Article pubs.acs.org/molecularpharmaceutics
Studies of Bicalutamide−Excipients Interaction by Combination of Molecular Docking and Molecular Dynamics Simulation Caixia Li,† Jie-Xin Wang,† Yuan Le,*,† and Jian-Feng Chen*,†,‡ †
State Key Laboratory of Organic−Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China Research Center of the Ministry of Education for High Gravity Engineering and Technology, Beijing University of Chemical Technology, Beijing 100029, China
‡
ABSTRACT: While the effects of hydrophilic excipients in enhancing the dissolution rate of water-insoluble drugs have been validated, the underlying mechanism remains poorly understood, particularly at a molecular level. In this work, a combination of docking calculations and MD simulations was applied to investigate the molecular interactions between bicalutamide (BIC) and each of three excipients: lactose (LAC), hydroxypropyl methylcellulose (HPMC), and mannitol (MAN). The calculated results indicated that BIC interacted with HPMC and MAN mainly by Lennard-Jones (LJ) interactions but with LAC mainly by Coulomb (Coul) interactions. There was no hydrogen bond formed between BIC and excipient. It was shown that BIC/LAC had the biggest total solvent accessible surface area with the biggest hydrophilic area and formed the most hydrogen bonds between excipient and water. In addition to the structure analyses, BIC/LAC had both the lowest interaction energy between BIC and excipient and the lowest interaction energy between BIC/excipient and water. All these led to the best dissolution performance of BIC/LAC, which could correspond to the experimental results of dissolution test. The present study suggests that a combination of docking calculations and MD simulations, which aims at complementing the experimental work, could provide a molecular insight into the interaction between drug and excipient. It also holds the great potential to simplify the optimization process of drug delivery system and reduce both time and costs. KEYWORDS: docking calculations, MD simulations, bicalutamide, excipient, dissolution, interaction
1. INTRODUCTION The clinical application of many existing drugs with potent pharmacological activities is limited because of their poor solubility, and more than 40% of newly discovered drugs also face the same problem.1 Hence, plenty of techniques are widely used to overcome this problem, mainly including particle size reduction, modification of the crystal habit, solubilization by surfactants, complexation, drug dispersion in carriers, prodrugs, and so on.1−7 Among them, the application of excipients would have an important influence on drug, such as morphology, drug-loading ability, stability, drug release kinetics, and even efficacy.8−11 Therefore, the selection of one proper excipient would be significant. Bicalutamide (BIC) is a pure, orally active, and nonsteroidal antiandrogen agent. It was approved by FDA in 1995 and is mainly used for the treatment of prostate cancer by blocking the action of androgens on the cancer cells.12 However, BIC belongs to class II (low solubility, high permeability) of the biopharmaceutics classification systems (BCS),13 and hence BIC is limited in its clinical application because of its poor solubility (5 mg/mL)14 and dissolution rate. Generally, BIC is available at 50 and 150 mg oral tablets.15 In our previous study,16 we have investigated the impact of excipients on BIC © XXXX American Chemical Society
dissolution by experiments, and the effects of hydrophilic excipients on controlling the particle size and enhancing the dissolution of water-insoluble drugs have been assured. Obviously, excipients have significant influence on drug properties, which justifies the need for understanding of drug−excipient interactions.17 As a complement to the experimental study of water-insoluble drug dissolution, molecular simulation offers unique possibilities for investigating molecular level phenomena that are difficult to probe experimentally.18 The rapid development of computer method makes it possible to obtain insights into inter- and intramolecular interactions between drug and excipient.19−21 Currently, there are mainly two kinds of computational methods performed for drug−excipient study. One is group contribution method (GCM), which has been used to calculate solubility parameters to predict drug−excipient miscibility or compatibility,22−24 but it failed to supply a reasonable estimation of polymers.25,26 The other is molecular dynamics Received: December 24, 2012 Revised: April 19, 2013 Accepted: April 22, 2013
A
dx.doi.org/10.1021/mp300727d | Mol. Pharmaceutics XXXX, XXX, XXX−XXX
Molecular Pharmaceutics
Article
2. METHODS 2.1. Experimental Methods. Raw BIC (form I, purity is 99.6%) was obtained from Beijing Guolian Chenghui Pharmaceutical Technology Co., Ltd. Lactose (LAC) and hydroxypropyl methylcellulose (HPMC) were supplied by Beijing Chemical Reagents Company. Mannitol (MAN) and sodium dodecyl sulfate were purchased from Beijing Yili Fine Chemicals Co., Ltd., and Tianjin Fuchen Chemical Reagent Factory respectively. Deionized water was prepared with Hitech-K flow water purification system (Hitech Instruments Co. Ltd., Shanghai, China). All solvents were analytical grade. BIC composites with different excipients (LAC, HPMC, and MAN) were prepared by solvent−antisolvent precipitation and spray-drying according to the reference.16 The dissolution rates of BIC composites were measured following the United States Pharmacopeia (USP) Apparatus 2 (paddle) method by a dissolution apparatus (D-800LS, Tianjin, China). The dissolution rates were studied separately in 1 wt % sodium dodecyl sulfate (1000 mL) maintained at 37 ± 0.5 °C and at a stirring speed of 50 rpm. Approximately 50 mg bicalutamide was added into vessels containing 1000 mL of the dissolution medium. Aliquots (5 mL) were withdrawn at different time intervals and filtered using a 0.45 μm filter. Then the concentrations of samples were measured by a spectrophotometer at 270 nm (UV-3000, Shimadzu, Japan). 