity factor) and f2 (similarity factor) factors. A higher f1 value and f2 value below 50 indicates difference between the two dissolution profiles.