Computer Simulation Studies of the Mechanism of Hydrotrope

Mar 16, 2016 - With the help of these observations we try to elucidate the hydrotropic action of hydrotrope SCS on the solubility of drug griseofulvin...
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Computer Simulation Studies of the Mechanism of HydrotropeAssisted Solubilization of a Sparingly Soluble Drug Molecule Shubhadip Das and Sandip Paul* Department of Chemistry, Indian Institute of Technology, Guwahati, Assam 781039, India S Supporting Information *

ABSTRACT: The effect of hydrotrope sodium cumene sulfonate (SCS) on the solubility of a sparingly water-soluble drug, griseofulvin, is studied by employing classical molecular dynamics simulation technique. We mainly focus on the underlying mechanism by which SCS enhances the solubility of a sparingly soluble or insoluble solute in water. The main observations are the following: (a) The self-aggregation of SCS molecules (through its hydrophobic tail) above the minimum hydrotrope concentration (MHC) causes the formation of micellar-like frameworks. Interestingly, though the drug griseofulvin possesses both polar and nonpolar groups, it prefers to get encapsulated inside the hydrophobic core of SCS aggregates. The decomposition of total SCS−drug interaction energy into van der Waals and electrostatic components suggests that the former plays a major role in this interaction. (b) The calculated Flory− Huggins interaction parameter values give a strong indication of the mixing ability of hydrotrope SCS and griseofulvin drug molecules. (c) As expected, we do not observe any strong effect of SCS aggregates on SCS−water and water−water average hydrogen-bond number, but it affects water−drug griseofulvin average hydrogen-bond number. With the help of these observations we try to elucidate the hydrotropic action of hydrotrope SCS on the solubility of drug griseofulvin.

I. INTRODUCTION The main problem which emerges from the drug discovery program is the poor water solubility of drug molecules. The strategy to accomplish a successful drug delivery for an orally administrated drug is the sufficient dissolution of the drug and its solubility in the gastrointestinal tract. Many newly developed drugs possess poor gastrointestinal tract solubility, which basically limits effective drug development, and the possible worst situation is the termination of a potential candidate.1 All these problems can be dealt with by the use of nontoxic, watersoluble molecules that act to enhance the solubility of an insoluble or sparingly soluble drug molecule. 2−5 This phenomenon is familiar as hydrotropy, and the solubilizing agent is known as the hydrotrope. The term hydrotrope and the concept of hydrotropy was first reported by Neuberg about a century ago.2 Hydrotropes are small amphiphilic organic molecules, and they have structural features reminiscent with classical surfactants, but their solution properties are different to common long-chain classical surfactants. Hydrotropes have many practical applications in industry, as well as they have some biological importance too.6−10 Similar to classical surfactant which structures micelle above its critical micelle concentration (cmc), hydrotropes also display aggregation property above their minimum hydrotrope concentrations (MHC).11 In spite of the fact that hydrotropic phenomenon has been familiar for more than a century, however, a definitive © 2016 American Chemical Society

conclusion of the mechanism hydrotropic action of a hydrotrope is yet to be achieved. The three hypotheses for hydrotropic action that are postulated so far are (i) the formation of complex between solute and hydrotrope,12 (ii) the breaking of tetrahedral network of water,13 and (iii) selfassociation of hydrotrope molecules.14 Note that these three hypotheses are not mutually exclusive. For example, in our previous studies,15,16 we observed the self-aggregation behavior of hydrotrope sodium cumene sulfonate (SCS) above its MHC and also supported the water structure breaking hypothesis.15 It is the organized self-assembly of hydrotropes which is thought to be acting as a vehicles for drug solubilization. This action is very similar to surfactant or polymer micelle-assisted solubilization.17,18 In contradiction to this, using fluctuation theory of solution (FTS), Booth et al.19 have analyzed the solubilization of two solutes (butyl acetate and butyl benzoate) in the presence of small-molecule hydrotropes (sodium benzoate and sodium salicylate), but their results are against the popular self-aggregation hypothesis. On the other hand, though Bauduin et al.20 agreed the fact that hydrotropes have solubilization property, their results did not support the selfaggregation hypothesis of hydrotropes. Received: December 5, 2015 Revised: March 15, 2016 Published: March 16, 2016 3540

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group of each hydrotrope molecule. To examine the solution properties of the ternary mixtures with a regime of concentrations, we consider eight different concentrations, and the same are presented in Table I. To prepare initial

In spite of the fact that hydrotropes are important and useful molecules, they are somewhat a neglected class of amphiphiles and receive a very little amount of scientific attention. It is reflected into the number of research works that have been devoted to understand the mode of actions of hydrotropes toward the solubilization process of sparingly soluble solute molecules (only a handful number of research works have been done so far). Mainly because of these reasons the generalized model of hydrotropic action of hydrotropes is way far behind from conclusive. Thus, the precise mechanism of hydrotropy and the structural property that defines a proper hydrotrope are a matter of discussion. This is the main motivation of this work. Here in this study, we consider griseofulvin as a drug molecule and SCS as a hydrotrope (Figure 1). Griseofulvin is a lipophilic

Table I. Ngriseofulvin, Nscs, Nwat, and Mscs Are the Number of Griseofulvin, Sodium Cumene Sulfonate, Water Molecules, and Molar Concentration of the Sodium Cumene Sulfonate, Respectively, For Different Systems system

Ngriseofulvin

Nscs

Nwat

vol (nm3)

Mscs

S0 S1 S2 S3 S4 S5 S6 S7

8 8 8 8 8 8 8 8

24 24 24 24 24 24 24 24

8400 7200 6000 4800 3600 2400 1800 1200

261.38 224.76 188.91 152.67 116.85 81.01 63.37 45.13

0.152 0.177 0.211 0.261 0.341 0.492 0.629 0.883

configurations of each of these systems PACKMOL package35 was used. Each of these simulations was performed using the AMBER12 package30 and using a time step of 2 fs. For all the systems, molecules were placed in a cubic box, and to remove the edge effect periodic boundary conditions were applied in all three directions. The energy of each of the systems was minimized for 10 000 steps in which the steepest descent minimization method is used for the first 4000 steps followed by the 6000 steps in conjugate gradient method. To avoid void formation, each of the systems was then heated slowly over 320 ps from 0 to 298 K in a canonical ensemble (NVT). Thereafter, the systems were equilibrated for 5 ns and followed by 80 ns production runs in isothermal−isobaric (NPT) ensemble at 298 K temperature and 1 atm pressure. We have used the Berendsen barostat36 with a pressure relaxation time of 2 ps to maintain the physical pressure. Temperature of each of the systems was controlled by the Langevin dynamics method with a collision frequency of 1 ps−1. We have used the SHAKE algorithm37 to constrain the bonds involving hydrogen atom. For all nonbonded interactions, a cutoff distance of 10.0 Å was specified. The long-ranged electrostatic interactions were evaluated by the application of the particle mesh Ewald method. Flory−Huggins (FH) theory (this theory is based on the lattice model) was originally developed to interpret the nonideal behavior of polymer solutions.38,39 The main assumption of this theory is that there is a random arrangement of molecules in the solution. Moreover, in consonance with FH theory, the mixing of polymers is predominantly based on (i) the change in alignment of the polymer chain from a fully ordered perfect state to a more disordered state (in the disordered state, there is a plasticity to put the polymer randomly in the lattice) and which is followed by (ii) the mixing process of the polymer chain and solvent molecules. As established by FH theory, for phase separation of a polymer− solvent mixture, the critical value of Flory−Huggins interaction parameter (χFH) is as follows: 1 1 1 χFH = + + (1) 2 2x x

