- Email: [email protected]
Replacing microemulsion formulations experimental solubility studies with in-silico methods comprising molecular dynamics and docking experiments
chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 453–456
Contents lists available at ScienceDirect
Chemical Engineering Research and Design journal homepage: www.elsevier.com/locate/cherd
Replacing microemulsion formulations experimental solubility studies with in-silico methods comprising molecular dynamics and docking experiments Abdelkader A. Metwally a,1 , Rania M. Hathout a,b,∗,1 a b
Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt Bioinformatics Program, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
a r t i c l e
i n f o
a b s t r a c t
Article history:
Usually, formulating hydrophobic drugs in microemulsions starts with screening the sol-
Received 3 December 2014
ubility of the active pharmaceutical ingredients in different oils and thereby selecting the
Received in revised form 2 May 2015
best candidate according to its solubilising power. We hypothesise that in-silico methods
Accepted 9 September 2015
such as molecular dynamics to simulate the oils domains together with docking of the
Available online 14 September 2015
investigated drug(s) on these simulated domains can offer extremely valuable tools saving researches long experimentation time in the laboratories and incalculable efforts exerted
Keywords:
in developing sensitive and accurate methods of analysing drugs in oils.
In-silico
© 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Microemulsions Solubility Molecular dynamics Docking
1.
Introduction
Microemulsions are excellent candidates for the delivery of hydrophobic drugs. They have been especially shown to overcome the hurdles that face the formulation of many weakly water-soluble drugs (Hathout and Woodman, 2012). One of the most important factors in developing microemulsion systems is the type of the solubilising oil used, and many microemulsion-based studies start with experimenting the solubility in different oil carriers (Patel and Vavia, 2007; Sharma and Kumar, 2012; Mehta et al., 2007; Zhao et al., 2005, 2006). This is usually performed prior to formulating a lipophilic drug such as testosterone in a microemulsion system to select the best oily carrier that can accommodate the drug (Hathout, 2010). The work in this study utilises
molecular dynamics and docking studies in order to predict the best solubilising oil for a model drug viz. testosterone hormone as an alternative to adopting exhausting solubility studies that need complicated and sensitive methods and instruments of analysis. Recently, a similar study was performed where modeling of mixed micelles was adopted using computer simulations in order to record the possible interactions between mixed micelles and drugs (Xie et al., 2014). In another recent study, molecular simulations were utilised to gain deep insights about the interand the intramolecular interactions between a hydrophobic drug viz. Vinpocetine and different hydrophilic excipients such as: hydroxyl propyl methyl cellulose, polyvinyl alcohol and lactose to select the suitable candidates for nanoparticles preparation (Li et al., 2014). Comparing the potential
∗ Corresponding author. Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, African Union St., 11566 Cairo, Egypt. Tel.: +20 100 5252919/+20 2 22912685; fax: +20 2 24011507. E-mail address: r [email protected] (R.M. Hathout). 1 The authors have equally contributed in this manuscript. http://dx.doi.org/10.1016/j.cherd.2015.09.003 0263-8762/© 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
454
chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 453–456
of two surfactants; Cremophor® EL and Tween® 80 to emulsify tocotrienol was also accomplished and the differences were interpreted according to their structural differences and spatial arrangement as revealed by molecular modeling and differences in binding energies obtained from docking results (Alayoubi et al., 2012). The work in this study comprised using molecular dynamics simulation in the preparation of simulated oily domains of four different oils: oleic acid (OA), ethyl oleate (EO), isopropyl myristate (IPM) and mineral oil (MO) together with an additional simulated water domain. Consequently, docking of a lipophilic model drug; testosterone on these simulated drug carriers was performed and the results were compared with the solubility wet experiments.
2.
