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Solubilization of drugs using sodium lauryl sulfate: Experimental data and modeling
Accepted Manuscript Solubilization of drugs using sodium lauryl sulfate: Experimental data and modeling
Mohammad Norouz Alizadeh, Ali Shayanfar, Abolghasem Jouyban PII: DOI: Reference:
S0167-7322(18)30265-4 doi:10.1016/j.molliq.2018.07.065 MOLLIQ 9386
To appear in:
Journal of Molecular Liquids
Received date: Revised date: Accepted date:
16 January 2018 16 June 2018 15 July 2018
Please cite this article as: Mohammad Norouz Alizadeh, Ali Shayanfar, Abolghasem Jouyban , Solubilization of drugs using sodium lauryl sulfate: Experimental data and modeling. Molliq (2018), doi:10.1016/j.molliq.2018.07.065
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ACCEPTED MANUSCRIPT Solubilization of drugs using sodium lauryl sulfate: Experimental data and modeling
Mohammad Norouz Alizadeh1,2, Ali Shayanfar*3, Abolghasem Jouyban4
Biotechnology Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences,
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Tabriz, Iran
Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
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Drug Applied Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences,
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Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical
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4
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Tabriz, Iran
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Science, Tabriz, Iran
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*Corresponding author: [email protected]
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ACCEPTED MANUSCRIPT
Abstract: Micellar solubilization is a great method for increasing drugs solubility in aqueous environments. At concentrations above the critical micelle concentration (CMC), micelles are formed and they
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are able to increase the apparent aqueous solubility of poorly soluble drugs. Sodium lauryl sulfate
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(SLS) is one of the common solubilizing agents in pharmaceutical sciences. Investigation on the
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water solubility of drugs in the presence of surfactants and the development of a relationship between drug solubility in the presence of SLS and structural descriptors is an important issue in
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the prediction and understanding of the solubilization mechanism. The aims of this study are:
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determination of experimental solubility of drugs in the presence of SLS and development of models for finding a relationship between solubilization factor by SLS and structural descriptors.
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Samples were prepared by adding excess amount of 19 drugs (with diverse structural and
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physicochemical properties) to water and an aqueous solution of SLS at different concentrations, that is, less than (0.1%) and above the CMC (0.5%). The mixtures were placed in a shaker-
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incubator for 72-96 h at 37°C. Then, the equilibrated samples were filtered and analyzed at
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maximum wavelengths by UV-spectrophotometry and the concentrations were calculated based on the calibration curves. Afterward, the molecular descriptors of drugs were computed and their
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relationship with solubilization factor in the presence of SLS was investigated. Most of the drugs showed a considerable increase in solubility above the CMC (0.5%) of SLS. Therefore, the effective mechanism for solubilization by surfactants is the formation of micelles. On the other hand, a good correlation was observed between structural descriptors and solubilization power in the presence of surfactant. Overall, SLS is a good solubilization agent and the solubility in aqueous solution of SLS depends on various structural descriptors. Keywords: Drug, Modeling, Solubility, Surfactant, SLS, micelle 2
ACCEPTED MANUSCRIPT Introduction: The solubility of drugs is an important aspect from the earliest stage of drug discovery to the latest stage of drug formulation [1, 2]. The most important challenge in designing oral drug systems is their poor bioavailability. Several factors affect oral bioavailability in which aqueous solubility is
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the most important one. Various strategies have been proposed to improve solubility of a solute
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and the most common methods are co-solvency, salt formation, crystal engineering, nanosizing
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and use of surfactant [3].
Surfactants are organic compounds that are amphiphilic and composed of hydrophilic heads and
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hydrophobic tails. When the concentration of surfactant molecules in a system is low, they adsorb
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onto surfaces or interfaces. At concentrations above the critical micelle concentration (CMC), micelles are formed. Micelles can increase the aqueous solubility of low soluble drugs; thus, at
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concentrations above the CMC, aqueous solubility increases linearly with the surfactant
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concentration [3, 4]. Therefore, micellar solubilization is a great method for increasing poorly soluble drugs in aqueous environments. Although, micellar solubility in aqueous media is just
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apparent because of the solute is not dispersed at molecular level but inside the micelles.
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Surfactants were used as solubilizing excipient of paclitaxel, cyclosporine, amiodarone hydrochloride and calcitriol [3].
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Anionic surfactant, sodium lauryl sulfate (SLS) is the most common surfactant which has several functional uses in medicinal [5] and cleaning products [6]. It was used in oral formulations of various drug products such as acetaminophen, hydrocodone bitartrate, alprazolam, amoxicillin trihydrate, buspirone hydrochloride, clonazepam, cyclobenzaprine hydrochloride, diazepam, gabapentin,
hydroxyzine
pamoate,
methocarbamol
and
tramadol
hydrochloride
(https://drugs.com). Moreover, research on aqueous solubility of drugs in the presence of
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ACCEPTED MANUSCRIPT surfactants and the development of a relationship between drug solubility in the presence of SLS and structural descriptors is an important issue in the prediction and understanding of solubilization mechanism. Various structural parameters could be used to model and predict physicochemical properties and activity of chemical compounds. However, common and simple parameters such as
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melting point (MP), molar mass (Mw), partition coefficient (log P) and topological polar surface
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area (TPSA) [7, 8] could provide a mechanistic interpretation for a given model [9]. These
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parameters are used to develop quantitative structure-activity relationships (QSAR) and quantitative structure-property relationships (QSPR) models. Moreover, Abraham solvation
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parameters of a solute as mechanistic descriptors which is composed of E (excess molar
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refraction), S (dipolarity/polarizability), A (hydrogen-bond acidity), B (hydrogen-bond basicity) and V (McGowan volume) were used to predict various biological and toxicological activities, and
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physicochemical properties such as solubility [10-13].
