Solubility of Acetaminophen and Ibuprofen in Polyethylene Glycol 600, N-Methyl Pyrrolidone and Water Mixtures

Transcription

1 J Solution Chem (2011) 40: DOI /s Solubility of Acetaminophen and Ibuprofen in Polyethylene Glycol 600, N-Methyl Pyrrolidone and Water Mixtures Shahla Soltanpour Abolghasem Jouyban Received: 24 July 2010 / Accepted: 10 April 2011 / Published online: 17 November 2011 Springer ScienceBusiness Media, LLC 2011 Abstract he solubility of acetaminophen and ibuprofen in binary and ternary mixtures of N-methyl pyrrolidone, polyethylene glycol 600 and water at 25 C were determined and the solubilities are mathematically represented by the Jouyban Acree model. he density of the solute-free solvent mixtures was measured and employed to train the Jouyban Acree model and then the densities of the saturated solutions were predicted. he overall mean relative deviations (OMRDs) for fitting the solubility data of acetaminophen and ibuprofen in binary mixtures are 3.2% and 6.0%, respectively. he OMRDs for fitting the solubilities in ternary solvent mixtures for acetaminophen and ibuprofen are 15.0% and 28.6%, respectively, and the OMRD values for predicting all solubilities of acetaminophen and ibuprofen by a trained version of the Jouyban Acree model are 9.4% and 17.8%, respectively. he prediction OMRD for the density of saturated solutions is 1.9%. Keywords Jouyban Acree model Acetaminophen Ibuprofen Solubility N-methyl pyrrolidone 1 Introduction Solutions, especially concentrated solutions, of pharmaceutical compounds have many advantages compared to solid forms. Solutions are easy to use and make excellent vehicles for carrying the uniform pharmaceutical compounds. hey provide rapid pharmacological S. Soltanpour Liver and Gastrointestinal Diseases Research Center, abriz University of Medical Sciences, abriz 51664, Iran Present address: S. Soltanpour Faculty of Pharmacy, Zanjan University of Medical Sciences, Zanjan 45139, Iran A. Jouyban ( ) Drug Applied Research Center and Faculty of Pharmacy, abriz University of Medical Sciences, abriz 51664, Iran ajouyban@hotmail.com

2 J Solution Chem (2011) 40: action, because it is not necessary to disintegrate and dissolve the compounds in the gastrointestinal fluids. Despite these advantages, some pharmaceutical compounds are not marketed in solution form due to their low solubility and/or chemical instability. Many pharmaceutically active compounds have low solubility and require relatively high volumes of the solvent for dissolution. Because of safety, compatibility, stability and economic considerations, the number of solvents to be used for making the liquid solutions is limited. Furthermore, using high volumes of solvents for solubilizing pharmaceuticals is not advised because there are some restrictions, e.g. for injections. One solution for overcoming this solubility problem is the addition of water-miscible co-solvents or surfactants to the formulation 1]. N-methyl pyrrolidone (NMP) is a chemically stable and powerful polar solvent. hese properties are very useful in some chemical reactions in which an inert medium is needed. Despite the stability of NMP, it can play an active role in certain reactions such as hydrolysis, oxidation, condensation, etc. NMP is a very strong solubilizing agent and is currently used in some commercially available pharmaceutical products 2]. NMP has various applications in pharmaceutical and medicinal fields such as a permeation enhancer in transdermal formulations 3, 4], improving the transdermal flux of both hydrophilic and hydrophobic drugs 5], is a solubilizing agent for poorly soluble drugs 6], provides entrapment of poorly water-soluble drugs in hybrid nanoparticles 7], enhances aqueous phase transdermal transport 5], increases the skin permeation of drugs 8] and is a co-surfactant in microemulsion systems 9]. Furthermore, various medical devices like self-hardening bone graft substitutes 10], dental barrier membranes 11] and subcutaneous drug delivery systems 12, 13] contain small amounts of NMP. he pharmaceutical applications of NMP have been reviewed in a recent work 14]. Polyethylene glycols (PEGs), called macrogols in the European pharmaceutical industry, are produced by polymerization of ethylene oxide 15, 16]. PEGs with a mean molecular weight up to 400 are non-volatile liquids at room temperature. PEG 600 shows a melting point of 17 to 22 C, so it may be a liquid at room temperature but a paste at lower temperatures. Solubility of PEGs in water is an important property, which makes them suitable in different applications. Liquid PEGs up to 600 are freely miscible with water 17]. he liquid PEGs have a slightly bitter taste, but it can be adjusted easily by suitable additives (sweeteners) and solid PEG grades have no taste. Solid PEGs are not soluble in liquid PEGs, but blending them with liquid PEGs leads to a white, pasty ointment with good solubility in water, good dissolving properties and suitable vehicles for many substances and ointment bases 18 20]. Solid PEGs are preferred bases for suppositories 21]. Producing tablets requires variable excipients with different functions, several of them covered by PEGs. hey may act as carriers, solubilizers, absorption improvers for active substances, lubricants and binders 22]. he relatively low melting point favors a sintering or compression technique. At the same time the PEG has a plasticizing effect, which facilitates the shaping of the tablet mass in the compression process and may counteract capping. he flexibility of sugar-coated tablets is increased by PEGs and, since PEG acts as an anticaking agent, the cores are prevented from sticking together. With film formers in sugar-free coating processes, PEGs act as a softener. PEGs 300 and 400 are listed as the active ingredients in ophthalmic demulcents 23]. With the two OH groups at the end of the PEG molecules, all typical reactions of alcohols, such as ester, carbonate and carbamate formation are possible. Methylether-capped PEGs, are available to avoid chain-building reactions, since these PEGs are only able to react at one end of the molecule. he PEG conjugation of drugs, e.g. anticancer drugs, makes them safer therapeutic agents which target malignant tissues with more selectivity 24, 25]. For achieving an optimized solvent mixture for dissolving a certain amount of a drug in a given volume of the solvent, the trial-and-error approach is usually employed, which is timeconsuming and expensive; therefore, employing cosolvency models could be an appropriate

