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Selecting optimum protein nano-carriers for natural polyphenols using chemoinformatics tools
Accepted Manuscript
Selecting optimum protein nano-carriers for natural polyphenols using chemoinformatics tools AbdelKader A. Metwally , Sherweit H. El-Ahmady , Rania M. Hathout PII: DOI: Reference:
S0944-7113(16)30195-7 10.1016/j.phymed.2016.10.020 PHYMED 52103
To appear in:
Phytomedicine
Received date: Revised date: Accepted date:
27 July 2016 30 September 2016 26 October 2016
Please cite this article as: AbdelKader A. Metwally , Sherweit H. El-Ahmady , Rania M. Hathout , Selecting optimum protein nano-carriers for natural polyphenols using chemoinformatics tools, Phytomedicine (2016), doi: 10.1016/j.phymed.2016.10.020
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ACCEPTED MANUSCRIPT
Selecting optimum protein nano-carriers for natural polyphenols using chemoinformatics tools AbdelKader A. Metwallya,1, Sherweit H. El-Ahmadyb,1 and Rania M. Hathouta,c,d,1* a
Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams
University, Cairo, Egypt. b
Bioinformatics program, Faculty of Computer and Information Sciences, Ain Shams
University, Cairo, Egypt. d
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c
Department of Pharmacognosy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt.
Department of Pharmaceutical Technology, Faculty of Pharmacy and Biotechnology,
German University in Cairo (GUC), Cairo, Egypt.
The authors have equally contributed in this manuscript.
*
Correspondence: Rania M. Hathout, Department of Pharmaceutics and Industrial
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1
Pharmacy, Faculty of Pharmacy, Ain Shams University, African Union Organization St.,11566 Cairo, Egypt. +2 (0) 100 5254919
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Tel:
+ 2 02 22912685
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Fax: +2 02 24011507
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E-mail: [email protected]; [email protected]
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ACCEPTED MANUSCRIPT Abstract Background: The normal fate of any natural product with a therapeutic potential is to be formulated into an effective medicine. However, the conventional methods of selecting the suitable formulations or carriers based on the formulator experiences, trial and errors as well as materials availability do not usually yield the optimal results. Hypothesis: We hypothesize the possibility of the virtual optimum selection of a protein carrier for two polyphenolic compounds widely investigated for their chemopreventive effects; resveratrol and curcumin
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using a combination of some chemoinformatics tools. Methods: Two protein-based nanoparticles namely; albumin and gelatin nanoparticles were compared as carriers for the two selected phytochemicals; resveratrol and curcumin. Resveratrol-albumin, resveratrolgelatin and curcumin-albumin results were gathered from the literature. While, a new combination (formulation), comprising curcumin as the cargo and gelatin nanoparticles as the
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carrier, was prepared and evaluated as a potential medicine for breast cancer. Combined chemoinformatics tools, namely; molecular dynamics and molecular docking were used to determine the optimum carrier for each of the two chemopreventive agents. Results: A new curcumin-gelatin nanoparticulate formulation was prepared and proven cytotoxic after an
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application period of 48 hours on MCF-7 breast cancer cell-lines scoring an IC50 value of 64.8 µg/ml. The utilized chemoinformatics tools comprising the molecular dynamics
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simulations of the protein nano-particulate drug-carriers followed by the molecular docking of phytochemical drugs on these carriers could capture the optimum protein carrier for each of the tested phytochemical and hence propose a successful formulation. Conclusion: This
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study presents one in a series that proves the novel addressed concept of the utilization of computational tools rather than wet-lab experimentation in providing better selection of drug-
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carrier pairs aiming for better formulations and the subsequent successful therapeutic effects.
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Keywords: curcumin; resveratrol; gelatin nanoparticles; albumin; docking; molecular dynamics
List of Abbreviations: DMSO: Dimethyl Sulphoxide; GNPs: Gelatin Nanoparticles; MTT: 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium
bromide;
modified Eagle Medium; TEM: transmission electron microscopy
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DMEM:
Dulbecco’s
ACCEPTED MANUSCRIPT 1. Introduction Since the pre-historic era, humans have utilized naturally-driven bioactive agents to treat a plethora of diseases. Recently, natural products have been explored for the prevention and treatment of serious diseases such as cancer (Mehanny et al., 2016a; Mehanny et al., 2016b). These products included phytochemicals such as: β-carotene, curcumin, epigallocatechin gallate, genistein, resveratrol, gingerol, and capsaicin. These agents specifically have been proven to have strong chemotherapeutic actions. However, the exploitation of these agents
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was challenged by their instabillity, poor (or sometimes extremely poor) aqueous solubility and the subsequent poor bioavailability limiting their use (Bharali et al., 2011). In the current study, resveratrol and curcumin were selected as examples of chemopreventive natural products. Resveratrol is considered a promising chemopreventive phytochemical which was first isolated from the roots of Veratrum glandiflorum O. Loes, then isolated from grapes,
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peanuts, mulberries and another 70 plant species. It has been shown to inhibit the growth of various cancer cells in culture as well as in implanted tumors in-vivo (Kundu and Surh, 2008; Singh et al., 2015). Curcumin is a major bioactive component isolated from the rhizomes of Curcuma longa L. (Zingiberaceae) and has been the scope of many publications for its wide
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range of pharmacological activities but limited clinical applications due to its poor aqueous solubility, multidrug pump P-pg efflux, extensive in vivo metabolism and rapid elimination
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(Patil et al., 2015). The anti-cancer activity of curcumin has been exhibited in breast, ovarian, colon and lung cancer (Saxena and Hussain, 2013; Patil et al., 2015; Yang et al., 2015; Wang et al., 2016).
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nanotechnology is a fast developing science gaining more grounds in medicine and therapy everyday. Nanoparticles have emerged as versatile nano-carriers for the specific and
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targeted delivery of drugs to organs and tissues which is particularly relevant in cancer therapy where most of the biological processes occur at the nanometer level (Li et al., 2012).
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Hence, the nanoparticle nanotechnology has been greatly appreciated as a potential tool for cancer diagnosis and treatment (Bharali et al., 2011). In this context, the search for the optimum smart materials, that can best accommodate the
active chemotherapeutic agents at the nano level, is an ongoing process. Accordingly, protein carriers have been sought of due to their many advantages including their biodegradablility, biocompatiblity, non-antigenicity, low cost and availability. Moreover, the surface of protein nanoparticles can be modified with site-specific ligands, cationized with amine derivatives or coated with polyethyl glycols to achieve targeted and sustained release drug delivery (Hathout and Omran, 2016). Compared to other colloidal carriers, protein nano-carriers are 3
ACCEPTED MANUSCRIPT better stable in biological fluids to provide the desired controlled and sustained release of entrapped drug molecules (Sahoo et al., 2015). Amongst the commonly used proteins, albumin and gelatin reside at the top due to their abundant resources. To this end, a usual question rises; what is the optimum proteinaceous carrier material for a specific drug? The conventional answer could be obtained by testing several protein-drug pairs in the wet laboratories till reaching the most suitable carrier. Nevertheless, another question has been raised; can computers and softwares, specifically, chemoinformatics tools,
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answer this question and thereby replace the exhausting and resources consuming wet-lab trials? We have recently answered this question and confirmed the possible usage of several chemo-informatics tools in optimizing and predicting the loading of drugs in several carriers (Metwally and Hathout, 2015a; Metwally and Hathout, 2015b).