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SLS is a common excipient in pharmaceutical formulations; nevertheless, a few studies have reported on the solubilization mechanism and its relation with the structure and physicochemical
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properties of drugs, to propose the QSPR models for prediction of solubilization ratio. Ghasemi et
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al. [14] proposed a model for solubilization of non-pharmaceutical compounds by SLS and Jouyban et al., [15] studied solubility of some anti-epileptic drugs in the presence of SLS in ethanol
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+ water mixture and the obtained data correlated with co-solvency models. The aims of the current study are to determine the experimental solubility of drug in the presence of SLS and development of models for finding a relationship between solubilization factor in the presence of SLS and common structural descriptors of drugs.
Methods and Materials: 4
ACCEPTED MANUSCRIPT Materials: Table 1 presents a summary of the drugs applied in this study. SLS supplied from Merck (Germany) and Lab-made distilled water was used for the preparation of the solutions. All the materials were of analytical grade and used without further purification. *********************************Table 1***************************************
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Apparatus and procedures for solubility determination: The solubility of the studied drugs was
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determined by equilibrating in water and the aqueous solution of SLS at two concentrations based
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on the CMC of SLS (0.24%) [16], that is, 0.1% (< CMC) and 0.5% (>CMC). For this purpose, an excess amount of the solid powder was added to the dissolution media using a shaker-incubator
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equipped with a temperature controlling system (Heidolph, Schwabach, Germany) at 37 ± 0.1°C.
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After a sufficient period of time (>72 h), the saturated solutions of the drugs were filtered through a 0.45 μm filter and diluted with appropriate solvent. The diluted samples were then assayed by
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UV–Vis spectrophotometer (Shimadzu, Japan), and the concentrations were determined from the
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calibration curves. Each experimental data point represents the average of at least three repetitive experiments. The details on calibration curves are summarized in Table S1 (supplementary
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information). The coefficient (R2) of all calibration curves were higher than 0.99.
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Computational Methods: The obtained solubilities in water and aqueous solutions of SLS (0.1 and 0.5%) were used to calculate the solubilization ratio of each drug at 37ºC by dividing solubility
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in the aqueous solution of SLS into solubility in water. The logarithm of solubilization ratio was used to correlate various structural parameters of solute, that is, Mw, TPSA, Abraham solvation parameters
and
MP.
(https://ilab.acdlabs.com/)
Structural and
parameters melting
of point
drugs
calculated
extracted
by
from
ACD-ilab literature
(https://chem.nlm.nih.gov/chemidplus/) and their numerical values are listed in Table 1. In aqueous solution of surfactant below CMC, data were divided into two groups, class I:
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ACCEPTED MANUSCRIPT solubilization ratio <1.3 and class II: solubilization ratio >1.3. Logistic regression [17] was used to develop a model for classification of drugs into the two mentioned sub-groups. Moreover, multiple linear regression was used to develop a model to predict solubilization ratio at above the CMC (0.5%). To validate the proposed models and assess their prediction capability, the leave-
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one out (LOO) method was used, where one solute is left out from the training set and the obtained
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model is used to predict the removed data point. All the computational analyses were performed
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using SPSS 17.
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Results and Discussion:
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Solubility of the studied drugs in water and aqueous solution of SLS (0.1% and 0.5%): Table 2 shows the experimental solubilities of 19 studied drugs in water and an aqueous solution
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of SLS (0.1% and 0.5%).
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*********************************Table 2*************************************** Several parameters can affect the validity of the solubility data and different solubility data have
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been reported for a drug at a certain temperature in the literature [34, 35]. Therefore, to check the
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accuracy of the experimental method for solubility determination of drugs, the obtained data of solubility in water was compared with previously published solubility data and good agreement
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was observed.
Solubility investigation in the presence of two CMCs gave different results. Only 50% of the drugs showed a considerable increase (>1.3) in solubility in 0.1% of SLS, therefore, the data were divided into two groups, that is, class I: solubilization ratio <1.3 and class II: solubilization ratio >1.3. However, majority of the drugs showed a significant increase in the presence of 0.5% SLS (>CMC). Minimum and maximum increases in solubility values were observed for acetaminophen (1.1-fold) and ketoconazole (173-fold). SLS is an anionic surfactant (amphiphilic compound); in 6
ACCEPTED MANUSCRIPT low concentration, it exists in monomer forms and at concentration above the CMC, it can form micelle and enhance the apparent solubility of poorly water-soluble drugs by providing a hydrophobic environment in the micelle core. Overall, solubilization effect is very low until the surfactant concentration reaches the CMC and then the solubility increases linearly with the
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concentration of surfactant. However, the drug properties are critical factors in solubilization by
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the surfactant.
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Computational modeling of drug solubilization in the presence of SLS:
Structural parameters of the studied drugs (Table 1) were used to develop models to predict
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solubility and interpret solubilization mechanism in aqueous SLS solution.
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Modeling results of solubility in aqueous solution of SLS (0.1%, < CMC) by regression analysis show poor correlation (R2<0.5) between the structural parameters of drugs and solubilization ratio
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in the presence of 0.1% SLS. The highest correlation coefficient (0.3) was obtained with V and
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Mw. This poor correlation could be related to nine compounds and they have a very low solubilization ratio (<1.3), and the solubility of only 10 drugs in 0.1% SLS was higher than 1.3.