3 2034 J Solution Chem (2011) 40: solution. Among the developed cosolvency models, the Jouyban Acree model is one of the most versatile. It provides accurate mathematical descriptions and shows how the solute solubility varies with both temperature and solvent composition. he model for representing the solubility of a solute in binary solvent mixtures at various temperatures is: ] w 1 w 2 log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, J i (w 1 w 2 ) i (1) where C m, is the solute s molar solubility in the binary solvent mixtures at temperature, w 1 and w 2 are the mass fractions of the solvents 1 and 2 in the absence of the solute, and C 1, and C 2, denote the molar solubility of the solute in the neat solvents 1 and 2, respectively. he J i terms are constants of the model and are computed by regressing (log 10 C m, w 1 log 10 C 1, w 2 log 10 C 2, ) against w 1w 2 w, 1 w 2 (w 1 w 2 ),and w 1w 2 (w 1 w 2 ) 2. his model was used to calculate multiple solubility maxima 26] and also to correlate other physico-chemical properties in solvent mixtures 27 32] and promises accurate mathematical representations. Equation 1 can be used to model the solubility data of a solute in a binary solvent at various temperatures. It requires C 1, and C 2, data and also a minimum number of solubility data in mixed solvents for the training process, which restricts its applications for prediction purposes. A number of attempts have been made to overcome this limitation. hese includes the presentation of trained versions of the Jouyban Acree model for specific cosolvents. he trained version of the model can be employed for predicting the solutes solubility in aqueous and non-aqueous solvent mixtures at various temperatures by using only C 1, and C 2, data. For PEG 400 {1} (to avoid reader confusion the solvent numbers used in the equations are reported in {}) water {2} mixtures, Eq. 1 was trained using experimental solubilities of drugs and the obtained model is 33]: log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 1w (w1 w 2 ) (w 1 w 2 ) 2], (2) for propylene glycol {1} water {2} mixtures is 34]: log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 1w (w1 w 2 ) ], (3) for ethanol {1} water {2} mixtures is 35]: log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 1w (w1 w 2 ) (w 1 w 2 ) 2], (4) and its updated version 36]: log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 1w (w1 w 2 ) (w 1 w 2 ) 2] 0.314w 1 w 2 log 10 P (5) in which log 10 P is the logarithm of the octanol water partition coefficient of the drug. he model for dioxane {1} water {2} mixtures is 37]: log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 1w (w1 w 2 ) (w 1 w 2 ) 2] (6)

