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.     

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.     

Mathematical representation of solubility of amino acids in binary aqueous-organic solvent mixtures at various temperatures using the Jouyban-Acree model

Applicability of a solution model for calculating solubility of amino acids in binary aqueous-organic solvent mixtures at various temperatures was shown. The accuracy of the proposed model was evaluated by computing mean percentage deviation (MPD) employing available solubility data of amino acids in binary solvents at various temperatures from the literature. The overall MPD (+/- SD) for correlation of solubility data was 16.5 +/- 8.8%. In addition, the equations calculating solubility of amino acids in binary solvent mixtures at a fixed temperature was revisited.     

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.     

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).     

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.     

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)%.     

Modeling the electrophoretic mobility of beta-blockers in capillary electrophoresis using artificial neural networks

Artificial neural networks were used for modeling the mobility of five beta-blockers (i.e., labetalol atenolol, practolol, timolol and propranolol) in running buffer with ternary solvent background electrolyte systems containing 80 mM acetate buffer dissolved in water, methanol, ethanol and their ternary mixtures. The volume fractions of two solvents (f(2), f(3)) and cologarithm of electrophoretic mobilities in pure solvents (i.e., -Lnmu(1), -Lnmu(2) and -Lnmu(3)) were used as inputs and cologarithm of the mobility in mixed solvents was the output of the networks. The number of neurons in hidden layer, learning rate, momentum and the number of epochs were optimized, in which two neurons in hidden layer, 0.2, 0.9 and 20000 were found the optimized values for learning rate, momentum and number of epochs, respectively. Mean percentage deviations (MPD) between calculated and experimental mobilities were computed as an accuracy criterion. To assess the correlative ability of the model, all data points in each set were used as training set and the mobilities were back-calculated by the trained networks, in which the overall MPD (OMPD)+/- standard deviation (SD) for correlative study was 3.1+/- 2.3. To evaluate the prediction capability of the proposed ANN model, the network was trained using 15 data points for each analyte and the remaining data points were predicted. The obtained OMPD (+/-SD) for this analysis was 3.6+/-3.0. To further investigate on the applicability of ANN, a generalized network was trained with 10 data points from each beta-blocker and then the network was employed to predict the mobilities of the analytes in ternary solvent electrolyte systems. The MPDs for predicted mobilities were 3.6%, 3.6%, 3.9%, 3.7% and 2.9% respectively for labetalol, atenolol, practolol, timolol and propranolol.     

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 %.     

Variation of acidity constants of peptides in acetonitrile-water mixtures with solvent composition: effect of preferential solvation

The dissociation constant values of a series of peptides in 5.54, 10, 16.30, 25.03 and 50% (w/w) acetonitrile-water mixed solvents at 298.15 K were determined according to the criteria endorsed by IUPAC. A pronounced change in the acid-base pK values of carboxylic, phenol and thiol groups was observed as the solvent was enriched in acetonitrile. By contrast, pK values of protonated amino-terminal groups were influenced slightly as the solvent was enriched in acetonitrile, although continually increasing pK values were observed. The variation of the pK values obtained, over the whole composition range studied, was explained by taking into account the preferential solvation of electrolytes in acetonitrile-water mixtures. To obtain pK values in all possible binary solvent acetonitrile-water mixtures, relationships between pK values and different bulk properties were examined and the Linear Solvation Energy Relationships methodology was applied. The pK_a values were then correlated with the Kamlet-Taft, π, α and β solvatochromic parameters of acetonitrile-water mixtures. The equations obtained allowed calculation of the pK values of peptides in acetonitrile-water mixtures up to 50% (w/w) and thus permitted the acid-base behavior of these substances in the widely used acetonitrile-water media to be known.     

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%.     

Electrophoretic behavior of alprenolol in mixed solvent electrolyte systems

The electrophoretic mobilities of alprenolol have been determined in a mixed solvent background electrolyte system containing sodium acetate (40 mM)+acetic acid (40 mM) as buffering agent and different volume fractions of water, methanol and ethanol using capillary electrophoresis. The mobility of alprenolol has been used to test the prediction capability of a model trained by previously reported mobility data of five beta-blocker drugs at the same electrophoretic conditions. The average percentage mean deviations (APMD) between experimental and predicted values were used as an accuracy criterion. The APMD (+/-SD) obtained for alprenolol data in binary/ternary solvent electrolyte system employing the mobility values in mono-solvent buffers was 4.37 (+/-3.50)% and the corresponding value for an ab initio prediction method was 7.65 (+/-4.30)%.     