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Therefore, the data were classified into two subgroups, that is, class I: solubilization factor <1.3
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and class II: solubilization factor >1.3. A model for classifying data in the defined groups was developed by a forward logistic regression and the developed model is:
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e(4.6162.141V ) p 1 e(4.6162.141V )
(1)
In this model, p is probability of a binary response (class I or II) based on V values. All the drugs with V value higher than 3 (five compounds) belong to class I and based on Eq. 1, 78.9% of the studied drugs can be classified in the correct group (80% of class 1 and 77.8% of class 2). LOO validation analysis showed no change in the predicted class and it confirmed the validity of the model. Therefore, V is a useful parameter for classification of data into two sub7
ACCEPTED MANUSCRIPT groups and drugs with higher V values can improve aqueous solubility more than 1.3 in the presence of 0.1% SLS. These results indicate the role of solute volume or size (there is a good correlation coefficient between V and Mw (R2>0.9)) in solubilization by SLS in low concentration (
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*********************************Table 3***************************************
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As compared to solubility investigation above CMC (micelle formation), few studies have been
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done on below CMC (surfactant monomers) in the presence of surfactants. Schacht et al. [36] reported on solubility of some polychlorinated dibenzodioxins (PCDDs) compounds; considerable
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solubility enhancement (up to 200-fold) in monomer solutions of SLS was observed for PCBs and
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a positive relation was observed between volume of PCBs and solubilization ratio. A possible mechanism is partitioning-like process of large volume compounds such as PCDDs between water
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and the non-polar moiety of the monomer. The formation of aggregates at surfactant
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concentrations below CMC in the presence of hydrophobic organic compounds such as hexadecane which has a large volume is another potential mechanism for solubilization [37].
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Relationship between solubilization ratios above the CMC of SLS, that is, 0.5% and structural
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parameters show a positive correlation, except A. However, only Mw, log P, V and A gave high coefficient of determination higher than 0.35 (R2=0.47, 0.46, 0.43 and 0.36, respectively) with
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solubilization ratio in the presence of the surfactant. Mw and V have a high intercorrelation (R2>0.9); therefore, a linear model by log P, Mw and A was developed as follows:
C log SLS,0.5% 0.863A 0.179log P 0.002Mw 0.136 Cw R2=0.772, q2LOO=0.565, F=16.9, p<0.001, MAPE=64.6%
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(2)
ACCEPTED MANUSCRIPT R2 and F values (Fisher constant) and corresponding p-value are acceptable and the q2 value of LOO cross validation >0.5 indicate the predictability of the developed model. All the descriptors are statistically significant (p<0.02). Mean absolute percentage error (MAPE) was used to check the accuracy of the model.
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model, and they can be considered as independent parameters [9].
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There is no considerable intercorrelation between the A, log P and Mw (R2<0.2) in the developed
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However, the intercept of the model is not significant (p>0.1); hence, the model was re-constructed by regression through origin (RTO) and the obtained model is:
R2=0.927, F=67.4, p<0.001, MAPE=66.1%
(3)
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C log SLS,0.5% 0.763A 0.192log P 0.002Mw Cw
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There is a significant difference in R2 of ordinary least square (OLS) and RTO because different
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equations are used in each case and comparison between R2 of OLS and RTO is not correct, thus,
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it should be avoided in modeling and validation of QSAR and QSPR models [38, 39]; however, there is no significant change in MAPE value of the models.
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Based on solubilization mechanism by surfactant, micelle formation is an important factor for improving solubility [3] and there is a positive correlation with log P, that is solute hydrophobicity
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is an important parameter for solubilization by SLS because low hydrophilic drugs can be placed in the core of the micelles and more solubilization by surfactant was observed. Similar pattern has been reported for log P of some drugs with molar micelle water partition coefficient of Tween 80 [40]. Moreover, the size of molecules is another parameter with a positive relation with solubilization, while the A of Abraham solvation parameters which shows the hydrogen bond acidity of molecules has a negative relation. Hydrogen bonding interactions between some poorly
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ACCEPTED MANUSCRIPT soluble antidiabetic drugs have been proposed as the main factor for solubilization by surfactant, that is, Tween 80 [41]. The results of this study indicated that drugs with hydrogen bond donor groups, that is NH and OH, could have a negative effect on solubilization in the presence of SLS. These findings showed that the molecules with higher hydrogen bond donor functional groups
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have a low affinity to locate the core of the micelles.
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The results of this study revealed that various structural parameters play a significant role in
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solubilization of drugs in the presence of SLS. However, only solubilization of 19 compounds by two concentrations of SLS is the limitation of the study and this should be considered in upcoming
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works.
Conclusion:
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SLS is a good solubilization agent and the apparent solubility in aqueous solution of SLS depends
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on various structural descriptors. Some drugs at below the CMC could increase the solubility, and volume is useful for classification into two groups: class I, solubilization factor <1.3 and class II,
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solubilization factor >1.3. Moreover, micelle formation (concentration above CMC) is the most
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important parameter for solubilization by SLS and it has some effects on aqueous solubility of most drugs. Log P is the main parameter for evaluating solubilization by surfactant. In addition,
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Mw is another solute parameter similar to log P which has a positive relation, while A (hydrogen bond acidity) is another structural parameter with a negative effect on solubilization by SLS.
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ACCEPTED MANUSCRIPT Acknowledgment: This article is a part of the results of M.N.A’s Pharm.D thesis No. 3983 registered at Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran. A.S. thanks the Ministry of Health
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and Medical Education (grant for young assistant professors), Tehran, Iran, for financial support.