Methods
2.1. Molecular dynamics simulations of the used oils and water All-atom molecular dynamics simulations were carriedout using GROMACS (Pronk et al., 2013) v4.6.5 software package. The parameters of the oils were obtained using CgenFF (Vanommeslaeghe et al., 2010) available on line (https://cgenff.paramchem.org/). The oil system was initially prepared with 300 molecules for each of ethyl oleate, isoporopyl myristate, mineral oil, and oleic acid. The mineral oil system was heterogenous and was prepared using 100 molecules of each of tetradecane, hexadecane, and octadecane. Prior to running molecular dynamics simulations, all systems were subjected to energy minimisation using the
steepest descent method. The oily or aqueous system was then subjected to a molecular dynamics run, with a time step of 2 fs, full periodic boundary conditions, and a cut-off dis˚ tance for van der Waal’s and electrostatic interactions of 12 A. PME was chosen to calculate long range electrostatic interactions. LINCS algorithm was used to constrain all bonds. The system was equilibrated at 25 ◦ C using a v-rescale thermostat, and at a pressure of 1 bar using a Berendsen barostat for 6 ns.
2.2. Preparation of the investigated model drug for docking The chemical structures of the studied drug; testosterone, was generated using ChemDraw® Ultra version 10 (Cambridgesoft, Waltham, MA). The corresponding Mol2 file needed for docking using the software adopted in this study was obtained using Chem3D® Ultra version 10 (Cambridgesoft, Waltham, MA) after energy minimisation using the MM2 force field of the same program.
2.3. Docking of the investigated drug on the simulated carriers The docking analysis was carried out using AutoDock vina (Molecular graphics laboratory, The Scripps research group, La Jolla, CA). Docking was performed on a grid box consisting ˚ cube (A box of 26 × 26 × 26 grid points corresponding to 9.75 A with a reasonable size to accommodate the docked drug) for all the carriers except for mineral oil where docking was only possible when the size of the grid box was increased to 35 × 35 × 35 ˚ Moreover, PyMol grid points corresponding to 13.125 A.
Fig. 1 – Successful docking of testosterone on the investigated oils: (A) oleic acid, (B) ethyl oleate and (C) isopropyl myristate ˚ (D) mineral oil. Images display spheres having a radius of 12 A.
chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 453–456
Fig. 2 – Comparing the experimental solubility results of testosterone in different oils and in water with the generated corresponding binding energies after the in-silico docking of the drug on the same molecular dynamics simulated carriers. software v0.99rc6 (Schrodinger LLC, Portland, OR) was used for the final display of the docked molecules on the investigated oils. Vina uses a sophisticated gradient optimisation method in its local optimisation procedure so that the calculation of the gradient effectively gives the optimisation algorithm a “sense of direction” from a single evaluation which leads to high accuracy (Trott and Olson, 2010).
2.4. Comparing docking results with previously obtained solubility values Afterwards, the obtained docking energies were compared to the experimental solubility values of testosterone in the investigated oils and in pure water obtained from Hathout (2010).
3.
455
Fig. 3 – Mathematical modeling of the relationship between the solubility of testosterone in different oils and water and the obtained binding energies after docking on the corresponding simulated domains. order (G is in kcal/mol): Oleic acid (G = −8.7) > Ethyl oleate (G = −8.0) and isopropyl myristate (G = −7.8) > mineral oil (G = −6.5) > water (G = −5.3). These results depict the sensitivity and accuracy of the docking results in reflecting the solubility of lipophilic drugs in oils. The obvious correlation between the docking binding energy of the drug and its corresponding solubility in oils and in water is attributed to the fact that the computation of the binding energy is based on the calculation of the binding energies due to van der Waal’s forces, hydrophobic forces and H-bonding (Wang et al., 2002) which are all involved in lipophilic drugs solubility. Furthermore, the aforementioned results imply an exponential relationship between the obtained solubilities and binding energies. Therefore, in an attempt to model the obtained results mathematically, linear regression analysis was performed between the log of the obtained solubilities of testosterone in the investigated oils and water and the corresponding docking binding energies. A mathematical model (Eq. (1)) was obtained as follows:
