key: cord-0707081-io5ewb0r
authors: Elsayed Khate, Shaymaa; El-khouly, Ahmed; Mohamed Abdel-Ba, Hend; Mohsen Al-mahallawi, Abdulaziz; Mahmoud Ghorab, Dalia
title: Fluoxetine hydrochloride loaded lipid polymer hybrid nanoparticles showed possible efficiency against SARS-CoV-2 infection
date: 2021-08-18
journal: Int J Pharm
DOI: 10.1016/j.ijpharm.2021.121023
sha: 9c3d4ce0166c1327916aa3dba8036bc27eed6b60
doc_id: 707081
cord_uid: io5ewb0r

Up to date, there were no approved drugs against coronavirus (COVID-19) disease that dangerously affects global health and the economy. Repurposing the existing drugs would be a promising approach for COVID-19 management. The antidepressant drugs, selective serotonin reuptake inhibitors (SSRIs) class, have antiviral, anti-inflammatory, and anticoagulant effects, which makes them auspicious drugs for COVID 19 treatment. Therefore, this study aimed to predict the possible therapeutic activity of SSRIs against COVID-19. Firstly, molecular docking studies were performed to hypothesize the possible interaction of SSRIs to the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-COV-2) main protease. Secondly, the candidate drug was loaded in lipid polymer hybrid (LPH) nanoparticles to enhance its activity. The studied SSRIs were Fluoxetine hydrochloride (FH), Atomoxteine, Paroxetine, Nisoxteine, Repoxteine RR, and Repoxteine SS. Interestingly, FH could effectively bind with SARS-COV-2 main protease via hydrogen bond formation with low binding energy (-6.7 kcal/mol). Moreover, the optimization of FH-LPH formulation achieved 65.1±2.7% encapsulation efficiency, 10.3±0.4% loading efficiency, 98.5±3.5 nm particle size, and -10.5±0.45 mV zeta potential. Additionally, it improved cellular internalization in a time-dependent manner with good biocompatibility on Human lung fibroblast (CCD-19Lu) cells. Therefore, the study suggested the potential activity of FH-LPH nanoparticles against the COVID-19 pandemic.

COVID-19 instigate severe acute respiratory syndrome caused by infection of coronavirus 2 (SARS-CoV2) (Gil et al., 2020) . The ability of both symptomatic and asymptomatic cases to spread COVID-19 infection makes it a pandemic disease in just a few months , which has negatively affected the global economy (Udugama et al., 2020) . Consequently, more efforts are needed to find new innovative approaches that could fundamentally change our understanding and management of this disaster (Nicola et al., 2020) .

SARS-COV-2 consisted of a single-strand positive Ribonucleic acid (ssRNA) genome that was enveloped within a membrane and surrounded by glycoprotein spikes (S-protein) with a crown-like appearance . The viral cellular penetration is prompted by the interaction of the viral spike (S) glycoprotein with the angiotensin-converting enzyme 2 (ACE2) receptor . Consequently, host transmembrane serine protease 2 cleaves the S protein to fuse to the host cell membrane and internalizes it via the endocytic pathway (Hoffmann et al., 2020) .

Moreover, the viral genome was split by the main proteases enzymes including 3 carbonlike proteases (3CLpro) and the papain-like protease (PLpro) to yield nonstructural proteins (nsp2−16), which are essential for replication−transcription complex formation (V'kovski et al., 2019) . Additionally, RNA-Dependent RNA Polymerase mediates the transcription and replication of the viral RNA genome through infection (Gil et al., 2020) . Particularly, all SARS-CoV2 enzymes and proteins that participated in the virus life cycle could be considered as potential targets for the treatment of this crisis (Gil et al., 2020) .

Up to date, there was no approved drug for COVID-19 treatment, so the repurposing of approved drugs could effectively shorten the required time and decreased the cost compared to the new drug discovery (Trezza et al., 2020) . Plenty of drugs have been subjected for drug repurposing to combat COVID-19 either antiviral or non-antiviral supporting agents and miscellaneous drugs (Elmezayen et al., 2020) . Molecular docking is a drug design approach used to determine the interaction of essential amino acids between the targeted protein and the candidate ligands with low binding energy (Carlesso et al., 2019) . Moreover, it predicts the binding affinity and the inhibition efficiency of the candidate ligand for the targeted protein (Huang et al., 2018) .