4 J Solution Chem (2011) 40: for glycerol {1} water {2} mixtures is 38]: ( ) w1 w 2 log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 1 w 2 log 10 P, (7) and for ethyl acetate {1} ethanol {2} mixtures the trained model is 39]: log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 1w (w1 w 2 ) (w 1 w 2 ) 2]. (8) It should be noted that the solvent composition of the mixtures and also the solute concentration can be expressed using different units. Our results (not shown here) revealed that these different expressions have no effect on the prediction capability of the model. he accuracies of Eqs. 2 8 were evaluated using various numbers of data sets (NDS) by computing the MRDs. he OMRDs (± SD) for the equations and their NDSs are 39.8% (SD = 46.7, NDS = 80), 24.1% (SD = 15.1, NDS = 27), 49.4% (SD = 88.3, NDS = 26), 35.5% (SD = 22.5, NDS = 32), 27.2% (SD = 14.3, NDS = 36), 40.7% (SD = 35.8, NDS = 5) and 13.1% (SD = 8.1, NDS = 26), respectively. As a general point, greater NDS and less diversity of the investigated solutes resulted in greater accuracy for the model. As it is shown in Eq. 5, addition of a term representing the solute s structure improves the prediction capability of the model. he Jouyban Acree model has theoretical justifications 40], among other cosolvency models it shows the most accurate results 41], and by employing the solubility data in mono-solvents, the solubility data in solvent mixtures at various temperatures can be easily predicted 33 39]. In addition to the binary solvent mixtures, the extended models for predicting the solubility data of drugs in ternary solvent mixtures are: ] w 1 w 2 log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 3 log 10 C 3, J i (w 1 w 2 ) i ] ] w 1 w 3 J i (w 1 w 3 ) i w 2 w 3 J i (w 2 w 3 ) i (9) log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 3 log 10 C 3, ] ] w 1 w 2 J i (w 1 w 2 ) i w 1 w 3 J i (w 1 w 3 ) i ] ] w 2 w 3 J i (w 2 w 3 ) i w 1 w 2 w 3 J i (w 1 w 2 w 3 ) i (10) where C 3, is the solute molar solubility in the solvent 3 at temperature,andw 3 is the mass fraction of the solvent 3 in the absence of the solute. he J i and J i terms are computed using the same procedure as for the J i terms. he J i terms are the ternary solvent interaction terms and are computed by regressing: ] log 10 Cm, Sat w 1 log 10 C1, Sat w 2 log 10 C2, Sat w 3 log 10 C3, Sat w 1 w 2 J i (w 1 w 2 ) i ] ] w 1 w 3 J i (w 1 w 3 ) i w 2 w 3 J i (w 2 w 3 ) i

5 2036 J Solution Chem (2011) 40: against w 1w 2 w 3, w 1w 2 w 3 (w 1 w 2 w 3 ),and w 1w 2 w 3 (w 1 w 2 w 3 ) 2. he existence of these model constants, which require a number of solubility data in solvent mixtures for the training process, is a limitation for the model when the solubility predictions are the goal of the computations in early drug discovery studies. Experimental solubilities of acetaminophen and ibuprofen in PEG 600 {1} water {3} mixtures were reported in a previous work 42]. In this work, the experimental solubility of these drugs in NMP {2} water {3}, PEG 600 {1} NMP {2} and PEG 600 {1} NMP {2} water {3} mixtures at 25 C are reported and the applicability of the Jouyban Acree model to predict the measured solubility data is shown. In addition, the applicability of the Jouyban Acree model for predicting the density of the saturated solutions by employing density of solute-free solutions of mixed solvents is shown. 2 Experimental Method 2.1 Materials Acetaminophen was purchased from Arastoo Pharmaceutical Company (Iran) and ibuprofen was purchased from Sobhan Pharmaceutical Company (Iran). he purity of the drugs was checked by determination of their melting points and comparing the measured solubilities in mono-solvents with the corresponding data from the literature 43, 44]. NMP was purchased from Merck (Germany), PEG 600 was a gift from Daana pharmaceutical company (Iran), and double distilled water was used for preparation of the solutions. 2.2 Apparatus and Procedures he binary solvent mixtures (100.0 g) were prepared by mixing the appropriate weights of the solvents with the uncertainty of 0.10 g. he solubilities of acetaminophen and ibuprofen in the solvent mixtures were determined by equilibrating excess amounts of drugs at 25 C using a shaker (Behdad, ehran, Iran) placed in an incubator equipped with a temperature controlling system maintained constant within ±0.2 C (Nabziran, abriz, Iran). Because of the high viscosity of PEG 600, after sufficient time (> 98 h), the saturated solutions of the drugs were centrifuged at 13,000 rpm for 15 min, diluted with water for acetaminophen and methanol for ibuprofen, and then assayed at 243 nm and 222 nm, respectively, using a UV vis spectrophotometer (Beckman DU-650, Fullerton, USA). Concentrations of the diluted solutions were determined from the calibration curves. Each experimental data point represents the average of at least three replicate experiments with the measured molar solubilities being reproducible to within ±3.1%. Densities of the saturated solutions and the solvent mixtures in the absence of the solute were measured using a 5 ml pycnometer. 2.3 Computational Methods he experimental solubility data for each drug, in the binary solvents, were fitted to Eq. 1, the model constants were computed, and the back-calculated solubilities were used to compute the MRD values. In the next analysis, Eq. 9 was used to calculate the solubility of each drug in the ternary solvents. In order to provide better predictions, the ternary interaction terms of Eq. 10 were calculated using a linear regression analysis. As discussed above, Eq. 1 should be trained using experimental solubility data. o provide generally trained versions of the model for PEG 600 {1} water {2} and NMP {1} water {2} mixtures, the generated data and collected data from the literature 42, 45 48]