Therefore, in the current study we hypothesize that chemoinformatics tools such as
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molecular dynamics simulations utilized to build virtual protein nano-carriers followed by the molecular docking of the investigated phytochemicals on these virtually built carriers can select the optimum protein-based nanoparticles material that can best accommodate naturally discovered chemotherapeutic agents (polyphenolic compounds having close chemical
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structures). This included the phytoalexin; resveratrol, and the diferuloylmethane; curcumin, (Figure 1) based on their physical and chemical potential interactions with the proposed
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nano-carriers. Several software packages and docking scores were evaluated in order to validate the hypothesis. In order to test this hypothesis, the mass of the loaded polyphenol per 100 mg carrier of resveratrol on each of albumin and gelatin nanoparticles was collected from
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the literature. Similarly, the loaded mass of curcumin on albumin nanoparticles was obtained. To the best of our knowledge, curcumin-gelatin nanoparticles, was not previously produced,
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and hence, we attempted to prepare this new formulation to compare to the curcumin-albumin counterpart.
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[Please insert Figure 1 here]
2. Methodology 2.1. Materials. Curcumin, gelatin, gluteraldehyde solution (25%, w/v) and glycine were purchased from Sigma-aldrich, Taufkirchen, Germany. Dimethyl Sulphoxide (DMSO), Trypan blue and MTT were purchased from Sigma (St.Louis, MO, USA). Fetal Bovine serum, DMEM, RPMI-1640,
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ACCEPTED MANUSCRIPT HEPES buffer solution, L-glutamine, gentamycin and 0.25% Trypsin-EDTA were purchased from Lonza, Belgium.
2.2. Methods. 2.2.1. Data mining using scientific literature databases. The mass of drug loaded per 100 mg protein of 2 different drugs; resveratrol (Li et al., 2012;
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Karthikeyan et al., 2013) and curcumin (Jithan et al., 2011) entrapped in protein-based nanoparticles were gathered from the scientific literature databases using PubMed, Scopus and Web of Science.
2.2.2. Preparation of curcumin-loaded gelatin nanospheres.
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Curcumin-loaded gelatin nanospheres were prepared using the single desolvation method (the same method that was previously used to prepare the curcumin-loaded albumin nanoparticles (Jithan et al., 2011)). Briefly, 10 mg curcumin were dissolved in 3 ml absolute ethanol. Meanwhile, gelatin (200 mg) was dissolved in 2 ml de-ionized water at 40⁰C. The alcoholic
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solution was then added to the aqueous solution drop-wise till a colloidal dispersion was obtained. Afterwards, 0.03 ml gluteraldehyde (25%, w/v) were added to the dispersion to
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cross-link the formed nanoparticles and prevent their swelling. Stirring for 1 hour was performed. Consequently, the colloidal dispersion volume was completed to 25 ml using deionized water. Finally, the prepared nanospheres were separated by centrifugation at
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13,000 rpm for 25 minutes (Abozeid et al., 2016).
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2.2.3. Characterization of the prepared curcumin-loaded gelatin nanospheres. 2.2.3.1. Particle size measurements.
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The particle size and polydispersity index of the prepared polyphenol-loaded nanospheres was measured using dynamic light scattering (DLS) (Malvern zetasizer, Nano ZS, Malvern, Worcestershire, UK) after re-dispersion of the centrifuged pellets in de-ionized water. The measurements were performed in triplicates.
2.2.3.2. Transmission electron microscopy (TEM). The morphology and the shape of the prepared nanoparticles were examined using a JEM1400 (JEOL, Tokyo, Japan) transmission electron microscope at a voltage of 100 kV. The
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ACCEPTED MANUSCRIPT aqueous dispersion of the particles was casted onto a carbon coated copper grid which was then air dried at room temperature before examination.
2.2.4. Determination of the mass of loaded curcumin in the prepared gelatin nanospheres. The mass of the loaded curcumin per 100 mg of the prepared gelatin nanoparticles was obtained by measuring the mass of the un-entrapped dissolved and precipitated amount of the drug. An aliquot of 1 ml of the supernatant was used to measure the un-entrapped drug
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colorimetrically at 427 nm (Shimadzu, Japan). For measuring the precipitated counterpart, the supernatant was discarded. After, an amount of 2 ml absolute ethanol was added to the centrifuged part containing the precipitated curcumin together with the prepared nanoparticles to selectively dissolve the curcumin. An aliquot of 1 ml was taken from the supernatant, diluted to a final volume of 10 ml with absolute ethanol and subsequently
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measured colorimetrically at a wavelength of 427 nm.
The mass of the loaded curcumin was determined as follows:
)
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( )
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(
(
2.2.5. Confirming the cytotoxic activity of curcumin gelatin nanoparticles using MTT assay.
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2.2.5.1. Mammalian cell lines.
MC7-F cells (human breast cancer cell lines) were obtained from the American Type Culture
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Collection (ATCC, Rockville, MD). 2.2.5.2. Cell-line propagation. The cells were grown on RPMI-1640 medium supplemented with 10% inactivated fetal calf
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serum, 1% L-glutamine, HEPES buffer and 50 µg/ml gentamycin. These cells were maintained at 37 ⁰C in a humidified atmosphere enriched with 5% CO2 and were subcultured two to three times a week. 2.2.5.3. Cytotoxicity evaluation using the viability test. The tumor cell lines were suspended at a concentration of 5
104 cell/well in Corning® 96-
well culture plates and was then incubated for 24 hours. After, the tested formulation was added (in triplicates) into the 96-well plates in order to achieve different concentrations of the tested compound in the formulation. Six vehicle controls with media or with 0.5% DMSO 6
ACCEPTED MANUSCRIPT were run for each of the 96 well plates as a control. After incubating for 48 hours, the number of viable cells was determined using the MTT assay. Briefly, the culture medium was removed from the 96 well plate and replaced with 100 µl of fresh culture RPMI-1640 medium without phenol red then 10 µl of the 12 mM MTT stock solution (5 mg MTT in 1 ml of PBS) to each well including the untreated controls. Then, the 96 well plates were incubated at 37⁰C for 5% CO2 for 4 hours. After, an 85 µl aliquot of the media was removed from the wells, and 50 µl of DMSO was added to each well and mixed thoroughly with the
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pipette and incubated at 37⁰C for 10 minutes. For the quantitative determination of the viable cells, the optical density was measured at 590 nm with a microplate reader (SunRise, TECAN Inc. USA). The percentage viability was calculated as follows: ( )
(
)
Equation (2)
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where, ODt is the mean optical density of wells treated with the tested formulation and ODc is the mean optical density of untreated cells. The relation between the percentage of surviving cells and the drug concentration on the tested formulation was plotted to get the survival curve in the specified cell-line. Accordingly, the 50% inhibitory concentration (IC50), which is the concentration required to cause a cytotoxic effect in 50% the intact cells was estimated
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from the graphical plots of the dose response curve using GraphPad Prism software (San
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Diego, CA, USA) (Mosmann, 1983).