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ACCEPTED MANUSCRIPT Table 1. Source of drug used and corresponding structural parameters i.e. partition coefficient (log P), molar mass (Mw), Abraham solvation parameters (E, S, A, B and V) and melting point (MP) in this study. Drug Acetaminophen Benzoic Acid Budesonide Carbamazepine Carvedilol Celecoxib Enrofloxacin Glibenclamide Ibuprofen Indomethacin Ketoconazole Lamotrigine Mycophenolate mofetil Naproxen Phenothiazine Phenytoin Piroxicam Salicylic Acid Tadalafil
Company Daana Pharma Co. (Iran) Merck (Germany) Pharmabios (Italy) Arasto Pharmaceutical Chemicals Inc. (Iran) Salehan Chemi (Iran) Zahravi Pharmaceutical Co. (Iran) Temad (Iran) Kimidaru (Iran) Daana Pharma Co. (Iran) Zahravi Pharmaceutical Co. (Iran) Arasto Pharmaceutical Chemicals Inc. (Iran) Arasto Pharmaceutical Chemicals Inc. (Iran) Zahravi Pharmaceutical Co. (Iran) Mahban Chemie (Iran) Merck (Germany) Alhavi (Iran) Zahravi Pharmaceutical Co. (Iran) Merck (Germany) Osveh (Iran)
D E
log P 0.34 2.06 3.14 2.67 4.11 3.96 2.54 3.75 3.72 3.10 3.55 -0.19 2.92 3.00 4.15 2.52 1.71 1.86 1.70
T P
E C
C A
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E 1.06 0.75 2.33 2.12 3.08 2.51 2.25 2.64 0.78 2.44 3.14 2.40 1.73 1.54 1.95 1.94 2.56 0.91 3.39
S 1.63 1.08 3.23 2.06 3.00 2.43 2.50 3.89 1.01 2.49 3.76 2.13 1.96 1.49 1.53 2.04 3.12 1.10 3.27
A 0.96 0.57 0.48 0.39 0.62 0.44 0.57 0.85 0.57 0.57 0.00 0.45 0.13 0.57 0.13 0.44 0.72 0.70 0.31
T P
I R
C S
U N
A
M
Mw 151 138 431 236 406 381 359 494 206 358 531 256 320 230 199 252 331 122 389
B 0.80 0.44 2.16 0.92 2.09 1.22 1.92 2.01 0.51 1.24 2.22 0.93 1.66 0.75 0.50 1.14 2.12 0.40 2.27
V 1.17 0.93 3.27 1.81 3.10 2.47 2.59 3.56 1.78 2.53 3.72 1.65 3.28 1.78 1.48 1.87 2.25 0.99 2.70
TPSA 55.4 37.3 99.3 46.8 78.4 78.9 64.1 126.9 40.8 68.8 57.8 89.0 69.1 49.9 37.3 58.2 104.1 68.2 71.1
MP(ºC) 170 122 226 190 115 158 220 169 76 158 146 217 141 153 188 286 199 158 302
ACCEPTED MANUSCRIPT Table 2. Solubility of studied drugs in water and aqueous solution of SLS (0.1% and 0.5%) at 37°C Drug Acetaminophen Benzoic Acid Budesonide Carbamazepine Carvedilol Celecoxib Enrofloxacin Glibenclamide Ibuprofen Indomethacin Ketoconazole Lamotrigine Mycophenolate mofetil Naproxen Phenothiazine Phenytoin Piroxicam Salicylic Acid Tadalafil a
T=25°C T=37°C c pH=7.4, T=37°C d T=35°C e no data in literature b
Aqueous solubility (g/L) 21.17 5.859 0.031 0.296 0.032 0.003 0.302 0.033 0.104 0.026 0.008 0.342 0.513
RSD (%) 7.2 7.3 11.2 4.7 9.5 14.5 5.6 11.4 3.4 13.9 10.4 4.7 7.9
Solubility data in literature (g/L) 21.0a 5.131b 0.028a 0.259b 0.031c 0.003d 0.146a 0.031a 0.082a 0.025d 0.01b 0.170a _e
References [18] [19] [20] [21] [22] [23] [24] [19] [25] [26] [27] [28] _d
C S
0.097 0.003 0.056 0.032 2.755 0.016
6.3 14.9 9.4 4.5 11.0 11.6
0.084d 0.003d 0.032a 0.026d 2.620b 0.018b
[29] [30] [31] [32] [19] [33]
0.133 0.004 0.056 0.053 2.870 0.017
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C A
E C
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0.1%SLS (g/L) 21.63 7.074 0.047 0.298 0.060 0.004 0.414 0.044 0.121 0.109 0.033 0.375 0.793
RSD (%) 6.3 9.1 10.4 4.1 8.4 14.3 5.5 10.8 7.5 10.5 11.1 3.2 6.9
0.5%SLS (g/L) 24.35 7.905 0.728 1.346 0.538 0.173 1.494 0.115 0.869 0.297 1.386 0.621 3.094
RSD (%) 5.0 6.9 10.7 6.4 7.5 11.9 10.2 10.5 3.4 4.3 9.2 3.7 9.9
I R
10.6 10.9 10.8 2.3 11.6 10.4
0.461 0.045 0.119 0.104 3.915 0.105
9.7 9.2 10.7 6.6 6.1 8.4
T P
ACCEPTED MANUSCRIPT Table 3. Solubilization ratio (SR) of each drug in presence of 0.1% of SLS, experimental class of each drug (class I, SR>1.3 and class II, SR<1.3), predicted class by Eq. 1 and probability (P) of a binary response (class I or II) based on V values Drug Acetaminophen Benzoic acid Budesonide Carbamazepine Carvedilol Celecoxib Enrofloxacin Glibenclamide Ibuprofen Indomethacin Ketoconazole Lamotrigine Mycophenolate mofetil Naproxen Phenothiazine Phenytoin Piroxicam Salicylic Acid Tadalafil
Solubilization ratio 1.02 1.21 1.52 1.01 1.88 1.18 1.37 1.33 1.16 4.19 4.13 1.10 1.55 1.37 1.33 1.00 1.66 1.04 1.12
D E
M
E C
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Predicted Class II II I II I I I I II I I II I II II II I II I
P 0.892 0.932 0.084 0.677 0.116 0.339 0.285 0.047 0.691 0.310 0.034 0.749 0.083 0.691 0.810 0.649 0.450 0.924 0.236
T P
I R
C S
U N
A
T P
C A
Class II II I II I II I I II I I II I I I II I II II
ACCEPTED MANUSCRIPT Graphical Abstract:
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ACCEPTED MANUSCRIPT Highlights Micellar solubilization is a method for increasing drugs solubility. Sodium lauryl sulfate (SLS) is one of the common surfactants in pharmaceutical sciences. Solubility of various drugs was studied at two concentrations of SLS.
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The most of drugs showed an increase in solubility above the critical micelle concentration (CMC).
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A good correlation was observed between structural descriptors and solubilization power of SLS.