Figure 1. (a) Structure of griseofulvin. (b) The structure and atomic number of sodium cumene sulfonate. Hydrogen atoms are left off for clarity in both structures.

oral administrated fungi-static antibiotic drug,21 and it is believed that it can also act as a potential anticancer drug.22 Griseofulvin is one of the highly prescribed antifungal drugs, with an annual worldwide business of USD 63.7 million, whereas its annual consumption is 85 000 kg.23 According to the biopharmaceutics classification system griseofulvin falls into Class II drugs that have low-solubility and high-permeability character.24 In this study, by carrying out classical molecular dynamics simulation of the sparingly soluble drug griseofulvin and hydrotrope SCS in water with a regime of concentrations, we try to examine the mechanism of hydrotropic action of SCS. At first we try to explore the outcome of MHC of SCS on the solubilization action of the drug griseofulvin. Our analysis is further extended to the microdetails of solubilization of sparingly soluble drug molecules. The remainder of this paper is split into three parts. In section II models and simulation details are described, the results are presented and discussed in section III, and our conclusions are outlined concisely in section IV.

II. MODELS AND SIMULATION METHOD Molecular dynamics (MD) simulations of griseofulvin−sodium cumene sulfonate−water ternary mixtures were performed with altering SCS composition at 298 K temperature and 1 atm pressure. Following earlier work,25 the griseofulvin molecule was parametrized using the general AMBER force field (GAFF),26 and the AM1-BCC27,28 calculation method within the antechamber29 module of AMBER1230 was used to compute the partial charges. CHARMM general force field (CGenFF)31,32 was taken for the parametrization of SCS molecule. We adopt SPC/E model for water.33 Note that, as we refer to in our previous works,15,16 the identical potential energy functions for nonbiological systems in CHARMM and AMBER and the implementation of CHAMBER tool kit34 make the CHARMM force field AMBER12 reconcilable. One Na+ ion was added in XLEAP of the AMBER12 package30 to counterbalance the single negative charge of the sulfonate

From eq 1, it can be inferred that the χFH value for the monomeric mixture (x = 1) is 2; on the other hand, χFH 3541

DOI: 10.1021/acs.jpcb.5b11902 J. Phys. Chem. B 2016, 120, 3540−3550

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Figure 2. Probability distribution (in percentage) of SCS clusters of different sizes. The numbers on different bar diagrams represent the average number of griseofulvin molecules present in a given SCS cluster.

volume because of mixing is negligible, so ΔEmix can favorably be substituted by enthalpy of mixing, ΔHmix.42 ΔHmix is determined from the enthalpy of binding ΔHbind of pure griseofulvin molecules [(ΔHbind)griseofulvin], pure SCS molecules [(ΔHbind)SCS], and griseofulvin−SCS mixture [(ΔHbind)griseofulvin−SCS], as outlined in eq 3:25,42,43

becomes 0.5 when x → ∞. Thus, for the polymer−solvent miscibility, the critical value of χFH is less than 0.5. The modified FH theory was used previously to predict the solubility of small molecules with the help of MD simulation technique.16,40 In our present study, we also consider the modified FH theory to measure the solubility of griseofulvin drug molecule. In contradiction to FH theory, the modified FH theory takes into account the intermolecular interactions between the component in the mixture, and it also enables the measurement of the concentration-dependent χFH.41 By using the following equation (eq 2) we have calculated χFH as40

χFH =

Vref ΔEmix RT

⎛ ΔHgriseofulvin−SCS ⎞ ⎛ ΔH ⎞ bind ΔHmix = ⎜ − ϕgriseofulvin⎜ ⎟ ⎟ V ⎝ ⎠griseofulvin−SCS ⎝ V ⎠griseofulvin ⎛ ΔH ⎞ bind − ϕSCS⎜ ⎟ ⎝ V ⎠SCS

(3)

where ϕgriseofulvin and ϕSCS represent the volume fractions of griseofulvin and SCS, respectively, and V is the total volume of the system. Following the recent works,16,25 primitive hydrotrope−drug mixtures of two dissimilar compositions are constructed. In the first system, we consider 24 hydrotrope SCS molecules and one griseofulvin molecule (in 24:1 ratio), whereas the second

(2)

The smaller molar volumes between the sparingly soluble griseofulvin drug molecules and hydrotrope SCS are represented by Vref. ΔEmix represents the energy of mixing between griseofulvin and SCS molecule. As the change of 3542