The selective serotonin reuptake inhibitors (SSRIs) are considered safe and effective antidepressant drugs that are mostly prescribed worldwide for the treatment of major depressive disorder disease (Kennedy et al., 2016) . Recently, several studies examined their activity against . Firstly, an observational study reported the potential association between the administration of certain antidepressant drugs including fluoxetine, venlafaxine, paroxetine, and mirtazapine, and the decrease in risk of intubation or death in hospitalized COVID-19 patients (Hoertel et al., 2020 (Hoertel et al., , 2021c . And this association was confirmed by several observational studies (Diez-Quevedo et al., 2021; Hoertel et al., 2021a Hoertel et al., , 2021b . Secondly, several preclinical in-vitro studies reported the potential activity of fluoxetine alone (Dechaumes et al., 2021; Zimniak et al., 2021) or in combination with antiviral drugs on COVID-19 (Schloer et al., 2021) . Finally, clinical trials suggested that the antidepressant drug, fluvoxamine, can inhibit clinical deterioration in acute COVID-19 outpatients (Lenze et al., 2020; Seftel and Boulware, 2021) .

The activity of antidepressant drugs on COVID-19 may be attributed to their ability to inhibit acid sphingomyelinase enzyme which decreases the release of ceramide on the epithelial cell surface (Gulbins et al., 2013; Kölzer et al., 2004) . Consequently, they can prevent the infection of epithelial cells with COVID-19 as reported by preclinical (Carpinteiro et al., 2021 (Carpinteiro et al., , 2020 and observational studies (Hoertel et al., 2021a (Hoertel et al., , 2021b . Additionally, their sigma-1 receptor agonists effect (Lenze et al., 2020; Rosen et al., 2019; Roumestan et al., 2007; Wang et al., 2019) decreases the elevated cytokine level in COVID 19 patients (Gordon et al., 2020; Hojyo et al., 2020) . Furthermore, they can reduce the plasma level of numerous inflammatory mediators that are associated with COVID-19 (Köhler et al., 2018; Marín-Corral et al., 2021) . Finally, their anticoagulant effect (Halperin and Reber, 2007) makes them the optimum choice for COVID-19 patients who have arterial and venous thrombosis (Hamed and Hagag, 2020) .

Moreover, drug loading in nanocarriers improves its therapeutic efficacy and reduces its side effects (Kumar et al., 2020) . Recently, lipid polymer hybrid (LPH) nanoparticles received great attention in scientific research . It consists of a polymeric core that is surrounded by a single or multiple layers of lipids. Besides, LPH could encapsulate hydrophilic, lipophilic, and amphiphilic drugs with good biocompatibility and easily functionalize with different targeting ligands . Therefore, this study firstly aimed to investigate the binding affinity of the SSRIs to the SARS-COV-2 main protease through the molecular docking study. Secondly, the candidate SSRIs molecule was encapsulated in LPH nanoparticles to improve its cellular uptake by the human lung fibroblast (CCD-19Lu) cells and consequently enhance its therapeutic activity against COVID-19.

(http://autodock.scripps.edu/resources/adt). The 3D crystal structures for SARS-COV-2 main protease in complex with 2-(5-cyanopyridin-3-yl)-N-(pyridine-3-yl) acetamide (5RGW) were retrieved from PDB (https://www.rcsb.org/structure/5RGW). The ligand, water molecules, and heteroatoms were removed, polar hydrogens were added, Kollman charges were added, Gasteiger charges were calculated (Anupama et al., 2019) . Furthermore, the atoms were assigned to AutoDock 4 type and consequently, the enzymes were converted to the PDBQT files using Autodock tools.

Chemical structures and the 3D conformers of the selected SSRIs were downloaded from Pubchem (https://pubchem.ncbi.nlm.nih.gov/) (Fig. S1 ) (FH (CID= 62857), Atomoxteine (CID= 54841), Paroxetine (CID= 43815), Nisoxteine hydrochloride (CID= 134453), Repoxteine RR (CID= 127150), Repoxteine SS (CID= 65856)). The energy of the 3D conformers was minimized by Avogadro (https://avogadro.cc/) using the Merck Molecular Force Field (MMFF94) and saved as a PDB file. Then its torsions were set and converted to PDBQT using AutoDock Tools.

The docking simulations were carried out by AutoDock Vina (http://vina.scripps.edu/) (Allouche, 2012) . Ligands centered with a spacing of 1.0 A˚, size_X = 15, size_Y = 12, size_Z = 15 and center_X = -8.433, center_y = -0.232 and center_z = 20.977. These dimensions and coordination were determined using AutoDock Tools Grid Box according to the co-crystallized ligand coordinates. All visualization of protein-ligand complexes were analyzed using the Autodock tools program and PyMOL molecular graphics program (https://pymol.org/2/) (L DeLano, 2002) . The two-dimensional (2D) schematic diagrams of protein-ligand interactions were generated by the LigPlot version (4.5.3) (https://www.ebi.ac.uk/thorntonsrv/software/LIGPLOT/) (Anupama et al., 2019) .