Results and discussion log (Solubility of testosterone in the oil) = −7.03–1.084 × (G)
Oleic acid was previously shown to exhibit best solubilising power and thereby testosterone was formulated in successful transdermal microemulsion formulations using oleic acid as the oily phase (Hathout et al., 2010; Hathout and Woodman, 2014). Fig. 1 demonstrates the successful docking of testosterone on the investigated oils. Fig. 2 shows the obtained docking energies of testosterone on the oils and on water simultaneously with the corresponding measured solubilities. It is obvious from the data that the experimental ranking of the oils in solubilising the drug correlated well with the rank of the same oils regarding the negative values of the binding energy (G) of testosterone docked on them where a more negative binding energy indicates better affinity to the drug and more accommodation and hence better solubilisation. Applying statistical analysis on the experimental solubility results using ANOVA followed by Tukey’s multiple comparison post-test to test differences between all pairs demonstrated extremely significant differences where P < 0.0001 was recorded between all pairs of the oils and between each oil and water except between the pair ethyloleate and isopropyl myristate. Similar results were equivalently obtained when applying ANOVA to the binding energy results, where again extremely significant results were obtained for all pairs (P = 0.0044) except for ethyloleate and isopropyl myristate. Hence, the solubilising ranking followed the consequent
(1) where G is the docking binding energy after docking testosterone on the same oil. Fig. 3 depicts the excellent goodness of fit of this mathematical relationship (r2 = 0.94). To this end, the computational time cost of our integrated approach is distributed among the molecular dynamics simulations, the docking of the drugs, mathematical modelling and the statistical analysis. The type of the used hardware usually affects the computational time for each procedure. However, in order to provide a preliminary estimate, the average time for molecular dynamics simulation for the systems investigated in this paper was about 1 ns of simulation per hour of computational cost using a desktop personal computer fitted with a modern multi-core processor. It is also worth noting that the size of the system (number of particles) would also affect the simulation computational time. Based on our experience, the corresponding ‘wet-lab’ experimental time was several folds more than the computational method adopted in this paper (for comparison with the computational method, wet lab experimental method of testosterone solubility determination in Hathout (2010) is provided in the Supplementary material). In addition, the exerted efforts and high expenses of experimentation were other triggers favouring the new introduced approach.
456
4.
chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 453–456
Conclusion
In light of the results obtained from the adopted study, it is then proposed that docking experiments could replace exhausting and time consuming solubility experiments which may also be unfavourable from the economical point of view. Moreover, the simplicity of utilising computer software is another advantage that encourages the feasible shift to this approach of in-silico testing rather than performing wet experiments. It is worth noting that docking drugs on oily domains could be beneficial in formulating other kinds of delivery systems that possess oily locus necessary to accommodate the drug such as: the nanostructured lipid carriers, nanoemulsions and lipid nanocapsules.
Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.cherd.2015.09.003.
References Alayoubi, A., Satyanarayanajois, S.D., Sylvester, P.W., Nazzal, S., 2012. Molecular modelling and multisimplex optimization of tocotrienol-rich self emulsified drug delivery systems. Int. J. Pharm. 426, 153–161. Hathout, R.M., Woodman, T.J., 2012. Applications of NMR in the characterization of pharmaceutical microemulsions. J. Control. Release 161, 62–72. Hathout, R.M., Woodman, T.J., 2014. NMR diffusion-ordered spectroscopy can explain differences in skin penetration enhancement between microemulsion formulations. Nanomedicine 10, 1389–1390. Hathout, R.M., Woodman, T.J., Mansour, S., Mortada, N.D., Geneidi, A.S., Guy, R.H., 2010. Microemulsion formulations for the transdermal delivery of testosterone. Eur. J. Pharm. Sci. 40, 188–196. Hathout, R.M., 2010. Increasing the Solubility and Bioavailability of a Slightly Soluble Drug Using Microemulsions. Faculty of Pharmacy, Ain Shams University, Cairo, Egypt.