2.2.6. Obtaining the bovine serum albumin virtual matrix.
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The crystal structure of bovine serum albumin protein (4F5S) was obtained from the protein
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data bank (http://www.rcsb.org).
2.2.7. Construction of the gelatin nanospheres matrix virtually using molecular dynamics simulations.
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All-atom molecular dynamics simulations were carried-out using GROMACS (Pronk et al., 2013) v4.6.5 software package. The parameters of gelatin were obtained using CgenFF (Vanommeslaeghe et al., 2010) available online (https://cgenff.paramchem.org/). The system contained 48 molecules each is composed of an 18 amino-acids peptide, representing gelatin, with amino acids sequence ALA, GLY, PRO, ARG, GLY, GLU, PRH, GLY, PRO, ALA, GLY, PRO, ASP, GLY, GLU, PRH, GLY, and PRO. The system was subjected to energy minimization using the steepest descent method. The systems was then subjected to a molecular dynamics run, with a time step of 2 fs, full periodic boundary conditions, and a 7
ACCEPTED MANUSCRIPT cut-off distance for Van der Waal’s and electrostatic interactions of 1.2 nm. PME was chosen to treat long-range electrostatic interactions. The LINCS algorithm was used to constrain all bonds. The system was equilibrated at 373 K using a v-rescale thermostat, and at a pressure of 1 bar using a Berendsen barostat for 3 ns.
2.2.8. Preparing the drugs chemical structures for docking. The isomeric SMILES corresponding to the chemical structures of the studied drugs were
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obtained using PubChem®. Subsequently, the corresponding 3D chemical structures were constructed using the builder function of MOE® version 2014.0901 software (Chemical Computing Group Inc., Montreal, Canada) (Hathout and Metwally, 2016). Further, energy minimization was carried out for all the investigated molecules using MMFF94x forcefield of
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the same software (Costache et al., 2009; Gooding et al., 2014).
2.2.9. Docking of the literature-gathered drugs on the investigated carrier. The docking analysis was employed using MOE version 2014.0901 (Chemical Computing Group Inc., Montreal, Canada). The pdb file of the protein nanoparticles matrix was imported
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to MOE where the identification of the binding site was performed using the MOE's "Site finder" tool (Elhefnawi et al., 2012). Subsequently, the docking was accomplished utilizing
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the "triangle matcher” as a placement method.
This software creates dummy atoms around the docking target atoms. These dummy atoms are considered the docking positions. The London ΔG and ASE scores were utilized for
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calculating the binding energies scoring values. The London ΔG scoring function estimates
Equation (3)
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terms:
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the free energy of binding of the ligand from a given pose. The functional form is a sum of
where c represents the average gain/loss of rotational and translational entropy; Eflex is the energy due to the loss of flexibility of the ligand (calculated from ligand topology only);fHB measures geometric imperfections of hydrogen bonds and takes a value in [0,1]; cHB is the energy of an ideal hydrogen bond; fM measures geometric imperfections of metal ligations and takes a value in [0,1]; cM is the energy of an ideal metal ligation; and Di is the desolvation energy of atom i. The difference in desolvation energies is calculated according to the formula
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Equation (4) where A and B are the protein and/or ligand volumes with atom i belonging to volume B; Ri is the solvation radius of atom i (taken as the OPLS-AA van der Waals sigma parameter plus 0.5 Å); and ci is the desolvation coefficient of atom i. The coefficients {c, cHB, cM, ci} were fitted from ~400 x-ray crystal structures of protein-ligand complexes with available
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experimental pKi data. Atoms are categorized into about a dozen atom types for the assignment of the ci coefficients. The triple integrals are approximated using Generalized Born integral formulas.
Like all commonly used scoring functions, lower binding energies (ΔG, kCal/mole) scores indicate more favourable interactions.
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On the other hand, the ASE score works in a different way. This score is proportional to the sum of the Gaussians R1R2 exp (-0.5d2) over all ligand atom - receptor atom pairs and ligand atom - alpha sphere pairs. R1 and R2 are the radii of the atoms in Å, or is -1.85 for alpha
3. Results and Discussion 3.1. Data mining results.
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spheres. ‘d’ is the distance between the pair in Å.
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Table 1 shows the loaded masses of the investigated drugs; resveratrol and curcumin per 100 mg of the studied protein nano-carriers as calculated from literature (Jithan et al., 2011; Li et
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al., 2012; Karthikeyan et al., 2013). To our knowledge, since the drug-nanocarrier pair corresponding to the curcumin-gelatin nanospheres has not been previously prepared, we
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endeavoured in this study to prepare this combination. By this, the comparison of curcumin loading on albumin and gelatin protein nanoparticles could be completed virtually (using
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chemoinformatics tools) and experimentally in the wet-lab. Moreover, this novel curcumin formulation was characterized, and evaluated for its cytotoxic activity on MCF-7 breast cancer cell lines. [Please insert Table 1 here]
It is worth noting that the results of the mass of curcumin loaded per 100 mg albumin obtained from Jithan et al. (Jithan et al., 2011) was estimated to be 10 mg approximately when prepared using the same initial amount. This was inferred from this literature finding that almost complete loading was observed for the curcumin per 100 mg BSA regardless its 9
ACCEPTED MANUSCRIPT initial added amount in the range of 50-100 mg. Logically, an amount of 10 mg would be easily completely entrapped, as well.
3.2. Particle size measurements of the prepared curcumin-loaded gelatin nanospheres. The prepared curcumin loaded gelatin nanospheres exhibited particle size of a 315 ± 0.376 nm, a value which conforms with the ranges usually reported in literature for the gelatin nanospheres particle size (Kuntworbe and Al-Kassas, 2012; Khatik et al., 2014). The
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recorded polydispersity index was 0.376 ± 0.041. This relatively high observed value is usually associated with the single desolvation preparation method (Li et al., 2012; Hathout and Omran, 2016) due to the heterogeneity of the molecular weight of the gelatin (Coester et al., 2000). Figure 2 demonstrates the obtained P.S. and PDI of the prepared nanospheres. However, nanoparticles of the range of 200-350 nm has proven successful in many animals
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and human studies especially through the pulmonary, nasal and transdermal routes (Bhaskar et al., 2009; Bolhassani et al., 2014; Muralidharan et al., 2015; Zhao et al., 2016). [Please insert Figure 2 here]
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3.3. Morphology study of the prepared curcumin-loaded gelatin nanospheres. The morphology of the prepared nanoparticles was studied using TEM imaging. Figure 3
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demonstrates the spherical structure of the particles which usually leads to better cellular uptake (Abozeid et al., 2016). Figure 3 (a) reveals the prepared matrix structure of the gelatin nanoparticles confirming the preparation of the nanospherical rather than nanocapsular type
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of the nanoparticles. Moreover, the photographed particles exhibited particle sizes
[Please insert Figure 3 here]
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conforming to a great extent with the DLS particles size values as obvious in Figure 3 (b).