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III. RESULTS AND DISCUSSION III.A. Incorporation of Drug Molecules Inside SCS Clusters. Similar to our previous simulation studies,15,16 here also we observed a tail-dependent aggregation behavior of SCS (see Figure 2 of the Supporting Information), and the selfassociation of SCS increases with increase in concentration of solution (see Figure 3 of the Supporting Information). Another notable observation, made from these visualizing snapshots (Figure 2 of the Supporting Information), is that griseofulvin molecules also get successfully enclosed into the lipophilic interior of the aggregates of SCS molecules. Moreover, for the estimation of SCS clusters in different systems we performed cluster structure analysis. For this, as used in previous work,16 in this study also we apply a geometric criterion in which a cutoff distance between C7 atoms two neighboring SCS molecules is considered. This cutoff distance is obtained from the C7−C7 correlation function of SCS molecules (see Figure 4a of the Supporting Information). In Figure 2, we show the probability distributions of different sizes of SCS clusters for all the systems. From these histograms, it can be seen that around the MHC region of SCS, a transition takes place between lower- and higher-order clusters formation and, once the MHC region is crossed, the size of the higher-order SCS aggregates is getting essentially ceased. These findings are very similar to what we found in our recent study16 in which hydrotrope SCS assisted solubilization of hydrophobic di-t-butylmethane (DTBM) is reported. Note that, in the context of hydrotropic action, the behaviors of hydrotrope SCS molecules are quite contradictory to that of amino acid proline, which is also classified as hydrotrope because of its amphiphilic nature and ability to solubilize hydrophobic solute. Although proline is considered as a hydrotrope, neither experimental nor simulation study evidenced the large-scale proline aggregation in aqueous solution.47,48 Since our goal is to investigate the role of SCS aggregation on the solubilization of the drug griseofulvin, if any, we extend our analysis further toward the estimation of the number of griseofulvin molecules inside the core of SCS clusters. As before,16 for this detailed examination, the following geometric criteria are set: (i) the size of the SCS cluster must be equal to or greater than three, (ii) the cutoff distance between the center of mass (COM) of a griseofulvin molecule and C7 atom of a cluster-forming SCS molecule should be within 9.65 Å, and at the same time (iii) if the distance between the COM of one griseofulvin molecule is within 8.85 Å of the COM atom of a neighboring griseofulvin molecule (see Figure 4, parts b and c, of the Supporting Information) and simultaneously if these two griseofulvin molecules reside within the cutoff distance of C7 atom of any of the cluster-forming SCS molecules then these two griseofulvin molecules are considered to be located within the same SCS cluster. Figure 2 also displays the total number of griseofulvin molecules that are present in the interior of SCS clusters of different sizes. It is quite apparent that, below the MHC level, the number of griseofulvin molecules within the SCS clusters is in lesser extent, but as soon as SCS begins to form higher-order aggregates (in the region of the MHC) a prominent increase in the number of griseofulvin molecules in the inner core of hydrotrope clusters is observed. These results indicate the creation of more hydrophobic layer in the interior of higher-order SCS aggregates increases the potential for encapsulating drug molecules into the inner core of these hydrotrope clusters. Here it is to be remembered that, unlike

mixture consists of 24 hydrotrope SCS molecules and two griseofulvin molecules (in 12:1 ratio). In both the systems, at first we placed all the SCS and griseofulvin molecules randomly. We have first equilibrated the initial configurations for each of the griseofulvin−SCS mixtures in the vacuum to acquire the initial compaction of the mixture. The vacuum simulations run were carried out in AMBER12 for 12 ns at 298 K with a time step of 2 fs. Then, we have generated a cubic box around the compacted complex by using the leap module of AMBER12 using 0 Å, buffer constant for all the three directions. To fill up the voids, mainly created at the corners of the cubic box, 405 and 369 SPC/E water molecules were added for the first and second mixture, respectively. Thereafter, we minimized the energy of each griseofulvin−SCS−water mixture for 10 000 steps, where the first 4000 steps are in steepest descent minimization method, and this is followed by 6000 steps in the conjugate gradient method. After that, each of the systems was heated slowly from 0 to 298 K for 320 ps in canonical ensemble (NVT). To relax the density of each system, additionally 4 ns equilibration run in an isothermal isobaric (NPT) ensemble at 298 K and 1 atm pressure was carried out. Finally, the systems were equilibrated for 20 ns in NPT at 298 K and 1 atm pressure. Periodic boundary conditions were employed in all the three directions. It is noticed that hydrotrope SCS molecules adopt a reverse micelle-like structure (see Figure 1a−c of the Supporting Information) in the gas phase (vacuum state) of the griseofulvin−SCS mixture. The griseofulvin molecules possess hydrophilic parts, but they are found at the surface of the reverse micelle structure of SCS. Interestingly, with the addition of water, to make favorable interactions with water molecules, hydrotrope molecules reorganize themselves in such a way that the small hydrophobic parts of SCS aggregates enclose griseofulvin molecules in their core while the hydrophilic part of SCS molecules is directing toward solvent water molecules (see Figure 1d−f of the Supporting Information). ΔHbind is calculated using the MM-GBSA44 method. We have taken the last 4 ns of production phase trajectories for the calculation purpose. Every single MM-GBSA calculation was performed with the help of Python script MMPBSA.py of the AMBER12 package. We have determined ΔHbind using the following set of equations: ΔHbind = ΔEvac + ΔGsolv

(4)

where ΔEvac represents the energy in the vacuum state and ΔGsolv is the solvation free energy.25,45 They can be determined as follows: ΔEvac = ΔEele + ΔEvdw

(5)

ΔGsolv = ΔGGB + ΔG NP

(6)

Here, ΔEele is the electrostatic component of energy, whereas ΔEvdw represents the van der Waals component. We have calculated the polar component, ΔGGB, by the use of the generalized-Born (GB) approach,46 whereas the nonpolar part, ΔGNP, of the solvation free energy (ΔGsolv) is determined with the help of the following equation:25,46 ΔG NP = γ(SASA) + β

(7)

where γ = 0.005 kcal/Å2, β = 0.0, and SASA is the solventaccessible surface area. 3543

DOI: 10.1021/acs.jpcb.5b11902 J. Phys. Chem. B 2016, 120, 3540−3550

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The Journal of Physical Chemistry B our previous study where pure hydrophobic DTBM molecules were used,16 in the present study we used drug griseofulvin which is not purely hydrophobic in nature. But, both DTBM and griseofulvin molecules behave in similar fashion (in the context of their encapsulation into the hydrophobic core of SCS clusters) as is evident from Figure 2. These findings further suggest that the aggregated structure of SCS plays a crucial role for the elevation of the solubility of a solute molecule by designing a hydrophobic layer in which the solute is getting encapsulated. From the above discussions it is evident that the interactions between griseofulvin drug and SCS molecules play an important role in the encapsulation of the former into the clusters of latter. In order to see the hydrotropes’ direct interaction pattern with the drug molecule, following earlier work,49 we have decomposed total griseofulvin−SCS interaction energy into electrostatic and van der Waals energy components for all the systems (S0−S7). Figure 3 represents

Figure 4. Radial distribution functions between (a) COM−OW (griseofulvin−water), (b) C7−OW (SCS−water), and (c) C8−OW (SCS−water).

