Development and characterization of gemcitabine hydrochloride loaded lipid polymer hybrid nanoparticles (LPHNs) using central composite design

Accepted Manuscript Development and characterization of gemcitabine hydrochloride loaded lipid polymer hybrid nanoparticles (lphns) using central composite design Tahir Emre Yalcin, Sibel Ilbasmis-Tamer, Sevgi Takka PII: DOI: Reference:

S0378-5173(18)30462-9 https://doi.org/10.1016/j.ijpharm.2018.06.063 IJP 17610

To appear in:

International Journal of Pharmaceutics

Received Date: Revised Date: Accepted Date:

7 February 2018 28 June 2018 28 June 2018

Please cite this article as: T.E. Yalcin, S. Ilbasmis-Tamer, S. Takka, Development and characterization of gemcitabine hydrochloride loaded lipid polymer hybrid nanoparticles (lphns) using central composite design, International Journal of Pharmaceutics (2018), doi: https://doi.org/10.1016/j.ijpharm.2018.06.063

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DEVELOPMENT AND CHARACTERIZATION OF GEMCITABINE HYDROCHLORIDE LOADED LIPID POLYMER HYBRID NANOPARTICLES (LPHNs) USING CENTRAL COMPOSITE DESIGN

Tahir Emre Yalcin, Sibel Ilbasmis-Tamer, and Sevgi Takka*

Gazi University, Faculty of Pharmacy, Department of Pharmaceutical Technology, 06330 Etiler, Ankara, TURKEY.

*Corresponding Author: Sevgi Takka Fax: +90 312 2127958 Phone: +90 312 2023045 E-mail: [email protected]

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Abstract Lipid polymer hybrid nanoparticles (LPHNs) combine the characteristics and beneficial properties of both polymeric nanoparticles and liposomes. The objective of this study was to design and optimize gemcitabine hydrochloride loaded LPHNs based on the central composite design approach for the treatment of breast cancer. PLGA 50:50/PLGA 65:35 mass ratio (w/w), soya phosphatidylcholine (SPC)/polymer mass ratio (%, w/w) and amount of DSPE-PEG were chosen as the investigated independent variables. The LPHNs were prepared with modified double emulsion solvent evaporation method and characterized by testing their particle size, encapsulation efficiency, and cumulative release. The composition of optimal formulation was determined as 1,5 (w/w) PLGA 50:50/PLGA 65:35 mass ratio, 30% (w/w) SPC/polymer mass ratio and 15 mg DSPE-PEG. The results showed that the optimal formulation gemcitabine hydrochloride loaded LPHNs had encapsulation efficiency of 45,2%, particle size of 237 nm and cumulative release of 62,3% at the end of 24 h. The morphology of LPHNs was found to be spherical by transmission electron microscopy (TEM) observation. Stability studies showed that LPHNs were physically stable until 12 months at 4°C and 9 months at 25°C/60% RH. The results suggest that the LPHNs can be an effective drug delivery system for hydrophilic active pharmaceutical ingredient.

Key Words: Gemcitabine hydrochloride, lipid polymer hybrid nanoparticles, Central Composite Design, stability, transmission electron microscopy