3.4. Cytotoxic evaluation of the prepared curcumin gelatin nanospheres. The biological cytotoxic evaluation in breast cancer cell lines was warranted since the curcumin-gelatin as a cargo-carrier combination is a newly addressed formulation pair (Mehanny et al., 2016a). Fig. 4 depicts the dose-response curve of curcumin gelatin nanospheres. This curve was utilized to calculate the IC50 which scored a value of 64.8 µg/ml. The activity of pure curcumin on MCF-7 cell-lines was previously confirmed (Banerjee et al., 2010) and the IC50 of curcumin after 48 hours incubation was specifically determined as 53.18 µg/ml (Khazaei 10
ACCEPTED MANUSCRIPT et al., 2015). A negative control comprising the media only was tested and scored 100% viability. Another positive control of 10% DMSO was tested and resulted in 0% viability. Moreover, the plain gelatin nanoparticles were also tested where they scored an IC 50 of 2.9 mg/ml. This high value is ascribed to the high safety margin and biocomptability of gelatin. The only source of slight toxicity in gelatin nanoparticles is usually attributed to the presence of gluteraldehyde; the used cross-linking agent (Hathout and Omran, 2016). The closeness of the IC50 of curcumin in gelatin nanospheres to its pure curcumin counterpart
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indicates the potency of this newly prepared formulation, bearing in mind the slow and sustained release of cargos from gelatin nanoparticles matrices (only 40 – 60 % cumulative release after 48 hours) especially after cross-linking with strong chemical compounds such as gluteraldehyde and in absence of collagenase enzyme that is responsible for gelatin metabolism in-vivo (Vandervoort and Ludwig, 2004; Kuntworbe and Al-Kassas, 2012;
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Abozeid et al., 2016). Augmenting the cytotoxic effect using gelatin nanoparticles is usually attributed to the better cellular internalization of this kind of particles using endocytosis mechanism (Ishikawa et al., 2012; Abozeid et al., 2016). The nano-range particle size was previously proven to cause better nanoparticles uptake and internalization. Expectedly, the
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smaller the nanoparticles diameter, the better internalization and hence the resultant cytotoxic effect (Panyam and Labhasetwar, 2003; Bivas-Benita et al., 2004; Farid et al., 2014).
will result in lower IC50.
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Accordingly, further attempts to decrease the prepared curcumin-loaded gelatin nanoparticles
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[Please insert Figure 4 here]
3.5. Docking results of the investigated drugs on the virtual nano-carriers.
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The crystal structure of bovine serum albumin was readily obtained from the protein data bank while the other carrier; gelatin was successfully simulated using molecular dynamics as
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revealed in Figure 5.
[Please insert Figure 5 here]
Table 2 shows the results of docking the two investigated drugs; resveratrol and curcumin on the two studied carriers; albumin and gelatin. It is obvious from the results, the superiority of albumin in accommodating more masses of both drugs. This can be inferred from the obtained lower binding energies in case of docking the two investigated polyphenols on albumin compared to the gelatin matrix. These results can be attributed to the higher hydrophobicity of bovine serum albumin in comparison with the denatured gelatin (Bull, 1937; Takahashi et al., 1993). Logically, since resveratrol and 11
ACCEPTED MANUSCRIPT curcumin are poorly soluble hydrophobic drugs therefore they possess higher affinity to the more hydrophobic carrier; bovine serum albumin. [Please insert Table 2 here]
Recently, we have proven the contribution of other factors (descriptors), as well, in the loading efficiencies of drugs on different carriers (Tripalmitin solid lipid nanoparticles and PLGA nanoparticles) (Metwally and Hathout, 2015a) where the drug molecular weight, total
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polar surface area and fragment complexity (including all non-hydrogen bonds, halogen atoms and the overall number of bonds in the drug structure) have shown significant difference on the mass of loaded drug per 100 mg carrier. All these factors play together resulting in a specific drug loading value (Ramezanpour et al., 2016).
The results in Table 2 conform to the reported and obtained results in Table 1 in an excellent
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way demonstrating the validity of our hypothesis.
Figure 6 demonstrates the successful docking of resveratrol on the two investigated carriers. [Please insert Figure 6 here]
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Similarly, Figure 7 depicts the success of docking curcumin on both carriers. The docking results demonstrated several interactions where an obvious hydrogen bonding between the
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drug and the bovine serum albumin can be observed. [Please insert Figure 7 here]
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4. Conclusions and Future Perspectives The work in this study continues in a series that presents a strong relationship between the
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masses of loaded drugs that are obtained experimentally and their computed binding energies resulting from their interactions with the simulated virtual carriers. The ultimate goal of these
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kinds of studies is to replace the highly tedious, time-consuming and resources-wasting wetlab experimentation with the addressed chemoinformatics tools; molecular dynamic simulations and molecular docking. Moreover, the current study introduced a new curcumin carrier formulation; gelatin nanoparticles intended for the treatment of breast cancer. However, according to the experimental and chemo-informatics tools that are used in this work, bovine serum albumin should be better recommended (compared to gelatin, though still successful) when a protein nano-carrier is warranted for polyphenolic compounds such as: resveratrol and curcumin.
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ACCEPTED MANUSCRIPT Conflict of Interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
Figures Captions
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Figure 1. The chemical structure of (A) Resveratrol and (B) Curcumin. Figure 2. Particle size and PDI measurement of the prepared curcumin-loaded gelatin nanospheres.
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Figure 3. Transmission electron microscope imaging of the prepared curcumin-loaded 19 gelatin nanospheres. (A) Under 25000X magnification, (B) Under 20000X magnification and (C) Under 12000X magnification. Figure 4. MCF-7 cell viability results of curcumin gelatin nanospheres. (Error bars represent Mean ± S.D.). Figure 5. (A) The crystal structure of Bovine serum albumin and (B) The molecular dynamics simulated gelatin.
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Figure 6. Docking of resveratrol on (A) Bovine serum albumin and (B) Gelatin.
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Figure 7. Docking of curcumin on (A) Bovine serum albumin and (B) Gelatin.
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ACCEPTED MANUSCRIPT Zhao,Y.Z., Jin,R.R., Yang,W., Xiang,Q., Yu,W.Z., Lin,Q., Tian,F.R., Mao,K.L., Lv,C.Z., Wang,Y.X., Lu,C.T., 2016. Using Gelatin Nanoparticle Mediated Intranasal Delivery of Neuropeptide Substance P to Enhance Neuro-Recovery in Hemiparkinsonian Rats. Plos One,
Table (1).
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11, e0148848.
Mass of the loaded investigated polyphenols per 100 mg of the protein nano-carrier. Mass loaded per 100 mg protein nano-carrier (mg) Drug Resveratrol Curcumin * The result was estimated from ref. [10].
Gelatin
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Albumin 39.4
1.96
10*
3.5
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50 mg drug was almost completely loaded in 100 mg carrier regardless of the drug : polymer ratio so 10 mg are estimated to be fully loaded as well.
Table (2).
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The obtained binding energies after docking the investigated polyphenols on the virtual protein nano-carriers.