2.2. Computational Methods. 2.2.1. Preparation of Structures for Docking Calculations. According to the twodimensional (2D) structures (Scheme 1), BIC, LAC, HPMC, and MAN were built in three-dimensional (3D) using PRODRG server.34 Only one repeat unit of HPMC was built by the following reasons: (a) one repeat unit contained all different atom groups, (b) the number of atoms in each repeat unit was close to the other two kinds of excipients, which would greatly enhance the comparability of data, (c) the calculation of a repeat unit could vastly save money and time. The geometry optimizations of four built molecules were performed at the DFT/B3LYP35,36 level of theory with the 6-311+G(d,p) basis set.37 All DFT calculations were carried out using the Gaussian 03 package.38 2.2.2. Docking Calculations. Due to the unknown specific binding sites, blind docking was employed to assess the possible binding mode and the binding energy qualitatively and quantitatively between BIC and each kind of three excipients. LAC, HPMC, and MAN was docked with BIC respectively using the Autodock4 software package.39 The files for docking calculations were prepared using AutoDock Tools (ADT).40 For all docking calculations, the size of grids was 80 Å × 80 Å × 80 Å with grid spaces of 0.375 Å. Lamarkian genetic algorithm (LGA)39 was applied to probe the most favorable drug− excipient complex geometry. The number of docking runs and energy evaluation were set to 100 and 25000000, respectively. The other docking parameters were set to the default values. 2.2.3. Molecular Dynamics Simulations. The most favorable BIC/excipient geometry obtained from docking calculations was regarded as the initial geometry for MD simulation. Three 10 ns simulations were done for the BIC/ excipient−water system and BIC/excipient system. For all the simulations, the GROMOS96 field41 were applied. Each BIC/ excipient system contained one BIC molecule and one excipient molecule. Each BIC/excipient−water system contained one BIC molecule, one excipient molecule, and over 800 water molecules. The water molecules were done using simple
(MD) simulation, which has a broad application in drug delivery system and has been successfully used to explain the excipient-assisted solubilization of water-insoluble drugs.21,27,28 It has been proved that MD simulation could provide the accurate structure and interaction results, but MD simulation consumes long computing time for searching binding positions between drug and excipient.9,21 Recently, Pajula et al.29 reported the application of docking approach for quick screening of small molecules to inhibit crystallization of amorphous drugs. It is worth noticing that docking computation provides a facile routine to get the binding conformation of drug and additive. Generally, docking is used to get initial conformation through fast screening with poor considering of protein flexibility, and MD simulation is then used to explore the structures of the final complexes in solvent in a flexible way and get accurate energy results. The combination of docking calculation and MD simulation complements the strengths and weaknesses of both docking calculation and MD simulation30 and is widely applied in the study of protein−ligand binding.31−33 In this paper, the combination of docking and MD simulation was tried to simulate and understand the drug−excipient interactions, which could help explain the experimental results. This work represents the step toward developing a computational approach to accelerate the selection of a suitable drug− excipient for reducing costs and time in the development of drug delivery system. The objective of this study is to investigate the interactions between drug and excipients at the molecular level using a combined docking-MD approach. The three model excipients, lactose (LAC), hydroxypropyl methylcellulose (HPMC), and mannitol(MAN), have been chosen for the following reasons: (a) LAC has been proved to be an appropriate excipient for BIC nanodispersions from our previous work,16 (b) HPMC is a representative polymer excipient for its extensive use in pharmaceutics, (c) MAN has many hydroxyl groups as LAC does. (Scheme 1) We first used docking calculation to generate Scheme 1. Two-Dimensional (2D) Atructures: (a) BIC, (b) LAC, (c) HPMC, (d) MAN
a set of drug−excipient binding configurations, which were the inputs of MD simulations in solvent. For each system of MD simulations, we examined the structure information of BIC and excipient molecules and calculated the interaction energy related with BIC and excipient molecules. The differences in the structures of BIC and excipient molecules and the interactions between BIC and excipient molecules were analyzed to find their impact on the properties of BIC. These differences were also compared with experimental dissolution results. B
dx.doi.org/10.1021/mp300727d | Mol. Pharmaceutics XXXX, XXX, XXX−XXX
Molecular Pharmaceutics
Article
Table 1. Results of BIC−Excipient Surface Docking cluster rank BIC-LAC lowest binding energy (kcal/mol) number in cluster
BIC-HPMC
BIC-MAN
1
2
3
1
2
3
1
2
3
−1.52 6
−1.48 76
−1.31 15
−2.02 5
−1.84 74
−1.64 3
−0.86 77
−0.78 8
−0.41 35
Figure 1. The surface mode of BIC docked with (a) LAC, (b) HPMC, (C) MAN. The color is based on the atom type: in gray are the carbon atoms, in red are oxygen atoms, in cyan are fluorine atoms, in purple are nitrogen atoms, in brown are sulfur atoms, in white are hydrogen atoms.