Complexation study of cinalukast and montelukast with cyclodextrines

A fluorimetric study on the spectral characteristics of two antileukotrienes, cinalukast and montelukast, has been performed. Ionization constants of both of them have been photometrically calculated. Cinalukast pK(a) in ethanol:water 50:50 (v/v) medium resulted to be 2.2+/-0.1. Because the spectral characteristics of montelukast are widely affected by the solvent nature, pK(a) was estimated in two different ethanol:water media, 70:30 (v/v) and 10:90 (v/v) and the values calculated were pK(a)=2.9+/-0.1, and pK(a1)=2.0+/-0.1 and pK(a2)=6.5+/-0.1, respectively. It has been proven that the fluorescence of both, cinalukast and montelukast, is significantly intensified in the presence of cyclodextrins (CyDs). The host-guest complexation processes between cinalukast and alpha-CyD or heptakis-(2,6-di-O-methyl)-beta-cyclodextrin (DIMEB) and between montelukast and DIMEB have been investigated by fluorescence spectroscopy. A 1:1 stoichiometric ratio was established for the three studied inclusion complexes. The changes produced on the fluorescence of cinalukast or montelukast, when they are included on the hydrophobic CyD cavity are used to calculate their association constants by a non-linear regression method. Semiempirical MO calculations using AM1 method were performed in order to characterize the studied inclusion complexes. A new method for cinalukast determination in human serum, based on the fluorescence of the complex cinalukast-DIMEB exhibiting limit of detection of 7.95 ng mL(-1) has been proposed with satisfactory results. Adequate recovery values between 95 and 103% were calculated at five different concentration levels.     

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.     

Measurement and correlation of solubility data for albendazole in ten pure solvents and two binary solvent systems from 278.15 K to 323.15 K

The equilibrium solubility of albendazole (ABZ) in ten single solvents and two binary solvent mixtures of different ratio was measured by a typical static method combined with ultraviolet (UV) spectrophotometry within the temperature range from 278.15 K to 323.15 K. Meanwhile, the modified Apelblat model, Van't Hoff equation and λh equation were used to correlate the solubility data of ABZ in pure solvent, the modified Apelblat model, λh equation, Sun model, GSM equation and NRTL model were used to correlate the solubility data of ABZ in binary mixed solvent, the 100RD, 100ARD, 103RMSD and 103ORMSD values of the above models were calculated respectively. The results show that the experimental data of six models have a good correlation with the calculated data. Especially, the Van't Hoff equation in pure solvent has the best fitting effect, and the GSM equation in binary mixed solvent has the best fitting effect. Additionally, the Van't Hoff equation was used to calculate and evaluate the thermodynamic properties of the ABZ dissolution process, including enthalpy (ΔdisH), entropy (ΔdisS) and Gibbs free energy (ΔdisG).     

Correlation of retention factor of analytes in quaternary solvent mobile phases using the Jouyban-Acree model

The Jouyban-Acree model has been used for the mathematical representation of retention factors of phenobarbital, phenytoin and carbamazepine in quaternary aqueous-organic solvent mobile phases. The accuracy of the proposed model is evaluated using average percentage deviation (APD) of experimental and calculated values as an accuracy criterion. The obtained mean and standard deviation of APDs of the model is 4.2 +/- 0.5%. The results showed that the Jouyban-Acree model provided accurate calculations and could be used in practice to speed up the method development process in which quaternary solvent mobile phases are required.     

An integrated process for measuring the physicochemical properties of drug candidates in a preclinical discovery environment

Automated log P, pK(a), solubility, and chemical stability systems comprise an integrated process that provides early stage physicochemical property data to the discovery research organization. Capillary electrophoresis (CE) techniques are used to experimentally determine pK(a) and log P. Solubility is determined using a quasi-equilibrium approach employing sample quantitation by flow injection analysis with ultraviolet (UV) detection at 256 nm. Chemical stability is assessed by challenging compounds with pH 2, pH 7, pH 12, and 3% hydrogen peroxide solutions overnight, and comparing the chromatographic profiles of the stability challenged solutions to that of a freshly prepared control. Validation of the log P method using a set of drug-like compounds demonstrates that the method yields log P values within +/-0.5 units of literature values. The log P method is valid over the range -0.5-5.0, and the technique is compatible with acidic, neutral, and basic compounds. The pK(a) technique yields results within +/-0.2 units of corresponding values obtained by potentiometric titration over a pK(a) range of 2 to 12. Solubility is reported in a 3-60 microg/mL range, and the results are generally within 20% of values measured by equilibrium solubility techniques. The current level of automation supports the measurement of the physicochemical properties of 100 compounds per week. Physicochemical property data for approximately 2000 compounds have been generated to date.     