Different LPH nanoparticles loaded with the selected drug from the docking study were prepared by a modified single-step nanoprecipitation self-assembly technique (Tahir et al., 2019) . Briefly, the polymer (PLGA) and the candidate drug were dissolved in acetonitrile to obtain the organic phase. LEC and tween 80 were dissolved in a 4% v/v hydroalcoholic solution, as the aqueous phase at 65 °C. Consequently, the organic phase was slowly dripped into the aqueous phase while stirring for 2 h at 65 °C. The organic to aqueous phase ratio was kept at 1:9 v/v. The resultant LPH dispersions were centrifuged at 15000 rpm for 15 min at 4 °C. The harvested pellets were washed twice with phosphate buffer saline (PBS pH 7.4), then resuspended in the same medium for further analysis. The fluorescently labeled optimized LPH formula was prepared by dissolving the fluorescent dye (DiI) into the lipid solution at 1% w/w. The candidate drug and PLGA polymer were dissolved in acetonitrile. Lecithin and tween 80 were dissolved in 4% v/v hydroalcoholic solution and heated at 65 °C. The drug-polymer organic solution was slowly dripped into the hydroalcoholic lipid phase with magnetic stirring at 65 °C for 2 h. LPH nanoparticles were harvested by ultrafiltration centrifugation (15000 rpm, 15 min) at 4 °C and resuspended in PBS pH 7.4 for further analysis.

LPH formulae were optimized using BBD by Design-Expert software (Design-Expert 9.0.5.2, State-Ease Inc., USA). The statistical models and the response surfaces were explored from the constructed matrix (Bachhav et al., 2017) . The selected critical process parameters (CPPs) under this study were; the PLGA amount, Drug amount, and the stirring speed were coded as A, B, C respectively. There were three levels for each variable; low (-1), medium (0), and high (+1). The critical quality attributes (CQAs) were particle size (Y 1 ), zeta potential (Y 2 ), and encapsulation efficiency (EE%) (Y 3 ). The quality target product profile (QTPP) of the optimized LPH formula was to achieve minimum particle size, maximum zeta potential, and maximum EE%. The determined CPPs and CQAs, as well as the required QTPP, are listed in Table 1 . Table 1 : Levels of critical process parameters, critical quality attributes, and quality target product profile for the preparation of drug-LPH using the Box-Behnken design. The polynomial equations were statistically validated by ANOVA and all observed responses were fitted to different models (linear, two-factor interactions (2FI) and quadratic). The statistical significance of different models was assessed using various statistical indices as Pvalues, F values, adjusted R 2 , predicted R 2 and predicted residual error sum of squares (PRESS).

The 3D response surface plots were constructed by the software and the polynomial equations were authenticated. According to the highest desirability, the design space was constructed to determine the optimum CPPs required to fabricate the optimized drug-LPH with the targeted QTPP (Al-mahallawi et al., 2019).

The dynamic light scattering (DLS) (Zeta sizer, Malvern Instruments, UK) was used to estimate the particle size, polydispersity index (PDI), and zeta potential of the optimized LPH formula. The measurements were performed using a 90° angle at 25 ºC (Wessam Hamdy Abd-Elsalam et al., 2018; Ahmed et al., 2019) .

The drug EE% was measured indirectly by the ultrafiltration centrifugation method (Ishak et al., 2017) . Briefly, 1mL of the prepared LPH dispersion was added to the Amicon tube ® (30,000 MWCO, Millipore, USA) and centrifuged at 15000 rpm for 15 min at 4 °C. The amount of free unentrapped drug in the filtrate was determined using a previously validated HPLC method (Dionex TM , Thermo Scientific TM , USA). A reverse phase C 18 column (150X4.6 mm, 5 µm, Hypersil ® ODS, USA) and a mobile phase consisting of acetonitrile and deionized water containing 10 mM aqueous triethylamine at a ratio of (55:45 v/v) with a flow rate of 1 mL/ min at 25 °C were employed. The UV detector was set at 226 nm. The calibration curve of the candidate drug in PBS (pH 7.4) in the concentration range of 1-100 µg/mL has a coefficient of determination (R 2 ) equal to 0.9994 with a limit of detection and quantification equal to 0.5 and 1 µg/mL, respectively. Moreover, the coefficient of variation percentage ranged from 2.1% to 4.9% and the accuracy for drug determination was 1.5% to 4.6% with a mean drug recovery percentage of 97.5±1.16%. The EE% and LE% of the candidate drug was calculated using the following equations:

The total amount of drug in dispersion -Amount of drug in the supernatant The total amount of drug in dispersion X 100

The total mass of drug -LPH NPs × 100

The morphological architecture of the optimized LPH was visualized by a transmission electron microscope (TEM) (Jeol, JEM-1230, Japan). Briefly, the examination was performed by depositing a drop of the dispersion on a carbon-coated copper grid (300-mesh) and dried for 10 min. Before imaging, one drop of 2% phosphotungstic acid was applied and dried for 5 min (Wessam H. Abd-Elsalam et al., 2018) . Moreover, the atomic force microscope (AFM) (Wet-SPM 9600, Scanning probe microscope, Shimadzu, Japan) was employed to visualize the topographical image and the 3D surface morphology of the optimized LPH dispersion. Briefly, one drop of the dispersion was deposited on a silicon wafer and air-dried. Consequently, it was scanned using a constant force model. A non-contact mode software was used in recording the AFM images under normal atmospheric conditions (Hamdi et al., 2020) .