6 J Solution Chem (2011) 40: were used to train the model. In order to show the capability of the Jouyban Acree model for a predicting drug s solubility in PEG 600 {1} water {2} and NMP {1} water {2} mixtures, one data set was excluded from the training process and its solubility values were predicted using the trained version of the model. his method is called leave one out crossvalidation. Densities of the saturated solutions are required to convert the molar solubilities into mole fraction solubilities. Any attempt to predict the density of the saturated solutions can save time and the cost of the experimental efforts. In a previous paper 49], the applicability of the Jouyban Acree model for the prediction of the densities of liquid mixtures at various temperatures was shown. he investigated liquid mixtures were solute free, so for showing the model applicability in predicting the densities of the saturated solutions composed of liquid mixtures, the model was fitted to the density of solute-free binary mixtures and the sub-binary constants were calculated for each system. hen, by using these constants, the model constants for ternary mixtures were obtained and the trained version of the model was used to predict the densities of the saturated solutions and the resulting prediction errors were within an acceptable range 42]. herefore, the same procedure can be used to predict the densities of saturated solutions investigated in this work. he experimental and calculated densities were used to convert the molar solubilities to the mole fraction scale. he mean relative deviation (MRD) between the calculated and observed (solubility/density) values are used to evaluate the accuracy of the model. he MRD values are calculated using: { Calculated Observed } Observed MRD = 100 (11) N where N is the number of data points in each set. 3 Results ables 1 and 2 list the experimental solubilities of acetaminophen and ibuprofen in the binary and ternary solvent mixtures along with the measured density of the saturated solutions at 25 C, respectively. he densities of the solute-free solvent mixtures are also listed in able 1. he minimum solubility of acetaminophen ( mol L 1 ) is observed for the aqueous solution and is in good agreement with previous data 50 52]. he maximum solubility of acetaminophen (8.60 mol L 1 ) in the solvent mixtures is observed for the PEG 600 {1} NMP {2} water {3} ( mass fractions) mixture. he aqueous solubility of ibuprofen is mol L 1 (or as mole fraction) and is comparable with the corresponding values from the literature 53, 54]. he maximum solubility of ibuprofen (8.95 mol L 1 ) in the solvent mixtures studied is observed in the PEG 600 {1} NMP {2} water {3} ( mass fractions) mixture. here are no published experimental data for drugs in the investigated solvent mixtures. Equations 9 and 10 were used to fit the data sets of acetaminophen and ibuprofen; the constants and MRD values are shown in able 3. In the binary mixtures of acetaminophen the lowest MRD value is for PEG 600 {1} NMP {2} mixtures with 1.0% and the highest MRD value belongs to NMP {2} water {3} mixtures with 7.4%. For ibuprofen in binary mixtures the lowest MRD is for PEG 600 {1} NMP {2} mixtures with 0.8% and the highest MRD value belongs to PEG 600 {1} water {3} mixtures with 9.3%. In the binary mixtures, the overall MRD (OMRD) values are 3.2% and 6.0%, respectively, for acetaminophen and ibuprofen. he MRD values for ternary mixtures are 15.0% and 28.6%, respectively, for

7 2038 J Solution Chem (2011) 40: able 1 Experimental molar solubilities (C m, ) of acetaminophen in PEG 600 {1} NMP {2} water {3} mixtures at 25 C and densities of the saturated and solute-free solutions w 1 w 2 w 3 C m, (mol L 1 ) Density of the saturated solutions (g ml 1 ) Density of solute-free solvent mixtures (g ml 1 )

8 J Solution Chem (2011) 40: able 1 (Continued) w 1 w 2 w 3 C m, (mol L 1 ) Density of the saturated solutions (g ml 1 ) Density of solute-free solvent mixtures (g ml 1 ) able 2 Experimental molar solubilities (C m, ) of ibuprofen in PEG 600 {1} NMP {2} water {3} mixtures at 25 C and density of the saturated solutions w 1 w 2 w 3 C m, (mol L 1 ) Density of the saturated solutions (g ml 1 )

9 2040 J Solution Chem (2011) 40: able 2 (Continued) w 1 w 2 w 3 C m, (mol L 1 ) Density of the saturated solutions (g ml 1 )