Figure 3. Average van der Waals and total interaction energy between griseofulvin and SCS.

oxygen atom of water, and the C8 atom of SCS and oxygen atom of water, respectively. From Figure 4a it is evident that the g(r) value of the system S0 at the first maximum is only 0.35 of the bulk density, and with increase in concentration of the SCS, a depletion in the peak height is observed for the COM−OW distribution function, but the effect is not remarkable for the systems below the MHC level of SCS. Here the oxygen atom of water is denoted by OW. In view to the effect of the MHC on C7−OW and C8−OW distribution functions, we find that with increase in concentration of SCS there is a modest decrease in the first peak height of both the distribution functions and the effect is more prominent for concentrated solution systems. These observations imply that, with an increase in concentration of SCS, the number of water molecules in the first solvation shell of the COM of griseofulvin, the C7 atom of SCS, and the C8 atom of SCS decreases. Furthermore, in an attempt to see the degree of solvation of griseofulvin and SCS molecules more perspicaciously, the number of first-shell water molecules (first-shell coordination number, CN) surrounding the COM of griseofulvin and different hydrophobic sites of hydrotrope SCS is determined. We have calculated the CN from the corresponding site−site correlation functions involving these atomic sites of SCS (and the COM of griseofulvin) and water oxygen atom by using eq 8:

the total interaction energy and van der Waals energy (averaged over the last 60 ns of simulation run) between griseofulvin and SCS molecules. Here we note that the electrostatic component of griseofulvin−SCS interactions is not a strong function of concentration. Nevertheless, as is evident from this figure, the electrostatic interaction energy component plays a very minor role in griseofulvin−SCS interactions (which is not surprising based on the findings discussed above) and its contribution to total direct interaction energy is also very small compared to van der Waals energy for all the systems (S0−S7) considered here. Hence, it can be inferred that van der Waals energy plays a dominant role in the interaction between hydrotrope and drug molecules. We also find that, with an increase in concentration of SCS, van der Waals interaction energy between griseofulvin and SCS decreases (more negative), but the effect is more noticeable for the systems S4−S7. This can be explained on the basis of the fact that, as the aggregation of SCS starts from the MHC level, the drug incorporation ability into the SCS hydrophobic cores also increases. This makes the van der Waals interaction between griseofulvin and SCS molecules more favorable. III.B. Hydrophobic Hydration. As the structural features of hydrotrope SCS and griseofulvin drug molecules in water are reflected in their hydration pattern also, we have additionally extended our investigation toward the radial distribution functions (rdfs) involving the COM of griseofulvin and water molecules and C7 and C8 atomic sites of SCS and water molecules. Figure 4a−c exhibits the rdfs between the COM of griseofulvin and oxygen atom of water, the C7 atom of SCS and

CN = 4πρβ

∫r

1

r2

r 2gαβ (r ) dr

(8)

where CN is the number of β atoms around a reference α atom in a shell extending from r1 to r2. ρβ represents the number density of species β in the system. To calculate the first 3544

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table. Below the MHC level of SCS, the difference between the actual and normalized (or expected) coordination number is not very significant, but this difference starts to increase dramatically from the MHC region. These results demonstrate that with increasing SCS concentration more and more dehydration of small hydrophobic tails of SCS and the COM of griseofulvin molecules is taking place. Here we note that a very similar hydration pattern of C7 and C8 atoms of hydrotrope SCS has also been observed in previous simulation study16 except for the fact that the number of first solvation shell water molecules surrounding hydrophobic tail parts of SCS is slightly less dehydrated in the presence of the drug griseofulvin. Moreover, as SCS molecules begin to selfaggregate, griseofulvin molecules start to penetrate into the interior of the core of SCS clusters. Therefore, a depletion in the number of first-shell water molecules surrounding the COM of griseofulvin molecules is also observed, and the influence is more pronounced as soon as SCS molecules acquired a more aggregated structure above the MHC region. To explore the change in water density around griseofulvin molecules upon SCS aggregation more precisely, further, we perform atomic mass density analysis by using the Visual Molecular Dynamics package.50 In Figure 5, we present the mass density map of the oxygen atom of water around a randomly selected griseofulvin molecule with a cell side of 0.5 Å within 5.55 Å. Below the MHC region, a very high water density around the griseofulvin molecule is noticed, but around the MHC region when SCS molecules start to self-aggregate a sharp depletion in the water density around the griseofulvin is perceived. This result can act as a corroborative proof of the

solvation shell coordination numbers, the particular values for r1 and r2 are specified to zero and the position of the first minimum in the corresponding correlation function. As can be seen from Table II, the number of first solvation shell water molecules around the COM of griseofulvin Table II. Number of First-Shell Water Molecules around the Center of Mass (COM) of Griseofulvin Molecules and the C7 and C8 Atoms of Sodium Cumene Sulfonate Moleculesa system S0 S1 S2 S3 S4 S5 S6 S7

COM 5.73 5.43 4.89 3.79 3.63 3.17 2.87 2.81

(5.71) (5.68) (5.60) (5.50) (5.28) (5.07) (4.75)

C7 25.47 24.99 24.60 23.66 22.00 18.63 16.65 13.80

(25.39) (25.23) (24.91) (24.44) (23.49) (22.53) (21.10)

C8 12.93 12.73 12.60 12.09 11.31 9.63 8.61 7.16

(12.89) (12.81) (12.65) (12.41) (11.92) (11.44) (10.71)

a The values in the parentheses represent the first-shell normalized coordination numbers due to change in the number density of water.

molecules and the C7 and C8 atoms of SCS molecules decreases as the concentration of SCS increases, and the effect is more profound for the systems S3−S7. Note that, as we move from system S0 to system S7, water number density decreases, and this might affect the coordination number values. To nullify the effect of reduced water density we normalized CNs with respect to that for system S0, and these normalized coordination number values are also incorporated in the same

Figure 5. Contours of solvent water density within 5.55 Å around griseofulvin molecule (pink balls). Panels a, b, c, and d are for systems S0, S3, S5, and S7, respectively. 3545

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Figure 6. Contours of hydrotrope SCS density within 9.65 Å around griseofulvin molecule (pink balls). Panels a, b, c, and d are for systems S0, S3, S5, and S7, respectively.

preceding discussions. We also carry out atomic mass density analysis of SCS surrounding a griseofulvin molecule possessing a cell side of 0.5 Å inside 9.65 Å, and the same is presented in Figure 6. As observed for the DTBM−SCS−water ternary system,16 in this study also we observe a low hydrotrope density around the griseofulvin molecule below the MHC region; however, with increasing concentration of hydrotrope, density of SCS around griseofulvin increases. Thus, these results can act as supportive evidence that water molecules are being substituted by SCS molecules around griseofulvin molecules at higher SCS concentrations. Here, we would like to mention that the findings of a recent fluctuation solution theory of hydrotropy argued that it is the excess number of hydrotropes around the solute (i.e., excess coordination number) which causes an enhancement in the solubility of the solute.51 This excess coordination number arises from the long-ranged changes in the solution structure around the solute. Here we note that the calculated coordination number, CN, is somewhat different from the excess coordination numbers. Although both are calculated by integrating the radial distribution functions, the latter (i.e., excess coordination number) provides information about the deviation from the ideal solvation model. In order to estimate the number of hydrotrope molecules in the first coordination shell of griseofulvin molecule, with the help of eq 8, we have calculated the number of first solvation shell SCS molecules around the griseofulvin molecule by considering the correlation function between the COM of griseofulvin and the C7 atom of SCS (see Figure 4 in the Supporting Information). From Table III, it can be seen that, with increasing concentration of the solution, the number of