The physical state of the candidate drug within the optimized LPH was investigated by the Differential scanning calorimetry (DSC) (Shimadzu Scientific instrument, USA). The free candidate drug, drug-loaded LPH, and blank LPH nanoparticles were accurately weighed and sealed in aluminum pans. The analysis was performed under a nitrogen flow rate of 30 mL/minute to prevent oxidation. The DSC thermograms were recorded at a temperature range of 25 °C-200 °C with a heating rate of 10 °C/ min (Elsherif et al., 2021; Tahir et al., 2019) .

The optimized LPH dispersion was incubated with 10 and 50% v/v fetal bovine serum (FBS) for 4 and 24 h at 37 °C. The in vitro serum stability was evaluated by recording the change in particle size, PDI, and zeta potential (Zhao et al., 2015) .

The dialysis membrane method was employed to assess the in vitro drug release from the optimized LPH formula (El-Gogary et al., 2014) . Briefly, A specified volume of the optimized LPH (equivalent to 2mg of the candidate drug) was diluted with 1mL PBS (pH 7.4). Then it was mixed with FBS (at a final concentration of 10 and 50% v/v) and added to the dialysis membrane (cut off: 10,000 Da). The dialysis bag was sealed and immersed in 25 mL of PBS (pH 7.4) at 37±0.5 °C, and 100 ±0.1 strokes/min. Different aliquots (0.2 mL) were withdrawn at predetermined time intervals and replaced by fresh media. The samples were analyzed using the previously validated HPLC method. The release of free drug was performed under the same conditions as a control. Moreover, the release data were fitted into different kinetics models to determine the possible release mechanism (Pardeshi et al., 2013) . Additionally, the similarity factor (ƒ 2 ) (Eq. 3) was calculated to compare the different release profiles, where the two dissolution profiles were considered similar when the ƒ 2 value is ≥50 (Shah et al., 1998) .

Where R t is the dissolution percentage of the reference (pre-change) formula at time t, T t is the dissolution percentage of the test (post-change) formula at time t, and n is the number of time points (Shah et al., 1998) .

Briefly, the optimized LPH dispersion was initially frozen at -80 °C, then primarily dried by heating the samples to -40 °C at pressure 100 µbar. The secondary drying step was performed at a temperature of 20 °C and pressure of 20 µbar." The freeze-dried optimized LPH formula was obtained after 48 h lyophilization and kept at 4 o C and 25 o C/ 60±5% relative humidity (RH) (WHO, 2017) for 28 days. The change in particle size, PDI, zeta potential, and EE% was evaluated after 7, 14, 21, and 28 days as previously described (Sengel-Turk and Hascicek, 2017).

The hemolytic effect of the optimized LPH dispersion was conducted on fresh red blood cells (RBCs) of male albino rats according to the ethical committee of the faculty of the pharmacy-Cairo university with license number (PI 2077). Briefly, the blood was withdrawn on a heparinized tube from the tail vein of male albino rats (aged 2-3 months, 200g ±10%). The blood was then centrifugated at 4000 rpm for 10 min. The collected RBCs were subjected to different concentrations of optimized LPH formula (20-200 µg/mL) and incubated for 2 h at 37 °C. After centrifugation of samples (4000 rpm, 5 min) at 4 °C, the absorbance of the supernatant was determined at 545 nm. The negative and positive controls were prepared by incubating RBCs with PBS (pH 7.4) and 0.5% w/v Triton X-100, respectively (Hamdi et al., 2020) . The percentage (%) hemolysis was calculated using the following equation:

Eq. (4) % Hemolysis = absorbance sample -absorbance negative control absorbance positive control -absorbance negative control * 100 2.2.4.9. In vitro cytocompatibility assay.