Li, C.X., Wang, H.B., Oppong, D., Wang, J.X., Chen, J.F., Le, Y., 2014. Excipient-assisted vinpocetine nanoparticles: experiments and molecular dynamic simulations. Mol. Pharm. 11, 4023–4035. Mehta, S.K., Kaur, G., Bhasin, K.K., 2007. Analysis of Tween based microemulsion in the presence of TB drug rifampicin. Colloids Surf., B: Biointerfaces 60, 95–104. Patel, A.R., Vavia, P.R., 2007. Preparation and in vivo evaluation of SMEDDS (self-microemulsifying drug delivery system) containing fenofibrate. AAPS J. 9, E344–E352. Pronk, S., P Éll, S.É., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M.R., Smith, J.C., Kasson, P.M., van der Spoel, D., Hess, B., Lindahl, E., 2013. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29, 845–854. Sharma, P.K., Kumar, A., 2012. Development, characterization, and evaluation of celecoxib microemulsion for topical delivery. J. Dispersion Sci. Technol. 33, 1411–1418. Trott, O., Olson, A.J., 2010. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461. Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., Darian, E., Guvench, O., Lopes, P., Vorobyov, I., Mackerell, A.D., 2010. CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31, 671–690. Wang, R., Lai, L., Wang, S., 2002. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J. Comput. Aided Mol. Des. 16, 11–26. Xie, X., Cardot, J.M., Garrait, G., Thery, V., El-Hajji, M., Beyssac, E., 2014. Micelle dynamic simulation and physicochemical characterization of biorelevant media to reflect gastrointestinal environment in fasted and fed states. Eur. J. Pharm. Biopharm. 88, 565–573. Zhao, X., Chen, D., Gao, P., Ding, P., Li, K., 2005. Synthesis of ibuprofen eugenol ester and its microemulsion formulation for parenteral delivery. Chem. Pharm. Bull. (Tokyo) 53, 1246–1250. Zhao, X., Liu, J.P., Zhang, X., Li, Y., 2006. Enhancement of transdermal delivery of theophylline using microemulsion vehicle. Int. J. Pharm. 327, 58–64.
Contents lists available at ScienceDirect
Chemical Engineering Research and Design journal homepage: www.elsevier.com/locate/cherd
Replacing microemulsion formulations experimental solubility studies with in-silico methods comprising molecular dynamics and docking experiments Abdelkader A. Metwally a,1 , Rania M. Hathout a,b,∗,1 a b
Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt Bioinformatics Program, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
a r t i c l e
i n f o
a b s t r a c t
Article history:
Usually, formulating hydrophobic drugs in microemulsions starts with screening the sol-
Received 3 December 2014
ubility of the active pharmaceutical ingredients in different oils and thereby selecting the
Received in revised form 2 May 2015
best candidate according to its solubilising power. We hypothesise that in-silico methods
Accepted 9 September 2015
such as molecular dynamics to simulate the oils domains together with docking of the
Available online 14 September 2015
investigated drug(s) on these simulated domains can offer extremely valuable tools saving researches long experimentation time in the laboratories and incalculable efforts exerted
Keywords:
in developing sensitive and accurate methods of analysing drugs in oils.
In-silico
© 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Microemulsions Solubility Molecular dynamics Docking
1.