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Mohammad Norouz Alizadeh, Ali Shayanfar, Abolghasem Jouyban PII: DOI: Reference:
S0167-7322(18)30265-4 doi:10.1016/j.molliq.2018.07.065 MOLLIQ 9386
To appear in:
Journal of Molecular Liquids
Received date: Revised date: Accepted date:
16 January 2018 16 June 2018 15 July 2018
Please cite this article as: Mohammad Norouz Alizadeh, Ali Shayanfar, Abolghasem Jouyban , Solubilization of drugs using sodium lauryl sulfate: Experimental data and modeling. Molliq (2018), doi:10.1016/j.molliq.2018.07.065
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ACCEPTED MANUSCRIPT Solubilization of drugs using sodium lauryl sulfate: Experimental data and modeling
Mohammad Norouz Alizadeh1,2, Ali Shayanfar*3, Abolghasem Jouyban4
Biotechnology Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences,
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1
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Tabriz, Iran
Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
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Drug Applied Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences,
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2
Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical
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4
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Tabriz, Iran
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Science, Tabriz, Iran
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*Corresponding author: [email protected]
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ACCEPTED MANUSCRIPT
Abstract: Micellar solubilization is a great method for increasing drugs solubility in aqueous environments. At concentrations above the critical micelle concentration (CMC), micelles are formed and they
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are able to increase the apparent aqueous solubility of poorly soluble drugs. Sodium lauryl sulfate
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(SLS) is one of the common solubilizing agents in pharmaceutical sciences. Investigation on the
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water solubility of drugs in the presence of surfactants and the development of a relationship between drug solubility in the presence of SLS and structural descriptors is an important issue in
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the prediction and understanding of the solubilization mechanism. The aims of this study are:
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determination of experimental solubility of drugs in the presence of SLS and development of models for finding a relationship between solubilization factor by SLS and structural descriptors.
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Samples were prepared by adding excess amount of 19 drugs (with diverse structural and
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physicochemical properties) to water and an aqueous solution of SLS at different concentrations, that is, less than (0.1%) and above the CMC (0.5%). The mixtures were placed in a shaker-
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incubator for 72-96 h at 37°C. Then, the equilibrated samples were filtered and analyzed at
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maximum wavelengths by UV-spectrophotometry and the concentrations were calculated based on the calibration curves. Afterward, the molecular descriptors of drugs were computed and their
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relationship with solubilization factor in the presence of SLS was investigated. Most of the drugs showed a considerable increase in solubility above the CMC (0.5%) of SLS. Therefore, the effective mechanism for solubilization by surfactants is the formation of micelles. On the other hand, a good correlation was observed between structural descriptors and solubilization power in the presence of surfactant. Overall, SLS is a good solubilization agent and the solubility in aqueous solution of SLS depends on various structural descriptors. Keywords: Drug, Modeling, Solubility, Surfactant, SLS, micelle 2
ACCEPTED MANUSCRIPT Introduction: The solubility of drugs is an important aspect from the earliest stage of drug discovery to the latest stage of drug formulation [1, 2]. The most important challenge in designing oral drug systems is their poor bioavailability. Several factors affect oral bioavailability in which aqueous solubility is
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the most important one. Various strategies have been proposed to improve solubility of a solute
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and the most common methods are co-solvency, salt formation, crystal engineering, nanosizing
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and use of surfactant [3].
Surfactants are organic compounds that are amphiphilic and composed of hydrophilic heads and
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hydrophobic tails. When the concentration of surfactant molecules in a system is low, they adsorb
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onto surfaces or interfaces. At concentrations above the critical micelle concentration (CMC), micelles are formed. Micelles can increase the aqueous solubility of low soluble drugs; thus, at
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concentrations above the CMC, aqueous solubility increases linearly with the surfactant
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concentration [3, 4]. Therefore, micellar solubilization is a great method for increasing poorly soluble drugs in aqueous environments. Although, micellar solubility in aqueous media is just
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apparent because of the solute is not dispersed at molecular level but inside the micelles.
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Surfactants were used as solubilizing excipient of paclitaxel, cyclosporine, amiodarone hydrochloride and calcitriol [3].
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Anionic surfactant, sodium lauryl sulfate (SLS) is the most common surfactant which has several functional uses in medicinal [5] and cleaning products [6]. It was used in oral formulations of various drug products such as acetaminophen, hydrocodone bitartrate, alprazolam, amoxicillin trihydrate, buspirone hydrochloride, clonazepam, cyclobenzaprine hydrochloride, diazepam, gabapentin,
hydroxyzine
pamoate,
methocarbamol
and
tramadol
hydrochloride
(https://drugs.com). Moreover, research on aqueous solubility of drugs in the presence of
3
ACCEPTED MANUSCRIPT surfactants and the development of a relationship between drug solubility in the presence of SLS and structural descriptors is an important issue in the prediction and understanding of solubilization mechanism. Various structural parameters could be used to model and predict physicochemical properties and activity of chemical compounds. However, common and simple parameters such as
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melting point (MP), molar mass (Mw), partition coefficient (log P) and topological polar surface
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area (TPSA) [7, 8] could provide a mechanistic interpretation for a given model [9]. These
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parameters are used to develop quantitative structure-activity relationships (QSAR) and quantitative structure-property relationships (QSPR) models. Moreover, Abraham solvation
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parameters of a solute as mechanistic descriptors which is composed of E (excess molar
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refraction), S (dipolarity/polarizability), A (hydrogen-bond acidity), B (hydrogen-bond basicity) and V (McGowan volume) were used to predict various biological and toxicological activities, and
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physicochemical properties such as solubility [10-13].