Table III. Number of First-Shell SCS Molecules around Griseofulvin Molecules system

NSCS

S0 S1 S2 S3 S4 S5 S6 S7

10.64 11.31 11.29 11.31 13.50 17.65 19.43 22.28

first-shell SCS molecules around griseofulvin increases, but a prominent rise in these numbers can only be observed at and above the MHC level. These results, further, infer the formation of more hydrophobic surfaces around griseofulvin molecules. Here, it is worth to mention that Busch et al.52 studied the concentrated aqueous solution of hydrotrope proline by the use of both neutron and X-ray diffraction (with isotropic substitution and inelastic neutron scattering) experiments as well as empirical potential structure refining (EPSR) and molecular dynamics simulation methods. They found that proline molecules form dimers in water via shortranged interactions in which the cyclic electrostatic interaction between the CO2− group of one proline molecule and the NH2+ group of a neighboring proline plays an important role (unlike SCS, proline does not form a large aggregated structure). Moreover, these proline dimers create small hydrophobic pockets that can associate with nonpolar parts 3546

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the corresponding distribution functions (see Figure 5 in the Supporting Information). In Table IV the average number of

of other solutes. Here it is also important to mention that neutron diffraction scattering experiments have also been carried out to investigate the self-association of different molecules in water.53−59 For example, McLain et al., by employing a combination of neutron diffraction and computer simulation techniques, examined the role of hydrophobic and hydrophilic interactions in the peptide association.57 For their study they used aqueous solutions of different peptides such as glycyl-L-alanine, glycyl-L-proline, and L-alanyl-L-proline. They argued that hydrophilic interactions play a major role in the peptide association process. Furthermore, by the use of experimental (neutron diffraction) followed by computational (EPSR) methods, Rhys et al.58 investigated the effect of increased temperature (from 297 to 333 K) on the selfassociation tendency of glutamine in water. They observed that, in aqueous solution, the hydrophilic interactions are responsible for the self-association of glutamine. This association causes the formation glutamine dimers (mostly), and the effect of temperature on it is very negligible. Furthermore, Mason et al.59 used a combination of small-angle neutron scattering (SANS) method and MD simulation technique to explore the aggregation property of isopropyl alcohol and pyridine in aqueous solution, and they also examined the impact of guanidinium chloride (GdmCl) on the clustering of these molecules. In specific, in aqueous solution pyridine molecules interact with the like molecules via T-type edge face interactions.55 The addition of GdmCl into aqueous pyridine solution causes a reduction in the self-aggregation propensity of pyridine molecules, whereas it (GdmCl) has a very negligible effect on the self-association tendency of isopropyl alcohol.59 They argued that GdmCl interacts weakly with the “lumpy” aliphatic group of the isopropyl alcohol molecule leading to a very minimal effect of GdmCl on isopropyl alcohol aggregation. On the other hand, it (GdmCl) interacts favorably with the planar aromatic faces of pyridine molecules, which leads to the breaking of pyridine clusters and enhances dissolution of pyridine molecules in water. III.C. Hydrogen-Bond Properties. The available electronegative oxygen atoms in the sulfonate group of SCS make it a potential hydrogen-bond acceptor. Thus, in aqueous solution, it can form hydrogen bonds with water molecules. Moreover, because of the presence of carbonyl groups in the griseofulvin molecules, in aqueous solution it also has a potential to act as a hydrogen-bond acceptor and form hydrogen bonds with water molecules. Since the carbonyl group of griseofulvin molecules and the sulfonate group of SCS molecule both can function only as hydrogen-bond acceptors, so each of these molecules cannot form hydrogen bonds between the like molecules, and also griseofulvin−SCS hydrogen bonds are not possible. Therefore, only three types of hydrogen bonds are possible, namely, water−water, griseofulvin−water, and SCS−water. Following earlier works,60−62 the average number of water− water (per water), griseofulvin−water (per griseofulvin), and SCS−water (per SCS) hydrogen bonds are estimated by considering certain geometric criteria. If the interatomic distance between oxygen atoms of two water molecules is less than 3.35 Å, and at the same time the oxygen−oxygen− hydrogen angle is less than 45°, then the two water molecules are taken as hydrogen-bonded. For griseofulvin−water and SCS−water hydrogen bonds, the cutoff distances are less than 3.25 and 3.15 Å, respectively, whereas the angle is the identical as that for water−water hydrogen bonds. We have taken these cutoff distances from the appearance of the first minimum in

Table IV. Average Number of Water−Water Per Water (HBwater−water), Griseofulvin−Water Per Griseofulvin (HBgriseofulvin−water), and SCS−Water Per SCS (HBSCS−water) Hydrogen Bonds for Different Systemsa system S0 S1 S2 S3 S4 S5 S6 S7

HBwater−water 3.28 3.26 3.24 3.21 3.15 3.08 3.00 2.89

(3.28) (3.26) (3.22) (3.16) (3.03) (2.91) (2.73)

HBgriseofulvin−water 2.59 2.45 2.18 2.03 2.02 1.82 1.67 1.56

(2.59) (2.57) (2.54) (2.49) (2.39) (2.30) (2.15)

HBSCS−water 4.83 4.79 4.75 4.64 4.63 4.43 4.28 4.18

(4.83) (4.80) (4.74) (4.65) (4.47) (4.29) (4.01)

a

The values in the parentheses are the expected number of hydrogen bonds due to change in water number density.