Introduction
Microemulsions are excellent candidates for the delivery of hydrophobic drugs. They have been especially shown to overcome the hurdles that face the formulation of many weakly water-soluble drugs (Hathout and Woodman, 2012). One of the most important factors in developing microemulsion systems is the type of the solubilising oil used, and many microemulsion-based studies start with experimenting the solubility in different oil carriers (Patel and Vavia, 2007; Sharma and Kumar, 2012; Mehta et al., 2007; Zhao et al., 2005, 2006). This is usually performed prior to formulating a lipophilic drug such as testosterone in a microemulsion system to select the best oily carrier that can accommodate the drug (Hathout, 2010). The work in this study utilises
molecular dynamics and docking studies in order to predict the best solubilising oil for a model drug viz. testosterone hormone as an alternative to adopting exhausting solubility studies that need complicated and sensitive methods and instruments of analysis. Recently, a similar study was performed where modeling of mixed micelles was adopted using computer simulations in order to record the possible interactions between mixed micelles and drugs (Xie et al., 2014). In another recent study, molecular simulations were utilised to gain deep insights about the interand the intramolecular interactions between a hydrophobic drug viz. Vinpocetine and different hydrophilic excipients such as: hydroxyl propyl methyl cellulose, polyvinyl alcohol and lactose to select the suitable candidates for nanoparticles preparation (Li et al., 2014). Comparing the potential
∗ Corresponding author. Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, African Union St., 11566 Cairo, Egypt. Tel.: +20 100 5252919/+20 2 22912685; fax: +20 2 24011507. E-mail address: r [email protected] (R.M. Hathout). 1 The authors have equally contributed in this manuscript. http://dx.doi.org/10.1016/j.cherd.2015.09.003 0263-8762/© 2015 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
454
chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 453–456
of two surfactants; Cremophor® EL and Tween® 80 to emulsify tocotrienol was also accomplished and the differences were interpreted according to their structural differences and spatial arrangement as revealed by molecular modeling and differences in binding energies obtained from docking results (Alayoubi et al., 2012). The work in this study comprised using molecular dynamics simulation in the preparation of simulated oily domains of four different oils: oleic acid (OA), ethyl oleate (EO), isopropyl myristate (IPM) and mineral oil (MO) together with an additional simulated water domain. Consequently, docking of a lipophilic model drug; testosterone on these simulated drug carriers was performed and the results were compared with the solubility wet experiments.
2.
Methods
2.1. Molecular dynamics simulations of the used oils and water All-atom molecular dynamics simulations were carriedout using GROMACS (Pronk et al., 2013) v4.6.5 software package. The parameters of the oils were obtained using CgenFF (Vanommeslaeghe et al., 2010) available on line (https://cgenff.paramchem.org/). The oil system was initially prepared with 300 molecules for each of ethyl oleate, isoporopyl myristate, mineral oil, and oleic acid. The mineral oil system was heterogenous and was prepared using 100 molecules of each of tetradecane, hexadecane, and octadecane. Prior to running molecular dynamics simulations, all systems were subjected to energy minimisation using the
steepest descent method. The oily or aqueous system was then subjected to a molecular dynamics run, with a time step of 2 fs, full periodic boundary conditions, and a cut-off dis˚ tance for van der Waal’s and electrostatic interactions of 12 A. PME was chosen to calculate long range electrostatic interactions. LINCS algorithm was used to constrain all bonds. The system was equilibrated at 25 ◦ C using a v-rescale thermostat, and at a pressure of 1 bar using a Berendsen barostat for 6 ns.
2.2. Preparation of the investigated model drug for docking The chemical structures of the studied drug; testosterone, was generated using ChemDraw® Ultra version 10 (Cambridgesoft, Waltham, MA). The corresponding Mol2 file needed for docking using the software adopted in this study was obtained using Chem3D® Ultra version 10 (Cambridgesoft, Waltham, MA) after energy minimisation using the MM2 force field of the same program.
2.3. Docking of the investigated drug on the simulated carriers The docking analysis was carried out using AutoDock vina (Molecular graphics laboratory, The Scripps research group, La Jolla, CA). Docking was performed on a grid box consisting ˚ cube (A box of 26 × 26 × 26 grid points corresponding to 9.75 A with a reasonable size to accommodate the docked drug) for all the carriers except for mineral oil where docking was only possible when the size of the grid box was increased to 35 × 35 × 35 ˚ Moreover, PyMol grid points corresponding to 13.125 A.