Albumin
Drug
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Binding energies (Kcal/mole) Gelatin
London ΔG score
ASE score
London ΔG score
ASE score
Resveratrol
-9.5 ± 0.5
-14.2 ± 0.1
- 8.3 ± 0.2
-9.21 ± 0.1
Curcumin
-13.2 ± 2.1
-21.28 ± 0.2
- 8.11 ± 0.1
-19.32 ± 0.01
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Graphical abstract
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Selecting optimum protein nano-carriers for natural polyphenols using chemoinformatics tools AbdelKader A. Metwally , Sherweit H. El-Ahmady , Rania M. Hathout PII: DOI: Reference:
S0944-7113(16)30195-7 10.1016/j.phymed.2016.10.020 PHYMED 52103
To appear in:
Phytomedicine
Received date: Revised date: Accepted date:
27 July 2016 30 September 2016 26 October 2016
Please cite this article as: AbdelKader A. Metwally , Sherweit H. El-Ahmady , Rania M. Hathout , Selecting optimum protein nano-carriers for natural polyphenols using chemoinformatics tools, Phytomedicine (2016), doi: 10.1016/j.phymed.2016.10.020
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Selecting optimum protein nano-carriers for natural polyphenols using chemoinformatics tools AbdelKader A. Metwallya,1, Sherweit H. El-Ahmadyb,1 and Rania M. Hathouta,c,d,1* a
Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams
University, Cairo, Egypt. b
Bioinformatics program, Faculty of Computer and Information Sciences, Ain Shams
University, Cairo, Egypt. d
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c
Department of Pharmacognosy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt.
Department of Pharmaceutical Technology, Faculty of Pharmacy and Biotechnology,
German University in Cairo (GUC), Cairo, Egypt.
The authors have equally contributed in this manuscript.
*
Correspondence: Rania M. Hathout, Department of Pharmaceutics and Industrial
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1
Pharmacy, Faculty of Pharmacy, Ain Shams University, African Union Organization St.,11566 Cairo, Egypt. +2 (0) 100 5254919
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Tel:
+ 2 02 22912685
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Fax: +2 02 24011507
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E-mail: [email protected]; [email protected]
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ACCEPTED MANUSCRIPT Abstract Background: The normal fate of any natural product with a therapeutic potential is to be formulated into an effective medicine. However, the conventional methods of selecting the suitable formulations or carriers based on the formulator experiences, trial and errors as well as materials availability do not usually yield the optimal results. Hypothesis: We hypothesize the possibility of the virtual optimum selection of a protein carrier for two polyphenolic compounds widely investigated for their chemopreventive effects; resveratrol and curcumin
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using a combination of some chemoinformatics tools. Methods: Two protein-based nanoparticles namely; albumin and gelatin nanoparticles were compared as carriers for the two selected phytochemicals; resveratrol and curcumin. Resveratrol-albumin, resveratrolgelatin and curcumin-albumin results were gathered from the literature. While, a new combination (formulation), comprising curcumin as the cargo and gelatin nanoparticles as the
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carrier, was prepared and evaluated as a potential medicine for breast cancer. Combined chemoinformatics tools, namely; molecular dynamics and molecular docking were used to determine the optimum carrier for each of the two chemopreventive agents. Results: A new curcumin-gelatin nanoparticulate formulation was prepared and proven cytotoxic after an
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application period of 48 hours on MCF-7 breast cancer cell-lines scoring an IC50 value of 64.8 µg/ml. The utilized chemoinformatics tools comprising the molecular dynamics
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simulations of the protein nano-particulate drug-carriers followed by the molecular docking of phytochemical drugs on these carriers could capture the optimum protein carrier for each of the tested phytochemical and hence propose a successful formulation. Conclusion: This
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study presents one in a series that proves the novel addressed concept of the utilization of computational tools rather than wet-lab experimentation in providing better selection of drug-
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carrier pairs aiming for better formulations and the subsequent successful therapeutic effects.
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Keywords: curcumin; resveratrol; gelatin nanoparticles; albumin; docking; molecular dynamics
List of Abbreviations: DMSO: Dimethyl Sulphoxide; GNPs: Gelatin Nanoparticles; MTT: 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium
bromide;
modified Eagle Medium; TEM: transmission electron microscopy
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DMEM:
Dulbecco’s
ACCEPTED MANUSCRIPT 1. Introduction Since the pre-historic era, humans have utilized naturally-driven bioactive agents to treat a plethora of diseases. Recently, natural products have been explored for the prevention and treatment of serious diseases such as cancer (Mehanny et al., 2016a; Mehanny et al., 2016b). These products included phytochemicals such as: β-carotene, curcumin, epigallocatechin gallate, genistein, resveratrol, gingerol, and capsaicin. These agents specifically have been proven to have strong chemotherapeutic actions. However, the exploitation of these agents
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was challenged by their instabillity, poor (or sometimes extremely poor) aqueous solubility and the subsequent poor bioavailability limiting their use (Bharali et al., 2011). In the current study, resveratrol and curcumin were selected as examples of chemopreventive natural products. Resveratrol is considered a promising chemopreventive phytochemical which was first isolated from the roots of Veratrum glandiflorum O. Loes, then isolated from grapes,
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peanuts, mulberries and another 70 plant species. It has been shown to inhibit the growth of various cancer cells in culture as well as in implanted tumors in-vivo (Kundu and Surh, 2008; Singh et al., 2015). Curcumin is a major bioactive component isolated from the rhizomes of Curcuma longa L. (Zingiberaceae) and has been the scope of many publications for its wide
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range of pharmacological activities but limited clinical applications due to its poor aqueous solubility, multidrug pump P-pg efflux, extensive in vivo metabolism and rapid elimination
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(Patil et al., 2015). The anti-cancer activity of curcumin has been exhibited in breast, ovarian, colon and lung cancer (Saxena and Hussain, 2013; Patil et al., 2015; Yang et al., 2015; Wang et al., 2016).
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nanotechnology is a fast developing science gaining more grounds in medicine and therapy everyday. Nanoparticles have emerged as versatile nano-carriers for the specific and
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targeted delivery of drugs to organs and tissues which is particularly relevant in cancer therapy where most of the biological processes occur at the nanometer level (Li et al., 2012).
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Hence, the nanoparticle nanotechnology has been greatly appreciated as a potential tool for cancer diagnosis and treatment (Bharali et al., 2011). In this context, the search for the optimum smart materials, that can best accommodate the
active chemotherapeutic agents at the nano level, is an ongoing process. Accordingly, protein carriers have been sought of due to their many advantages including their biodegradablility, biocompatiblity, non-antigenicity, low cost and availability. Moreover, the surface of protein nanoparticles can be modified with site-specific ligands, cationized with amine derivatives or coated with polyethyl glycols to achieve targeted and sustained release drug delivery (Hathout and Omran, 2016). Compared to other colloidal carriers, protein nano-carriers are 3
ACCEPTED MANUSCRIPT better stable in biological fluids to provide the desired controlled and sustained release of entrapped drug molecules (Sahoo et al., 2015). Amongst the commonly used proteins, albumin and gelatin reside at the top due to their abundant resources. To this end, a usual question rises; what is the optimum proteinaceous carrier material for a specific drug? The conventional answer could be obtained by testing several protein-drug pairs in the wet laboratories till reaching the most suitable carrier. Nevertheless, another question has been raised; can computers and softwares, specifically, chemoinformatics tools,
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answer this question and thereby replace the exhausting and resources consuming wet-lab trials? We have recently answered this question and confirmed the possible usage of several chemo-informatics tools in optimizing and predicting the loading of drugs in several carriers (Metwally and Hathout, 2015a; Metwally and Hathout, 2015b).
