Solubility prediction of salicylic acid in water-ethanol-propylene glycol mixtures using the Jouyban-Acree model

To show the applicability of a solution model, i.e. the Jouyban-Acree model, for predicting the solubility of a solute in ternary solvent systems based on model constants computed using solubility data of the solute in binary solvent systems, the solubility of salicylic acid in water-ethanol, water-propylene glycol, ethanol-propylene glycol mixtures was determined. A minimum number of three data points from each binary system was used to calculate the binary interaction parameters of the model. Then the solubility in other binary solvent compositions and also in a number of ternary solvents was predicted, and the mean percentage deviation (MPD) was calculated as an accuracy criterion. The overall MPD (+/-SD) was 7.3 (+/-7.3)% and those of a similar predictive model was 15.7 (+/-11.5)%. The mean difference between the proposed and a previous model was statistically significant (paired t-test, p < 0.004).     

Mathematical representation of apparent dissociation constants in aqueous-organic solvent mixtures

A mathematical model for calculating apparent acid dissociation constants (pK(a)) in hydroorganic mixtures with respect to the concentration of organic solvent in a binary mixture is proposed. The correlation ability of the proposed model is evaluated by employing pK(a) value of 75 different weak acids in 13 water-cosolvent systems. The results show that the equation is able to correlate the pK(a) values with an overall mean percentage differences (MPD) of 0.52+/-0.43%. In order to test the prediction capability of the model, four experimental pK(a) values for each data set have been employed to train the model, then the pK(a) values at other solvent compositions predicted and the overall MPD obtained is 1.41+/-1.15%. The applicability of the model to correlate/predict pK(a) values of structurally related drugs in a given binary solvent has been shown. The obtained overall MPD for correlation and prediction capabilities are 1.60+/-2.16 and 2.89+/-3.22%, respectively.     

Prediction of drug solubility in water-propylene glycol mixtures using Jouyban-Acree model

A trained version of the Jouyban-Acree model was presented to predict drug solubility in water-propylene glycol mixtures at various temperatures. The model is able to predict the solubility in various solubility units and requires the experimental solubility of a solute in mono-solvent systems. The mean percentage deviation (MPD) of predicted solubilities was computed to show the accuracy of the predicted data and 24% was found as the average MPD for 27 data sets studied. The proposed model enables the researchers to predict solubiliy in water-propylene glycol mixtures at various temperatures and reduces the number of required experimental data from five to two points.     

Modeling drug solubility in water-cosolvent mixtures using an artificial neural network

Application of the artificial neural network (ANN) to calculate the solubility of drugs in water-cosolvent mixtures was shown using 35 experimental data sets. The networks employed were feedforward backpropagation errors with one hidden layer. The topology of neural network was optimized and the optimum topology achieved was a 6-5-1 architecture. All data points in each set were used to train the ANN and the solubilities were back-calculated employing the trained networks. The differences between calculated solubilities and experimental values was used as an accuracy criterion and defined as mean percentage deviation (MPD). The overall MPD (OMPD) and its S.D. obtained for 35 data sets was 0.90 +/- 0.65%. To assess the prediction capability of the method, five data points in each set were used as training set and the solubility at other solvent compositions were predicted using trained ANNs whereby the OMPD (+/-S.D.) for this analysis was 9.04 +/- 3.84%. All 496 data points from 35 data sets were used to train a general ANN model, then the solubilities were back-calculated using the trained network and MPD (+/-S.D.) was 24.76 +/- 14.76%. To test the prediction capability of the general ANN model, all data points with odd set numbers from 35 data sets were employed to train the ANN model, the solubility for the even data set numbers were predicted and the OMPD (+/-S.D.) was 55.97 +/- 57.88%. To provide a general ANN model for a given cosolvent, the experimental data points from each binary solvent were used to train ANN and back-calculated solubilities were used to calculate MPD values. The OMPD (+/-S.D.) for five cosolvent systems studied was 2.02 +/- 1.05%. A similar numerical analysis was used to calculate the solubility of structurally related drugs in a given binary solvent and the OMPD (+/-S.D.) was 4.70 +/- 2.02%. ANN model also trained using solubility data from a given drug in different cosolvent mixtures and the OMPD (+/-S.D.) obtained was 3.36 +/- 1.66%. The results for different numerical analyses using ANN were compared with those obtained from the most accurate multiple linear regression model, namely the combined nearly ideal binary solvent/Redlich-Kister equation, and the ANN model showed excellent superiority to the regression model.     