Fig. 1 – Successful docking of testosterone on the investigated oils: (A) oleic acid, (B) ethyl oleate and (C) isopropyl myristate ˚ (D) mineral oil. Images display spheres having a radius of 12 A.
chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 453–456
Fig. 2 – Comparing the experimental solubility results of testosterone in different oils and in water with the generated corresponding binding energies after the in-silico docking of the drug on the same molecular dynamics simulated carriers. software v0.99rc6 (Schrodinger LLC, Portland, OR) was used for the final display of the docked molecules on the investigated oils. Vina uses a sophisticated gradient optimisation method in its local optimisation procedure so that the calculation of the gradient effectively gives the optimisation algorithm a “sense of direction” from a single evaluation which leads to high accuracy (Trott and Olson, 2010).
2.4. Comparing docking results with previously obtained solubility values Afterwards, the obtained docking energies were compared to the experimental solubility values of testosterone in the investigated oils and in pure water obtained from Hathout (2010).
3.
455
Fig. 3 – Mathematical modeling of the relationship between the solubility of testosterone in different oils and water and the obtained binding energies after docking on the corresponding simulated domains. order (G is in kcal/mol): Oleic acid (G = −8.7) > Ethyl oleate (G = −8.0) and isopropyl myristate (G = −7.8) > mineral oil (G = −6.5) > water (G = −5.3). These results depict the sensitivity and accuracy of the docking results in reflecting the solubility of lipophilic drugs in oils. The obvious correlation between the docking binding energy of the drug and its corresponding solubility in oils and in water is attributed to the fact that the computation of the binding energy is based on the calculation of the binding energies due to van der Waal’s forces, hydrophobic forces and H-bonding (Wang et al., 2002) which are all involved in lipophilic drugs solubility. Furthermore, the aforementioned results imply an exponential relationship between the obtained solubilities and binding energies. Therefore, in an attempt to model the obtained results mathematically, linear regression analysis was performed between the log of the obtained solubilities of testosterone in the investigated oils and water and the corresponding docking binding energies. A mathematical model (Eq. (1)) was obtained as follows:
Results and discussion log (Solubility of testosterone in the oil) = −7.03–1.084 × (G)
Oleic acid was previously shown to exhibit best solubilising power and thereby testosterone was formulated in successful transdermal microemulsion formulations using oleic acid as the oily phase (Hathout et al., 2010; Hathout and Woodman, 2014). Fig. 1 demonstrates the successful docking of testosterone on the investigated oils. Fig. 2 shows the obtained docking energies of testosterone on the oils and on water simultaneously with the corresponding measured solubilities. It is obvious from the data that the experimental ranking of the oils in solubilising the drug correlated well with the rank of the same oils regarding the negative values of the binding energy (G) of testosterone docked on them where a more negative binding energy indicates better affinity to the drug and more accommodation and hence better solubilisation. Applying statistical analysis on the experimental solubility results using ANOVA followed by Tukey’s multiple comparison post-test to test differences between all pairs demonstrated extremely significant differences where P < 0.0001 was recorded between all pairs of the oils and between each oil and water except between the pair ethyloleate and isopropyl myristate. Similar results were equivalently obtained when applying ANOVA to the binding energy results, where again extremely significant results were obtained for all pairs (P = 0.0044) except for ethyloleate and isopropyl myristate. Hence, the solubilising ranking followed the consequent
(1) where G is the docking binding energy after docking testosterone on the same oil. Fig. 3 depicts the excellent goodness of fit of this mathematical relationship (r2 = 0.94). To this end, the computational time cost of our integrated approach is distributed among the molecular dynamics simulations, the docking of the drugs, mathematical modelling and the statistical analysis. The type of the used hardware usually affects the computational time for each procedure. However, in order to provide a preliminary estimate, the average time for molecular dynamics simulation for the systems investigated in this paper was about 1 ns of simulation per hour of computational cost using a desktop personal computer fitted with a modern multi-core processor. It is also worth noting that the size of the system (number of particles) would also affect the simulation computational time. Based on our experience, the corresponding ‘wet-lab’ experimental time was several folds more than the computational method adopted in this paper (for comparison with the computational method, wet lab experimental method of testosterone solubility determination in Hathout (2010) is provided in the Supplementary material). In addition, the exerted efforts and high expenses of experimentation were other triggers favouring the new introduced approach.
