J. Phys. Chem. B 2010, 114, 13455–13462
13455
Arginine-Assisted Solubilization System for Drug Substances: Solubility Experiment and Simulation Atsushi Hirano,†,| Tomoshi Kameda,‡,| Tsutomu Arakawa,§ and Kentaro Shiraki*,† Institute of Applied Physics, UniVersity of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan, Computational Biology Research Center, AdVanced Industrial Science and Technology, Koto, Tokyo 135-0064, Japan, and Alliance Protein Laboratories, Thousand Oaks, California 91360, United States ReceiVed: March 3, 2010; ReVised Manuscript ReceiVed: September 3, 2010
The poor aqueous solubility of drug substances hampers their broader applications. This paper describes a de novo strategy to increase the aqueous solubility of drug substances using an arginine-assisted solubilization system (AASS) with alkyl gallates as model drug substances. Solubility experiments of alkyl gallates showed that arginine greatly increases the aqueous solubility of different alkyl gallates, whose aqueous solubilities differ widely. In contrast, lysine showed marginal effects on alkyl gallates solubility. Molecular dynamic simulation indicated a greater interaction of arginine with alkyl gallates than that of lysine, which reflects favorable interaction between the guanidinium group of arginine and the aromatic ring of alkyl gallates. Such interaction apparently disrupts association of alkyl gallate molecules, leading to solubilization. These results indicate AASS as a promising approach to solubilize poorly soluble drug substances containing aromatic ring structures. Introduction The aqueous solubility of drug substances plays a key role in two phases of drug development: drug discovery and pharmacological evaluation. Drug substances are screened in aqueous solution against functional target molecules such as native proteins. For that reason, they must be soluble in solution during the screening process. In the second phase, drug substances must be pharmacologically available during in vivo drug administration. Bioavailability of poorly soluble drug substancessas exemplified by biopharmaceutical classification system (BCS) type II and type IV drugssthrough oral administration is limited by the dissolution rate.1-4 Many techniques for enhancement of dissolution performance have been provided through alteration of various operating parameters, such as supercritical solid dispersion and cosolvent formulations,5,6 inclusion complexation,7-16 precipitation techniques,17-19 and cryogenic engineering.16,20-24 Dissolution rates of drugs depend on the surface area of the particle and their solubility, as shown by the Nernst-Noyes-Whitney equation. Therefore, the particle size and morphology affect dissolution rates. Although drug formulation at supersaturated states obtained from metastable solid provides a high dissolution rate, the particles are often kinetically unstable and subsequently relax into equilibrium states through precipitation or crystallization, which can be inhibited by the inclusion of polymer in some cases.25-31 In addition to the kinetics of dissolution, equilibrium solubility is an important parameter that defines the degree of supersaturation and can be enhanced by biocompatible solutes. Arginine is a highly established aggregation suppressor for protein. It is used in various applications, including storage and refolding.32-36 Although the arginine effect has been interpreted * To whom correspondence should be addressed. Phone: +81-29-8535306. Fax: +81-29-853-5215. E-mail:
[email protected]. † University of Tsukuba. ‡ Advanced Industrial Science and Technology. § Alliance Protein Laboratories. | These authors contributed equally to this paper.
in terms of its thermodynamic binding to the protein, the physical mechanism that confers arginine binding is unknown. Recently, several groups have reported the solubilization and stabilization of low molecular weight aromatic compounds by arginine,37-42 implying that arginine interacts with the aromatic moiety of the compounds. Such a capability of arginine to suppress protein aggregation and increase small molecule solubility might be applicable and beneficial to drug development. As described in this paper, we provide a novel method to increase the solubility of poorly soluble drug substances: an arginine-assisted solubilization system (AASS). We systematically investigated the interaction between arginine and alkyl gallates with an aromatic ring. Gallic acid (3,4,5-trihydroxybenzoic acid) is obtained conventionally using alkaline or acid hydrolysis of the tannins from nutgalls. Some alkyl gallate derivatives have been characterized extensively in terms of their physiological activity and cytotoxicity.43 They have been used as antioxidant food additives with E numbers, such as E-310 (propyl gallate), E-311 (octyl gallate), and E-312 (lauryl gallate). The Ministry of Health, Labour and Welfare of Japan recognizes both propyl gallate and octyl gallate as quasi-drugs. In this study, we compared the solubilities of alkyl gallates in the presence of arginine and lysine because the positively charged amino acids can possibly confer electrostatic interactions between their positive charge of the side chains and alkyl gallates. Additionally, we use molecular dynamics simulation to elucidate how alkyl gallates mutually interact and how arginine interacts with alkyl gallates. Experimental Section Solubility Measurement. All alkyl gallates used for this study were obtained from Tokyo Kasei Kogyo Co., Ltd. (Tokyo, Japan). Arginine hydrochloride and lysine hydrochloride were acquired from Wako Pure Chemical Industries, Ltd. (Osaka, Japan). Aqueous arginine and lysine solutions were prepared at the indicated concentrations by weighing these amino acid powders and water. The weight concentration was converted
10.1021/jp101909a 2010 American Chemical Society Published on Web 10/06/2010
13456
J. Phys. Chem. B, Vol. 114, No. 42, 2010
Hirano et al.