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1. Introduction Liposomes, polymeric nanoparticles, micelles, solid lipid nanoparticles, dendrimers, gold nanoparticles, niosomes, and carbon nanotubes have been commonly used as nanocarriers for many years (van Rijt et al., 2014; Rangsimawong et al., 2016). Among these nanocarrier systems, liposomes and polymeric nanoparticles are two important classes that are currently under investigation. While liposomes and polymeric nanoparticles have many advantages, these systems also have some limitations in terms of their physicochemical and biological properties (Zhang and Zhang, 2010). Liposomes are spherical lipid vesicles that have been widely used to deliver both hydrophilic and hydrophobic drugs (Eloy et al., 2014; Yalcin et al., 2018). Liposomes are attractive drug delivery systems because of their superior biocompatible and biodegradable properties (Sercombe et al., 2015). The limitations of liposomes include low drug loading capacity, burst release kinetics of encapsulated drugs, limited physical and chemical stability, and short circulation half-life of vesicles (Sharma and Sharma, 1997; Daraee et al., 2016). Polymeric nanoparticles have shown significant therapeutic potential as nanocarriers. Compared to liposomes, polymeric nanoparticles have high structural integrity and high stability during storage (Cheow and Hadinoto, 2011). Nevertheless, encapsulation of hydrophilic drugs in polymeric nanoparticles and liposomes is difficult due to their escape to the external aqueous phase (Xu et al., 2012; Mandal et al., 2013). Recently, integrated systems known as lipid–polymer hybrid nanoparticles (LPHNs), which have been developed to overcome all of the limitations, that incorporate the positive attributes of both liposomes and polymeric nanoparticles (Fang et al., 2009). LPHNs are core–shell nanoparticle structures comprising of polymer cores and lipid/lipid–PEG shells, which exhibit mechanical benefits of polymeric core and biomimetic benefits of the phospholipid shell (Mandal et al., 2016).

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This system involves three fundamental constituents: (i) an inner polymeric core in which therapeutic substances are encapsulated, (ii) a lipid layer surrounding the polymeric core, the main function of which is to supply biocompatibility to the polymer core, and (iii) an outer lipid–PEG layer to prolong in vivo circulation time of the LPHNs as well as increasing the biological and physical stability (Zhang et al., 2009; Hadinoto et al., 2013). As a result of this unique structural design, the LPHNs exhibit good biocompatibility and in vivo stability, high structural integrity, capability of multiple

drugs

entrapment,

controlled

release

capability,

and

a

favorable

pharmacokinetic profile (Sengel-Turk and Hascicek, 2017). In the present study, one of the most widely used and well tolerated anticancer drug (Kushwah et al., 2018), gemcitabine hydrochloride was chosen as the active substance. Gemcitabine hydrochloride is a highly hydrophilic molecule (Trickler et al., 2010) and currently used in clinics for the treatment of several types of human cancers, including breast, thyroid, colon, ovarian, non-small cell lung, bladder, and pancreatic cancers (Stella et al., 2007; Federico et al., 2012). A commercial product for gemcitabine is GEMZAR® (Eli Lilly and Company, Indianapolis, IN, USA) which is used as a parental formulation with an intravenous (i.v.) infusion, shows good clinical effect in cancer chemotherapy (Dubey et al., 2015). Moreover, after systemic administration, gemcitabine hydrochloride is rapidly converted into the inactive metabolite by cytidine deaminase (Vandana and Sahoo, 2010). Due to its short halflife and rapid metabolism to its inactive metabolite, continuous i.v. infusion of gemcitabine is needed to achieve therapeutic concentration (Dubey et al., 2016). When effective anti-tumor doses of this drug were used, vascular and other side effects appeared (Dasanu, 2008; Paolino et al., 2010).

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Central Composite Design (CCD) is one of the most popular designs in response surface methodology and it has been successfully applied to the optimization of formulations (Hao et al., 2012). The CCD comprise of integrated factorial design, group of star points for the estimation of curvature, and center points for the determination of experimental reproducibility (Lamarra et al., 2016). Poly (lactic-co-glycolic acid) (PLGA) was used as the core material while soya phosphatidylcholine (SPC) and DSPE-PEG were used for coating the polymeric core and lipid-PEG shell layer at the outer surface, respectively. The aim of this study was to develop and optimize the gemcitabine hydrochloride loaded LPHNs by using central composite design to increase encapsulation efficiency of hydrophilic drug.

2. Materials and methods 2.1. Materials PLGA 50/50 (MW:38-54 kDa), PLGA 65:35 (MW:24-38 kDa) and poly(vinyl alcohol) (PVA, MW 31-50 kDa, 87-89% hydrolyzed) were purchased from Sigma– Aldrich

(USA).