The in vitro cytocompatibility assay of the optimized LPH formula was assessed on human lung fibroblast (CCD-19Lu) Cells by MTT assay . Briefly, cells were seeded in a 96-well plate (10,000 cells/ well) in the DMEM culture media enriched with 100 mg/mL of streptomycin, 100 units/mL of penicillin, 1% L-glutamine, and 10% heat-inactivated FBS for 24 h at 37 °C. The human lung fibroblast (CCD-19Lu) cells were then treated with serial drug concentrations ranging from 0.01-100 µM and incubated for 72 h. Afterward, the media was replaced with 120 μL of MTT solution for 4 h at 37 °C and 5% CO 2 . The obtained formazan crystals were dissolved by a 200 μL of DMSO and then the absorbance was measured at 570 nm using a plate reader (ChroMate-4300, FL, USA). The percentage of cell viability was calculated using the following equation: After incubation, the cells were washed twice with PBS (pH 7.4) and trypsinized for 5 min. The obtained cells were centrifuged (1750 rpm, 3 min) at 4 °C and re-suspended into PBS (pH 7.4).

The fluorescence intensity was measured using the FL-2 detector and the collected data were scrutinized using the FlowJo software (Oh et al., 2018) .

All experiments of the present study were conducted in triplicates and the results are expressed as the mean ± SD. The difference between the two variables was compared by the Student t-test while the difference between groups was assessed by ANOVA followed by the Tukey HSD test. The SPSS 18 (Chicago, USA) was applied to assess the statistical analysis and the differences were considered significant at (p) value <0.05.

Among several available crystal structures of SARS-COV-2 main protease in the PDB, The (5RGW) crystal structure was selected as the size of a co-crystalized ligand is quite similar to the size of the compounds to be docked and correct fitting in the pocket, i.e. ligand in (5RGK) is too small while ligand in (5R82) is out site the binding site (Fig.S2) . Moreover, the docking protocol was validated by redocking the same conformer of the co-crystalized ligand with a Root Mean Square Deviation (RMSD) of 1.021 A° (Fig.S3 ), which indicated a good solution as the calculated RMSD ≤ 2.0 Å (Gohlke et al., 2000) .

Docking and molecular interaction of the selected SSRIs were studied where the best 20 modes of binding with the binding site were recorded and the binding affinity values of the best mode are shown in Table. 2. According to the binding energy, the affinity of different tested SSRIs to the SARS-COV-2 main protease could be arranged as follows paroxetine > reboxetine S, S > FH > reboxetine R, R > Atomoxetine > nisoxetine. Furthermore, Fig 2 shows the binding interactions between different SSRIs and the SARS-COV-2 main protease. Among these compounds, only FH was able to form hydrogen bonding by the interaction of the trifluoromethyl group with the binding site residues as well as hydrophobic interactions while the other compounds only were able to bind hydrophobically. Interestingly, the presence of hydrogen bonds promotes the stability of the interaction of ligands with the active sites of protein (Chen et al., 2016; Patil et al., 2010) . Moreover, Fig.3 shows the 2D and 3D ligand-protein interaction of FH with the binding site of SARS-COV-2 main protease; FH was able to form hydrogen bonding with Histidine 163 ( Fig.3 C, F) and additionally sometimes can form hydrogen bonding with Serine 144 (Fig.3 E) . According to the binding affinities of the studied compounds and the ligand-protein interaction, we concluded that FH could be considered as a potential SARS-COV-2 main protease inhibitor. In this study, the suggested activity of FH on COVID-19 is based on its highest binding affinity to SARS-COV-2 main protease. On the other hand, the recent observational (Hoertel et al., 2021a (Hoertel et al., , 2021b and preclinical (Carpinteiro et al., 2020) consequently, FH could prevent the cytokine storm of COVID-19 that is associated with respiratory failure (SARS), organ failure, and death (Xu et al., 2020; Ye et al., 2020) . Therefore, FH was selected for the forthcoming formulation study to improve its therapeutic activity against COVID-19.

The candidate drug from the molecular docking study, FH, was loaded in LPH by a modified nanoprecipitation self-assembly method (Tahir et al., 2019) . During this approach, the addition of the organic phase to the aqueous phase decreased the interfacial tension. Moreover, the levigation of the two phases produced turbulence which enhanced the diffusion of the watermiscible-organic solvent through the aqueous phase. Consequently, the drug and polymer migrate into the aqueous phase and form a precipitate that is considered a nucleation point for nanoparticles formation (Guterres et al., 2007) .

The self-assembly of the lipids surrounding the produced polymeric core was derived by the hydrophobic interactions between their hydrophobic tail and the polymeric core, while their hydrophilic head would protrude to the external aqueous surrounding forming a homogenous surfactant stabilized LPH (Hadinoto et al., 2013) . Based on the highest adjusted and predicted R 2 values with a difference below 0.2 and the least PRESS value after omitting the non-significant factors (Huang et al., 2004) , the quadratic model was elected as the best fit statistical model for all responses (particle size, EE%, and zeta potential) Tables S1-S3.