Solubility prediction of clonazepam in aqueous mixtures of ethanol,polyethylene glycol 200 and propylene glycol at 30 °C

Solubility of clonazepam in aqueous binary mixtures of ethanol, polyethylene glycol 200 and propylene glycol was determined at 30 °C using the shake flask method. The maximum solubility of clonazepam was observed at volume fraction of 0.90 of ethanol, whereas for aqueous mixtures of polyethylene glycol 200 and propylene glycol, the maximum values were observed in the neat cosolvents. The generated data was fitted to the Jouyban-Acree model and its constants were computed, then the back-calculated solubilities were compared with the corresponding experimental values by calculating the mean percentage deviation (MPD) in which the overall MPD for three cosolvent systems was 7.0 %. The solubility data in cosolvent + water mixtures was predicted using previously trained versions of the Jouyban-Acree model and the prediction MPDs were 13.4, 54.2 and 24.9 %, respectively for ethanol, polyethylene glycol 200 and propylene glycol mixtures and the overall MPD was 30.8 %.     

In silico prediction of drug solubility in water-dioxane mixtures using the Jouyban-Acree model

A numerical method based on the Jouyban-Acree model was presented for prediction of drug solubility in water-dioxane mixtures at various temperatures. The method requires drug solubility in monosolvent systems, i.e. two data points for each temperature of interest. The mean percentage deviation (MPD) of predicted solubilities was calculated to show the accuracy of the predicted data and 27% was found as the average MPD for 36 data sets studied. The proposed numerical method reduced the number of required experimental data from five to two points and could also be extended to predict solubility at various temperatures.     

Solubility of pioglitazone hydrochloride in ethanol,N-methyl pyrrolidone,polyethylene glycols and water mixtures at 298.20 °K

Background and the purpose of the study

Solubility of pharmaceuticals is still a challenging subject and solubilization using cosolvents is the most common technique used in the pharmaceutical industry. The purpose of this study was reporting and modeling the experimental molar solubility of pioglitazone hydrochloride (PGZ-HCl) in binary and ternary mixtures of ethanol (EtOH), N-methyl pyrrolidone (NMP), polyethylene glycols (PEGs) 200, 400, 600 and water along with the density of saturated solutions at 298.2 °K.

Methods

To provide a computational method, the Jouyban-Acree model was fitted to the solubilities of the binary solvents, and solubilities of the ternary solvents were back-calculated by employing the solubility data in mono-solvents. In the next step, the ternary interaction terms were added to the model and the prediction overall mean percentage deviation (MPD) of the ternary data was reduced. Also a previously proposed version of the model was used to predict the solubility of PGZ-HCl in binary and ternary mixtures employing the experimental solubility data in mono-solvents.

Results

The overall MPD of the model for fitting the binary data and predicted data of ternary solvents were 2.0 % and 50.5 %, respectively. The overall MPD of the predicted solubilities in ternary solvents using the ternary interaction terms in the model was 34.2 %, and by using the proposed version of the Jouyban-Acree model for binary and ternary data the overall correlation and prediction errors were 18.0 and 15.0 %, respectively.

Conclusion

The solubility of PGZ-HCl was increased by addition of EtOH, NMP, PEGs 200, 400 and 600 to aqueous solutions. The reported data extended the available solubility data of pharmaceuticals which are crucial in formulation of liquid dosage forms. The constants of the Jouyban-Acree model using the generated data are also reported which provides the possibility of solubility prediction in other solvent mixtures and temperatures.     

A simple relationship between dielectric constant of mixed solvents with solvent composition and temperature

A simple computational method for calculating dielectric constants of solvent mixtures based on Redlich-Kister extension was proposed. The model was applied to the experimental dielectric constant of binary and ternary solvent mixtures at fixed and/or various temperatures and showed accurate results. Overall average percentage deviation (OAPD) between calculated and experimental dielectric constants was calculated as an accuracy criterion. The OAPDs for correlative and predictive analyses of dielectric constants in binary solvents at a fixed temperature were 0.56 and 1.42%, respectively. The corresponding values for binary solvents at different temperatures were 1.29 and 1.92%, respectively. The OAPDs for correlative and predictive analyses of dielectric constants of a nonaqueous ternary solvent mixture at various temperatures were 1.61 and 3.05%. The accuracy of the proposed models has also been compared with those of previously published models and results showed that the proposed models were superior and capable of providing more accurate results.     