Soya

phosphatidylcholine,

and

1,2-distearoyl-sn-glycero-3-

phosphoethanolamine-N-[methoxy (polyethylene glycol)-2000] (ammonium salt) (DSPE-PEG2000) were obtained from Avanti Polar Lipids (USA). Gemcitabine hydrochloride was kindly gifted by Koçak Farma (Turkey). Dialysis membrane (MWCO: 12400 Da) was obtained from Sigma-Aldrich. Dichloromethane, acetone, and acetonitrile were purchased from Sigma–Aldrich. All the other chemicals were of analytical quality.

2.2. Preparation of gemcitabine hydrochloride loaded LPHNs

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Gemcitabine hydrochloride loaded LPHNs were prepared using modified double emulsion solvent evaporation method (Mandal et al., 2013; Devrim et al., 2016). Briefly, SPC and PLGA 50:50/PLGA 65:35 mixture (totally 120 mg) was dissolved in 2 mL of dichloromethane: acetone (1:2, v/v) mixture as organic solvent to form the oil phase (O). 4 mg of gemcitabine hydrochloride was dissolved in 1% (w/v) PVA solution (0,8 mL) to form the internal aqueous phase (W1). Then, the internal aqueous phase solution was added to the oil phase to obtain a W1/O emulsion and followed by probe sonication for 60 s at 20 W (Sonics-VCX 130 FSJ, USA) over an ice bath. Next, this primary emulsion was poured into external aqueous solution (3 mL) of PVA (2% w/v) containing DSPE-PEG2000 (W2) to form multiple emulsion (W1/O/W2). The multiple emulsion was again sonicated for 60 s. Finally, the organic phase was evaporated under reduced pressure and LPHNs were recovered by centrifugation (Allegra X-30R Beckman Coulter, Germany) at 27400 x g for 45 min. After this process, the LPHNs were resuspended in distilled water (1,5 mL) containing trehalose (10% w/w) as cryoprotectant. The samples were frozen at −80°C for 30 min then lyophilized for 40 h, at -55°C (Christ Alpha 1-2 LD plus, Germany).

2.3. Central Composite Design (CCD) In this study, optimization of LPHNs was done by using CCD and the data were analyzed using Design Expert® software (Trial version 6.0.6, Stat-Ease Inc., Minneapolis, MN, USA). The effect of PLGA 50:50/PLGA 65:35 mass ratio (X1), SPC/polymer mass ratio (X2) and the amount of DSPE-PEG (X3) on mean particle size (Y1), encapsulation efficiency (Y2) and cumulative drug release (Y3) were optimized using a three-factor, five-level CCD. All independent variables and responses with their coded and actual levels are described in Table 1. Each factor was set to five levels; plus

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and minus alpha are axial points, plus and minus one are factorial points and the centre point. These values were determined by taking our previous experiments on liposomes and polymeric nanoparticles (Yalcin et al., 2018) and the available literature (Chan et al., 2009) into consideration. The CCD was used with totally 20 experiments including three factorial points (23), three axial points (2*3) and the center point replicated six times. In order to maximize the encapsulation efficiency (Y2), prolong gemcitabine hydrochloride release (Y3), and to minimize the particle size (Y1) of LPHNs, the optimization of independent variables (X1, X2, X3) was planned.

2.4. Characterization of LPHNs 2.4.1. Zeta potential, particle size and polydispersity index analyses Gemcitabine hydrochloride loaded LPHNs were characterized for the zeta potential, mean particle size, and polydispersity index (PDI), using a Malvern ZetaSizer Nano ZS instrument (Malvern Instruments, UK). The surface charge measurements were based on the electrophoretic mobility of LPHNs and the particle size and PDI of LPHNs were determined by Dynamic Light Scattering (DLS) technique. Prior to the measurements, freeze-dried LPHNs were dispersed in distillated water and sonicated for 5 min. Three batches of each formulation were analyzed at room temperature and the results were represented as mean value ± SD (n = 3).