10 J Solution Chem (2011) 40: able 3 he constants of the Jouyban Acree model and the mean relative deviations (MRD) of backcalculation for the solubility of acetaminophen and ibuprofen in binary and ternary solvent mixtures Drug N Solvent system J 0 J 1 J 2 MRD% Acetaminophen 11 PEG 600 {1} NMP {2} Acetaminophen a 11 PEG 600 {1} water {3} Acetaminophen 11 NMP {2} water {3} Acetaminophen 36 PEG 600 {1} NMP {2} water {3} OMRD 6.2 Ibuprofen 11 PEG 600 {1} NMP {2} Ibuprofen a 11 PEG 600 {1} water {3} Ibuprofen 11 NMP {2} water {3} Ibuprofen 36 PEG 600 {1} NMP {2} water {3} OMRD 11.7 a Solubility data taken from a previous work 42] acetaminophen and ibuprofen. he MRD values for ternary solvent mixtures are higher than those of the binary solvent mixtures. After calculating the sub-binary and ternary constants for each drug (details are shown in able 3), the trained versions of the Jouyban Acree model were used to predict the solubility of acetaminophen and ibuprofen in both binary and ternary solvent mixtures. he OMRD values for predicting the acetaminophen and ibuprofen solubility in binary and ternary solvent mixtures are 6.2% and 11.7%, respectively. In addition to those trained versions of the Jouyban Acree model, two generally trained models for PEG 600 {1} water {2} and NMP {1} water {2} mixtures, using data sets taken from previous work 42, 45 48], are presented in this work. he trained version for PEG 600 {1} water {2} mixtures is: log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 1w 2 and the trained version for NMP {1} water {2} mixtures is: (12) log 10 C m, = w 1 log 10 C 1, w 2 log 10 C 2, w 1w (w1 w 2 ) (w 1 w 2 ) 2]. (13) he back-calculated MRDs of Eqs. 12 and 13 and the references of the data sets are shown in able 4. In PEG 600 {1} water {2} mixtures the lowest (18.4%) and highest (127.9%) MRDs are observed for lamotrigine and clonazepam, respectively. Also in NMP {1} water {2} mixtures the lowest and highest MRDs belong to lamotrigine (25.6%) and clonazepam (149.9%), respectively. he OMRD values for Eqs. 12and 13are 56.9% and 58.1%, respectively. Comparing the MRD values of Eqs. 12 and 13 with those of Eq. 1 (listed in able 3) reveal that the MRD values of Eq. 1 are less than those of Eqs. 12 and 13. Butthe main advantage of Eqs. 12 and 13 is that they need experimental solubility data of drugs in mono-solvents, but for computing the constants of Eq. 1 at least three solubility data in mixed solvents are required. herefore, with the advantage and weakness of Eqs. 12 and 13, the MRD values of these equations are acceptable. In the leave one out cross-validation method, one drug was excluded from the training sets and Eq. 1 was trained with the rest of the data sets, and then by using the trained ver-

11 2042 J Solution Chem (2011) 40: able 4 he details of data sets and the mean relative deviations (MRD) of the back-calculation and leave one out methods Drug N Solvent system Reference MRD% (backcalculation) MRD% (leave one out) Acetaminophen 11 PEG 600 {1} water {3} 42] Ibuprofen 11 PEG 600 {1} water {3} 42] Pioglitazone HCl 12 PEG 600 {1} water {3} 48] Diazepam 11 PEG 600 {1} water {3} 46] Lamotrigine 11 PEG 600 {1} water {3} 46] Clonazepam 11 PEG 600 {1} water {3} 46] OMRD Acetaminophen 11 NMP {2} water {3} his work Ibuprofen 11 NMP {2} water {3} his work Pioglitazone HCl 14 NMP {2} water {3} 45] Diazepam 11 NMP {2} water {3} 47] Lamotrigine 11 NMP {2} water {3} 47] Clonazepam 11 NMP {2} water {3} 47] Phenobarbital 11 NMP {2} water {3} 47] OMRD sion, the solubility of the excluded drug was predicted. he details of the MRD values for the leave one out method are shown in able 4. In PEG 600 {1} water {2} mixtures the lowest and highest MRDs belong to lamotrigine (20.4%) and clonazepam (172.9%), respectively, and for NMP {1} water {2} mixtures the lowest and highest MRDs belong again to lamotrigine (30.1%) and clonazepam (198.8%), respectively. he OMRD values for PEG 600 {1} water {2} and NMP {1} water {2} mixtures are 69.2% and 70.8%, respectively. For predicting the density of the saturated solutions, the densities of solute free binary and ternary solvent mixtures were measured. hen, by the density of the binary mixtures, the constants of Eq. 14 were computed: ] w 1 w 2 log 10 ρ m, = w 1 log 10 ρ 1, w 2 log 10 ρ 2, w 3 log 10 ρ 3, J i (w 1 w 2 ) i w 1 w 3 ] J i (w 1 w 3 ) i w 2 w 3 J i (w 2 w 3 ) i ]. (14) hen by using these sub-binary constants, the ternary constants of Eq. 15 were computed: log 10 ρ m, = w 1 log 10 ρ 1, w 2 log 10 ρ 2, w 3 log 10 ρ 3, ] ] w 1 w 2 J i (w 1 w 2 ) i w 1 w 3 J i (w 1 w 3 ) i w 2 w 3 ] J i (w 2 w 3 ) i w 1 w 2 w 3 J i (w 1 w 2 w 3 ) i ]. (15)