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SLS is a common excipient in pharmaceutical formulations; nevertheless, a few studies have reported on the solubilization mechanism and its relation with the structure and physicochemical
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properties of drugs, to propose the QSPR models for prediction of solubilization ratio. Ghasemi et
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al. [14] proposed a model for solubilization of non-pharmaceutical compounds by SLS and Jouyban et al., [15] studied solubility of some anti-epileptic drugs in the presence of SLS in ethanol
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+ water mixture and the obtained data correlated with co-solvency models. The aims of the current study are to determine the experimental solubility of drug in the presence of SLS and development of models for finding a relationship between solubilization factor in the presence of SLS and common structural descriptors of drugs.
Methods and Materials: 4
ACCEPTED MANUSCRIPT Materials: Table 1 presents a summary of the drugs applied in this study. SLS supplied from Merck (Germany) and Lab-made distilled water was used for the preparation of the solutions. All the materials were of analytical grade and used without further purification. *********************************Table 1***************************************
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Apparatus and procedures for solubility determination: The solubility of the studied drugs was
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determined by equilibrating in water and the aqueous solution of SLS at two concentrations based
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on the CMC of SLS (0.24%) [16], that is, 0.1% (< CMC) and 0.5% (>CMC). For this purpose, an excess amount of the solid powder was added to the dissolution media using a shaker-incubator
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equipped with a temperature controlling system (Heidolph, Schwabach, Germany) at 37 ± 0.1°C.
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After a sufficient period of time (>72 h), the saturated solutions of the drugs were filtered through a 0.45 μm filter and diluted with appropriate solvent. The diluted samples were then assayed by
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UV–Vis spectrophotometer (Shimadzu, Japan), and the concentrations were determined from the
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calibration curves. Each experimental data point represents the average of at least three repetitive experiments. The details on calibration curves are summarized in Table S1 (supplementary
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information). The coefficient (R2) of all calibration curves were higher than 0.99.
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Computational Methods: The obtained solubilities in water and aqueous solutions of SLS (0.1 and 0.5%) were used to calculate the solubilization ratio of each drug at 37ºC by dividing solubility
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in the aqueous solution of SLS into solubility in water. The logarithm of solubilization ratio was used to correlate various structural parameters of solute, that is, Mw, TPSA, Abraham solvation parameters
and
MP.
(https://ilab.acdlabs.com/)
Structural and
parameters melting
of point
drugs
calculated
extracted
by
from
ACD-ilab literature
(https://chem.nlm.nih.gov/chemidplus/) and their numerical values are listed in Table 1. In aqueous solution of surfactant below CMC, data were divided into two groups, class I:
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ACCEPTED MANUSCRIPT solubilization ratio <1.3 and class II: solubilization ratio >1.3. Logistic regression [17] was used to develop a model for classification of drugs into the two mentioned sub-groups. Moreover, multiple linear regression was used to develop a model to predict solubilization ratio at above the CMC (0.5%). To validate the proposed models and assess their prediction capability, the leave-
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one out (LOO) method was used, where one solute is left out from the training set and the obtained
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model is used to predict the removed data point. All the computational analyses were performed
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using SPSS 17.
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Results and Discussion:
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Solubility of the studied drugs in water and aqueous solution of SLS (0.1% and 0.5%): Table 2 shows the experimental solubilities of 19 studied drugs in water and an aqueous solution
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of SLS (0.1% and 0.5%).
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*********************************Table 2*************************************** Several parameters can affect the validity of the solubility data and different solubility data have
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been reported for a drug at a certain temperature in the literature [34, 35]. Therefore, to check the
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accuracy of the experimental method for solubility determination of drugs, the obtained data of solubility in water was compared with previously published solubility data and good agreement
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was observed.
Solubility investigation in the presence of two CMCs gave different results. Only 50% of the drugs showed a considerable increase (>1.3) in solubility in 0.1% of SLS, therefore, the data were divided into two groups, that is, class I: solubilization ratio <1.3 and class II: solubilization ratio >1.3. However, majority of the drugs showed a significant increase in the presence of 0.5% SLS (>CMC). Minimum and maximum increases in solubility values were observed for acetaminophen (1.1-fold) and ketoconazole (173-fold). SLS is an anionic surfactant (amphiphilic compound); in 6
ACCEPTED MANUSCRIPT low concentration, it exists in monomer forms and at concentration above the CMC, it can form micelle and enhance the apparent solubility of poorly water-soluble drugs by providing a hydrophobic environment in the micelle core. Overall, solubilization effect is very low until the surfactant concentration reaches the CMC and then the solubility increases linearly with the
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concentration of surfactant. However, the drug properties are critical factors in solubilization by
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the surfactant.
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Computational modeling of drug solubilization in the presence of SLS:
Structural parameters of the studied drugs (Table 1) were used to develop models to predict
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solubility and interpret solubilization mechanism in aqueous SLS solution.
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Modeling results of solubility in aqueous solution of SLS (0.1%, < CMC) by regression analysis show poor correlation (R2<0.5) between the structural parameters of drugs and solubilization ratio
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in the presence of 0.1% SLS. The highest correlation coefficient (0.3) was obtained with V and
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Mw. This poor correlation could be related to nine compounds and they have a very low solubilization ratio (<1.3), and the solubility of only 10 drugs in 0.1% SLS was higher than 1.3.
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Therefore, the data were classified into two subgroups, that is, class I: solubilization factor <1.3
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and class II: solubilization factor >1.3. A model for classifying data in the defined groups was developed by a forward logistic regression and the developed model is:
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e(4.6162.141V ) p 1 e(4.6162.141V )
(1)
In this model, p is probability of a binary response (class I or II) based on V values. All the drugs with V value higher than 3 (five compounds) belong to class I and based on Eq. 1, 78.9% of the studied drugs can be classified in the correct group (80% of class 1 and 77.8% of class 2). LOO validation analysis showed no change in the predicted class and it confirmed the validity of the model. Therefore, V is a useful parameter for classification of data into two sub7
ACCEPTED MANUSCRIPT groups and drugs with higher V values can improve aqueous solubility more than 1.3 in the presence of 0.1% SLS. These results indicate the role of solute volume or size (there is a good correlation coefficient between V and Mw (R2>0.9)) in solubilization by SLS in low concentration (
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*********************************Table 3***************************************
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As compared to solubility investigation above CMC (micelle formation), few studies have been
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done on below CMC (surfactant monomers) in the presence of surfactants. Schacht et al. [36] reported on solubility of some polychlorinated dibenzodioxins (PCDDs) compounds; considerable
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solubility enhancement (up to 200-fold) in monomer solutions of SLS was observed for PCBs and
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a positive relation was observed between volume of PCBs and solubilization ratio. A possible mechanism is partitioning-like process of large volume compounds such as PCDDs between water
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and the non-polar moiety of the monomer. The formation of aggregates at surfactant
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concentrations below CMC in the presence of hydrophobic organic compounds such as hexadecane which has a large volume is another potential mechanism for solubilization [37].