2.4.2. Drug encapsulation efficiency (EE%) To determine the encapsulation efficiency of the gemcitabine hydrochloride in the LPHNs formulations, the HPLC analysis technique was used. The HPLC system (Agilent 1220 LC, Germany) was used with a reverse phase C18 column (250 mm x 4,6 mm, 5 μm, Waters Xselect, Ireland). Briefly, 5 mg lyophilized LPHNs were mixed with

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5 mL of dichloromethane. Then, 10 mL of phosphate buffer solution (pH 7,4) was added into this mixture and sonicated in an ultrasonic bath for 60 min. Finally, the remaining aqueous dispersion was filtered and the samples were analyzed by HPLC at a wavelength of 268 nm. The mobile phase was composed of 95:5 v/v water and acetonitrile. The flow rate was 1 mL/min and retention time of 5 ± 0,5 min. Drug encapsulation efficiency was calculated as (mass of drug in gemcitabine hydrochloride loaded LPHNs/total mass of drug used) ×100.

2.4.3. Transmission electron microscopy (TEM) The surface morphologies of the LPHNs were observed by High Contrast Transmission Electron Microscopy (CTEM). A drop of LPHNs suspension (in distilled water) was placed on a copper grid and negatively stained with 1% uranyl acetate. Observations were conducted at 120 kV with a FEI Tecnai G2 Spirit BioTwin CTEM Microscope (Hillsboro, OR, USA). The grid was allowed to dry before characterization. 2.5. Drug release from LPHNs Gemcitabine hydrochloride release was evaluated by Franz diffusion technique. The Franz diffusion cells consisted of donor and receptor chambers between which there is a dialysis membrane (MW: 12400 Da). Before experiments, the membranes were kept overnight in the phosphate buffer solution (PBS) of pH 7,4. A volume of 1 mL of LPHNs (2 mg) was applied to the donor compartment. The receptor phase consisted of 2,5 mL PBS (pH 7,4) which was maintained at 37 C. During all the experiment, a magnetic bar was stirring in each cell at 100 rpm. At determined time intervals (0.5, 1, 2, 3, 4, 6, 12 and 24 h), 2,5 mL of the media were withdrawn from the receptor chambers and the same volume was replaced by fresh release media. Samples

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were then analyzed by HPLC at 268 nm. The release studies were carried out in triplicate and the average values were taken.

2.6. Storage stability In order to evaluate storage stability of the freeze dried LPHNs, the samples were put into capped glass containers and stored at 4°C, 25°C/60% RH for 12 months and 40°C/75% RH for 6 months in a stability chamber. Periodically, the parameters such as particle size, polydispersity index, zeta potential, and retention rate were evaluated after reconstitution. The retention rate (RR, %) was calculated using the following formula: ncaps lated amo nt of emcita ine hydrochloride after stora e ncaps lated amo nt of emcita ine hydrochloride initially prepared

00

2.7. Statistical analysis All the results were presented as mean ± standard deviation (SD) (n =3). All analyses were performed using GraphPad Prism 5.0 (GraphPad Software Inc.). A statistical analysis was performed using one-way ANOVA with post hoc T key’s test. A difference with p < 0,05 was considered to be statistically significant.

3. Results and discussion 3.1. Central Composite Design Preparation of lipid–polymer hybrid nanoparticles is a complicated process as it involves several processing factors. These factors show significant interactions among themselves that affect the characteristics of the formulations. The results of the CCD studies are shown in Table 2. ANOVA was performed to determine the effect of each 9

factor on mean particle size (Y1), encapsulation efficiency (Y2) and cumulative release (Y3). To select the best model that determines the correlation between critical factors and response, the obtained data were fitted in different designs such as linear, 2FI (twofactor interaction), cubic, and quadratic models. The best fit was chosen for each response based on a significant p-value from sequential model sum of squares analysis and a non-significant lack of fit p-value. 2FI model was chosen based on a significant pvalue from sum of squares analysis and a non-significant lack of fit p-value for all responses, compared to the linear, cubic, and quadratic model. Therefore, the 2FI model was adopted to describe the effects of the variables. Each experimental response could be represented by the following two-factor interaction Eq. (2) of the response surface: 0