to the molar concentration from the density of the prepared solutions. Alkyl gallates were transferred into test tubes, to which 0.5 mL of water or amino acid solution was added. The suspension was heated at 40 °C for 1 h with frequent vortex mixing for complete resolution of alkyl gallate powders. The solution was then incubated at 25 °C for 3 days with frequent vortex mixing. The suspension was centrifuged at 25 °C and 16 000g for 20 min to obtain supernatant saturated with alkyl gallate. After appropriate dilution with water, the absorbance of the supernatant was determined spectrophotometrically at 271 nm on a UV-vis spectrophotometer (ND-1000; NanoDrop Technologies, Inc.; Wilmington, DE). The absorbance value was converted to the concentration based on the standard curve determined for each alkyl gallate. The determined solubility was an average of triplicate experiments. The average and standard error were obtained. Calculation of Transfer Free Energy. The transfer free energy of alkyl gallates from water to amino acid solutions was calculated according to
∆Gtr ) µ0a - µw0 ) -RT ln(xa /xw)
(1)
where
{
µw ) µw0 + RT ln xw µa ) µ0a + RT ln xa
{
xw ) ng,w /(ng,w + nH2O,w) xa ) ng,a /(ng,a + nH2O,a + 2na,a)
(2)
box with a side length of about 60.8 Å under periodic boundary conditions. The temperature and pressure were controlled using Berendsen methods48 with respective relaxation times of 0.1 and 1.0 ps. Electrostatics were treated using the particle mesh Ewald (PME) method49 with a 8.0 Å cutoff distance and 1.2 Å grid spacing for the Fourier transform in the reciprocal space. The van der Waals interactions were expressed by the twin range cutoff method with a 8.0 or 12.0 Å cutoff distance. Covalent bonds involving hydrogen atoms of alkyl gallates were constrained using the Linear Constraint Solver (LINCS).50 Covalent bonds of water were constrained using the SETTLE algorithm.51 The integration time step was 2 fs. The simulations were conducted using the GROMACS 3.3.1 simulator.52,53 Free Energy Calculation of an Ethyl Gallate and an Amino Acid. The thermodynamic nature of the amino acid (arginine or lysine)-ethyl gallate-water system was investigated using MD simulation. We performed the umbrella sampling simulation to determine the free energy profile of the system. In umbrella sampling, the change of the free energy A along the order parameter ξ is acquired, as calculated by combining the potential mean force (PMF) along ξ using MD or Monte Carlo simulation. These simulations are performed with a series of bias potentials for efficient sampling of the entire range of the order parameter. The relevant range of the order parameter is divided into bins; then each biased potential wi(ξ) is assigned to each window. The simulation produces the PMF of the biased system as
1 Aib(ξ) ) - ln Pib(ξ) β
(4)
(3)
In the equations presented above, µa and µw are the chemical potential of alkyl gallates, µa0 and µw0 are the corresponding standard chemical potentials of alkyl gallate, xa and xw, respectively, represent the corresponding mole fraction of alkyl gallate in the presence and absence of additive (i.e., arginine hydrochloride and lysine hydrochloride). In addition, ni,a and ni,w are the molarities of component i at saturation, where subscript g, H2O, and a corresponding to subscript i, respectively, denote the alkyl gallate, water, and amino acid in the presence and absence of additive. In those equations, R and T, respectively, represent the gas constant and absolute temperature. Therein, µa represents the chemical potential of alkyl gallate in aqueous amino acid solution; ng,a is the molarity of alkyl gallate in the same solution. The activity coefficient was considered to be close to unity because of the poor solubility of alkyl gallate.
where β ) 1/kBT; the PME of the unbiased system in each window is
1 Aiu(ξ) ) - ln Pib(ξ) - wi(ξ) + Fi β
(5)
whereFi is a constant. The weighted histogram analysis method (WHAM) is the most popular among the combined PMFs of the biased system.54 Umbrella integration (UI) has been further developed for WHAM with some advantages.55,56 For this study, we used UI. Each bin of UI is calculated from the derivative of unbiased PME as b ∂Aiu(ξ) dwi(ξ) 1 ∂ ln Pi (ξ) )∂ξ β ∂ξ dξ
(6)
Computational Section Molecular Dynamics Simulation for Alkyl Gallates. The microscopic state of alkyl gallate was studied using molecular dynamics (MD) simulations of methyl gallate, ethyl gallate, propyl gallate, and butyl gallate in a water box. Three 10 ns calculations were done for the alkyl gallate-water system. The system contained 27 alkyl gallates and 7149 water molecules, which corresponded to ca. 0.2 M. That value is about 4-fold higher than the solubility of methyl gallate and far above the solubility of propyl gallate and butyl gallate. The atomic coordinates of alkyl gallates were described using the General AMBER Force Field (GAFF).44 Those of water molecules were done using the TIP3P model.45 An AM1-BCC charge was used for alkyl gallates assigned by the Antechamber.46,47 Simulations were conducted at the NPT ensemble (300 K, 1 bar) in a cubic
Ka¨stner and Thiel showed that if the restraint potential has a harmonic formula, then
1 wi(ξ) ) K(ξ - ξic)2 2
(7)
where ξci is the center of the window and Pbi (ξ) is well approximated as a normal distribution. Then, eq 6 can be described as
¯b ∂Aiu(ξ) 1 ξ - ξi - K(ξ - ξic) )∂ξ β (σb)2 i
(8)
Drug Solubilization by Arginine
J. Phys. Chem. B, Vol. 114, No. 42, 2010 13457
TABLE 1: Solubility of Alkyl Gallates in the Presence of Amino Acids solubility (g/100 g solvent) amino acid (M)
methyl gallate
ethyl gallate
propyl gallate
butyl gallate
arginine 0 0.25 0.5 0.75 1
0.951 ( 0.005 1.355 ( 0.009 1.783 ( 0.021 2.217 ( 0.027 2.607 ( 0.036
1.288 ( 0.016 1.822 ( 0.018 2.418 ( 0.027 3.021 ( 0.055 3.668 ( 0.042
0.285 ( 0.002 0.387 ( 0.004 0.500 ( 0.005 0.617 ( 0.005 0.739 ( 0.003
0.150 ( 0.002 0.201 ( 0.001 0.254 ( 0.003 0.317 ( 0.005 0.379 ( 0.004
lysine 0 0.25 0.5 0.75 1
0.951 ( 0.005 1.033 ( 0.007 1.094 ( 0.012 1.143 ( 0.009 1.195 ( 0.011
1.288 ( 0.016 1.322 ( 0.011 1.388 ( 0.023 1.445 ( 0.013 1.531 ( 0.020
0.285 ( 0.002 0.281 ( 0.002 0.284 ( 0.001 0.291 ( 0.002 0.299 ( 0.003
0.150 ( 0.002 0.146 ( 0.001 0.145 ( 0.002 0.145 ( 0.001 0.149 ( 0.001
where ξjbi is the mean of the biased simulation in the window i and variance σbi is the variance. To acquire the derivative (∂Au(ξ))/(∂ξ) in the entire range of ξ, Aui (ξ) are combined according to a weighted average as
∂A(ξ) ) ∂ξ
windows
∑
pi(ξ)
i
∂Aiu(ξ) ∂ξ
(9)
where windows
pi(ξ) ) NiPib(ξ)/
∑
NiPib(ξ)
(10)
i