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Relationship between solubilization ratios above the CMC of SLS, that is, 0.5% and structural
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parameters show a positive correlation, except A. However, only Mw, log P, V and A gave high coefficient of determination higher than 0.35 (R2=0.47, 0.46, 0.43 and 0.36, respectively) with
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solubilization ratio in the presence of the surfactant. Mw and V have a high intercorrelation (R2>0.9); therefore, a linear model by log P, Mw and A was developed as follows:
C log SLS,0.5% 0.863A 0.179log P 0.002Mw 0.136 Cw R2=0.772, q2LOO=0.565, F=16.9, p<0.001, MAPE=64.6%
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(2)
ACCEPTED MANUSCRIPT R2 and F values (Fisher constant) and corresponding p-value are acceptable and the q2 value of LOO cross validation >0.5 indicate the predictability of the developed model. All the descriptors are statistically significant (p<0.02). Mean absolute percentage error (MAPE) was used to check the accuracy of the model.
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model, and they can be considered as independent parameters [9].
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There is no considerable intercorrelation between the A, log P and Mw (R2<0.2) in the developed
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However, the intercept of the model is not significant (p>0.1); hence, the model was re-constructed by regression through origin (RTO) and the obtained model is:
R2=0.927, F=67.4, p<0.001, MAPE=66.1%
(3)
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C log SLS,0.5% 0.763A 0.192log P 0.002Mw Cw
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There is a significant difference in R2 of ordinary least square (OLS) and RTO because different
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equations are used in each case and comparison between R2 of OLS and RTO is not correct, thus,
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it should be avoided in modeling and validation of QSAR and QSPR models [38, 39]; however, there is no significant change in MAPE value of the models.
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Based on solubilization mechanism by surfactant, micelle formation is an important factor for improving solubility [3] and there is a positive correlation with log P, that is solute hydrophobicity
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is an important parameter for solubilization by SLS because low hydrophilic drugs can be placed in the core of the micelles and more solubilization by surfactant was observed. Similar pattern has been reported for log P of some drugs with molar micelle water partition coefficient of Tween 80 [40]. Moreover, the size of molecules is another parameter with a positive relation with solubilization, while the A of Abraham solvation parameters which shows the hydrogen bond acidity of molecules has a negative relation. Hydrogen bonding interactions between some poorly
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ACCEPTED MANUSCRIPT soluble antidiabetic drugs have been proposed as the main factor for solubilization by surfactant, that is, Tween 80 [41]. The results of this study indicated that drugs with hydrogen bond donor groups, that is NH and OH, could have a negative effect on solubilization in the presence of SLS. These findings showed that the molecules with higher hydrogen bond donor functional groups
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have a low affinity to locate the core of the micelles.
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The results of this study revealed that various structural parameters play a significant role in
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solubilization of drugs in the presence of SLS. However, only solubilization of 19 compounds by two concentrations of SLS is the limitation of the study and this should be considered in upcoming
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works.
Conclusion:
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SLS is a good solubilization agent and the apparent solubility in aqueous solution of SLS depends
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on various structural descriptors. Some drugs at below the CMC could increase the solubility, and volume is useful for classification into two groups: class I, solubilization factor <1.3 and class II,
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solubilization factor >1.3. Moreover, micelle formation (concentration above CMC) is the most
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important parameter for solubilization by SLS and it has some effects on aqueous solubility of most drugs. Log P is the main parameter for evaluating solubilization by surfactant. In addition,
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Mw is another solute parameter similar to log P which has a positive relation, while A (hydrogen bond acidity) is another structural parameter with a negative effect on solubilization by SLS.
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ACCEPTED MANUSCRIPT Acknowledgment: This article is a part of the results of M.N.A’s Pharm.D thesis No. 3983 registered at Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran. A.S. thanks the Ministry of Health
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and Medical Education (grant for young assistant professors), Tehran, Iran, for financial support.
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phenothiazine in water, ethanol, and propylene glycol at (298.2 to 338.2) K and their binary and ternary mixtures at 298.2 K, J. Chem. Eng. Data, 56 (2011) 4352-4355.
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technical note, AAPS PharmSciTech, 9 (2008) 431-436.