12 J Solution Chem (2011) 40: he final trained version is: log 10 ρ m, = w 1 log 10 ρ 1, w 2 log 10 ρ 2, w 3 log 10 ρ 3, (w 1w 2 ) (w 1w 3 ) w 2w (w2 w 3 ) 0.336(w 2 w 3 ) 2] w 1w 2 w (w1 w 2 w 3 ) ]. (16) Equation 16 was used to predict the densities of the saturated solutions. he prediction MRDs were 1.4% and 2.4%, respectively, for acetaminophen and ibuprofen and the OMRD was 1.9%. he experimental and calculated densities were used to convert the molar solubility to the mole fraction solubility, and the OMRD value for the difference of mole fraction solubilities from experimental and predicted densities was 1.9%. 4 Discussion Experimental solubilities of acetaminophen and ibuprofen are reported in aqueous and nonaqueous mixtures of PEG 600 and NMP. Aqueous mixtures data could be used in liquid drug formulation whereas non-aqueous data could be used in other formulations such as softgels. he Jouyban Acree model fits well to the experimental solubility data for drugs at all compositions of the solvent mixtures. Also, it fits well to the experimental solubilities of drugs in ternary solvents with given fractions of the cosolvents. hese findings are also supported by the small MRD values of the back-calculated and experimental solubility data. Although the MRDs are very low, especially for sub-binary solvents, it should be kept in mind that the constants are computed using the solubility of acetaminophen and/or ibuprofen, which need experimental measurements. Generally, the overall MRDs observed in these predictions show that the Jouyban Acree model provided more accurate predictions in the presence of one or two cosolvents. According to the density prediction results, it s not necessary to measure the density of all saturated solutions, and by measuring the density of the solute-free solvent mixtures and with trained version of the Jouyban Acree model, the density of the saturated solutions could be predicted within an acceptable MRD. Acknowledgements he authors would like to thank the Research Affairs of abriz University of Medical Sciences for the partial financial support under grant No. 5/4/5452, and also Daana pharmaceutical company for supplying the drug powders and materials used in this work. References 1. Coapman, S.D.: Progress for solubilizing difficulty soluble pharmaceutical actives. Patent No , 25 August Strickly, A.: Solubilizing excipients in oral and injectable formulations. Pharm. Res. 21, (2004) 3. Sasaki, H., Kojima, M., Mori, Y., Nakamura, J., Shibasaki, J.: Enhancing effect of pyrrolidone derivatives on transdermal drug delivery I. Int. J. Pharm. 44, (1988) 4. Yoneto, K., Li, S.K., Ghanem, A.H., Crommelin, D.J., Higuchi, W.I.: A mechanistic study of the effects of the 1-alkyl-2-pyrrolidones on bilayer permeability of stratum corneum lipid liposome: a comparison with hairless mouse skin studies. J. Pharm. Sci. 84, (1955) 5. Lee, P.J., Langer, R., Shastri, V.P.: Role of N-methyl pyrrolidone in the enhancement of aqueous phase transdermal transport. J. Pharm. Sci. 94, (2005) 6. Uch, A.S., Hesse, U., Dressman, J.B.: Use of 1-methyl-pyrrolidone as a solubilizing agent for determining the uptake of poorly soluble drugs. Pharm. Res. 16, (1999)