Therefore, in the current study we hypothesize that chemoinformatics tools such as
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molecular dynamics simulations utilized to build virtual protein nano-carriers followed by the molecular docking of the investigated phytochemicals on these virtually built carriers can select the optimum protein-based nanoparticles material that can best accommodate naturally discovered chemotherapeutic agents (polyphenolic compounds having close chemical
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structures). This included the phytoalexin; resveratrol, and the diferuloylmethane; curcumin, (Figure 1) based on their physical and chemical potential interactions with the proposed
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nano-carriers. Several software packages and docking scores were evaluated in order to validate the hypothesis. In order to test this hypothesis, the mass of the loaded polyphenol per 100 mg carrier of resveratrol on each of albumin and gelatin nanoparticles was collected from
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the literature. Similarly, the loaded mass of curcumin on albumin nanoparticles was obtained. To the best of our knowledge, curcumin-gelatin nanoparticles, was not previously produced,
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and hence, we attempted to prepare this new formulation to compare to the curcumin-albumin counterpart.
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[Please insert Figure 1 here]
2. Methodology 2.1. Materials. Curcumin, gelatin, gluteraldehyde solution (25%, w/v) and glycine were purchased from Sigma-aldrich, Taufkirchen, Germany. Dimethyl Sulphoxide (DMSO), Trypan blue and MTT were purchased from Sigma (St.Louis, MO, USA). Fetal Bovine serum, DMEM, RPMI-1640,
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ACCEPTED MANUSCRIPT HEPES buffer solution, L-glutamine, gentamycin and 0.25% Trypsin-EDTA were purchased from Lonza, Belgium.
2.2. Methods. 2.2.1. Data mining using scientific literature databases. The mass of drug loaded per 100 mg protein of 2 different drugs; resveratrol (Li et al., 2012;
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Karthikeyan et al., 2013) and curcumin (Jithan et al., 2011) entrapped in protein-based nanoparticles were gathered from the scientific literature databases using PubMed, Scopus and Web of Science.
2.2.2. Preparation of curcumin-loaded gelatin nanospheres.
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Curcumin-loaded gelatin nanospheres were prepared using the single desolvation method (the same method that was previously used to prepare the curcumin-loaded albumin nanoparticles (Jithan et al., 2011)). Briefly, 10 mg curcumin were dissolved in 3 ml absolute ethanol. Meanwhile, gelatin (200 mg) was dissolved in 2 ml de-ionized water at 40⁰C. The alcoholic
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solution was then added to the aqueous solution drop-wise till a colloidal dispersion was obtained. Afterwards, 0.03 ml gluteraldehyde (25%, w/v) were added to the dispersion to
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cross-link the formed nanoparticles and prevent their swelling. Stirring for 1 hour was performed. Consequently, the colloidal dispersion volume was completed to 25 ml using deionized water. Finally, the prepared nanospheres were separated by centrifugation at
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13,000 rpm for 25 minutes (Abozeid et al., 2016).
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2.2.3. Characterization of the prepared curcumin-loaded gelatin nanospheres. 2.2.3.1. Particle size measurements.
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The particle size and polydispersity index of the prepared polyphenol-loaded nanospheres was measured using dynamic light scattering (DLS) (Malvern zetasizer, Nano ZS, Malvern, Worcestershire, UK) after re-dispersion of the centrifuged pellets in de-ionized water. The measurements were performed in triplicates.
2.2.3.2. Transmission electron microscopy (TEM). The morphology and the shape of the prepared nanoparticles were examined using a JEM1400 (JEOL, Tokyo, Japan) transmission electron microscope at a voltage of 100 kV. The
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2.2.4. Determination of the mass of loaded curcumin in the prepared gelatin nanospheres. The mass of the loaded curcumin per 100 mg of the prepared gelatin nanoparticles was obtained by measuring the mass of the un-entrapped dissolved and precipitated amount of the drug. An aliquot of 1 ml of the supernatant was used to measure the un-entrapped drug
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colorimetrically at 427 nm (Shimadzu, Japan). For measuring the precipitated counterpart, the supernatant was discarded. After, an amount of 2 ml absolute ethanol was added to the centrifuged part containing the precipitated curcumin together with the prepared nanoparticles to selectively dissolve the curcumin. An aliquot of 1 ml was taken from the supernatant, diluted to a final volume of 10 ml with absolute ethanol and subsequently
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measured colorimetrically at a wavelength of 427 nm.
The mass of the loaded curcumin was determined as follows:
)
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( )
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(
(
2.2.5. Confirming the cytotoxic activity of curcumin gelatin nanoparticles using MTT assay.
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2.2.5.1. Mammalian cell lines.
MC7-F cells (human breast cancer cell lines) were obtained from the American Type Culture
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Collection (ATCC, Rockville, MD). 2.2.5.2. Cell-line propagation. The cells were grown on RPMI-1640 medium supplemented with 10% inactivated fetal calf
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serum, 1% L-glutamine, HEPES buffer and 50 µg/ml gentamycin. These cells were maintained at 37 ⁰C in a humidified atmosphere enriched with 5% CO2 and were subcultured two to three times a week. 2.2.5.3. Cytotoxicity evaluation using the viability test. The tumor cell lines were suspended at a concentration of 5
104 cell/well in Corning® 96-
well culture plates and was then incubated for 24 hours. After, the tested formulation was added (in triplicates) into the 96-well plates in order to achieve different concentrations of the tested compound in the formulation. Six vehicle controls with media or with 0.5% DMSO 6
ACCEPTED MANUSCRIPT were run for each of the 96 well plates as a control. After incubating for 48 hours, the number of viable cells was determined using the MTT assay. Briefly, the culture medium was removed from the 96 well plate and replaced with 100 µl of fresh culture RPMI-1640 medium without phenol red then 10 µl of the 12 mM MTT stock solution (5 mg MTT in 1 ml of PBS) to each well including the untreated controls. Then, the 96 well plates were incubated at 37⁰C for 5% CO2 for 4 hours. After, an 85 µl aliquot of the media was removed from the wells, and 50 µl of DMSO was added to each well and mixed thoroughly with the
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pipette and incubated at 37⁰C for 10 minutes. For the quantitative determination of the viable cells, the optical density was measured at 590 nm with a microplate reader (SunRise, TECAN Inc. USA). The percentage viability was calculated as follows: ( )
(
)
Equation (2)
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where, ODt is the mean optical density of wells treated with the tested formulation and ODc is the mean optical density of untreated cells. The relation between the percentage of surviving cells and the drug concentration on the tested formulation was plotted to get the survival curve in the specified cell-line. Accordingly, the 50% inhibitory concentration (IC50), which is the concentration required to cause a cytotoxic effect in 50% the intact cells was estimated
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from the graphical plots of the dose response curve using GraphPad Prism software (San
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Diego, CA, USA) (Mosmann, 1983).