Where,

X

X

3X3

X

3X 3

3X 3

(2)

is the meas red response; X , X and X3 are the independent varia les; 0 is

the intercept;

-

3 are the model coefficients of respective variables and their

interactional terms calculated by experimental data. X12, X13 and X23 are the interaction of the independent variables. 3.1.1. Effect on CCD on particle size As shown in Table 2, the mean particle size of formulations is found between the range of 185 to 275 nm, based on the different value of independent variables. Formulation LPHN13 showed the smallest (185 nm), while the formulation LPHN16 showed the highest particle size (275 nm). The 2FI model design indicates the effect on particle size and it is given in Eq. (3):

ean particle size

)

,

3,5 X

,08X3

10

, 0X X

, 0 X X3

The sign and value of the quantitative effect indicate propensity and magnitude of the term’s effect on the response, respectively. In the regression equation, a positive value demonstrates synergistic effect between the factor and the response; however, a negative value shows opposite or antagonistic effect (Verma et al., 2009). The R2 value of the model was calculated as 0,9739 which indicates the good correlation between response and selected factors. The ANOVA analysis shown in Table 3 indicates that the 2FI model is valid with a significant p-value (<0,0001) and insignificant lack of fit value (p=0,3352), and SPC/polymer mass ratio and the amount of DSPE-PEG have a significant effect on the particle size. The effect of various factors on the particle size is shown in Fig. 1. At lower levels of PLGA 50:50/PLGA 65:35 mass ratio, there was an increasing trend in the values of particle size, as the ratio of SPC/polymer increased from lower to high levels. The particle size increased with the increase of DSPE-PEG amount. It can be due to the increase of the amount of DSPE lipid, which affected the increase of particle size (Kim et al., 2012). The highest particle size (275 nm) was obtained with high level of SPC/polymer mass ratio (30%) and high level of DSPE-PEG amount (15 mg) (LPHN16). LPHN13 with a combination of low PLGA 50:50/PLGA 65:35 mass ratio, low SPC/polymer mass ratio, and low DSPE-PEG amount had the lowest particle size (185 nm). The lowest size of formulations may be due to the presence of thin SPC and DSPE-PEG layers at the surface of polymeric core.

3.1.2. Effect on CCD on encapsulation efficiency The drug encapsulation efficiency is also an important parameter for the hybrid nanocarrier drug delivery systems. Values of the encapsulation efficiency of individual formulations ranged between 21,4% and 45,2%, as demonstrated in Table 2. The

11

coefficient of determination (R2) was calculated to be 0,9764, indicating that 2FI model can explain about 98% variability in the response and only 2% of the variability is due to noise. Also, the adjusted R2 (0,9654) was in a good agreement with the coefficient of determination (R2) that shows the adequacy of the model to predict the response (Varshosaz et al., 2010). The signal-to-noise ratio is measured by Adeq precision value. The obtained adeq precision values for encapsulation efficiency were higher than 4 (38,8), indicating an acceptable signal to noise ratio, so the ability of the model to navigate the design space (Aghamohammadi et al., 2007). The regression Eq. (4) of the fitted model constructed for encapsulation efficiency was presented below:

ncaps lation efficiency

)

0, 3 , X 0,3 X

,

X3 0,8 X X

,

X X3 0,0 X X3 (4)

As shown in Table 4, all of the independent parameters (PLGA 50:50/PLGA 65:35 mass ratio, SPC/polymer mass ratio and DSPE-PEG amount) were found to have significant effect on the encapsulation efficiency. As shown in Fig. 2A, the encapsulation efficiency was positively correlated to the PLGA 50:50/PLGA 65:35 mass ratio and the SPC/polymer mass ratio. The highest drug encapsulation efficiency was observed in formula LPHN17, containing high amount of PLGA 50:50 (PLGA 50:50/PLGA 65:35 mass ratio 1,5), high ratio of SPC (30%) and high amount of DSPEPEG (15 mg). This could be due to the high amount of PLGA 50:50, which has low lactide content compared to PLGA 65:35. Owing to the more hydrophilic nature of PLGA 50:50, hydrophilicity of polymer mixture increased, so that the polymeric core becomes more suitable for hydrophilic drug gemcitabine hydrochloride loading. At the same time, this could be explained by the presence of the thick layer of SPC. As shown in Fig. 2A and Fig 2C, increasing of SPC value in formulations positively affected the