hydrogen bonds between water−water, griseofulvin−water, and SCS−water is presented. It is apparent that the average number of these hydrogen bonds decreases as the concentration of SCS increases. Since, increased concentration causes a depletion in water number density (as mentioned above), a modest decrease in these numbers is expected. To negate this effect, in the parentheses of the same Table IV the expected hydrogen-bond numbers are also included. Negligible differences in the expected and calculated hydrogen-bond numbers are observed for water−water and SCS−water hydrogen bonds for all the systems. Therefore, the availability of the polar hydrogenbonding group of the SCS molecule for water remains unaffected in the aggregated structure of hydrotropes, indicating that the self-accumulation of SCS has an imperceptible impact on the average number of water−water and water−SCS hydrogen bonds. Similar observations were also reported in which DTBM molecules were used as solute molecules in water−SCS−DTBM ternary system.16 But, for griseofulvin−water hydrogen bonds, with increasing concentration, we observed a decrease in the average number of this type of hydrogen bond. We further noticed that the difference of expected and calculated hydrogen-bond numbers is less below the MHC level and this difference starts to rise from the MHC level. This indirectly supports that, although griseofulvin molecule carries hydrophilic parts, it favors to stay in the hydrophobic environment over the hydrophilic aqueous environment. III.D. Flory−Huggins Interaction Parameters for Griseofulvin−SCS Interactions. As mentioned above, for the calculations of Flory−Huggins interaction parameters we consider two different drug−hydrotrope mixtures with varying compositions. The calculated values of enthalpy of mixing (ΔHmix) and Flory−Huggins interaction parameters (χFH) for the first mixture (24:1 ratio) are −30.83 cal cm−3 and −8.99, respectively, whereas for the second mixture (12:1 ratio) they are −55.37 cal cm−3 and −16.16, respectively. The negative χFH values that we obtain for both the mixtures suggest the favorable hydrotrope SCS−drug griseofulvin interactions. Besides this, the griseofulvin-encapsulated SCS clusters are tightly wrapped by the water molecules. Thus, we can infer that, though SCS contains a much shorter hydrophobic chain in contrast to classical surfactants or long chain polymers, its ability to incorporate the griseofulvin molecules into its cluster 3547

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The Journal of Physical Chemistry B makes it a potential drug solubilizer. Here it is worth mentioning that the χFH and ΔHmix values for both the SCS− griseofulvin mixtures are more negative when compared to that for the respective SCS−DTBM mixtures,16 indicating a much higher mixing ability for the former mixtures than the latter.

IV. SUMMARY AND CONCLUSIONS Employing classical molecular dynamics simulation and the MM-GBSA method, the basic mechanism of hydrotropic action of hydrotrope sodium cumene sulfonate on sparingly soluble griseofulvin drug molecules is studied. Eight different systems with varying SCS concentrations at ambient temperature and pressure conditions are considered. The snapshots of different systems show that the SCS molecules start to self-aggregate through their small hydrotropic tail parts at and above minimum hydrotrope concentration (MHC) and drug griseofulvin molecules get incorporated into the inner core of SCS clusters. As concentration increases, the exclusion of water molecules from the first shell of different hydrophobic moieties of SCS and the COM of griseofulvin molecules has also been observed, and this effect is more prominent above the MHC level of SCS. These observations are further probed by the atomic mass density analysis of water and SCS density around the griseofulvin molecule. At and above the MHC level, the formation of higher-order SCS clusters is confirmed by cluster structure analysis. Furthermore, when SCS molecules start to form higher-order clusters, the number of incorporated griseofulvin molecules within the inner core of SCS aggregates exhibits a prominent enhancement, and above the MHC level, nearly all griseofulvin molecules are encapsulated into the SCS aggregates. The decomposition of drug−hydrotrope interactions into electrostatic and van der Waals components implies that the contribution of the latter is more significant in spite of the fact that the drug griseofulvin possesses both polar and nonpolar moieties. Interestingly, the estimation of average number of hydrogen bonds suggests that, though the selfassociation of SCS molecules has insignificant effect on water− SCS hydrogen bonds, it influences the water−griseofulvin hydrogen bonds. Furthermore, Flory−Huggins interaction parameter (χFH) successfully explains the reason behind the incorporation of drug griseofulvin into the core of SCS clusters. In this regard, we would like to mention that the recent studies of statistical thermodynamic theory of hydrotropic action, which is based upon the rigorous Kirkwood−Buff theory of solution, reported that the MHC of hydrotrope is caused by the solute (drug) persuaded increment of hydrotrope aggregation and bulk phase self-association of hydrotropes causes a reduction in the solubilization efficiency.63,64 It has further been claimed that it is the nonstoichiometric accumulation of the hydrotrope molecules around the drug which is responsible for its enhanced solubility in water.64 In this study also, though we observe a sharp increase in the number of SCS molecules in the first solvation shell of griseofulvin above the MHC level, our results are somewhat different from the findings of refs 63 and 64, where it has been reported that the preferential interactions between solute molecules and hydrotropes play a major role for the solubilization of solutes, not the aggregation of hydrotropes.





Snapshots for SCS−griseofulvin mixture in vacuum and in water, the distribution functions involving C7−C7 (SCS−SCS), COM−C7 (griseofulvin−SCS), COM− COM (griseofulvin−griseofulvin), O−OW (griseofulvin−water), O−O w (SCS−water), and O w −O w (water−water), contours of a C7 atom of SCS density around a reference C7 atom of an SCS molecule, and visualizing snapshots of different systems (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.



ACKNOWLEDGMENTS Part of the research was done using the computational facility of C-DAC, Pune, India.



REFERENCES

(1) Leeson, P. D.; Springthorpe, B. The Influence of Drug-Like Concepts on Decision-Making in Medicinal Chemistry. Nat. Rev. Drug Discovery 2007, 6, 881−890. (2) Neuberg, C. Ü ber Hydrotropic. Biochem. Z. 1916, 76, 107−176. (3) Hodgdon, T. K.; Kaler, E. W. Hydrotropic Solutions. Curr. Opin. Colloid Interface Sci. 2007, 12, 121−128. (4) Subbarao, C. V.; Chakravarthy, I. P. K.; Sai Bharadwaj, A. V. S. L.; Prasad, K. M. M. Functions of Hydrotropes in Solutions. Chem. Eng. Technol. 2012, 35, 225−237. (5) Saleh, A. M.; Elkhordagui, L. K. Hydrotropic Agents−a New Definition. Int. J. Pharm. 1985, 24, 231−238. (6) Woolfson, A. D.; McCafferty, D. F.; Launchbury, A. P. Stabilisation of Hydrotropic Temazepam Parenteral Formulations by Lyophilisation. Int. J. Pharm. 1986, 34, 17−22. (7) Jain, N. K.; Patel, V. V.; Taneja, L. N. Formulation and Evaluation of Nifedipine Injection. Pharmazie 1988, 43, 254−255. (8) Osborne, D. W. The Effect of the Monosodium Salt of a C21 Dicarboxylic Acid Hydrotrope on Biosurfactant bilayers: Transdermal Delivery Considerations. Colloids Surf. 1987, 30, 13−23. (9) Koparkar, Y. P.; Gaikar, V. G. Solubility of o−/p− Hydroxyacetophenones in Aqueous Solutions of Sodium Alkyl Benzene Sulfonate Hydrotropes. J. Chem. Eng. Data 2004, 49, 800− 803. (10) Raney, K. H.; Miller, C. A. Optimum Detergency Conditions with Nonionic Surfactants: II. Effect of Hydrophobic Additives. J. Colloid Interface Sci. 1987, 119, 539−549. (11) Kim, J. Y.; Kim, S.; Papp, M.; Park, K.; Pinal, R. Hydrotropic Solubilization of Poorly Water-Soluble Drugs. J. Pharm. Sci. 2010, 99, 3953−3965. (12) Dempsey, G.; Molyneux, P. Solubility of the Cosolutes 4Hydroxybenzoic Acid and Its Alkyl Esters (‘Alkylparabens’)in Aqueous Urea: Evidence for 1:1 Cosolute-Urea Association in Solution and Evaluation of the Methylene Group Contribution to the Free Energy of Association. J. Chem. Soc., Faraday Trans. 1992, 88, 971−977. (13) Ferreira, G.; Perigo, D. M.; Politi, M. J.; Schreier, S. Effect of Anions from the Hofmeister Series and Urea on the Binding of the Charged and Uncharged forms of the Local Anesthetic Tetracaine to Zwitterionic Micelles. Photochem. Photobiol. 1996, 63, 755−761. (14) Coffman, R. E.; Kildsig, D. O. Effect of Nicotinamide and Urea on the Solubility of Riboflavin in Various Solvents. J. Pharm. Sci. 1996, 85, 951−954. (15) Das, S.; Paul, S. Exploring Molecular Insights of Aggregation of Hydrotrope Sodium Cumene Sulfonate in Aqueous Solution: A Molecular Dynamics Simulation Study. J. Phys. Chem. B 2015, 119 (7), 3142−3154.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcb.5b11902. 3548