Solubility prediction of solutes in aqueous mixtures of ethylene glycols

The applicability of a trained version of the Jouyban-Acree model, for predicting the solubility of solutes in aqueous mixtures of ethylene glycol and its polymerized forms was shown. The solubilities of 8 drugs in binary mixtures were determined and the mean percentage deviation (MPD) was calculated as a prediction accuracy criterion and the overall MPD (+/- SD) was 23.2 (+/- 13.1)%.     

Influence of solvent composition on the solid phase at equilibrium with saturated solutions of quinolones in different solvent mixtures

The dissolution profiles and solubilities of three quinolonic drugs (oxolinic, pipemidic, and nalidixic acids) in different solvent mixtures were studied. The behavior of the solid phase, during solubility experiments was in-depth investigated with the aim of detecting possible crystalline modifications, such as polymorphic transitions or solvate formations, that might modify drug stability and/or solubility properties. In order to test the influence of both the nature and polarity of the co-solvents, aqueous and non-aqueous binary mixtures have been prepared by using Lewis base (dioxane and ethyl acetate) and amphiprotic co-solvents (ethanol and water). Differential scanning calorimetry (DSC), hot stage microscopy, IR spectroscopy and X-ray powder diffraction were used in combination with solubility and dissolution studies to characterize and investigate the solid state properties of the original powders and the corresponding ones at equilibrium with the different pure solvents and solvent mixtures examined. The solid phases of nalidixic and oxolinic acids did not show any change after equilibration with the various pure solvents or binary solvent mixtures, regardless the chemical nature of the examined solvents. On the contrary, in the case of pipemidic acid, the different analytical techniques used to characterize the drug solid state enabled identification of a solvated form at equilibrium with pure dioxane and a trihydrated form in aqueous mixtures of water with both ethanol (amphiprotic) or dioxane (Lewis base) in a concentration range from 10 to 100% water.     

Prediction of the optimized solvent composition for solubilization of drugs in water-cosolvent mixtures

The capability of the Jouyban-Acree model for predicting the optimized solvent composition of binary solvents for solubilization of drugs is shown employing solubility of drugs in aqueous mixtures of dioxane, ethanol and polyethylene glycol 400. The established model constants of the Jouyban-Acree model and solubility of drugs in water and cosolvent are used to predict the maximum solubility of in the binary solvent mixture (log Xm(max)) and the corresponding solvent composition (f1,max). The accuracy of the predicted log Xm(max) and f1,max is studied using average absolute error (AAE) of predicted and observed values. The AAEs were 0.10 +/- 0.12 and 0.08 +/- 0.10, respectively for log Xm(max) and f1,max. The method provided acceptable predictions and is recommended for practical applications. The main advantage of the proposed method is its extension to temperatures higher/lower than room temperature.     

Extended hildebrand solubility approach: satranidazole in mixtures of dioxane and water

The extended Hildebrand solubility parameter approach is used to estimate the solubility of satranidazole in binary solvent systems. The solubility of satranidazole in various dioxane-water mixtures was analyzed in terms of solute-solvent interactions using a modified version of Hildebrand-Scatchard treatment for regular solutions. The solubility of satranidazole in the binary solvent, dioxane-water shows a bell-shaped profile with a solubility maximum well above the ideal solubility of the drug. This is attributed to solvation of the drug with the dioxane-water mixture, and indicates that the solute-solvent interaction energy is larger than the geometric mean (δ(1)δ(2)) of regular solution theory. The new approach provides an accurate prediction of solubility once the interaction energy is obtained. In this case, the energy term is regressed against a polynomial in δ(1) of the binary mixture. A quartic expression of W in terms of solvent solubility parameter was found for predicting the solubility of satranidazole in dioxane-water mixtures. The method has potential usefulness in preformulation and formulation studies during which solubility prediction is important for drug design.     