state, but BIC/LAC was the quickest. We also find that BIC/ LAC and BIC/HPMC were much more stable than BIC/MAN.
point charge (SPC) model.42 The geometry of molecular system was optimized using the steepest descent method. The simulations were conducted at the NPT ensemble (300 K, 1 bar) in a octahedral box with a volume of about 27.4 nm3 under periodic boundary conditions. The temperature and the pressure were maintained by virtue of velocity rescaling thermostat43 and Berendsen pressure coupling.44 Electrostatic interactions were treated with the particle mesh Ewald (PME) method45,46 with a 1.0 nm cutoff distance. The van der Waals interactions were handled with a 1.4 nm cutoff. The integral calculations of the classical Newton equation of motion were dealt with application of leapfrog method.47The integration time step was 2 fs. The simulations were conducted using GROMACS 4.5 package.48 The different interaction energy and structures were analyzed by GROMACS 4.5 package. All the pictures were processed by PyMOL software.49
3. RESULTS AND DISCUSSION 3.1. Docking Calculations. Docking can explore the fitting of two molecules with a stable configuration and a favorable energy. The docked complexes of BIC with LAC, HPMC, and MAN were selected in terms of the free energy of binding and the statistic information of the population of the complexs. The free energy of binding included intermolecular energy (van der Waals, H-bonding interactions, desolvation, and electrostatic energies) and torsional free energy. An advantage of this docking is that it supplies not only the possible physical interactions but also the possible complex configuration. The majority of the results concerning BIC−excipient docking were shown in Table 1. Most of BIC-LAC dockings were distributed in one cluster with 76 elements. The lowest energy in this cluster was −1.48 kcal/mol. BIC-HPMC docking fell in a cluster of 74 elements. The best docked BIC-HPMC had a binding energy of −1.84 kcal/mol. BIC-MAN dockings were mostly distributed in two clusters (with 77 and 35 elements). The lower energy within two clusters was −0.86 kcal/mol. The conformation with lowest binding energy in each of the highest populated clusters was selected as the starting configuration for the following MD simulations (see Figure 1). 3.2. Structure Analyses of MD Simulations. The rootmean-square deviation (RMSD) is usually used to study the stability of the structure. The structure changes of BIC/ excipient were investigated by calculation of RMSD. From Figure 2, all the BIC/excipient could arrive at the equilibrium
Figure 2. Time evolution of RMSD values of BIC/excipient complex.
Figure 3 shows freeze frames of BIC with different excipient molecules. From Figure 3a, the benzene ring with cyano of BIC oriented to the hexatomic ring in lactose. From Figure 3b, benzene ring with cyano in BIC was near to the branch of hexatomic ring in HPMC. The hexatomic ring in HPMC was near to −CF3 in BIC, and we also find that the benzene ring with cyano in BIC closed to one end of MAN from Figure 3c. Therefore, we can infer that the benzene ring with cyano in BIC was apt to close to the excipient. The solvent accessible surface area (SASA) of solute is usually calculated to compare and demonstrate the hydrophilic property of solute.18,19 The hydrophilic, hydrophobic, and total solvent accessible surface area (SASA) of BIC/excipient complexs in water were calculated with a solvent probe of 0.14 nm radius to give insight into dissolution differences (Table 2). It could be easily found that BIC/LAC had the biggest total area with the biggest hydrophilic area. At the same time, BIC/HPMC had bigger hydrophilic area and lower hydrophobic area than BIC/MAN but had a slightly bigger total area than BIC/MAN. The differences in total area were mainly from the hydrophilic part. These results may suggest that BIC/ LAC had the best hydrophilic property, and the hydrophilic C
dx.doi.org/10.1021/mp300727d | Mol. Pharmaceutics XXXX, XXX, XXX−XXX
Molecular Pharmaceutics
Article
Figure 3. Images of the trajectories with labeled atoms of (a) LAC and BIC, (b) HPMC and BIC, and (C) MAN and BIC. The bicalutamide molecules are in blue. The other molecules are based on the atom type: in gray are the carbon atoms, in red are oxygen atoms, in white are hydrogen atoms. The atoms are labeled by number.