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encapsulation efficiency. This biocompatible shell could prevent leakage of the drug from the polymeric core (Hadinoto et al., 2013). When the lower PLGA 50:50/PLGA 65:35 mass ratio was used (<1), the encapsulation efficiency decreased with using high amount of DSPE-PEG (Fig. 2B) and LPH16 showed the lowest drug encapsulation efficiency. In contrast, when high level PLGA 50:50/PLGA 65:35 mass ratio (>1,25) was used, the increase in DSPE-PEG was found to increase the encapsulation efficiency. 3.1.3. Effect on CCD on drug release The in vitro drug release from the LPHNs were conducted in PBS (pH 7,4) at 37 °C, by using the Franz diffusion technique, to estimate the in vivo drug release behavior. At the end of 24 h, the cumulative release of gemcitabine hydrochloride from formulations are shown in Table 2. The cumulative release of all LPHNs formulations ranged from 39,7% to 73,9% at 24 h. According to the regression coefficients calculated for the cumulative drug release response (Y3), the model can be represented by the following Eq. (5):

Drug release

3) 3,0

3 , X - 0, X X3 (5)

The R2 and adjusted R2 for the cumulative release were found to be 0,9535 and 0,9321, respectively. The model is highly statistically significant (p<0,0001) with insignificant lack of fit value (F=2,40; p=0,1751) (Table 5). The ANOVA results revealed that PLGA 50:50/PLGA 65:35 mass ratio and two interactions of SPC/polymer mass ratio and DSPE-PEG amount exerted a statistically significant effect on the release. As shown in Fig. 3, the low cumulative drug release was obtained when the higher SPC/polymer ratio and the higher amount of DSPE-PEG were used. This result suggested that the

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lipid monolayer at the interface of the PLGA core and the PEG shell increased the hydrophobic properties of LPHNs (Zhang et al., 2009) and PEG acts as a barrier against the diffusion of hydrophilic drugs (Panwar et al., 2010). The highest drug release was obtained from LPHN18, which has high PLGA 50:50/PLGA 65:35 mass ratio and low DSPE-PEG (5 mg). Formulations of the centre, axial and factorial points are presented in Fig. 4A-4C, respectively. The drug release from the LPHNs showed a biphasic release pattern as observed in previously reported gemcitabine hydrochloride loaded liposomes (Cosco et al., 2012) and PLGA nanoparticles (Martín-Banderas et al., 2013). This biphasic release was characterized by an initial burst release, followed by a slower continuous up to 24 h. The initial release is related to the presence of gemcitabine hydrochloride at or near the surface of LPHNs, while the slow and continuous release may be attributed to the diffusion through the polymeric core, PLGA degradation and the lipid present in the LPHNs (Tahir et al., 2017). Statistically, the center points formulations had similar cumulative release ranged from 55,6% to 60,1% (Table 2) at the end of 24 h (p >0,05), that shows experimental reproducibility.

3.2. Polydispersity Index (PDI) and zeta potential The zeta potential and the polydispersity index (PDI) of obtained LPHNs listed in Table 6. The PDI is a measure of distribution of sizes of particles and if it is closer to 1, it represents the polydisperse system. Most researchers recognize the PDI values lower than 0,3 as optimum values (Dragicevic-Curic et al., 2009; Vighi et al., 2010). PDI value of all LPHNs formulations ranged from 0,129 to 0,286, indicating homogenous populations (PDI < 0,3) of LPHNs.