A model to represent solvent effects on the chemical stability of solutes in mixed solvent systems

The applicability of the combined nearly ideal binary solvent/Redlich-Kister (CNIBS/R-K) equation for quantification of solvent effects on the stability of a solute is shown employing the experimental data of three solutes in different aqueous binary solvents. The proposed model provides a simple computational method to correlate/predict the instability rate constant of a drug in mixed solvent systems. The accuracy of the model is compared with that of a model proposed by Connors and co-workers employing various methods including mean percentage deviation (MPD) as comparison criteria. The obtained overall MPD values for the proposed model to correlate and predict the instability rate constants are 2.05 +/- 1.44 and 4.41 +/- 3.21%, respectively, where the corresponding values for Connors' model are 4.34 +/- 3.28 and 10.74 +/- 9.86%. The results suggest that by using only five experimental instability rate constants at different concentrations of the cosolvent in a binary mixture, it is possible to predict unmeasured values falling between data points within an acceptable error range.     

Mathematical representation of solute solubility in binary mixture of supercritical fluids by the Jouyban-Acree model

Applicability of a solution model for calculating the solute solubility in binary mixtures of supercritical fluids at different SCF compositions and pressures was shown using phenanthrene solubility data in supercritical carbon dioxide and supercritical ethane at 313 K and a pressure range of 100-350 bar. The correlation ability of the proposed model was evaluated by fitting all data points and computing error term employing back-calculated solubilities. The prediction capability of the model was assessed by dividing each data set to two subsets, namely training and test subsets. The predicted solubilities using trained models were used to calculate the prediction error term. The results show that both correlative and predictive error terms were less than the experimentally obtained RSD values.     

Solution thermodynamics and solubility prediction of glibenclamide in Transcutol + water co-solvent mixtures at 298.15–333.15 K

Solution thermodynamics and solubility of glibenclamide (GBN) in binary co-solvent mixtures of Transcutol + water at temperature range of 298.15–333.15 K were investigated in present study. The modified Apelblat model was used to predict the solubility of GBN in co-solvent mixtures at various temperatures. The highest and lowest solubility of GBN were observed in pure Transcutol and pure water, respectively. Moreover, all co-solvent mixtures had highest solubility at 333.15 K. The experimental solubility data of GBN was correlated well with the modified Apelblat model at each temperature studied with relative absolute deviation in the range of 0.008–5.903 %. The correlation coefficients in co-solvent mixtures were observed in the range of 0.995–0.999 which indicated good fitting of experimental data with calculated one. The enthalpies and entropies for GBN dissolution were observed in the range of 2.012–38.215 kJ mol^?1 and 6.748–114.709 J mol^?1 K^?1, respectively indicating its dissolution is endothermic and an entropy-driven process. These results indicated that Transcutol can be used as a co-solvent in preformulation studies and formulation development of GBN.     

Modeling the electrophoretic mobility of analytes in binary solvent electrolyte systems in capillary electrophoresis using an artificial neural network

An artificial neural network (ANN) methodology was used to model the electrophoretic mobility of basic analytes in binary solvent electrolyte systems. The electrophoretic mobilities in pure solvent electrolytes, and the volume fractions of the solvents in mixtures were used as input. The electrophoretic mobilities in mixed solvent buffers were employed as the output of the network. The optimized topology of the network was 3-3-1. 32 experimental mobility data sets collected from the literature were employed to test the correlation ability and prediction capability of the proposed method. The mean percentage deviation (MPD) between the experimental and calculated values was used as an accuracy criterion. The MPDs obtained for different numerical analyses varied between 0.21% and 13.74%. The results were also compared with similar calculated mobilities which were derived from the best multiple linear model from the literature. From these results it was found that the ANN methodology is superior to the multiple linear model.     

Correlation of surface tension of mixed solvents with solvent composition

A simple computational method for calculating surface tension of solvent mixtures based on the Redlich-Kister extension was proposed. The model was applied to the experimental surface tension of binary solvent mixtures and showed accurate results. Overall average percentage deviation (APD) between calculated and experimental surface tensions was calculated as an accuracy criterion. The overall APD for correlating surface tensions in binary solvents was 4.30%. The accuracy of the proposed model has also been compared with those of previously published models and the results showed that the proposed model was superior and capable of providing more accurate results. An extension of the model was also proposed to correlate surface tension of ternary solvents and the overall APD for ternary solvent data was 2.06%.