Table 2. SASA of BIC/Excipient and Their Hydrophilic and Hydrophobic Area
BIC/LAC BIC/HPMC BIC/MAN
hydrophilic area (nm2)
hydrophobic area (nm2)
total area (nm2)
2.29 1.89 1.27
6.14 6.04 6.46
8.43 7.93 7.73
Table 3. The Mean Radius of Gyration (Rg) of BIC and Excipient in Different BIC/Excipient System complex in water
BIC excipient
property of BIC/HPMC was slightly better than BIC/MAN. According to the in vitro dissolution test in Figure 4, a
complex in vacuum
BIC/ LAC
BIC/ HPMC
BIC/ MAN
BIC/ LAC
BIC/ HPMC
BIC/ MAN
0.46 0.35
0.45 0.32
0.45 0.26
0.44 0.35
0.44 0.33
0.44 0.26
water environment had no influence on the Rgs of BIC and excipients. The hydrogen bonds (HBs) between solute and solvent were also found to be related with the solubility of solute.20 In this work, hydrogen bonds were determined based on a 30° cutoff angle and a 0.35 nm cutoff radius. The mean numbers of HBs between BIC and excipient, BIC and water, as well as excipient and water were displayed in Table 4. From Table 4a, it shows Table 4. (a) The Mean Number of Hydrogen Bonds between Bic and Excipient; (b) The Mean Number of Hydrogen Bonds between BIC and Water, between Excipient and Water, and between BIC/Excipient and Water (a) BIC/excipient in water
BIC/excipient in vacuum
0.05 0.07 0.03
0.31 0.34 0.21
BIC/LAC BIC/HPMC BIC/MAN
(b)
Figure 4. Dissolution profile of BIC composites. BIC/LAC BIC/HPMC BIC/MAN
significantly high dissolution rate for BIC/LAC was observed, 95 wt % of drug dissolved in 10 min while almost 65% of drug dissolved for both BIC/HPMC and BIC/MAN at that time. After 45 min, almost complete dissolution of the BIC/LAC was observed. In contrast, BIC/HPMC and BIC/MAN showed slower release, only dissolved 85 and 78 wt %, respectively. Dissolution performances reveal that LAC was the most effective excipient. Thus, it is suggested that the SASA stemmed from different BIC/excipient complexes, especially hydrophilic area, may contribute to the dissolution performances of BIC composites. In Table 3, while the BIC in different BIC/excipient systems exhibited similar radius of gyration (Rg), LAC had the biggest Rg, and MAN had the smallest Rg. So, the Rgs of excipients in water may be the main reason for the differences in BIC/ excipient total area. In addition, it shows that the different SASA did not come from BIC but came from the excipient, and we also find that Rgs of BIC and excipients were almost the same from water system to vacuum system, which means that
BIC−water
excipient−water
BIC/excipent−water
9.49 9.61 9.88
3.69 1.75 2.36
13.18 11.36 12.24
that there was almost no HB formed between BIC and excipient in the water or vacuum systems. From Table 4b, we find that the numbers of HBs between BIC and water in different system were approximate, but LAC formed more HBs with water than HPMC and MAN, which may let BIC/LAC complex become easier to interact with water. This finding indicates that excipient−water HBs may have the effect on the dissolution performances and make BIC/LAC dissolved better. Furthermore, the radial distribution functions g(r) (RDF) was used to understand hydrogen bonds (HBs) between excipient and water. The labeled O atoms on the excipients were presented in Figure 3. The RDFs between each O atom of excipients and H atoms of water were calculated, and those exhibiting obvious HB features were presented in Figure 5. For LAC, it is observed that the O43−Hwater, O46−Hwater, O63− Hwater, and O68−Hwater RDFs showed first peaks of high amplitude at a distance of 3.0−3.5 A, while the O52−Hwater and D
dx.doi.org/10.1021/mp300727d | Mol. Pharmaceutics XXXX, XXX, XXX−XXX
Molecular Pharmaceutics
Article
Figure 5. Calculated Oexcipient−Hwater RDFs: (a) OLAC−Hwater RDFs, (b) OHPMC−Hwater RDFs, (c) OMAN−Hwater RDFs..
O54−Hwater had no clear peaks. Therefore, LAC could form hydrogen bonds with water close to O43, O46, O63, and O68 atoms. At the same time, O52 and O54 might have no hydrogen bond with water. Also, in the case of OHPMC−Hwater RDFs, the results indicate that the position next to O41 was the most reliable site to form hydrogen bond with water. By inspecting the OMAN−Hwater RDFs, it could be seen that O39 and O55 of MAN were the potential atoms forming HBs. These results demonstrate the difference of HBs between excipient and water at atomic level. The RDFs of water around BIC/excipient complex were also investigated and shown in Figure 6. From Figure 6, a clear peak could be observed at a distance of approximately 0.2 nm in all of the three curves, which implies that the three BIC/excipient systems had similar water distribution. SASA and HBs are usually used as main structure information to understand the solubilization property.18−20,50,51 That means SASA and HBs are the important elements to supply structure information of drug hydrophilic property, which takes a key role in selecting a proper kind of excipient. Our calculated results of SASA and HB indicate that BIC/LAC had the best dissolution performance, which is consistent with our experimental results (Figure 4). 3.3. Energy Analyses of MD Simulations. The interaction energy between BIC and excipient and between BIC/excipient and water were computed in terms of LennardJones (LJ) and Coulomb (Coul) interactions (Table 5). According to Table 5a, the LJ term was the driving force for the interaction between BIC and excipient for HPMC and MAN but the Coul term for LAC. The big differences in total interaction energy between BIC and excipient for LAC and
Figure 6. BIC/excipient−water RDFs in water.