Pharmacological characterization of muscarinic receptors in mouse isolated urinary bladder smooth muscle

The pharmacological characteristics of muscarinic receptors in the male mice urinary bladder smooth muscle were studied. (+)-Cis-dioxolane, oxotremorine-M, acetylcholine, carbachol and pilocarpine induced concentration-dependent contractions of the urinary bladder smooth muscle (pEC(50)=6.6+/-0.1, 6.9+/-0.1, 6.7+/-0.1, 5.8+/-0.1 and 5.8+/-0.1, E(Max)=3.2+/-0.8 g, 2.7+/-0.4 g, 1.0+/-0.1 g, 2.7+/-0.3 and 0.9+/-0.2 g, respectively, n=4). These contractions were competitively antagonized by a range of muscarinic receptor antagonists (pK(B) values): atropine (9.22+/-0.09), pirenzepine (6.85+/-0.08), 4-DAMP (8.42+/-0.14), methoctramine (5.96+/-0.05), p-F-HHSiD (7.48+/-0.09), tolterodine (8.89+/-0.13), AQ-RA 741 (7.04+/-0.12), s-secoverine (8.21+/-0.09), zamifenacin (8.30+/-0.17) and darifenacin (8.70+/-0.09). In this tissue, the pK(B) values correlated most favourably with pK(i) values for these compounds at human recombinant muscarinic M(3) receptors. A significant correlation was also noted at human recombinant muscarinic m5 receptors given the poor discriminative ability of ligands between M(3) and m5 receptors. In recontraction studies, in which the muscarinic M(3) receptor population was decreased, and conditions optimized to study M(2) receptor activation, methoctramine exhibited an affinity estimate consistent with muscarinic M(3) receptors (pK(B)=6.23+/-0.14; pA(2)=6.16+/-0.03). Overall, these data study suggest that muscarinic M(3) receptors are the predominant, if not the exclusive, subtype mediating contractile responses to muscarinic agonists in male mouse urinary bladder smooth muscle.     

Selectivity of d[Cha4]AVP and SSR149415 at human vasopressin and oxytocin receptors: evidence that SSR149415 is a mixed vasopressin V1b/oxytocin receptor antagonist

1 A possible role of arginine vasopressin (AVP) V(1b) receptor subtype in stress-related disorders has been recently highlighted by the discovery of the agonist [1-deamino-4-cyclohexylalanine] AVP (d[Cha(4)]AVP) and the antagonist SSR149415. Both compounds have been proposed to target specifically V(1b) receptors, since the reported affinities for the related V(1a), V(2) and oxytocin receptors are in the micromolar or submicromolar range. In the present study, we further investigated the binding affinities of d[Cha(4)]AVP and SSR149415 at recombinant human vasopressin V(1b) (hV(1b)) and oxytocin (hOT) receptors expressed in Chinese hamster ovary (CHO) cells and functional properties of both compounds at hV(1b), hV(1a), hV(2) and hOT receptors. 2 d[Cha(4)]AVP bound to hV(1b) receptors and hOT receptors with pK(i) values of 9.68+/-0.06 and 7.68+/-0.09, respectively. SSR149415 showed pK(i) values of 9.34+/-0.06 at hV(1b) and 8.82+/-0.16 at hOT receptors. 3 d[Cha(4)]AVP stimulated [Ca(2+)](i) increase in hV(1b)-CHO cells with a pEC(50) value of 10.05+/-0.15. It showed pEC(50) values of 6.53+/-0.17 and 5.92+/-0.02 at hV(1a) and hV(2) receptors, respectively, and behaved as a weak antagonist at hOT receptors (pK(B)=6.31+/-0.12). SSR149415 inhibited the agonist-induced [Ca(2+)](i) increase with pK(B) values of 9.19+/-0.07 in hV(1b)-CHO and 8.72+/-0.15 in hOT-CHO cells. A functional pK(i) value of 7.23+/-0.10 was found for SSR1494151 at hV(1a) receptors, whereas it did not inhibit 20 nM AVP response at hV(2) receptors up to 3 microM. 4 Data obtained confirmed the high potency and selectivity of d[Cha(4)]AVP at hV(1b) receptors, but revealed that SSR149415, in addition to the high potency at hV(1b) receptors, displays a significant antagonism at hOT receptors.