The fabricated FH-LPH had a particle size ranging from 110.8 nm to 240.5 nm as shown in Table 3 . All formulae were monodispersed systems with PDI values less than 0.3 (Danaei et al., 2018) . Eq. (6) described the effect of various significant CPPs on the particle size as following:

Y 1 = +214.46 +38.49A +7.62B -5.26C +17.88AB +7.9 AC -17.55A 2 -38.88 B 2 Eq. (6) The regression coefficients of all assessed CPPs and the ANOVA results were listed in Table S4 and S5 , respectively. Based on Eq. (6) and Fig. S4 , it is clear that FH-LPH particle size was positively correlated with PLGA amount (A), and drug amount (B). The increase in polymer and drug amounts increased the viscosity of the organic phase, decreased its evaporation rate, and consequently produced a large particle size (Ravi et al., 2015) . Furthermore, this viscus dispersion decreased the impact of the shear force of stirring and would prevent the breaking of the produced droplets into smaller ones (Gajra et al., 2016) . Additionally, the positive coefficient of the interaction (AB) between PLGA amount and drug amount indicated the enhanced effect of both variables on the viscosity of the organic phase yielding LPH with a large particle size (Fig. 4A) .

On the other hand, the FH-LPH particle size was negatively correlated with stirring speed (C) (Fig. S4) . As the increase in stirring speed generates high mechanical and hydraulic shear that produces nanoparticles of small particle size (Maleki Dizaj et al., 2016) . But the positive coefficient of the interaction (AC) between PLGA amount and the stirring speed indicated the prime effect of PLGA amount on the particle size of LPH, and the particle size was significantly increased (P< 0.05) (Fig. 4B ).

The positive interaction (AB) between PLGA and drug amounts showed a positive effect on FH-LPH particle size. (B) Interaction (AC) between PLGA amount and stirring speed showed a positive effect on FH-LPH particle size.

The obtained FH-LPH had a drug EE% varied between 41.5% and 73.1% as shown in Table S7 shows the ANOVA results. Eq. 7 shows a positive correlation between all factors' effects and EE% as shown in Fig. S5 . The PLGA amount (A) had the highest positive significant effect on the drug EE% (P< 0.05) because it formed a large core that incorporates large amounts of the drug (Gajra et al., 2015) . Moreover, the higher PLGA amount increased the viscosity of the organic phase and produced a large particle size, which would prevent the production of porous particles with the acquirement of a longer diffusion path for the drug that enhances the EE% (Lalani et al., 2012) . These results were in agreement with Hamdi et al, and Tahir et al, who reported that the increase in PLGA amount increased EE% of entecavir and doxorubicin respectively (Hamdi et al., 2020; Tahir et al., 2019) .

Additionally, the rise in drug amount (B) (a hydrophilic drug, pKa= 10, Log p= 1.2) (Kwon and Armbrust, 2008) would compensate for its leakage from the prepared nanoparticles and significantly increased its EE% (P< 0.05) (Fig. S5 ) (Song et al., 2008) . Furthermore, the rise in stirring speed (C) significantly increased the FH EE% ( Fig. S5) 5A ). As the (AB) interaction is associated with the formation of large particle sizes (Fig. 4A) ,

which enhances the encapsulation of large amounts of the drug (Gajra et al., 2015) . Moreover, the positive coefficient of the interaction (BC) between drug amount and the stirring speed indicated the significant (P< 0.05) synergistic effect of their interaction on FH EE% (Fig. 5B ).

The interaction (BC) between drug amount and the stirring speed had a positive influence on FH EE%. Eq. (8) and Fig. S6 illustrated that the absolute value of zeta potential was negatively correlated with PLGA amount (A) and drug amount (B). On the contrary, it was positively correlated with the stirring speed (C). The cationic nature of FH might be responsible for the significant decrease in the absolute value of zeta potential (P< 0.05) (Pham et al., 2018) .

Furthermore, the increase in PLGA amount is associated with higher EE% of the drug (Fig. S5) and consequently, the absolute value of zeta potential was significantly decreased (P< 0.05). On the contrary, the rise in stirring speed (C) significantly decreased the particle size of FH-LPH (P< 0.05) and this might be associated with a high absolute value of zeta potential (Ding and Kan, 2017) .

Additionally, the negative coefficient of both (AB) interaction between PLGA amount and drug amount and (BC) interaction between drug amount and stirring speed showed a significant antagonistic effect of their interactions on the absolute value of zeta potential (P< 0.05) (Fig. 6A and B), respectively. As either (AB) or (BC) interactions associated with higher EE% of the drug (Fig. 5) . 