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The zeta potential plays an important role for the stability of a colloidal system and biological applications of nanoparticles. Positive or negative high zeta potential could cause strong repulsion forces among particles to inhibit aggregation of the formulations. All LPHNs formulations were negatively charged with zeta potential values ranging from -16,7 to -24,5 mV (Table 6). The negative zeta potential of LPHNs might be derived from the DSPE-PEG coating (Kim et al., 2012). As a result, LPHN17 was selected as an optimum formulation due to the highest encapsulation efficiency, desired particle size, particle size distribution, zeta potential, and release properties of gemcitabine hydrochloride. Then the optimized formulation LPHN17 was prepared and subjected to further characterization.

3.3. TEM imaging of the optimized formulation The shape and morphology of LPHN17 was investigated through TEM imaging as shown in Fig. 5. The images exhibit that LPHN17 came out as homogeneous and spherical particles with an inner dark polymeric core surrounded by the lighter phospholipid layer. The particle size of LPHN17 measured by TEM correlated well with the results obtained by DLS. According to TEM images, LPHN17 showed discrete structures without aggregation. This could be due to the repulsion forces among the negatively-charged LPHNs.

3.4. Stability studies Storage stability study was performed at different conditions (4°C, 25°C and 40°C) in order to evaluate the potential of optimum formulation (LPHN17) to withstand against environmental changes. The mean particle size, PDI value, zeta potential, and retention rate of the LPHN17 were determined during the stability study and the results are

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shown in Table 7. At 4°C and 25°C, the particle size of LPHN17 showed physical stability for a period of 9 and 6 months, respectively (p>0,05). However, LPHN17 showed stability for a storage period of 3 months at 40°C (p>0,05), by the end of 6 months, the particle size significantly increased from 216 to 336 nm with a high polydispersity index (0,648) at the same climatic condition. After a storage period of 6 months, the lowest retention rate was obtained at 40°C storage condition (36,3%). This could be explained by that the lipid layer may be damaged by high humidity and storage temperature. Lipid layer acts as a barrier which prevents leakage of the drug from the polymeric core (Sengel-Turk and Hascicek, 2017). The zeta potential values of LPHN17 at both storage conditions and time points were negative. PDI values of the LPHN17 were seen to escalate with the effects of temperature, humidity, and time in all climatic conditions throughout the six month period. The observed results demonstrated that 4°C was more suitable for the storage of LPHNs than 25°C and 40°C.

Conclusion The LPHNs system, a new lipid-polymer nano delivery system comprising of PLGA as a polymeric core, SPC as a lipid monolayer and DSPE-PEG as a lipid shell was successfully developed. Optimization of LPHNs formulation is a complicated process that necessitates the consideration of a large number of variables and their interactions with each other. The CCD clearly showed the feasibility of the optimization procedure in developing gemcitabine hydrochloride loaded LPHNs. Responses such as particle size, encapsulation efficiency, and cumulative release that permit optimizing of the system were dependent on the design parameters: PLGA 50:50/PLGA 65:35 mass ratio, SPC/polymer mass ratio, and the amount of DSPE-PEG. SPC/polymer mass ratio was identified as the most significant factor for particle size and encapsulation efficiency.

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TEM images revealed that the optimum LPHNs formulation has a discrete spherical structure. LPHNs were physically stable until 12 months at 4°C and 9 months at 25°C/60% RH. When harder conditions are employed (40°C/75% RH), LPHNs lost the physical stability. Encapsulation efficiency and drug release from the LPHNs are dependent on characteristics of drugs. Although gemcitabine hydrochloride is a hydrophilic drug, lipid-polymer hybrid nanoparticle systems increased the encapsulation efficiency and retarded the release profile of drug. These findings indicate that LPHNs provided better encapsulation efficiency and release properties than the results

of

gemcitabine

hydrochloride

loaded

liposomes

and

polymeric

nanoparticles in our previous study. According to findings of the current study, it can be concluded that the lipid-polymer hybrid nanoparticles are encouraging delivery systems for hydrophilic drugs can be promising delivery systems for also other hydrophilic drugs. Further in vivo animal studies are recommended in order to assess the effectiveness of this optimum hybrid formulation on breast various cancer treatments.

Declaration of interest: The authors report no declaration of interest.

Acknowledgments The Scientific and Technological Research Council of Turkey (TUBITAK) with a grant number 113S841 supported this work.