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(37) Ryckaert, J. P.; Ciccotti, G.; Berendsen, H. J. C. Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes. J. Comput. Phys. 1977, 23, 327−341. (38) Flory, P. J. Thermodynamics of High Polymer Solutions. J. Chem. Phys. 1942, 10, 51−61. (39) Huggins, M. L. Solutions of Long Chain Compounds. J. Chem. Phys. 1941, 9, 440−440. (40) Huynh, L.; Grant, J.; Leroux, J.-C.; Delmas, P.; Allen, C. Predicting the Solubility of the Anti-Cancer Agent Docetaxel in Small Molecule Excipients using Computational Methods. Pharm. Res. 2008, 25, 147−157. (41) Case, F. H.; Honeycutt, D. J. Will My Polymers Mix? Methods for Studying Polymer Miscibility. Trends Polym. Sci. 1994, 2, 256. (42) Patel, S.; Lavasanifar, A.; Choi, P. Application of Molecular Dynamics Simulation to Predict the Compatability between WaterInsoluble Drugs and Self-Associating Poly(Ethylene Oxide)-b-Poly(εCaprolactone)Block Copolymers. Biomacromolecules 2008, 9, 3014− 3023. (43) Xiang, T. X.; Anderson, B. D. Molecular Dynamics Simulation of Amorphous Indomethacin-Poly (Vinylpyrrolidone) Glasses: Solubility and Hydrogen Bonding Interactions. J. Pharm. Sci. 2013, 102, 876−891. (44) Srinivasan, J.; Cheatham, T. E.; Cieplak, P.; Kollman, P. A.; Case, D. A. Continuum Solvent Studies of the Stability of DNA, RNA, and Phosphoramidate-DNA Helices. J. Am. Chem. Soc. 1998, 120, 9401−9409. (45) Jayaram, B.; Sprous, D.; Beveridge, D. L. Solvation Free Energy of Biomacromolecules: Parameters For a Modified Generalized Born Model Consistent with the AMBER Force Field. J. Phys. Chem. B 1998, 102, 9571−9576. (46) Onufriev, A.; Bashford, D.; Case, D. A. Exploring Protein Native States and Large-Scale Conformational Changes with a Modified Generalized Born Model. Proteins: Struct., Funct., Genet. 2004, 55, 383−394. (47) Troitzsch, R. Z.; Martyna, G. J.; McLain, S. E.; Soper, A. K.; Crain, J. Structure of Aqueous Proline via Parallel Tempering Molecular Dynamics and Neutron Diffraction. J. Phys. Chem. B 2007, 111, 8210−8222. (48) McLain, S. E.; Soper, A. K.; Terry, A. E.; Watts, A. Structure and Hydration of L-Proline in Aqueous Solutions. J. Phys. Chem. B 2007, 111, 4568−4580. (49) Paul, S.; Paul, S. Investigating the Counteracting Effect of Trehalose on Urea-Induced Protein Denaturation Using Molecular Dynamics Simulation. J. Phys. Chem. B 2015, 119 (34), 10975−10988. (50) Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual Molecular Dynamics. J. Mol. Graphics 1996, 14, 33−38. (51) Shimizu, S.; Booth, J. J.; Abbott, S. Hydrotropy: Binding Models Vs. Statistical Thermodynamics. Phys. Chem. Chem. Phys. 2013, 15, 20625−20632. (52) Busch, S.; Lorenz, C. D.; Taylor, J.; Pardo, L. C.; McLain, S. E. Short-Range Interactions of Concentrated Proline in Aqueous Solution. J. Phys. Chem. B 2014, 118, 14267−14277. (53) Bowron, D. T.; Finney, J. L.; Soper, A. K. Structural Investigation of Solute-Solute Interactions in Aqueous Solutions of Tertiary Butanol. J. Phys. Chem. B 1998, 102, 3551−3563. (54) Malardier-Jugroot, C.; Bowron, D. T.; Soper, A. K.; Johnson, M. E.; Head-Gordon, T. Structure and Water Dynamics of AqueousPeptide Solutions in the Presence of Co-solvents. Phys. Chem. Chem. Phys. 2010, 12, 382−392. (55) Mason, P. E.; Neilson, G. W.; Dempsey, C. E.; Price, D. L.; Saboungi, M.-L.; Brady, J. W. Observation of Pyridine Aggregation in Aqueous Solution Using Neutron Scattering Experiments and MD Simulations. J. Phys. Chem. B 2010, 114, 5412−5419. (56) Mason, P. E.; Neilson, G. W.; Price, D.; Saboungi, M.-L.; Brady, J. W. Simulation and Neutron Diffraction Studies of Small Biomolecules in Water. Food Biophys. 2011, 6, 210−216. (57) McLain, S. E.; Soper, A. K.; Daidone, I.; Smith, J. C.; Watts, A. Charge-Based Interactions Between Peptide Observed as the