Design space was constructed to optimize all the investigated CPPs to fulfill QTPP criteria; minimum particle size, maximum zeta potential (absolute value), and maximum EE% (Fig. S7-S9 ). Based on the highest desirability (combined value 0.902), one formula was selected as a checkpoint to authenticate the obtained statistical models. Table 4 and Fig. S10 -S12 illustrate its composition, where the optimum level for each factor was 5 mg, 20 mg, and 1000 rpm for PLGA amount (desirability 1), drug amount (desirability 1), and the stirring speed (desirability 1), respectively. Moreover, the comparison of the observed and predicted values of particle size (Y 1 ) (desirability 1), EE% (Y 2 ) (desirability 0.697), and zeta potential (Y 3 ) (desirability 1) was shown in table 4 with a % predicted error ranged between 0.3 and 3.8%. The small values of the % predicted error verified the applicability of the generated models to optimize FH-LPH (Abdelbary and Aboughaly, 2015). So, the optimized formula, referred to as OFH-LPH was selected for further studies. (Asthana et al., 2015) , for ropinirole hydrochloride (9.9-12.5) (Pardeshi et al., 2013) , and etoposide (8-10%) (Duan et al., 2017) .

The TEM image of the OFH-LPH demonstrated spherical non-aggregated particles with a white polymeric core surrounded by a gray lipid shell (Fig. 7A ) (Mandal et al., 2016) . The particles have a size range of 100-120 nm which was consistent with the DLS measurements.

Furthermore, the 2D and the 3D structure of the OFH-LPH nanoparticles showed scattered particles with a particle size consistent with both DLS and TEM (Fig. 7-B, C) . OFH-LPH showed a core-shell structure with a particle size in accordance with the DLS measurement. Differential scanning calorimetry thermograms of FH, blank LPH, and OFH-LPH (D). FH shows an endothermic peak at 160 °C which is completely disappeared in the thermogram of OFH-LPH. Both OFH-LPH and blank LPH show an endothermic peak at 50 °C that is correlated to the glass transition temperature of PLGA.

The DSC thermograms of FH, blank LPH, and OFH-LPH are illustrated in Fig. 7D .

According to the FH thermogram, FH has a crystalline nature with a melting point at 160 °C as previously reported (Silva et al., 2007) . The endothermic peak of FH was completely disappeared in the DSC thermogram of OFH-LPH indicating the transformation of the drug into the amorphous state as a result of its encapsulation in the LPH nanoparticles (Ishak et al., 2017; Mandal et al., 2016) . Moreover, the PLGA polymer prevents the recrystallization of the drug during the preparation of LPH nanoparticles (Mandal et al., 2016) . Additionally, both blank LPH and OFH-LPH thermograms represent an endothermic peak at 50 °C which is correlated to the glass transition temperature of PLGA (Ishak et al., 2014) which indicated a negligible effect of formulation procedures on PLGA (Sanna et al., 2015) .

Fig. 8A-C shows the effect of serum incubation on particle size, PDI, and zeta potential of OFH-LPH. The OFH-LPH was stable in 10% v/v FBS with a non-significant effect on particle size, PDI, and zeta potential at all tested time points (P> 0.05). Moreover, the presence of 50% v/v FBS non-significantly affects the particle size, PDI, and zeta potential of OFH-LPH (P> 0.05) after 4 h incubation. But after 24 h incubation, there was a significant increase in the particle size, PDI, and zeta potential of OFH-LPH (P< 0.05).

The negativity of the OFH-LPH surface (-10.5 mV) might be sufficient to prevent the adsorption of a low concentration of serum proteins (10% v/v FBS) by the electrostatic repulsion (Zhao et al., 2015) . Contrarily, the presence of a high concentration of serum proteins (50% v/v FBS) for a long incubation time (24 h) might enhance the adsorption of a large number of serum proteins to the OFH-LPH surface and consequently, the particle size, PDI, and zeta potential were increased.

The effect of serum incubation on particle size, PDI, and zeta potential of OFH-LPH. The OFH-LPH was incubated with 10 and 50% v/v FBS for 4, and 24 h then particle size (A), PDI (B), and zeta potential (C) were measured using DLS as described before. Data points represent mean and SD (n=3). Statistical analysis was carried out using one-way ANOVA followed by the Tukey HSD test and P<0.05 was considered significant. Serum proteins had a nonsignificant effect on OFH-LPH particle size, PDI, or zeta potential at 10% v/v FBS (P˃0.05), but they had a significant effect on OFH-LPH particle size, PDI, or zeta potential at 50% v/v FBS after 24 h (P<0.05). attributed to their architecture as they consist of a polymeric core that encapsulates the drug and a lipid shell that inhibits the drug leakage (Date et al., 2018) . Besides, it reduces the water penetration to the polymeric core and consequently decreases its hydrolysis rate (Hadinoto et al., 2013) . On the other hand, 50% v/v FBS increased FH release and achieved 100% release after 24 h with ƒ2 value of 42.5%. The high concentration of serum proteins and lipase enzyme might induce the hydrolysis of the lipid shell and consequently enhances the polymeric core dissolution and increases the drug release (Mat Azmi et al., 2015) .