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ACCEPTED MANUSCRIPT Table 1. Source of drug used and corresponding structural parameters i.e. partition coefficient (log P), molar mass (Mw), Abraham solvation parameters (E, S, A, B and V) and melting point (MP) in this study. Drug Acetaminophen Benzoic Acid Budesonide Carbamazepine Carvedilol Celecoxib Enrofloxacin Glibenclamide Ibuprofen Indomethacin Ketoconazole Lamotrigine Mycophenolate mofetil Naproxen Phenothiazine Phenytoin Piroxicam Salicylic Acid Tadalafil
Company Daana Pharma Co. (Iran) Merck (Germany) Pharmabios (Italy) Arasto Pharmaceutical Chemicals Inc. (Iran) Salehan Chemi (Iran) Zahravi Pharmaceutical Co. (Iran) Temad (Iran) Kimidaru (Iran) Daana Pharma Co. (Iran) Zahravi Pharmaceutical Co. (Iran) Arasto Pharmaceutical Chemicals Inc. (Iran) Arasto Pharmaceutical Chemicals Inc. (Iran) Zahravi Pharmaceutical Co. (Iran) Mahban Chemie (Iran) Merck (Germany) Alhavi (Iran) Zahravi Pharmaceutical Co. (Iran) Merck (Germany) Osveh (Iran)
D E
log P 0.34 2.06 3.14 2.67 4.11 3.96 2.54 3.75 3.72 3.10 3.55 -0.19 2.92 3.00 4.15 2.52 1.71 1.86 1.70
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E C
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E 1.06 0.75 2.33 2.12 3.08 2.51 2.25 2.64 0.78 2.44 3.14 2.40 1.73 1.54 1.95 1.94 2.56 0.91 3.39
S 1.63 1.08 3.23 2.06 3.00 2.43 2.50 3.89 1.01 2.49 3.76 2.13 1.96 1.49 1.53 2.04 3.12 1.10 3.27
A 0.96 0.57 0.48 0.39 0.62 0.44 0.57 0.85 0.57 0.57 0.00 0.45 0.13 0.57 0.13 0.44 0.72 0.70 0.31
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A
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Mw 151 138 431 236 406 381 359 494 206 358 531 256 320 230 199 252 331 122 389
B 0.80 0.44 2.16 0.92 2.09 1.22 1.92 2.01 0.51 1.24 2.22 0.93 1.66 0.75 0.50 1.14 2.12 0.40 2.27
V 1.17 0.93 3.27 1.81 3.10 2.47 2.59 3.56 1.78 2.53 3.72 1.65 3.28 1.78 1.48 1.87 2.25 0.99 2.70
TPSA 55.4 37.3 99.3 46.8 78.4 78.9 64.1 126.9 40.8 68.8 57.8 89.0 69.1 49.9 37.3 58.2 104.1 68.2 71.1
MP(ºC) 170 122 226 190 115 158 220 169 76 158 146 217 141 153 188 286 199 158 302
ACCEPTED MANUSCRIPT Table 2. Solubility of studied drugs in water and aqueous solution of SLS (0.1% and 0.5%) at 37°C Drug Acetaminophen Benzoic Acid Budesonide Carbamazepine Carvedilol Celecoxib Enrofloxacin Glibenclamide Ibuprofen Indomethacin Ketoconazole Lamotrigine Mycophenolate mofetil Naproxen Phenothiazine Phenytoin Piroxicam Salicylic Acid Tadalafil a
T=25°C T=37°C c pH=7.4, T=37°C d T=35°C e no data in literature b
Aqueous solubility (g/L) 21.17 5.859 0.031 0.296 0.032 0.003 0.302 0.033 0.104 0.026 0.008 0.342 0.513
RSD (%) 7.2 7.3 11.2 4.7 9.5 14.5 5.6 11.4 3.4 13.9 10.4 4.7 7.9
Solubility data in literature (g/L) 21.0a 5.131b 0.028a 0.259b 0.031c 0.003d 0.146a 0.031a 0.082a 0.025d 0.01b 0.170a _e
References [18] [19] [20] [21] [22] [23] [24] [19] [25] [26] [27] [28] _d
C S
0.097 0.003 0.056 0.032 2.755 0.016
6.3 14.9 9.4 4.5 11.0 11.6
0.084d 0.003d 0.032a 0.026d 2.620b 0.018b
[29] [30] [31] [32] [19] [33]
0.133 0.004 0.056 0.053 2.870 0.017
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0.1%SLS (g/L) 21.63 7.074 0.047 0.298 0.060 0.004 0.414 0.044 0.121 0.109 0.033 0.375 0.793
RSD (%) 6.3 9.1 10.4 4.1 8.4 14.3 5.5 10.8 7.5 10.5 11.1 3.2 6.9
0.5%SLS (g/L) 24.35 7.905 0.728 1.346 0.538 0.173 1.494 0.115 0.869 0.297 1.386 0.621 3.094
RSD (%) 5.0 6.9 10.7 6.4 7.5 11.9 10.2 10.5 3.4 4.3 9.2 3.7 9.9
I R
10.6 10.9 10.8 2.3 11.6 10.4
0.461 0.045 0.119 0.104 3.915 0.105
9.7 9.2 10.7 6.6 6.1 8.4
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ACCEPTED MANUSCRIPT Table 3. Solubilization ratio (SR) of each drug in presence of 0.1% of SLS, experimental class of each drug (class I, SR>1.3 and class II, SR<1.3), predicted class by Eq. 1 and probability (P) of a binary response (class I or II) based on V values Drug Acetaminophen Benzoic acid Budesonide Carbamazepine Carvedilol Celecoxib Enrofloxacin Glibenclamide Ibuprofen Indomethacin Ketoconazole Lamotrigine Mycophenolate mofetil Naproxen Phenothiazine Phenytoin Piroxicam Salicylic Acid Tadalafil
Solubilization ratio 1.02 1.21 1.52 1.01 1.88 1.18 1.37 1.33 1.16 4.19 4.13 1.10 1.55 1.37 1.33 1.00 1.66 1.04 1.12
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Predicted Class II II I II I I I I II I I II I II II II I II I
P 0.892 0.932 0.084 0.677 0.116 0.339 0.285 0.047 0.691 0.310 0.034 0.749 0.083 0.691 0.810 0.649 0.450 0.924 0.236
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T P
C A
Class II II I II I II I I II I I II I I I II I II II
ACCEPTED MANUSCRIPT Graphical Abstract:
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ACCEPTED MANUSCRIPT Highlights Micellar solubilization is a method for increasing drugs solubility. Sodium lauryl sulfate (SLS) is one of the common surfactants in pharmaceutical sciences. Solubility of various drugs was studied at two concentrations of SLS.
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The most of drugs showed an increase in solubility above the critical micelle concentration (CMC).
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A good correlation was observed between structural descriptors and solubilization power of SLS.
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