HPMC, MAN was largely from Coul term of LAC, but for HPMC and MAN was largely from the LJ term. From Table 5b, the Coul term played an important role in the interaction between BIC/excipient and water, and the differences in interaction energy between BIC/excipient and water mainly came from the LJ term. It is obvious that BIC/LAC had both the smallest interaction energy between BIC and excipient and the smallest interaction energy between BIC/excipient and water from Table 5. Meanwhile, the analyses of excipient−water HBs and Oexcipient−Hwater RDFs support this strong interaction between BIC/excipient and water. These results suggest that BIC had a E
dx.doi.org/10.1021/mp300727d | Mol. Pharmaceutics XXXX, XXX, XXX−XXX
Molecular Pharmaceutics
Article
MAN has many hydroxyl groups as LAC. But from our MD simulation analyses, BIC/LAC showed bigger hydrophilic area, formed more HBs between excipient and water, and had almost twice the interaction energy between drug and excipient than BIC/MAN, which may make BIC/LAC dissolved more easily than BIC/MAN. It illustrates that there was much difference in BIC/excipient structures (SASA and HBs) and BIC−excipient interaction for LAC and MAN in spite of the same hydroxyl groups. It also demonstrates that different excipients may affect the dissolutions of drug complexes through structure and interaction, although there are same chemical groups in different excipients. A combination of MD simulations and docking calculations was used to model and predict polymer−drug interactions in self-assembled nanoparticles and was also expected to become a powerful prescreening tool in new drug delivery system,9 but there is still difference in priority order of docking and MD simulation with our approach. Just due to this difference, our approach holds the bigger potential of becoming a screening tool: (a) docking first will be in favor of screening from large excipient library, which could save time and costs vastly, (b) final MD simulation in solvent will provide an accurate and comprehensive understanding of drug−excipient interactions.30,31 Thus, more work should be undertaken to further test and improve this approach for the most efficient excipient for a desired drug. Our next step is to dock BIC with a large range of excipient for selecting only several excipients to do MD simulations, the results of which will be compared with the corresponding experimental results.
Table 5. Energy Characterization: (a) Energy between BIC and Excipient, (b) Energy between Complex and Water (a) Coul energy (kJ/mol)
LJ energy (kJ/mol)
total (kJ/mol)
−58.68 −22.58 −22.28
−38.45 −47.60 −31.14
−97.13 −70.18 −53.42
BIC-LAC BIC-HPMC BIC-MAN
(b)
BIC/LAC−water BIC/HPMC− water BIC/MAN−water
Coul energy (kJ/mol)
LJ energy (kJ/mol)
total (kJ/mol)
−638.55 −629.95
−178.7 −150.46
−817.25 −780.40
−633.52
−140.05
−773.57
strongest interaction with LAC and could be dissolved more easily than the other two, which can be in good agreement with the experimental data. Among them, it is worth noting that the strong Coul interaction between BIC and LAC, which is greatly different from the Coul interaction between BIC and HPMC/ MAN, may be the crucial parameter for the better dissolution performance of BIC/LAC. This may help toward the elucidation of the key parameters that are involved in the formation of effective carriers for BIC molecules used in cancer chemotherapy. Both the structure and energy analyses of MD simulations suggest BIC/LAC could dissolve most, but we got different results for BIC/HPMC and BIC/MAN from the structure and energy analyses of MD simulations. On one hand, fewer HBs between BIC/HPMC and water were formed in BIC/HPMC system than in BIC/MAN system, which means that BIC/ MAN may give a better dissolution performance than BIC/ HPMC. On the other hand, BIC/HPMC had a bigger hydrophilic area and a lower hydrophobic area than BIC/ MAN and had over twice the interaction energy between BIC and excipient than BIC/MAN, which suggests that BIC/ HPMC may be dissolved better than BIC/MAN. In addition, the slightly bigger interaction energy between BIC/excipient and water existed in BIC/HPMC than in BIC/MAN (Table 5(b)), which may relate with the similar dissolution of BIC/ HPMC and BIC/MAN. But from experimental results in Figure 4, BIC/MAN and BIC/HPMC displayed approximately as a whole. Until after 120 min, the dissolution rate of BIC/ HPMC and BIC/MAN reached 93% and 87%, respectively. According to the comparisons of simulation analyses and dissolution results of BIC/MAN and BIC/HPMC, it is concluded that the interpretation of dissolution performances depends on the combination of these structure and energy elements including SASA, HBs, and interaction energy. It is because of different advantages and disadvantages of BIC/ MAN and BIC/HPMC in SASA, HBs, interaction energy that BIC/MAN and BIC/HPMC had approximate dissolution performance. Generally, the interaction energy between drug and excipient dominated the position to analyze the effects of the excipient on solubilization.21 However, we find that the effects of SASA and HBs cannot be underestimated. Costache et al.9 also pointed out that some subtle structure characteristics could cause changes that cannot be explained by interaction energy. Therefore, structure and energy analyses should be taken into consideration at the same time to give the reasonable explaination to the dissolution results.