For use in these equations, A(ξ) is obtained by numerically integrating the derivative (eq 9) over ξ. For this study, we defined the order parameter ξ as the distance between the center of mass of the ethyl gallate and that of the amino acid. Umbrella sampling was conducted for ξ ) 1.5-13.0 Å, which was divided into 24 bins with window lengths of 0.5 Å. Using K ) 0.239 kcal/mol Å2, six 10 ns MD simulations were done in each window; the last 9 ns of data were analyzed and used to acquire the free energy. The total simulation time was 2880 ns. The biased-simulation procedure was almost identical to that of the MD simulation of the ethyl gallate-water system described above. Briefly, the atomic coordinates of the amino acids were described by an Amber ff99SB force field.57 The charges of side chains were assigned as a neutral pH condition. Simulations were conducted in a rectangular box with side lengths of about 50 Å (x axis) and 35 Å (y axis and z axis) under a periodic boundary condition. The system contained 2004 water molecules and one chloride ion, which were added to neutralize the net charge of the system. Fujitani et al. have previously shown that Amber ff99 and GAFF with AM1-BCC charge, which was similar to our system, are used for prediction of the binding affinity between protein and small compounds; the calculated data were in good agreement with those of the experiment.58 In other research, the solvation free energies of many drug-like compounds were calculated using GAFF with AM1-BCC charge, which were also in good agreement with experimental data.59-62 Thus, we concluded that this force field is applicable to MD simulation for alkyl gallates, though their experimental data such as the solvation free energy were not reported to our best knowledge. To analyze the free energy landscape, principal component analysis (PCA) was used.63-65 First, we selected 129 600
configurations of the ethyl gallate and the amino acid (both arginine and lysine) from the last 9 ns snapshots of simulation at ξ ) 4.0-10.0 Å. Noting that the side chains of lysine and arginine differ, we considered the common region of the system: all heavy atoms of ethyl gallate, all heavy atoms of the backbone of the amino acid, and the Cβ, Cγ, and Cδ atoms of the amino acid were used for PCA; all other atoms of the side chains were omitted. Next, these conformations were superimposed onto a reference conformation and the positions of ethyl gallate were fitted to that of ethyl gallate of a reference conformation. Then, the variance-covariance matrix of the ensemble was diagonalized to obtain eigenvectors (PC axes) and eigenvalues λi (the standard deviations of the conformational distribution along the ith PC axis). Here, λi/∑iλi is regarded as the relative contribution of the distribution along the ith PC. Results Solubility Measurement and Analysis. Solubility of Alkyl Gallates in Amino Acid Solution. The solubility in grams per 100 g of solvents of four different alkyl gallates (methyl gallate, ethyl gallate, propyl gallate, and butyl gallate) was measured in aqueous solutions containing arginine and lysine of various concentrations. The solubility data are shown in Table 1 along with the standard errors. The observed increase in solubility is statistically significant because the experimental errors of solubility measurements are less than 2%. The solubility of all four alkyl gallates derivatives increased concomitantly with increasing concentrations of the amino acids. Results showed that arginine solubilizes each alkyl gallate more effectively than lysine, suggesting the importance of the guanidinium group of the arginine side chain. The increase of the solubility of alkyl gallates by arginine did not depend upon further incubation; therefore, alkyl gallates were thermodynamically stabilized by arginine in the equilibrium state. Contrary to our expectation, the solubility of four alkyl gallates in each solvent increased not in the order of alkyl chain length but in the order of butyl gallate < propyl gallate < methyl gallate < ethyl gallate. The solubility data are shown against the molar concentration of the solvent additive (Figure 1A). The solubility of all alkyl gallates sharply increased in the presence of arginine, almost linearly with the concentration. Figure 1B shows that changes in solubility attributable to addition of arginine are more clearly apparent by the ratio of the solubility in amino acid solution to the solubility in water. The solubility of all four alkyl gallates increased 2.5-3.0-fold in the presence of 1 M arginine compared to that in water (Figure 1B). On the other hand, lysine had marginal effects on alkyl gallate solubility, in particular those with longer alkyl chains. Apparently, solubility enhance-
13458
J. Phys. Chem. B, Vol. 114, No. 42, 2010
Figure 1. Solubility (A) and solubility ratio (B) of alkyl gallates in the presence of arginine (black symbols) or lysine (white symbols): methyl gallate, circles; ethyl gallate, squares; propyl gallate, triangles; butyl gallate, diamonds.
Figure 2. Solubility ratio of alkyl gallates to methyl gallate as a function of the carbon number in the alkyl chain length: 1 M arginine, black circles; 1 M lysine, white circles; no additive, crosses.
ment by arginine was higher for methyl gallate and ethyl gallate than for propyl gallate and butyl gallate (Figure 1B). The results suggest that the solubilizing effect of arginine decreases slightly with elongation of alkyl chains in alkyl gallate derivatives. Lysine increased the solubilities of only methyl and ethyl gallate (at most 1.3-fold at 1 M) and increased the solubilities of propyl gallate and butyl gallate even less effectively. It is interesting that alkyl gallate solubility increased in the order of butyl gallate < propyl gallate < methyl gallate < ethyl gallate, independent of the presence of the additive. To illustrate this alkyl chain length dependence of alkyl gallate solubility more clearly, the ratio of alkyl gallate solubility to methyl gallate solubility was calculated in the absence of additive, in 1 M arginine, and in 1 M lysine as presented in Figure 2. The solubility of each alkyl gallate changes similarly in these three different solvents: ethyl gallate has the highest solubility of any of the solvents. These results suggest that the solid state of each alkyl gallate is independent of the solvent, whether the solvent is water, 1 M arginine solution, or 1 M lysine solution. Transfer Energy from Water to Amino Acid Solution. The transfer free energy of alkyl gallate from the water to solution containing additives (e.g., arginine) was calculated from the solubility data in Table 1 using eq 1, assuming that the free energy of the solid phase (precipitate) is independent of the presence of the additive, as stated above. In other words, the chemical equilibrium of alkyl gallate solutions with and without the additive was established against the same solid state. This assumption is normally made for calculation of the transfer free energy from the solubility data.66-69 Because the solubility increased in the presence of arginine and lysine, the transfer free energy was negative. Alkyl gallate is more thermodynamically stable in the presence of amino acids (Figure 3). Transfer energy from water to arginine was more negative than the value for lysine. Therefore, the interaction between arginine and alkyl gallates is more favorable than that
Hirano et al.
Figure 3. Transfer free energy of alkyl gallates from water to arginine solutions (black symbols) or lysine solutions (white symbols): methyl gallate, circles; ethyl gallate, squares; propyl gallate, triangles; butyl gallate, diamonds.