456
4.
chemical engineering research and design 1 0 4 ( 2 0 1 5 ) 453–456
Conclusion
In light of the results obtained from the adopted study, it is then proposed that docking experiments could replace exhausting and time consuming solubility experiments which may also be unfavourable from the economical point of view. Moreover, the simplicity of utilising computer software is another advantage that encourages the feasible shift to this approach of in-silico testing rather than performing wet experiments. It is worth noting that docking drugs on oily domains could be beneficial in formulating other kinds of delivery systems that possess oily locus necessary to accommodate the drug such as: the nanostructured lipid carriers, nanoemulsions and lipid nanocapsules.
Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.cherd.2015.09.003.
References Alayoubi, A., Satyanarayanajois, S.D., Sylvester, P.W., Nazzal, S., 2012. Molecular modelling and multisimplex optimization of tocotrienol-rich self emulsified drug delivery systems. Int. J. Pharm. 426, 153–161. Hathout, R.M., Woodman, T.J., 2012. Applications of NMR in the characterization of pharmaceutical microemulsions. J. Control. Release 161, 62–72. Hathout, R.M., Woodman, T.J., 2014. NMR diffusion-ordered spectroscopy can explain differences in skin penetration enhancement between microemulsion formulations. Nanomedicine 10, 1389–1390. Hathout, R.M., Woodman, T.J., Mansour, S., Mortada, N.D., Geneidi, A.S., Guy, R.H., 2010. Microemulsion formulations for the transdermal delivery of testosterone. Eur. J. Pharm. Sci. 40, 188–196. Hathout, R.M., 2010. Increasing the Solubility and Bioavailability of a Slightly Soluble Drug Using Microemulsions. Faculty of Pharmacy, Ain Shams University, Cairo, Egypt.
Li, C.X., Wang, H.B., Oppong, D., Wang, J.X., Chen, J.F., Le, Y., 2014. Excipient-assisted vinpocetine nanoparticles: experiments and molecular dynamic simulations. Mol. Pharm. 11, 4023–4035. Mehta, S.K., Kaur, G., Bhasin, K.K., 2007. Analysis of Tween based microemulsion in the presence of TB drug rifampicin. Colloids Surf., B: Biointerfaces 60, 95–104. Patel, A.R., Vavia, P.R., 2007. Preparation and in vivo evaluation of SMEDDS (self-microemulsifying drug delivery system) containing fenofibrate. AAPS J. 9, E344–E352. Pronk, S., P Éll, S.É., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M.R., Smith, J.C., Kasson, P.M., van der Spoel, D., Hess, B., Lindahl, E., 2013. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29, 845–854. Sharma, P.K., Kumar, A., 2012. Development, characterization, and evaluation of celecoxib microemulsion for topical delivery. J. Dispersion Sci. Technol. 33, 1411–1418. Trott, O., Olson, A.J., 2010. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461. Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., Darian, E., Guvench, O., Lopes, P., Vorobyov, I., Mackerell, A.D., 2010. CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31, 671–690. Wang, R., Lai, L., Wang, S., 2002. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J. Comput. Aided Mol. Des. 16, 11–26. Xie, X., Cardot, J.M., Garrait, G., Thery, V., El-Hajji, M., Beyssac, E., 2014. Micelle dynamic simulation and physicochemical characterization of biorelevant media to reflect gastrointestinal environment in fasted and fed states. Eur. J. Pharm. Biopharm. 88, 565–573. Zhao, X., Chen, D., Gao, P., Ding, P., Li, K., 2005. Synthesis of ibuprofen eugenol ester and its microemulsion formulation for parenteral delivery. Chem. Pharm. Bull. (Tokyo) 53, 1246–1250. Zhao, X., Liu, J.P., Zhang, X., Li, Y., 2006. Enhancement of transdermal delivery of theophylline using microemulsion vehicle. Int. J. Pharm. 327, 58–64.