13 2044 J Solution Chem (2011) 40: Ishihara,., Goto, M., Kanazawa, H., Higaki, M., Mizushima, Y.: Efficient entrapment of poorly watersoluble pharmaceuticals in hybrid nanoparticles. J. Pharm. Sci. 98, (2008) 8. Koizumi, A., Makiko, F., Kondoh, M., Watanabe, Y.: Effect of N-methyl pyrrolidone on skin permeation of estradiol. Eur. J. Pharm. Biopharm. 57, (2004) 9. Bachhav, Y.G., Date, A.A., Patravale, V.B.: Exploring the potential of N-methyl pyrrolidone as a cosurfactant in the microemulsion systems. Int. J. Pharm. 326, (2006) 10. Hellerbrand, K., Siedler, M., Schutz, A., Pompe, C., Friess, W.: In situ hardening paste, its manufacturing and use. Publication No. US 2009/ , 19 February Pirhonen, E., Nieuwenhuis, J., Kaikkonen, A., Nieminen,., Weber, F.: Resorbable polymer compositions, implant and method of making implant. Patent No , 9 August Malook, S.U., Boon, P.F.G., Morgan, J.P.: Method of reducing the swelling or pain associated with antibiotics compositions. Patent No , 20 September Dornhofer, W., Embrechts, E.: Injection solution for intramascular and subcutaneous administration to animal. Patent No , 19 May Jouyban, A., Fakhree, M.A.A., Shayanfar, A.: Review of pharmaceutical applications of N-methyl-2- pyrrolidone. J. Pharm. Pharmaceut. Sci. 13, (2010) 15. Cheng, G., Fan, X., ian, W., Liu, Y., Liu, G.: Study on anionic polymerization of ethylene oxide initiated by ammonium/triisobutylaluminum. J. Polym. Res. 17, (2010) 16. Mark, H.F. (ed.): 1,2-Epoxide Polymers: Ethylene Oxide Polymers and Copolymers. In: Encyclopedia of Polymer Science and Engineering, pp Wiley, New York (1986) 17. Polyethylene Glycols (PEGs) and the pharmaceutical industry. (2009). Accessed on 22 November Polyethylene Glycol Ointment (PEG Ointment), USP 24th revision, p (2000) 19. Macrogol Ointment (Polyethylene Glycol Ointment), 13th edn., p he Japanese Pharmacopoeia (1996) 20. Macrogol 1500 (Polyethylene Glycol 1500), 13th edn., pp he Japanese Pharmacopoeia (1996) 21. Nagatomi, A., Mishima, M., suzuki, O., Ohdo, S., Higuchi, S.: Utility of a rectal suppository containing the antiepileptic drug zonisamide. Biol. Pharm. Bull. 20, (1997) 22. Al-Nasassrah, M., Podczeck, F., Newton, J.M.: he effect on an increase in chain lengthon the mechanical properties of polyethylene glycols. Eur. J. Pharm. Biopharm. 46, (1998) 23. FDA, Ophthalmic drug products for over-the-counter human use: active ingredients: ophthalmic demulcents. Code of Federal Regulation 21, Part URL cfdocs/cfcfr/cfrsearch.cfm?fr= (2009). Accessed 22 Nov Parnaud, G.: Polyethylene glycol suppresses colon cancer. Cancer Res. 59, (1999) 25. Sakanoue, Y.: he efficacy of whole gut irrigation with polyethylene glycol electrolyte solution. Acta Chim. Scand. 156, (1990) 26. Jouyban-Gharamaleki, A., Acree, W.E. Jr.: Comparison of models for describing multiple peaks in solubility profiles. Int. J. Pharm. 167, (1998) 27. Jouyban-Gharamaleki, A., Khaledi, M.G., Clark, B.J.: Calculation of electrophoretic mobilities in water organic modifier mixtures in capillary electrophoresis. J. Chromatogr. A 868, (2000) 28. Jouyban, A., Fathi-Azarbayjani, A., Acree, W.E. Jr.: Surface tension calculation of mixed solvents with respect to solvent composition and temperature by using Jouyban Acree model. Chem. Pharm. Bull. 52, (2004) 29. Jouyban, A., Khoubnasabjafari, M., Vaez-Gharamaleki, Z., Fekari, Z., Acree, W.E. Jr.: Calculation of the viscosity of binary liquids at various temperatures using Jouyban Acree model. Chem. Pharm. Bull. 53, (2005) 30. Jouyban, A., Chan, H.K., Barzegar-Jalali, M., Acree, W.E. Jr.: A model to represent solvent effects on the chemical stability of solutes in mixed solvent systems. Int. J. Pharm. 243, (2002) 31. Jouyban, A., Soltani, S., Chan, H.K., Acree, W.E. Jr.: Modeling acid dissociation constant of analytes in binary solvents at various temperatures using Jouyban Acree model. hermochim. Acta 428, (2005) 32. Jouyban, A., Rashidi, M.R., Vaez-Gharamaleki, Z., Matin, A.A., Djozan, D.j.: Mathematical representation of analyte s capacity factor in binary solvent mobile phases using Jouyban Acree model. Pharmazie 60, (2005) 33. Jouyban, A.: Solubility prediction of drugs in water polyethylene glycol 400 mixtures using Jouyban Acree model. Chem. Pharm. Bull. 54, (2006) 34. Jouyban, A.: Prediction of drug solubility in water propylene glycol mixtures using Jouyban Acree model. Pharmazie 62, (2007) 35. Jouyban, A., Acree, W.E. Jr.: In silico prediction of drug solubility in water ethanol mixtures using Jouyban Acree model. J. Pharm. Pharmaceut. Sci. 9, (2006)