Figure 4. Derivative of the transfer free energy of alkyl gallates with respect to the concentration of arginine (black symbols) or lysine (white symbols): methyl gallate, circles; ethyl gallate, squares; propyl gallate, triangles; butyl gallate, diamonds.
between lysine and alkyl gallates. The transfer free energy of these alkyl gallates from water to 1 M arginine is ca. -3 kJ/ mol (ca. -700 cal/mol). This transfer free energy is significantly greater than the value observed for the side chains of phenylalanine (ca. -150 cal/mol), tyrosine (ca. -300 cal/mol), and tryptophan (ca. -450 cal/mol),34 indicating that the interaction of arginine with alkyl gallates is much more favorable than that with aromatic amino acids. The difference might be attributable to the steric hindrance between the aromatic ring in phenylalanine or tryptophan and the guanidinium group of arginine. A small but significant difference was found in the transfer free energy between different alkyl gallates. The interaction is less favorable for the longer alkyl chain of alkyl gallates than the shorter ones, which suggests an unfavorable interaction between arginine and alkyl chain moiety. Such an unfavorable interaction can be offset by the favorable free energy of arginine with aromatic moiety. The difference in the transfer free energy between these alkyl gallates is more apparent in lysine solutions, suggesting that the unfavorable interaction of lysine with the alkyl chain moiety of alkyl gallates is greater than that of arginine. It is evident from Figure 3 that the transfer free energy does not increase linearly with the arginine concentration. The slopes of the transfer free energy at each concentration are portrayed in Figure 4. The slope decreased with increasing arginine concentration, meaning that the solubilizing effect of arginine becomes weaker at higher concentrations. MD Simulation and Analysis. Conformational InWestigation of Solubilization by MD Simulation. To elucidate the observed alkyl chain length dependence of solubility, we conducted MD simulations of the alkyl gallate-water system and investigated how alkyl gallates mutually interact in water. Figure 5 portrays the representative configurations of methyl gallate (Figure 5A)
Drug Solubilization by Arginine
J. Phys. Chem. B, Vol. 114, No. 42, 2010 13459
Figure 5. Representative structures of methyl gallate (A) and butyl gallate (B) in the MD simulation.
Figure 7. Two-dimensional free energy landscape of arginine-ethyl gallate-water (A) and lysine-ethyl gallate-water (B) in a tightly bound state (ξ ) ca. 5 Å) along principal component axes, PC-1 (horizontal axis) and PC-2 (vertical axis). Lower figures correspond to the representative structures in the respective minima of free energy.
Figure 6. Free energy of arginine-ethyl gallate-water (solid line) and lysine-ethyl gallate-water (dotted line) along order parameter ξ, which is the distance between the respective centers of mass of ethyl gallate and the amino acid.
and butyl gallate (Figure 5B): the concentration of alkyl gallate we used was far greater than the solubility. For that reason, MD calculation is expected to give more information related to the precipitated state. The molecules of methyl gallate were aggregated into large clusters comprising more than 15 molecules. In the clusters, most molecules mutually associate through aromatic stacking, suggesting that π-π stacking is the driving force for the observed clustering. Although butyl gallate also produced large clusters, it rarely formed aromatic stacking. Actually, butyl gallate forms a cluster mainly through hydrophobic interactions of alkyl chains (Figure 5B), which might sterically hinder the stacking of aromatic rings. The observed lower solubility of butyl gallate than methyl gallate, as presented in Figure 2, might be attributable to a stronger contribution of hydrophobic interaction to the solubility than that of aromatic stacking. Free Energy Profile of the Amino Acid-Alkyl Gallate-Water System. We examined the thermodynamic interaction between amino acids and ethyl gallate using umbrella sampling simulations. The free energy profile of the interaction was obtained using one ethyl gallate, one additive (arginine or lysine), and water (see Computational Section for details). Figure 6 shows the free energy landscape along the order parameter ξ, which is the distance between the center of mass of ethyl gallate and that of the additive (i.e., arginine or lysine). The minimum of the free energy of both amino acid additive systems was located around ξ ) ca. 5 Å; at that distance, the interaction of arginine or lysine with ethyl gallate was most favorable. At ξ > 10 Å, the free energy of both systems became constant, indicating that the two molecules lacked interaction. Therefore, the free energy difference between the plateau region and the minimum point represents a microscopic transfer of free energy from the unbound state to the tightly bound state. The microscopic transfer free energy was ca. -35 kJ/mol for arginine-ethyl gallate-water and ca. -21 kJ/mol for lysine-ethyl gallate-water. These results suggest that arginine binds to alkyl gallates more tightly and can thereby dissolve them more effectively than
lysine, which is qualitatively consistent with the experimental data. In the range of 5 < ξ < 10 Å, a shoulder was observed for each amino acid (Figure 6). Through a more detailed analysis, two representative conformations were observed in the range: tightly bound and nonbound states between the side chains of the amino acids and the ring of gallate (data not shown). The populations of the tightly bound states decreased gradually with increasing distance ξ, which may be responsible for the shoulder in the free energy profile. Next, we investigated the conformational characteristics of the system. The conformational space of the system is too large. For that reason, we needed fewer axes by which major conformations can be distinguished to support intuitive understanding. For this purpose, principal component (PC) axes are usually used, which tend to classify sampled conformations to the greatest extent possible.64,65,70 In this study, we also conducted principal component analysis (PCA) and described the free energy landscape using two PC axes. In fact, the contribution of PC1 was 97.7% and that of PC2 was 1.1%. The total contribution of two PC was quite high (98.8%). Consequently, the free energy landscape of the amino acid-ethyl gallate-water system was well described by two axes. Figure 7A and 7B depicts the computed free energy of arginine-ethyl gallate-water and lysine-ethyl gallate-water in the tightly bound state (ξ ) ca. 5 Å) by PC1 (horizontal axis) and PC2 (vertical axis). These figures show that the conformational ensembles differ greatly from each other. In arginine-ethyl gallate-water (Figure 7A), two minima of free energy are visible: (a) (PC1, PC2) ) (3, -16) (F ) -4.01) and (b) (PC1, PC2) ) (11, 12) (F ) -3.95). In contrast, in lysine-ethyl gallate-water (Figure 7B), three minima were observed: (c) (PC1, PC2) ) (-6, -9) (F ) -3.83), (d) (PC1, PC2) ) (7, 11) (F ) -3.78), and (e) (PC1, PC2) ) (-3, 14) (F ) -3.56). In arginine-ethyl gallate-water, the guanidinium group of arginine interacted with the aromatic rings of ethyl gallate at the minima (Figure 7A (a and b)). Such an interaction mode suggests that arginine binds to ethyl gallate mainly via π-π interaction. Moreover, the guanidinium group of arginine is positively charged and the center of aromatic ring of ethyl gallate is negatively charged (Figure S1 of the Supporting Information), leading to electrostatic interaction (cation-π interaction) and resultant stabilization of the bound state. Meanwhile, in the lysine-ethyl gallate-water system, the alkyl part of the side chain of lysine (Cβ-C) interacted with the aromatic rings of ethyl gallate (Figure 7B (c-e)), suggesting that only the