2.2.6. Obtaining the bovine serum albumin virtual matrix.
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The crystal structure of bovine serum albumin protein (4F5S) was obtained from the protein
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data bank (http://www.rcsb.org).
2.2.7. Construction of the gelatin nanospheres matrix virtually using molecular dynamics simulations.
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All-atom molecular dynamics simulations were carried-out using GROMACS (Pronk et al., 2013) v4.6.5 software package. The parameters of gelatin were obtained using CgenFF (Vanommeslaeghe et al., 2010) available online (https://cgenff.paramchem.org/). The system contained 48 molecules each is composed of an 18 amino-acids peptide, representing gelatin, with amino acids sequence ALA, GLY, PRO, ARG, GLY, GLU, PRH, GLY, PRO, ALA, GLY, PRO, ASP, GLY, GLU, PRH, GLY, and PRO. The system was subjected to energy minimization using the steepest descent method. The systems was then subjected to a molecular dynamics run, with a time step of 2 fs, full periodic boundary conditions, and a 7
ACCEPTED MANUSCRIPT cut-off distance for Van der Waal’s and electrostatic interactions of 1.2 nm. PME was chosen to treat long-range electrostatic interactions. The LINCS algorithm was used to constrain all bonds. The system was equilibrated at 373 K using a v-rescale thermostat, and at a pressure of 1 bar using a Berendsen barostat for 3 ns.
2.2.8. Preparing the drugs chemical structures for docking. The isomeric SMILES corresponding to the chemical structures of the studied drugs were
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obtained using PubChem®. Subsequently, the corresponding 3D chemical structures were constructed using the builder function of MOE® version 2014.0901 software (Chemical Computing Group Inc., Montreal, Canada) (Hathout and Metwally, 2016). Further, energy minimization was carried out for all the investigated molecules using MMFF94x forcefield of
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the same software (Costache et al., 2009; Gooding et al., 2014).
2.2.9. Docking of the literature-gathered drugs on the investigated carrier. The docking analysis was employed using MOE version 2014.0901 (Chemical Computing Group Inc., Montreal, Canada). The pdb file of the protein nanoparticles matrix was imported
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to MOE where the identification of the binding site was performed using the MOE's "Site finder" tool (Elhefnawi et al., 2012). Subsequently, the docking was accomplished utilizing
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the "triangle matcher” as a placement method.
This software creates dummy atoms around the docking target atoms. These dummy atoms are considered the docking positions. The London ΔG and ASE scores were utilized for
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calculating the binding energies scoring values. The London ΔG scoring function estimates
Equation (3)
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terms:
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the free energy of binding of the ligand from a given pose. The functional form is a sum of
where c represents the average gain/loss of rotational and translational entropy; Eflex is the energy due to the loss of flexibility of the ligand (calculated from ligand topology only);fHB measures geometric imperfections of hydrogen bonds and takes a value in [0,1]; cHB is the energy of an ideal hydrogen bond; fM measures geometric imperfections of metal ligations and takes a value in [0,1]; cM is the energy of an ideal metal ligation; and Di is the desolvation energy of atom i. The difference in desolvation energies is calculated according to the formula
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Equation (4) where A and B are the protein and/or ligand volumes with atom i belonging to volume B; Ri is the solvation radius of atom i (taken as the OPLS-AA van der Waals sigma parameter plus 0.5 Å); and ci is the desolvation coefficient of atom i. The coefficients {c, cHB, cM, ci} were fitted from ~400 x-ray crystal structures of protein-ligand complexes with available
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experimental pKi data. Atoms are categorized into about a dozen atom types for the assignment of the ci coefficients. The triple integrals are approximated using Generalized Born integral formulas.
Like all commonly used scoring functions, lower binding energies (ΔG, kCal/mole) scores indicate more favourable interactions.
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On the other hand, the ASE score works in a different way. This score is proportional to the sum of the Gaussians R1R2 exp (-0.5d2) over all ligand atom - receptor atom pairs and ligand atom - alpha sphere pairs. R1 and R2 are the radii of the atoms in Å, or is -1.85 for alpha
3. Results and Discussion 3.1. Data mining results.
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spheres. ‘d’ is the distance between the pair in Å.
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Table 1 shows the loaded masses of the investigated drugs; resveratrol and curcumin per 100 mg of the studied protein nano-carriers as calculated from literature (Jithan et al., 2011; Li et
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al., 2012; Karthikeyan et al., 2013). To our knowledge, since the drug-nanocarrier pair corresponding to the curcumin-gelatin nanospheres has not been previously prepared, we
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endeavoured in this study to prepare this combination. By this, the comparison of curcumin loading on albumin and gelatin protein nanoparticles could be completed virtually (using
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chemoinformatics tools) and experimentally in the wet-lab. Moreover, this novel curcumin formulation was characterized, and evaluated for its cytotoxic activity on MCF-7 breast cancer cell lines. [Please insert Table 1 here]
It is worth noting that the results of the mass of curcumin loaded per 100 mg albumin obtained from Jithan et al. (Jithan et al., 2011) was estimated to be 10 mg approximately when prepared using the same initial amount. This was inferred from this literature finding that almost complete loading was observed for the curcumin per 100 mg BSA regardless its 9
ACCEPTED MANUSCRIPT initial added amount in the range of 50-100 mg. Logically, an amount of 10 mg would be easily completely entrapped, as well.
3.2. Particle size measurements of the prepared curcumin-loaded gelatin nanospheres. The prepared curcumin loaded gelatin nanospheres exhibited particle size of a 315 ± 0.376 nm, a value which conforms with the ranges usually reported in literature for the gelatin nanospheres particle size (Kuntworbe and Al-Kassas, 2012; Khatik et al., 2014). The
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recorded polydispersity index was 0.376 ± 0.041. This relatively high observed value is usually associated with the single desolvation preparation method (Li et al., 2012; Hathout and Omran, 2016) due to the heterogeneity of the molecular weight of the gelatin (Coester et al., 2000). Figure 2 demonstrates the obtained P.S. and PDI of the prepared nanospheres. However, nanoparticles of the range of 200-350 nm has proven successful in many animals
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and human studies especially through the pulmonary, nasal and transdermal routes (Bhaskar et al., 2009; Bolhassani et al., 2014; Muralidharan et al., 2015; Zhao et al., 2016). [Please insert Figure 2 here]
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3.3. Morphology study of the prepared curcumin-loaded gelatin nanospheres. The morphology of the prepared nanoparticles was studied using TEM imaging. Figure 3
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demonstrates the spherical structure of the particles which usually leads to better cellular uptake (Abozeid et al., 2016). Figure 3 (a) reveals the prepared matrix structure of the gelatin nanoparticles confirming the preparation of the nanospherical rather than nanocapsular type
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of the nanoparticles. Moreover, the photographed particles exhibited particle sizes
[Please insert Figure 3 here]
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conforming to a great extent with the DLS particles size values as obvious in Figure 3 (b).