4. CONCLUSION A combination of docking calculations and MD simulations was used to examine the interactions between BIC and each kind of excipients represented by LAC, HPMC, and MAN. The study emphasized the structure and energy differences of different BIC/excipient complexes in simulation, which may underpin the different dissolution performances of BIC/excipient complexes. BIC/LAC complex had the biggest total SASA with the biggest hydrophilic area and the biggest excipient− water HB number; BIC/HPMC had a lower excipient−water HB number but bigger hydrophilic area and lower hydrophobic area than BIC/MAN. Meanwhile, the driving force between BIC and LAC was strongest due to Coul interaction, while it was the LJ interaction for both the BIC/HPMC and BIC/MAN complexes; HPMC had over twice interaction energy with BIC than MAN. Both structure and energy analyses demonstrated that BIC/LAC showed the best dissolution performance, and BIC/MAN and BIC/HPMC had approximate dissolution performance, which were in agreement with the experimental data. After all, we really hope that this study may supply a computational method that could help provide a good understanding of drug−excipient interactions at a molecular level, could assist screening from a large excipient library, and could speed up the process of selecting a suitable excipient in the future.
■
AUTHOR INFORMATION
Corresponding Author
*For Y.L.: phone, +86 10 64447274; fax, +86 10 64423474; Email,
[email protected]. For J.F.C.: phone, +86 10 64446466; fax, +86 10 64434784; E-mail,
[email protected]. edu.cn. F
dx.doi.org/10.1021/mp300727d | Mol. Pharmaceutics XXXX, XXX, XXX−XXX
Molecular Pharmaceutics
Article
Author Contributions
(13) Kanfer, I. Report on the internationalworkshop on the biopharmaceutics classification system (BCS): scientific and regulatory aspects in practice. J. Pharm. Sci. 2000, 5, 1−4. (14) Fradet, Y. Bicalutamide (Casodex) in the treatment of prostate cancer. Expert Rev. Anticancer Ther. 2004, 4 (1), 37−48. (15) Danquah, M.; Fujiwara, T.; Mahato, R. I. Self-assembling methoxy poly (ethylene glycol)-b-poly(carbonate-co-L-lactide) block copolymers for drug delivery. Biomaterials 2010, 31 (8), 2358−2370. (16) Li, C.; Li, C. X.; Le, Y.; Chen, J.-F. Formation of BIC nanodispersion for dissolution rate enhancement. Int. J. Pharm. 2011, 404, 257−263. (17) Io, T.; Fukami, T.; Yamamoto, K.; Suzuki, T.; Xu, J. D.; Tomono, K.; Ramamoorthy, A. Homogeneous nanoparticles to enhance the efficiency of a hydrophobic drug, antihyperlipidemic probucol, characterized by solid-State NMR. Mol. Pharmaceutics 2010, 7, 299−305. (18) Yang, C.; Lu, D. N.; Liu, Z. How PEGylation enhances the stability and potency of insulin: a molecular dynamics simulation. Biochemistry 2011, 50, 2585−2593. (19) Wang, T.; Chipot, C.; Shao, X. G.; Cai, W. S. Structural characterization of micelles formed of cholesteryl-functionalized cyclodextrins. Langmuir 2011, 27, 91−97. (20) Matziari, M.; Dellis, D.; Dive, V.; Yiotaki, A.; Samios, J. Conformational and solvation studies via computer simulation of the novel large scale diastereoselectively synthesized phosphinic MMP inhibitor RXP03 diluted in selected solvents. J. Phys. Chem. B 2010, 114, 421−428. (21) Hirano, A.; Kameda, T.; Arakawa, T.; Shiraki, K. Argininesssisted solubilization system for drug substances: solubility experiment and simulation. J. Phys. Chem. B 2010, 114, 13455−13462. (22) Forster, A.; Hempenstall, J.; Tucker, I.; Rades, T. Selection of excipients for meltextrusion with two poorly water-soluble drugs by solubility parameter calculation and thermal analysis. Int. J. Pharm. 2001, 226, 147−161. (23) Liu, J.; Xiao, Y.; Allen, C. Polymer−drug compatibility: a guide to the development of delivery systems for the anticancer agent, ellipticine. J. Pharm. Sci. 2004, 93, 132−143. (24) Zhang, L. J.; Yu, Q.; Long, C. X.; Chen, Y. Systematic procedures for formulation design of drug-loaded solid lipid microparticles: selection of carrier material and stabilizer. Ind. Eng. Chem. Res. 2008, 47, 6091−6100. (25) Van Krevelen, D. W. Properties of Polymers: Their Correlation with Chemical Structure, Their Numerical Estimation and Prediction from Additive Group Contributions, 3rd ed.; Elsevier: New York, 1990; pp 189−124. (26) Nair, R.; Nyamweya, N.; Gönen, S.; Martínez-Miranda, L. J.; Hoag, S. W. Influence of various drugs on the glass trasition temperature of poly-(vinylpyrrolidine): a thermodynamic and spectroscopic investigation. Int. J. Pharm. 2001, 225, 83−96. (27) Stephenson, B. C.; Rangel-Yagui, C. O.; Pessoa Junior, A.; Costa Tavares, L.; Beers, K.; Blankschtein, D. Experimental and theoretical investigation of the micellar-assisted solubilization of ibuprofen in aqueous media. Langmuir 2006, 22, 1514−1525. (28) Menger, F. M.; Zhang, H. L.; de Joannis, J.; Kindt, J. T. Solubilization of paclitaxel (taxol) by peptoad self-assemblies. Langmuir 2007, 23, 2308−2310. (29) Pajula, K.; Lehto, V.−P.; Ketolainen, J.; Korhonnen, O. Computational approach for fast screening of small molecular candidates to inhibit crystallization in amorphous drugs. Mol. Pharmaceutics 2012, 9, 2844−2855. (30) Alonso, H.; Bliznyuk, A. A.; Gready, J. E. Combining docking and molecular dynamic simulations in drug design. Med. Res. Rev. 2006, 26, 531−568. (31) Wang, Z. G.; Ling, B. P.; Zhang, R.; Liu, Y. J. Docking and molecular dynamics study on the inhibitory activity of coumarins on aldose reductase. J. Phys. Chem. B 2008, 112, 10033−10040. (32) Choi, J.-S.; Braymer, J. J.; Nanga, R. P. R.; Ramamoorthy, A.; Lim, M. H. Design of small molecules that target metal-Abeta species
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS This work was supported by NSF of China (no. 21121064) and Program for New Century Excellent Talents in University of China (NCET-12-0760). We thank Professor Min Pu for his help in computations with the Gaussian 03 package.