13460
J. Phys. Chem. B, Vol. 114, No. 42, 2010
hydrophobic interaction played a major role in the binding. No electrostatic interaction was significant between lysine and ethyl gallate, which might explain why lysine bound ethyl gallate less tightly than arginine. Discussion Many drugs have low solubility or low permeability, as do BCS type II and type IV drugs. These properties engender low bioavailability upon administration. Therefore, they must be formulated into a solid state that can be readily dispersed and solubilized. We described in an earlier report that arginine increases the solubility of aromatic compounds, whether aromatic hydrocarbons or heteroaromatic compounds.41,42,71 However, the mechanism of the solubilizing effects of arginine remains partially unclear. Consequently, our objective in this study was clarification of the molecular mechanism and demonstration of the availability of arginine on the solubilization of drugs through the experiment and MD simulation. We designated this system the “arginine-assisted solubilization system (AASS)”, which is anticipated as a new formulation for solubilization of organic drugs. Solubility measurements revealed that the solubility of alkyl gallates as model drug substances decreases concomitantly with increasing alkyl chains, except ethyl gallate, whose solubility is higher than that of methyl gallate (Figure 2). In fact, MD simulation showed that a dominant contribution to the solubility of methyl gallate is aromatic stacking, whereas that to the butyl gallate solubility is extensive hydrophobic interaction, which causes disruption of aromatic stacking. The observed higher solubility of ethyl gallate might be attributed to the weaker hydrophobic interaction of the ethyl group and the interference of the hydrophobic interaction with the aromatic stacking. The MD simulation showed that the transfer free energy of ethyl gallate is negative in both aqueous arginine and lysine solutions but at different magnitudes (Figure 6). The interaction of arginine with ethyl gallate was much more stable, consistent with the observed difference in the experimental transfer free energy between arginine and lysine (Figure 3). The difference in effectiveness between arginine and lysine is attributable to the nature of the interactions with alkyl gallates. Arginine interacted with alkyl gallates through both mechanisms that occur between the guanidinium group of the side chain and the aromatic ring of alkyl gallates, i.e., π-π and cation-π interactions, and similarly on proteins,34,72,73 whereas lysine interacted with alkyl gallates only through hydrophobic interaction. Here, it is noteworthy that the present calculation is based on a classical force field. Nonbonded interaction is represented by summation of Coulomb and van der Waals terms. In this force field, the partial charges of atoms are fixed. Instantaneous polarization, being the source of π-π and cation-π interactions, is not treated explicitly but is implicitly considered by nonbonded interaction terms. In fact, results of many reports have described that this force field approach can describe these interactions quite well.74-79 The experimental results show that the effects of arginine on the transfer free energy of alkyl gallates depended little on the chain length (Figure 1) despite the large difference in the solubility (Figure 3). This relative lack of dependence suggests that arginine increases the solubility of alkyl gallates through π-π and cation-π interactions, even when the contribution of the hydrophobic interaction becomes greater as the mechanism of precipitation. The interaction may be derived from the guanidinium group of arginine in the same manner as from guanidinium ions.80 In contrast, the lysine effect showed a
Hirano et al. considerable degree of dependence (Figure 3). The interaction of lysine with alkyl gallates was less favorable with a longer alkyl chain, which implies that lysine cannot provide a sufficient hydrophobic interaction for a longer chain alkyl group and is therefore less effective in increasing the solubility of longer chain alkyl gallates. The effect of arginine, but not lysine, on the transfer free energy appears to be concentration dependent to a great degree (see Figure 3 for a nonlinear increase for arginine). The slope at different arginine concentrations, portrayed in Figure 4, underscores this point. The slope becomes about one-half at 1 M arginine solution. Such a decreased effectiveness indicates the gradual saturation of π-π and cation-π interactions with increasing arginine concentration, suggesting the limitation of arginine in this system. Another important result in this study is its support for interpretation of the interaction between arginine and protein. The mechanism of aggregation suppression of protein by arginine has been studied extensively, especially in terms of preferential interaction.34,81-83 The preferential interaction analyses have revealed that arginine differs from structure-stabilizing amino acids.84,85 Although the latter amino acids are preferentially excluded from the protein surface and show no tendency to bind to the proteins, arginine tends to bind to the proteins. Arginine is similar to guanidine hydrochloride in solubilizing aromatic amino acids and other aromatic compounds34,37-39,41 but shows no denaturing effect.32,86,87 How does arginine suppress the aggregation of protein without exerting a denaturing effect? Baynes and Trout suggested that such a binding property of arginine combined with a large excluded volume engenders an increased free energy of the transition state of aggregates, which increases the activation energy toward aggregation.85 Unlike a complex protein surface, MD simulation of small molecules such as alkyl gallates provides a mechanistic understanding of how arginine binds to the surface of organic moieties that might mimic the protein surface. The present results indicate the greater binding property of arginine to the aromatic moiety in the solution phase, suggesting that arginine interacts similarly with the surfaces of peptides and proteins. Conclusion Arginine not only solubilizes small molecules but also suppresses aggregation of proteins without effects on protein structure and stability, even at 1-2 M. These solubility enhancing and nondenaturing effects of arginine might find application in drug discovery research, where organic solvents are used to dissolve drug substances and can therefore denature target proteins and disrupt protein-drug interactions. Their respective concentrations must be reduced below the levels at which they affect the proteins. Nevertheless, such a dilution process can denature proteins by a transient high concentration of organic solvents. Such a process might also cause aggregation of the proteins. In this context, AASS can ameliorate these problems because of its solubilizing and aggregation-suppressing effects. Future challenges include the application of AASS for drug development studies. Acknowledgment. This work was partly supported by Tsukuba University and partly by a Grant-in-Aid for Scientific Research No. 18750140 from MEXT of Japan and the Tsukuba Industrial Liaison and Cooperative Research Center. Supporting Information Available: Charge distributions of the side chain of arginine and ethyl gallate. This material is available free of charge via the Internet at http://pubs.acs.org.