(16) Das, S.; Paul, S. Mechanism of Hydrotropic Action of Hydrotrope Sodium Cumene Sulfonate on the Solubility of Di-tButyl-Methane: A Molecular Dynamics Simulation Study. J. Phys. Chem. B 2016, 120, 173−183. (17) Zaheer, A.; Naveen, M.; Santosh, M. K.; Imran, K. Solubility Enhancement of Poorly Water Soluble Drugs. Int. J. Pharm. Technol. 2011, 3 (1), 807−823. (18) Nidhi, K.; Indrajeet, S.; Khushboo, M.; Gauri, K.; Sen, D. J. Hydrotropy: A Promising Tool for Solubility Enhancement. Int. J. Drug. Dev. Res. 2011, 3 (2), 26−33. (19) Booth, J. J.; Abbott, S.; Shimizu, S. Mechanism of Hydrophobic Drug Solubilization by Small Molecule Hydrotropes. J. Phys. Chem. B 2012, 116, 14915−14921. (20) Bauduin, P.; Testard, F.; Zemb, Th. Solubilization in Alkanes by Alcohols as Reverse Hydrotropes or “Lipotropes”. J. Phys. Chem. B 2008, 112, 12354−12360. (21) Chan, Y. C.; Friedlander, S. F. New Treatments for Tinea Capitis. Curr. Opin. Infect. Dis. 2004, 17, 97−103. (22) Panda, D.; Rathinasamy, K.; Santra, M. K.; Wilson, L. Kinetic Suppression of Microtubule Dynamic Instability by Griseofulvin: Implications for Its Possible Use in the Treatment of Cancer. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 9878−9883. (23) Thomson Reuters NEWPORT database, 2011. (24) Kasim, N. A.; Whitehouse, M.; Ramachandran, C.; Bermejo, M.; Lennernäs, H.; Hussain, A. S.; Junginger, H. E.; Stavchansky, S. A.; Midha, K. K.; Shah, V. P.; Amidon, G. L. Molecular Properties of WHO Essential Drugs and Provisional Biopharmaceutical Classification. Mol. Pharmaceutics 2004, 1, 85−96. (25) Kasimova, O. A.; Pavan, M. G.; Danani, A.; Modan, K.; Cristiani, A.; Scapozza, L.; Gurny, R.; Möller, M. Validation of a Novel Molecular Dynamics Simulation Approach for Lipophilic Drug Incorporation into Polymer Micelles. J. Phys. Chem. B 2012, 116, 4338−4345. (26) Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and Testing of a General Amber Force Field. J. Comput. Chem. 2004, 25, 1157−1174. (27) Jakalian, A.; Bush, B. L.; Jack, D. B.; Bayly, C. I. Fast, Efficient Generation of High-Quality Atomic Charges. AM1-BCC Model: I. Method. J. Comput. Chem. 2000, 21, 132−146. (28) Jakalian, A.; Jack, D. B.; Bayly, C. I. Fast, Efficient Generation of High-Quality Atomic Charges. AM1-BCC Model: II. Parameterization and Validation. J. Comput. Chem. 2002, 23, 1623−1641. (29) Wang, J.; Wang, W.; Kollman, P. A.; Case, D. A. Automatic Atom Type and Bond Type Perception in Molecular Mechanical Calculations. J. Mol. Graphics Modell. 2006, 25, 247−260. (30) Case, D. A.; Darden, T. A.; Cheatham, T. E., III; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R.; Walker, R. C.; Zhang, W.; Merz, K. M.; et al. AMBER12; University of California: San Francisco, CA, 2012. (31) Vanommeslaeghe, K.; Hatcher, E.; Acharya, C.; Kundu, S.; Zhong, S.; Shim, J.; Darian, E.; Guvench, O.; Lopes, P.; Vorobyov, I.; Mackerell, A. D., Jr CHARMM general force field: A Force Field for Drug-Like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Fields. J. Comput. Chem. 2010, 31, 671−690. (32) He, X.; Guvench, O.; MacKerell, A. D.; Klein, M. L. Atomistic Simulation Study of Linear Alkylbenzene Sulfonates at the Water/Air Interface. J. Phys. Chem. B 2010, 114, 9787−9794. (33) Berendsen, H. J. C.; Grigera, J. R.; Straatsma, T. P. The Missing Term in Effective Pair Potentials. J. Phys. Chem. 1987, 91, 6269−6271. (34) Crowley, M. F.; Williamson, M. J.; Walker, R. C. CHAMBER: Comprehensive Support for CHARMM Force Fields Within the AMBER Software. Int. J. Quantum Chem. 2009, 109, 3767−3772. (35) Martinez, L.; Andrade, R.; Birgin, E. G.; Martinez, J. M. PACKMOL: A Package for Building Initial Configurations for Molecular Dynamics Simulations. J. Comput. Chem. 2009, 30, 2157− 2164. (36) Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; DiNola, A.; Haak, J. R. Molecular Dynamics with Coupling to an External Bath. J. Chem. Phys. 1984, 81, 3684−3690. 3549

DOI: 10.1021/acs.jpcb.5b11902 J. Phys. Chem. B 2016, 120, 3540−3550

Article

The Journal of Physical Chemistry B Dominant Force for Association in Aqueous Solution. Angew. Chem., Int. Ed. 2008, 47, 9059−9062. (58) Rhys, N. H.; Soper, A. K.; Dougan, L. Hydrophilic Association in a Dilute Glutamine Solution Persists Independent of Increasing Temperature. J. Phys. Chem. B 2015, 119, 15644−15651. (59) Mason, P. E.; Dempsey, C. E.; Neilson, G. W.; Kline, S. R.; Brady, J. W. Preferential Interactions of Guanidinum Ions with Aromatic Groups over Aliphatic Groups. J. Am. Chem. Soc. 2009, 131, 16689−16696. (60) Luzar, A.; Chandler, D. Effect of Environment on Hydrogen Bond Dynamics in Liquid Water. Phys. Rev. Lett. 1996, 76, 928−931. (61) Chandra, A. Effects of Ion Atmosphere on Hydrogen-Bond Dynamics in Aqueous Electrolyte Solutions. Phys. Rev. Lett. 2000, 85, 768−771. (62) Chandra, A. Dynamical Behavior of Anion-Water and WaterWater Hydrogen Bonds in Aqueous Electrolyte Solutions: A Molecular Dynamics Study. J. Phys. Chem. B 2003, 107, 3899−3906. (63) Shimizu, S.; Matubayasi, N. Hydrotropy: Monomer-Micelle Equilibrium and Minimum Hydrotrope Concentration. J. Phys. Chem. B 2014, 118, 10515−10524. (64) Booth, J. J.; Omar, M.; Abbott, S.; Shimizu, S. Hydrotrope Accumulation Around the Drug: the Driving Force for Solubilization and Minimum Hydrotrope Concentration for Nicotinamide and Urea. Phys. Chem. Chem. Phys. 2015, 17, 8028−8037.

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DOI: 10.1021/acs.jpcb.5b11902 J. Phys. Chem. B 2016, 120, 3540−3550