14 J Solution Chem (2011) 40: Jouyban, A., Soltanpour, Sh., Acree, W.E. Jr.: Improved prediction of drug solubilities in ethanol water mixtures at various temperatures. Biomed. Int. 1, (2010) 37. Jouyban, A.: In silico prediction of drug solubility in water dioxane mixtures using the Jouyban Acree model. Pharmazie 62, (2007) 38. Jouyban, A., Fakhree, M.A.A., Mirzaei, Sh., Ghafourian,., Soltanpour, Sh., Nokhodchi, A.: Solubility prediction of paracetamol in water glycerol mixtures at 25 and 30 C using the Jouyban Acree model. Asian J. Chem. 21, (2009) 39. Jouyban, A., Acree, W.E. Jr.: Prediction of drug solubility in ethanol ethyl acetate mixtures at various temperatures using the Jouyban Acree model. J. Drug Deliv. Sci. echnol. 17, (2007) 40. Acree, W.E. Jr.: Mathematical representation of thermodynamic properties. Part II. Derivation of the combined nearly ideal binary solvent (NIBS)/Redlich-Kister mathematical representation from a twobody and three-body interactional mixing model. hermochim. Acta 198, (1992) 41. Jouyban-Gharamaleki, A., Valaee, L., Barzegar-Jalali, M., Clark, B.J., Acree, W.E. Jr.: Comparison of various cosolvency models for calculating solute solubility in water cosolvent mixtures. Int. J. Pharm. 177, (1999) 42. Soltanpour, Sh., Jouyban, A.: Solubility of acetaminophen and ibuprofen in binary and ternary mixtures of polyethylene glycol 600, ethanol and water. Chem. Pharm. Bull. 58, (2010) 43. Pacheco, D.P., Manrique, Y.J., Martinez, F.: hermodynamic study of the solubility of ibuprofen and naproxen in some ethanol propylene glycol mixtures. Fluid Phase Equilib. 262, (2007) 44. Manzo, R.H., Ahumada, A.A.: Effects of solvent medium on solubility. V. Enthalpic and entropic contributions to the free energy changes of disubstituted benzene derivatives in ethanol:water and ethanol:cyclohexane mixtures. J. Pharm. Sci. 79, (1990) 45. Soltanpour, Sh., Jouyban, A.: Solubility of pioglitazone hydrochloride in aqueous solutions of ethanol, propylene glycol, and N-methyl-2-pyrrolidone at K. AAPS Pharm. Sci. ech. 10, (2009) 46. Soltanpour, Sh., Acree, W.E. Jr., Jouyban, A.: Solubility of 5-(2-chlorophenyl)-7-nitro-1,3-dihydro-1,4-benzodiazepin-2-one, 7-chloro-1-methyl-5-phenyl-3H-1,4-benzodiazepin-2-one, and 6-(2,3- dichlorophenyl)-1,2,4-triazine-3,5-diamine in the mixtures of polyethylene glycol 600, ethanol, and water at K. J. Chem. Eng. Data 55, (2010) 47. Shayanfar, A., Acree, W.E. Jr., Jouyban, A.: Solubility of clonazepam, diazepam, lamotrigine and phenobarbital in N-methyl-2-pyrrolidone water mixtures at K. J. Chem. Eng. Data 54, (2009) 48. Jouyban, A., Soltanpour, Sh.: Solubility of pioglitazone hydrochloride in polyethylene glycol 600 ethanol water mixtures at 25 C. Latin Am. J. Pharm. 29, (2010) 49. Jouyban, A., Fathi-Azarbayjani, A., Khoubnasabjafari, M., Acree, W.E. Jr.: Mathematical representation of the density of liquid mixtures at various temperatures using Jouyban Acree model. Indian J. Chem. A 44, (2005) 50. Jouyban, A., Chan, H.K., Chew, N.Y.K., Khoubnasabjafari, M., Acree, W.E. Jr.: Solubility prediction of paracetamol in binary and ternary solvent mixtures using Jouyban Acree model. Chem. Pharm. Bull. 54, (2006) 51. Dearden, J.C., Patel, N.C.: Dissolution kinetics of some alkyl derivatives of acetaminophen. Drug Dev. Ind. Pharm. 4, (1978) 52. Paruta, A.N., Irani, S.A.: Dielectric solubility profiles in dioxane water mixtures for several antipyretic drugs. Effect of substituents. J. Pharm. Sci. 54, (1965) 53. Rytting, E., Lentz, K.A., Chen, X.Q., Qian, F., Venkatesh, S.: Aqueous and cosolvent solubility data for drug-like organic compounds. AAPS J., 7, E78 E105 (2005) 54. Fini, A., Laus, M., Orienti, I., Zecchi, V.: Dissolution and partition thermodynamic functions of some nonsteroidal anti-inflammatory drugs. J. Pharm. Sci. 75, (1986)