Drug Solubilization by Arginine References and Notes (1) Galia, E.; Nicolaides, E.; Horter, D.; Lobenberg, R.; Reppas, C.; Dressman, J. B. Evaluation of various dissolution media for predicting in vivo performance of class I and II drugs. Pharm. Res. 1998, 15, 698–705. (2) Leuner, C.; Dressman, J. Improving drug solubility for oral delivery using solid dispersions. Eur. J. Pharm. Biopharm. 2000, 50, 47–60. (3) Pedersen, B. L.; Mullertz, A.; Brondsted, H.; Kristensen, H. G. A comparison of the solubility of danazol in human and simulated gastrointestinal fluids. Pharm. Res. 2000, 17, 891–894. (4) Horter, D.; Dressman, J. B. Influence of physicochemical properties on dissolution of drugs in the gastrointestinal tract. AdV. Drug DeliVery ReV. 2001, 46, 75–87. (5) Moneghini, M.; Kikic, I.; Voinovich, D.; Perissutti, B.; FilipovicGrcic, J. Processing of carbamazepine-PEG 4000 solid dispersions with supercritical carbon dioxide: preparation, characterisation, and in vitro dissolution. Int. J. Pharm. 2001, 222, 129–138. (6) Zerrouk, N.; Chemtob, C.; Arnaud, P.; Toscani, S.; Dugue, J. In vitro and in vivo evaluation of carbamazepine-PEG 6000 solid dispersions. Int. J. Pharm. 2001, 225, 49–62. (7) Muller, B. W.; Brauns, U. Solubilization of drugs by modified betacyclodextrins. Int. J. Pharm. 1985, 26, 77–88. (8) Badawy, S. I. F.; Ghorab, M. M.; Adeyeye, C. M. Characterization and bioavailability of danazol-hydroxypropyl beta-cyclodextrin coprecipitates. Int. J. Pharm. 1996, 128, 45–54. (9) Esclusa-Diaz, M. T.; Guimaraens-Mendez, M.; Perez-Marcos, M. B.; Vila-Jato, J. L.; Torres-Labandeira, J. J. Characterization and in vitro dissolution behaviour of ketoconazole/beta- and 2-hydroxypropyl-betacyclodextrin inclusion compounds. Int. J. Pharm. 1996, 143, 203–210. (10) Arias, M. J.; Moyano, J. R.; Gines, J. M. Investigation of the triamterene-beta-cyclodextrin system prepared by co-grinding. Int. J. Pharm. 1997, 153, 181–189. (11) Thompson, D. O. Cyclodextrins-enabling excipients: their present and future use in pharmaceuticals. Crit. ReV. Ther. Drug Carrier Syst. 1997, 14, 1–104. (12) Ventura, C. A.; Tirendi, S.; Puglisi, G.; Bousquet, E.; Panza, L. Improvement of water solubility and dissolution rate of ursodeoxycholic acid and chenodeoxycholic acid by complexation with natural and modified beta-cyclodextrins. Int. J. Pharm. 1997, 149, 1–13. (13) Williams, R. O.; Mahaguna, V.; Sriwongjanya, M. Characterization of an inclusion complex of cholesterol and hydroxypropyl-beta-cyclodextrin. Eur. J. Pharm. Biopharm. 1998, 46, 355–360. (14) Uekama, K.; Hirayama, F.; Irie, T. Cyclodextrin drug carrier systems. Chem. ReV. 1998, 98, 2045–2076. (15) Szente, L.; Szejtli, J. Highly soluble cyclodextrin derivatives: chemistry, properties, and trends in development. AdV. Drug DeliVery ReV. 1999, 36, 17–28. (16) Rogers, T. L.; Nelsen, A. C.; Hu, J.; Brown, J. N.; Sarkari, M.; Young, T. J.; Johnston, K. P.; Williams, R. O. A novel particle engineering technology to enhance dissolution of poorly water soluble drugs: sprayfreezing into liquid. Eur. J. Pharm. Biopharm. 2002, 54, 271–280. (17) Chen, X.; Young, T. J.; Sarkari, M.; Williams, R. O.; Johnston, K. P. Preparation of cyclosporine A nanoparticles by evaporative precipitation into aqueous solution. Int. J. Pharm. 2002, 242, 3–14. (18) Sarkari, M.; Brown, J.; Chen, X.; Swinnea, S.; Williams, R. O.; Johnston, K. P. Enhanced drug dissolution using evaporative precipitation into aqueous solution. Int. J. Pharm. 2002, 243, 17–31. (19) Rogers, T. L.; Gillespie, I. B.; Hitt, J. E.; Fransen, K. L.; Crowl, C. A.; Tucker, C. J.; Kupperblatt, G. B.; Becker, J. N.; Wilson, D. L.; Todd, C.; Broomall, C. F.; Evans, J. C.; Elder, E. J. Development and characterization of a scalable controlled precipitation process to enhance the dissolution of poorly water-soluble drugs. Pharm. Res. 2004, 21, 2048– 2057. (20) Hu, J.; Rogers, T. L.; Brown, J.; Young, T.; Johnston, K. P.; Williams, R. O. Improvement of dissolution rates of poorly water soluble APIs using novel spray freezing into liquid technology. Pharm. Res. 2002, 19, 1278–1284. (21) Rogers, T. L.; Hu, J.; Yu, Z.; Johnston, K. P.; Williams, R. O. A novel particle engineering technology: spray-freezing into liquid. Int. J. Pharm. 2002, 242, 93–100. (22) Rogers, T. L.; Johnston, K. P.; Williams, R. O. Physical stability of micronized powders produced by spray-freezing into liquid (SFL) to enhance the dissolution of an insoluble drug. Pharm. DeV. Technol. 2003, 8, 187–197. (23) Rogers, T. L.; Nelsen, A. C.; Sarkari, M.; Young, T. J.; Johnston, K. P.; Williams, R. O. Enhanced aqueous dissolution of a poorly water soluble drug by novel particle engineering technology: spray-freezing into liquid with atmospheric freeze-drying. Pharm. Res. 2003, 20, 485–493. (24) Rogers, T. L.; Overhoff, K. A.; Shah, P.; Santiago, P.; Yacaman, M. J.; Johnston, K. P.; Williams, R. O. Micronized powders of a poorly water soluble drug produced by a spray-freezing into liquid-emulsion process. Eur. J. Pharm. Biopharm. 2003, 55, 161–172.