■
ABBREVIATIONS USED LAC, lactose; HPMC, hydroxypropyl methylcellulose; MAN, mannitol; RMSD, root-mean-square; LJ, Lennard-Jones; Coul, Coulomb; BCS, biopharmaceutics classification systems; GCM, group contribution method; MD, molecular dynamics; Rg, radius of gyration; SASA, solvent accessible surface area; RDF, radial distribution functions g(r)
■
REFERENCES
(1) Gupta, U.; Agashe, H. B.; Asthana, A.; Jain, N. K. Dendrimers: novel polymeric nanoarchitectures for solubility enhancement. Biomacromolecules 2006, 7, 649−658. (2) Neha; Preeti, C.; Atin, K.; Rajan, P.; Kumar, M. R.; Santanu, M.; Pardeep, K.; Munsab, A. Approaches to improve the solubility and bioavailability of poorly soluble drugs and different parameter to screen them. Novel Sci. Int. J. Pharm. Sci. 2012, 1, 171−182. (3) Kennedy, M.; Hu, J.; Gao, P.; Li, L.; Ali-Reynolds, A.; Chal, B.; Gupta, V.; Ma, C.; Mahajan, N.; Akrami, A.; Surapaneni, S. Enhanced bioavailability of a poorly soluble VR1 antagonist using an amorphous solid dispersion approach: a case study. Mol. Pharmaceutics 2008, 5, 981−993. (4) Babu, N. J.; Nangia, A. Solubility advantage of amorphous drugs and pharmaceutical cocrystals. Cryst. Growth Des. 2011, 11, 2662− 2679. (5) Lvov, Y. M.; Pattekari, P.; Zhang, X. C.; Torchilin, V. Converting poorly soluble materials into stable aqueous nanocolloids. Langmuir 2011, 27, 1212−1217. (6) Io, T.; Fukami, T.; Yamamoto, K.; Suzuki, T.; Xu, J. D.; Tomono, K.; Ramamoorthy, A. Homogeneous nanoparticles to enhance the efficiency of a hydrophobic drug, antihyperlipidemic probucol, characterized by solid-state NMR. Mol. Pharmaceutics 2010, 7, 299− 305. (7) Van Eerdenbrugh, V.; Vermant, J.; Martens, J. A.; Froyen, L.; Van Humbeeck, J.; Van den Mooter, G.; Augustijns, P. Solubility increases associated with crystalline drug nanoparticles: methodologies and significance. Mol. Pharmaceutics 2010, 7, 1858−1870. (8) Ragab, D. M.; Rohani, S. Particle engineering strategies via crystallization for pulmonary drug delivery. Org. Process Res. Dev. 2009, 13, 1215−1223. (9) Costache, A. D.; Sheihet, L.; Zaveri, Z.; Knight, D. D.; Kohn, J. Polymer−drug interactions in tyrosine-derived triblock copolymer nanospheres: a computational modeling approach. Mol. Pharmaceutics 2009, 6, 1620−1627. (10) Thommes, T.; Ely, D. R.; Teresa Carvajal, M.; Pinal, R. Improvement of the dissolution rate of poorly soluble drugs by solid crystal suspensions. Mol. Pharmaceutics 2011, 8, 727−735. (11) Liu, Y.; Huang, L.; Liu, F. Paclitaxel nanocrystals for overcoming multidrug resistance in cancer. Mol. Pharmaceutics 2010, 3, 863−869. (12) Vega, D. R.; Polla, G.; Martinez, A.; Mendioroz, E.; Reinoso, M. Conformational polymorphism in bicalutamide. Int. J. Pharm. 2007, 328, 112−118. G
dx.doi.org/10.1021/mp300727d | Mol. Pharmaceutics XXXX, XXX, XXX−XXX
Molecular Pharmaceutics
Article
and regulate metal-induced Abeta aggregation and neurotoxicity. Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 21990−21995. (33) Kandasamy, S. K.; Lee, D.-K.; Nanga, R. P. R.; Xu, J.; Santos, J. S.; Larson, R.; Ramamoorthy, A. Solid-state NMR and molecular dynamics simulations reveal the oligomeric ion-channels of TM2GABAA stabilized by intermolecular hydrogen bonding. Biochim. Biophys. Acta, Biomembr. 