The application of different kinetics models to the in vitro release data revealed that the diffusion mechanism was attained for the FH release profile in PBS pH7.4 with/without 10% v/v FBS with an R 2 value of 0.9969 and 0.9972, respectively. On the other hand, the FH release profile in the presence of 50% v/v FBS followed the Korsmeyer-Peppas model with an R 2 value of 0.9989 and a release exponent value of n= 0.773. Therefore, it represents a non-fickian diffusion, that resulted from the combination of both drug diffusion and polymer dissolution (Costa and Sousa Lobo, 2001; Ho et al., 2017) . These results were in agreement with those of Tahir et al., who reported that the doxorubicin hydrochloride release pattern from LPH obeyed the diffusion mechanism (Tahir et al., 2019) . Positive and negative controls were 0.5% w/v Triton X-100 and PBS (pH 7.4), respectively. Samples were centrifuged at 4000 rpm for 5 min at 4 °C and the absorbance of the released hemoglobin was determined at 545 nm. Datapoint represents mean and SD (n=3). The dotted line represents the acceptable hemolysis range. OFH-LPH is a biocompatible formula.

A positive correlation between OFH-LPH concentration and hemolysis % (Fig. 9B ) could be attributed to the presence of tween 80 (Ishak et al., 2017; Sun et al., 2017) . However, the % hemolysis at all tested concentrations did not exceed the acceptable limit of nanocarriers (5%)

according to the new consensus ASTM E2524-08-Standard test (Ishak et al., 2017) . Therefore, the prepared OFH-LPH could be considered a biocompatible platform.

The stability study of the OFH-LPH formula was performed on the freeze-dried form to decrease accumulation and avoid leakage of drug from the polymeric core and lipid layer during storage (Dave et al., 2017; Yuan et al., 2018) . At different time intervals 7, 14, 21, and 28 days, the freeze-dried formula was redispersed in deionized water and characterized for its morphology.

It has a non-significant change in its particle size, PDI, zeta potential, and EE% after 28 days of storage at 4 o C and 25 o C/ 60±5% RH (P> 0.05) as illustrated in Table S10 . Furthermore, these results were in agreement with the previous studies (Dave et al., 2017; Sengel-Turk and Hascicek, 2017) ."

3.3.8. In vitro cytocompatibility assay. 

The cellular uptake quantification of the DiI-labeled OFH-LPH (50 nM) by the Human lung fibroblast (CCD-19Lu) cells was assessed for 4 and 24 h. Where DiI is a fluorescence lipophilic carbocyanine dye that has a high fluorescence efficiency and photo-stability (Cheng et al., 2014) . Additionally, it successfully labeled different lipidic and polymeric nanoparticles (Hamdi et al., 2020; Mousseau et al., 2019; Snipstad et al., 2017) . The fluorescence was highly intensified following the treatment of cells with the DiI-labeled OFH-LPH in a time-dependent manner as illustrated in Fig.10A and B. As the longer incubation time improved the cellular uptake by cells (Hamdi et al., 2020) . Moreover, the small particle size of OFH-LPH (98.5 nm) might enhance their entry to the cells via clathrin and caveolin-mediated endocytosis (Panariti et al., 2012; Thorley et al., 2014) . Therefore, LPH could efficiently improve the cellular internalization of FH into Human lung fibroblast (CCD-19Lu) Cells. . Cellular uptake was quantified by mean fluorescence intensity (MFI) using flow cytometry and FL-2 detector (B). LPH uptake was intensified in a time-dependent manner. Data points represent mean and SD (n=3). Statistical analysis was carried out using one-way ANOVA followed by the Tukey HSD test and P<0.05 was considered significant.

In this study, we investigate the binding affinity of different SSRIs drugs to the SARS-COV-2 main protease to assess their potential therapeutic activity against COVID-19. Among the studied drugs, FH had a promising SARS-COV-2 main protease inhibitory activity with a binding energy of (-6.7 kcal/mol) and a hydrogen bonding formation with histidine163 and serine 144 amino acids. Moreover, the encapsulation of FH in the LPH nanoparticle improved its EE%, promote its biocompatibility, and enhance its cellular uptake by the Human lung fibroblast (CCD-19Lu) cells. Therefore the fabricated FH-LPH formula represents a promising therapy for the COVID-19 pandemic. Additionally, these results along with the prior works on FH, reinforce the scientific presumption of FH efficacy in the COVID-19 pandemic, and that large-scale phase 3 trials testing FH and FH-LPH are urgently needed. 

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Author Statement Formal analysis

Writing -original draft: Hend Mohamed Abdel-Bar

Writing -review& editing: Dalia Mahmoud Ghorab

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