J. Phys. Chem. B, Vol. 114, No. 42, 2010 13461 (25) Raghavan, S. L.; Trividic, A.; Davis, A. F.; Hadgraft, J. Effect of cellulose polymers on supersaturation and in vitro membrane transport of hydrocortisone acetate. Int. J. Pharm. 2000, 193, 231–237. (26) Raghavan, S. L.; Kiepfer, B.; Davis, A. F.; Kazarian, S. G.; Hadgraft, J. Membrane transport of hydrocortisone acetate from supersaturated solutions; the role of polymers. Int. J. Pharm. 2001, 221, 95–105. (27) Raghavan, S. L.; Trividic, A.; Davis, A. F.; Hadgraft, J. Crystallization of hydrocortisone acetate: influence of polymers. Int. J. Pharm. 2001, 212, 213–221. (28) Hancock, B. C.; Parks, M. What is the true solubility advantage for amorphous pharmaceuticals. Pharm. Res. 2000, 17, 397–404. (29) Gupta, P.; Chawla, G.; Bansal, A. K. Physical stability and solubility advantage from amorphous celecoxib: the role of thermodynamic quantities and molecular mobility. Mol. Pharm. 2004, 1, 406–413. (30) Matteucci, M. E.; Miller, M. A.; Williams, R. O.; Johnston, K. P. Highly Supersaturated Solutions of Amorphous Drugs Approaching Predictions from Configurational Thermodynamic Properties. J. Phys. Chem. B 2008, 112, 16675–16681. (31) Matteucci, M. E.; Paguio, J. C.; Miller, M. A.; Williams Iii, R. O.; Johnston, K. P. Highly Supersaturated Solutions from Dissolution of Amorphous Itraconazole Microparticles at pH 6.8. Mol. Pharm. 2009, 6, 375–385. (32) Shiraki, K.; Kudou, M.; Fujiwara, S.; Imanaka, T.; Takagi, M. Biophysical effect of amino acids on the prevention of protein aggregation. J. Biochem. (Tokyo) 2002, 132, 591–595. (33) Arakawa, T.; Tsumoto, K. The effects of arginine on refolding of aggregated proteins: not facilitate refolding, but suppress aggregation. Biochem. Biophys. Res. Commun. 2003, 304, 148–152. (34) Arakawa, T.; Ejima, D.; Tsumoto, K.; Obeyama, N.; Tanaka, Y.; Kita, Y.; Timasheff, S. N. Suppression of protein interactions by arginine: a proposed mechanism of the arginine effects. Biophys. Chem. 2007, 127, 1–8. (35) Arakawa, T.; Tsumoto, K.; Kita, Y.; Chang, B.; Ejima, D. Biotechnology applications of amino acids in protein purification and formulations. Amino Acids 2007, 33, 587–605. (36) Arakawa, T.; Tsumoto, K.; Nagase, K.; Ejima, D. The effects of arginine on protein binding and elution in hydrophobic interaction and ionexchange chromatography. Protein Expression Purif. 2007, 54, 110–116. (37) Mura, P.; Maestrelli, F.; Cirri, M. Ternary systems of naproxen with hydroxypropyl-beta-cyclodextrin and aminoacids. Int. J. Pharm. 2003, 260, 293–302. (38) Mura, P.; Bettinetti, G. P.; Cirri, M.; Maestrelli, F.; Sorrenti, M.; Catenacci, L. Solid-state characterization and dissolution properties of naproxen-arginine-hydroxypropyl-beta-cyclodextrin ternary system. Eur. J. Pharm. Biopharm. 2005, 59, 99–106. (39) Das, U.; Hariprasad, G.; Ethayathulla, A. S.; Manral, P.; Das, T. K.; Pasha, S.; Mann, A.; Ganguli, M.; Verma, A. K.; Bhat, R.; Chandrayan, S. K.; Ahmed, S.; Sharma, S.; Kaur, P.; Singh, T. P.; Srinivasan, A. Inhibition of protein aggregation: supramolecular assemblies of arginine hold the key. PLoS ONE 2007, 2, e1176. (40) Ain-Ai, A.; Gupta, P. K. Effect of arginine hydrochloride and hydroxypropyl cellulose as stabilizers on the physical stability of high drug loading nanosuspensions of a poorly soluble compound. Int. J. Pharm. 2008, 351, 282–288. (41) Arakawa, T.; Kita, Y.; Koyama, A. H. Solubility enhancement of gluten and organic compounds by arginine. Int. J. Pharm. 2008, 355, 220– 223. (42) Hirano, A.; Arakawa, T.; Shiraki, K. Arginine increases the solubility of coumarin: comparison with salting-in and salting-out additives. J. Biochem. 2008, 144, 363–369. (43) van der Heijden, C. A.; Janssen, P. J.; Strik, J. J. Toxicology of gallates: a review and evaluation. Food Chem. Toxicol. 1986, 24, 1067– 1070. (44) 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. (45) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, 926–935. (46) 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. (47) Wang, J.; Wang, W.; Kollman, P. A.; Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graph. Model. 2006, 25, 247–260. (48) 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. (49) Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103, 8577–8593.