2009, 1788, 686−695. (34) PRODRG Server; http://davapc1.bioch.dundee.ac.uk/cgi-bin/ prodrg_beta (accessed April 2013). (35) Lee, C.; Yang, W.; Parr, R. G. Development of the Colle− Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B 1988, 37, 785−789. (36) Becke, A. D. Density-functional thermochemistry. III. The role of exact exchange. J. Chem. Phys. 1993, 98, 5648−5652. (37) Le, Y.; Ji, H.; Chen, J.-F.; Shen, Z. G.; Yun, J.; Pu, M. Nanosized bicalutamide and its molecular structure in solvents. Int. J. Pharm. 2009, 370, 175−180. (38) Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Zakrzewski, V. G.; Montgomery, J. A., Jr.; Stratmann, R. E.; Burant, J. C.; Dapprich, S.; Millam, J. M.; Daniels, A. D.; Kudin, K. N.; Strain, M. C.; Farkas, O.; Tomasi, J.; Barone, V.; Cossi, M.; Cammi, R.; Mennucci, B.; Pomelli, C.; Adamo, C.; Clifford, S.; Ochterski, J.; Petersson, G. A.; Ayala, P. Y.; Cui, Q.; Morokuma, K.; Malick, D. K.; Rabuck, A. D.; Raghavachari, K.; Foresman, J. B.; Cioslowski, J.; Ortiz, J. V.; Stefanov, B. B.; Liu, G.; Liashenko, A.; Piskorz, P.; Komaromi, I.; Gomperts, R.; Martin, R. L.; Fox, D. J.; Keith, T.; Al-Laham, M. A.; Peng, C. Y.; Nanayakkara, A.; Gonzalez, C.; Challacombe, M.; Gill, P. M. W.; Johnson, B. G.; Chen, W.; Wong, M. W.; Andres, J. L.; Head-Gordon, M.; Replogle, E. S.; Pople, J. A. Gaussian 03, revision B.01; Gaussian, Inc.: Pittsburgh, PA, 2003. (39) Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. J. Automated docking using a lamarckian genetic algorithm and empirical binding free energy function. J. Comput. Chem. 1998, 19, 1639−1662. (40) ADT/AutoDockTools; Molecular Graphics Laboratory, The Scripps Research Institute: La Jolla, CA, 2007; http://autodock. scripps.edu/resources/adt/index_html (accessed April 2013). (41) Van Gunsteren, W. F.; Billeter, S. R.; Eising, A. A.; Hunenberger, P. H.; Kruger, P.; Mark, A. E.; Scott, W. R. P.; Tironi, I. G. Biomolecular Simulation: The GROMOS96 Manual and User Guide; Hochschulverlag AG an der ETH Zurich: Zurich, Switzerland, 1996. (42) Berendsen, H. J. C.; Postma, J. P. M.; Van Gunsteren,W. F.; Hermans, J. Interaction Models for Water in Relation to Protein Hydration in Intermolecular Forces; Pullman, B. Ed.; Reidel: Dordrecht, The Netherlands, 1981; pp 331−342. (43) Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007, 126, 014101. (44) Berendsen, H. J. C.; Postma, J. P. M.; DiNola, A.; Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984, 81, 3684−3690. (45) Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J. Chem. Phys. 1993, 98, 10089−10092. (46) Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. A smooth particle mesh Ewald potential. J. Chem. Phys. 1995, 103, 8577−8592. (47) Van Gunsteren, W. F.; Berendsen, H. J. C. A leap-frog algorithm for stochastic dynamics. Mol. Simul. 1988, 1, 173−185. (48) Van Der Spoel, D.; Lindahl, E.; Hess, B.; Van Buuren, A. R.; Apol, E.; Meulenhoff, P. J.; Tieleman, D. P.; Sijbers, A. L. T. M.; Feenstra, K. A.; Van Drunen, R.; Berendsen, H. J. C. Gromacs User Manual, version 4.5; Gromacs: Groningen, The Netherlands, 2010; www.gromacs.org. (49) PyMOL; http://www.pymol.org (accessed April 2013). (50) Jorgensen, L. W.; Duffy, M. E. Prediction of drug solubility from Monte Carlo simulations. Bioorg. Med. Chem. Lett. 2000, 10, 1155− 1158.
(51) Jorgensen, L. W.; Duffy, M. E. Prediction of drug solubility from structure. Adv. Drug Delivery Rev. 2002, 54, 355−366.
H
dx.doi.org/10.1021/mp300727d | Mol. Pharmaceutics XXXX, XXX, XXX−XXX