3.4. Cytotoxic evaluation of the prepared curcumin gelatin nanospheres. The biological cytotoxic evaluation in breast cancer cell lines was warranted since the curcumin-gelatin as a cargo-carrier combination is a newly addressed formulation pair (Mehanny et al., 2016a). Fig. 4 depicts the dose-response curve of curcumin gelatin nanospheres. This curve was utilized to calculate the IC50 which scored a value of 64.8 µg/ml. The activity of pure curcumin on MCF-7 cell-lines was previously confirmed (Banerjee et al., 2010) and the IC50 of curcumin after 48 hours incubation was specifically determined as 53.18 µg/ml (Khazaei 10
ACCEPTED MANUSCRIPT et al., 2015). A negative control comprising the media only was tested and scored 100% viability. Another positive control of 10% DMSO was tested and resulted in 0% viability. Moreover, the plain gelatin nanoparticles were also tested where they scored an IC 50 of 2.9 mg/ml. This high value is ascribed to the high safety margin and biocomptability of gelatin. The only source of slight toxicity in gelatin nanoparticles is usually attributed to the presence of gluteraldehyde; the used cross-linking agent (Hathout and Omran, 2016). The closeness of the IC50 of curcumin in gelatin nanospheres to its pure curcumin counterpart
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indicates the potency of this newly prepared formulation, bearing in mind the slow and sustained release of cargos from gelatin nanoparticles matrices (only 40 – 60 % cumulative release after 48 hours) especially after cross-linking with strong chemical compounds such as gluteraldehyde and in absence of collagenase enzyme that is responsible for gelatin metabolism in-vivo (Vandervoort and Ludwig, 2004; Kuntworbe and Al-Kassas, 2012;
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Abozeid et al., 2016). Augmenting the cytotoxic effect using gelatin nanoparticles is usually attributed to the better cellular internalization of this kind of particles using endocytosis mechanism (Ishikawa et al., 2012; Abozeid et al., 2016). The nano-range particle size was previously proven to cause better nanoparticles uptake and internalization. Expectedly, the
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smaller the nanoparticles diameter, the better internalization and hence the resultant cytotoxic effect (Panyam and Labhasetwar, 2003; Bivas-Benita et al., 2004; Farid et al., 2014).
will result in lower IC50.
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Accordingly, further attempts to decrease the prepared curcumin-loaded gelatin nanoparticles
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[Please insert Figure 4 here]
3.5. Docking results of the investigated drugs on the virtual nano-carriers.
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The crystal structure of bovine serum albumin was readily obtained from the protein data bank while the other carrier; gelatin was successfully simulated using molecular dynamics as
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revealed in Figure 5.
[Please insert Figure 5 here]
Table 2 shows the results of docking the two investigated drugs; resveratrol and curcumin on the two studied carriers; albumin and gelatin. It is obvious from the results, the superiority of albumin in accommodating more masses of both drugs. This can be inferred from the obtained lower binding energies in case of docking the two investigated polyphenols on albumin compared to the gelatin matrix. These results can be attributed to the higher hydrophobicity of bovine serum albumin in comparison with the denatured gelatin (Bull, 1937; Takahashi et al., 1993). Logically, since resveratrol and 11
ACCEPTED MANUSCRIPT curcumin are poorly soluble hydrophobic drugs therefore they possess higher affinity to the more hydrophobic carrier; bovine serum albumin. [Please insert Table 2 here]
Recently, we have proven the contribution of other factors (descriptors), as well, in the loading efficiencies of drugs on different carriers (Tripalmitin solid lipid nanoparticles and PLGA nanoparticles) (Metwally and Hathout, 2015a) where the drug molecular weight, total
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polar surface area and fragment complexity (including all non-hydrogen bonds, halogen atoms and the overall number of bonds in the drug structure) have shown significant difference on the mass of loaded drug per 100 mg carrier. All these factors play together resulting in a specific drug loading value (Ramezanpour et al., 2016).
The results in Table 2 conform to the reported and obtained results in Table 1 in an excellent
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way demonstrating the validity of our hypothesis.
Figure 6 demonstrates the successful docking of resveratrol on the two investigated carriers. [Please insert Figure 6 here]
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Similarly, Figure 7 depicts the success of docking curcumin on both carriers. The docking results demonstrated several interactions where an obvious hydrogen bonding between the
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drug and the bovine serum albumin can be observed. [Please insert Figure 7 here]
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4. Conclusions and Future Perspectives The work in this study continues in a series that presents a strong relationship between the
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masses of loaded drugs that are obtained experimentally and their computed binding energies resulting from their interactions with the simulated virtual carriers. The ultimate goal of these
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kinds of studies is to replace the highly tedious, time-consuming and resources-wasting wetlab experimentation with the addressed chemoinformatics tools; molecular dynamic simulations and molecular docking. Moreover, the current study introduced a new curcumin carrier formulation; gelatin nanoparticles intended for the treatment of breast cancer. However, according to the experimental and chemo-informatics tools that are used in this work, bovine serum albumin should be better recommended (compared to gelatin, though still successful) when a protein nano-carrier is warranted for polyphenolic compounds such as: resveratrol and curcumin.
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ACCEPTED MANUSCRIPT Conflict of Interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
Figures Captions
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Figure 1. The chemical structure of (A) Resveratrol and (B) Curcumin. Figure 2. Particle size and PDI measurement of the prepared curcumin-loaded gelatin nanospheres.
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Figure 3. Transmission electron microscope imaging of the prepared curcumin-loaded 19 gelatin nanospheres. (A) Under 25000X magnification, (B) Under 20000X magnification and (C) Under 12000X magnification. Figure 4. MCF-7 cell viability results of curcumin gelatin nanospheres. (Error bars represent Mean ± S.D.). Figure 5. (A) The crystal structure of Bovine serum albumin and (B) The molecular dynamics simulated gelatin.
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Figure 6. Docking of resveratrol on (A) Bovine serum albumin and (B) Gelatin.
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Figure 7. Docking of curcumin on (A) Bovine serum albumin and (B) Gelatin.
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Table (1).
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11, e0148848.
Mass of the loaded investigated polyphenols per 100 mg of the protein nano-carrier. Mass loaded per 100 mg protein nano-carrier (mg) Drug Resveratrol Curcumin * The result was estimated from ref. [10].
Gelatin
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Albumin 39.4
1.96
10*
3.5
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50 mg drug was almost completely loaded in 100 mg carrier regardless of the drug : polymer ratio so 10 mg are estimated to be fully loaded as well.
Table (2).
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The obtained binding energies after docking the investigated polyphenols on the virtual protein nano-carriers.
Albumin
Drug
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Binding energies (Kcal/mole) Gelatin
London ΔG score
ASE score
London ΔG score
ASE score
Resveratrol
-9.5 ± 0.5
-14.2 ± 0.1
- 8.3 ± 0.2
-9.21 ± 0.1
Curcumin
-13.2 ± 2.1
-21.28 ± 0.2
- 8.11 ± 0.1
-19.32 ± 0.01
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Fig. 7
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Graphical abstract
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