13462
J. Phys. Chem. B, Vol. 114, No. 42, 2010
(50) Hess, B.; Bekker, H.; Berendsen, H. J. C.; Fraaije, J. G. E. M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 1997, 18, 1463–1472. (51) Miyamoto, S.; Kollman, P. A. Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. J. Comput. Chem. 1992, 13, 952–962. (52) Berendsen, H. J. C.; van der Spoel, D.; van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 1995, 91, 43–56. (53) Lindahl, E.; Hess, B.; van der Spoel, D. GROMACS 3.0: a package for molecular simulation and trajectory analysis. J. Mol. Model. 2001, 7, 306–317. (54) Ferrenberg, A. M.; Swendsen, R. H. New Monte Carlo technique for studying phase transitions. Phys. ReV. Lett. 1988, 61, 2635–2638. (55) Kastner, J.; Thiel, W. Bridging the gap between thermodynamic integration and umbrella sampling provides a novel analysis method: ”Umbrella integration”. J. Chem. Phys. 2005, 123, 144104. (56) Kastner, J.; Thiel, W. Analysis of the statistical error in umbrella sampling simulations by umbrella integration. J. Chem. Phys. 2006, 124, 234106. (57) Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 2006, 65, 712–725. (58) Fujitani, H.; Tanida, Y.; Ito, M.; Jayachandran, G.; Snow, C. D.; Shirts, M. R.; Sorin, E. J.; Pande, V. S. Direct calculation of the binding free energies of FKBP ligands. J. Chem. Phys. 2005, 123, 084108. (59) Mobley, D. L.; Dumont, E.; Chodera, J. D.; Dill, K. A. Comparison of charge models for fixed-charge force fields: small-molecule hydration free energies in explicit solvent. J. Phys. Chem. B 2007, 111, 2242–2254. (60) Nicholls, A.; Mobley, D. L.; Guthrie, J. P.; Chodera, J. D.; Bayly, C. I.; Cooper, M. D.; Pande, V. S. Predicting small-molecule solvation free energies: an informal blind test for computational chemistry. J. Med. Chem. 2008, 51, 769–779. (61) Mobley, D. L.; Bayly, C. I.; Cooper, M. D.; Dill, K. A. Predictions of hydration free energies from all-atom molecular dynamics simulations. J. Phys. Chem. B 2009, 113, 4533–4537. (62) Mobley, D. L.; Bayly, C. I.; Cooper, M. D.; Shirts, M. R.; Dill, K. A. Small molecule hydration free energies in explicit solvent: An extensive test of fixed-charge atomistic simulations. J. Chem. Theory Comput. 2009, 5, 350–358. (63) Garcia, A. E. Large-amplitude nonlinear motions in proteins. Phys. ReV. Lett. 1992, 68, 2696–2699. (64) Kitao, A.; Hayward, S.; Go, N. Energy landscape of a native protein: jumping-among-minima model. Proteins 1998, 33, 496–517. (65) Ikeda, K.; Galzitskaya, O. V.; Nakamura, H.; Higo, J. beta-Hairpins, alpha-helices, and the intermediates among the secondary structures in the energy landscape of a peptide from a distal beta-hairpin of SH3 domain. J. Comput. Chem. 2003, 24, 310–318. (66) Nozaki, Y.; Tanford, C. The solubility of amino acids and related compounds in aqueous urea solutions. J. Biol. Chem. 1963, 238, 4074– 4081. (67) Nozaki, Y.; Tanford, C. The solubility of amino acids and related compounds in aqueous ethylene glycol solutions. J. Biol. Chem. 1965, 240, 3568–3575. (68) Nozaki, Y.; Tanford, C. The solubility of amino acids, diglycine, and triglycine in aqueous guanidine hydrochloride solutions. J. Biol. Chem. 1970, 245, 1648–1652.
Hirano et al. (69) Nozaki, Y.; Tanford, C. The solubility of amino acids and two glycine peptides in aqueous ethanol and dioxane solutions. Establishment of a hydrophobicity scale. J. Biol. Chem. 1971, 246, 2211–2217. (70) Kameda, T.; Takada, S. Secondary structure provides a template for the folding of nearby polypeptides. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 17765–17770. (71) Arakawa, T.; Hirano, A.; Shiraki, K.; Kita, Y.; Koyama, A. H. Stabilizing and destabilizing effects of arginine on deoxyribonucleic acid. Int. J. Biol. Macromol. 2010, 46, 217–222. (72) Woods, A. S. The mighty arginine, the stable quaternary amines, the powerful aromatics, and the aggressive phosphate: their role in the noncovalent minuet. J. Proteome Res. 2004, 3, 478–484. (73) Crowley, P. B.; Golovin, A. Cation-π interactions in protein-protein interfaces. Proteins 2005, 59, 231–239. (74) Jorgensen, W. L.; Severance, D. L. Aromatic-aromatic interactions: free energy profiles for the benzene dimer in water, chloroform, and liquid benzene. J. Am. Chem. Soc. 1990, 112, 4768–4774. (75) Duffy, E. M.; Kowalczyk, P. J.; Jorgensen, W. L. Do denaturants interact with aromatic-hydrocarbons in water. J. Am. Chem. Soc. 1993, 115, 9271–9275. (76) Chipot, C.; Jaffe, R.; Maigret, B.; Pearlman, D. A.; Kollman, P. A. Benzene dimer: A good model for pi-pi interactions in proteins? A comparison between the benzene and the toluene dimers in the cas phase and in an aqueous solution. J. Am. Chem. Soc. 1996, 118, 11217–11224. (77) Hart, K.; Nystrom, B.; Ohman, M.; Nilsson, L. Molecular dynamics simulations and free energy calculations of base flipping in dsRNA. RNA 2005, 11, 609–618. (78) Sponer, J.; Riley, K. E.; Hobza, P. Nature and magnitude of aromatic stacking of nucleic acid bases. Phys. Chem. Chem. Phys. 2008, 10, 2595– 2610. (79) Paton, R. S.; Goodman, J. M. Hydrogen bonding and pi-stacking: how reliable are force fields? A critical evaluation of force field descriptions of nonbonded interactions. J. Chem. Inf. Model. 2009, 49, 944–955. (80) 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. (81) Baynes, B. M.; Wang, D. I.; Trout, B. L. Role of arginine in the stabilization of proteins against aggregation. Biochemistry. 2005, 44, 4919– 4925. (82) Schneider, C. P.; Trout, B. L. Investigation of cosolute-protein preferential interaction coefficients: new insight into the mechanism by which arginine inhibits aggregation. J. Phys. Chem. B 2009, 113, 2050– 2058. (83) Shukla, D.; Shinde, C.; Trout, B. L. Molecular computations of preferential interaction coefficients of proteins. J. Phys. Chem. B 2009, 113, 12546–12554. (84) Kita, Y.; Arakawa, T.; Lin, T. Y.; Timasheff, S. N. Contribution of the surface free energy perturbation to protein-solvent interactions. Biochemistry 1994, 33, 15178–15189. (85) Baynes, B. M.; Trout, B. L. Rational design of solution additives for the prevention of protein aggregation. Biophys. J. 2004, 87, 1631–1639. (86) Kudou, M.; Shiraki, K.; Fujiwara, S.; Imanaka, T.; Takagi, M. Prevention of thermal inactivation and aggregation of lysozyme by polyamines. Eur. J. Biochem. 2003, 270, 4547–4554. (87) Ishibashi, M.; Tsumoto, K.; Tokunaga, M.; Ejima, D.; Kita, Y.; Arakawa, T. Is arginine a protein-denaturant. Protein Expression Purif. 2005, 42, 1–6.
JP101909A