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References Aghamohammadi, N., Aziz, H.B., Isa, M.H., Zinatizadeh, A.A., 2007. Powdered activated carbon augmented activated sludge process for treatment of semi-aerobic landfill leachate using response surface methodology. Bioresour Technol. 98(18), 35703578. Chan, J.M., Zhang, L., Yuet, K.P., Liao, G., Rhee, J.W., Langer, R., Farokhzad, O.C., 2009. PLGA-lecithin-PEG core-shell nanoparticles for controlled drug delivery. Biomaterials. 30(8), 1627-1634. Cheow, W.S., Hadinoto, K., 2011. Factors affecting drug encapsulation and stability of lipid-polymer hybrid nanoparticles. Colloids Surf B Biointerfaces. 85(2), 214-220. Cosco, D., Paolino, D., Cilurzo, F., Casale, F., Fresta, M., 2012. Gemcitabine and tamoxifen-loaded liposomes as multidrug carriers for the treatment of breast cancer diseases. Int J Pharm. 422(1-2):229-237. Daraee, H., Etemadi, A., Kouhi, M., Alimirzalu, S., Akbarzadeh, A., 2016. Application of liposomes in medicine and drug delivery. Artif Cells Nanomed Biotechnol. 44(1), 381-391. Dasanu, C.A., 2008. Gemcitabine: vascular toxicity and prothrombotic potential. Expert Opin Drug Saf. 7(6), 703-716. Devrim, B., Kara, A., V ral, İ., Bozkır, A.,

0

. Lysozyme-loaded lipid-polymer

hybrid nanoparticles: preparation, characterization and colloidal stability evaluation. Drug Dev Ind Pharm. 42(11), 1865-1876.

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Dragicevic-Curic, N., Scheglmann, D., Albrecht, V., Fahr, A., 2009. Development of liposomes containing ethanol for skin delivery of temoporfin: characterization and in vitro penetration studies. Colloids Surf B Biointerfaces. 74(1), 114-122. Dubey, R.D., Alam, N., Saneja, A., Khare, V., Kumar, A., Vaidh, S., Mahajan, G., Sharma P.R., Singh, S.K., Mondhe, D.M., Gupta, P.N., 2015. Development and evaluation of folate functionalized albumin nanoparticles for targeted delivery of gemcitabine. Int J Pharm. 492 (1-2), 80-91. Dubey, R.D., Saneja, A., Gupta, P.K., Gupta, P.N., 2016. Recent advances in drug delivery strategies for improved therapeutic efficacy of gemcitabine. Eur J Pharm Sci. 93, 147-162. Eloy, J.O., Claro de Souza, M., Petrilli, R., Barcellos, J.P., Lee, R.J., Marchetti, J.M., 2014. Liposomes as carriers of hydrophilic small molecule drugs: strategies to enhance encapsulation and delivery. Colloids Surf B Biointerfaces. 1(123), 345-363. Fang, R.H., Aryal, S., Hu, C.M., Zhang, L., 2009. Quick synthesis of lipid-polymer hybrid nanoparticles with low polydispersity using a single-step sonication method. Langmuir. 26(22), 16958-16962. Federico, C., Morittu, V.M., Britti, D., Trapasso, E., Cosco, D., 2012. Gemcitabineloaded liposomes: rationale, potentialities and future perspectives. Int J Nanomedicine. 7, 5423-5436. Hadinoto, K., Sundaresan, A., Cheow, W.S., 2013. Lipid-polymer hybrid nanoparticles as a new generation therapeutic delivery platform: a review. Eur J Pharm Biopharm. 85, 427-443. Hao, J., Wang, F., Wang, X., Zhang, D., Bi, Y., Gao, Y., Zhao, X., Zhang, Q., 2012. Development and optimization of baicalin-loaded solid lipid nanoparticles prepared by coacervation method using central composite design. Eur J Pharm Sci. 47(2), 497-505. Kim, C.E., Lim, S.K., Kim, J.S., 2012. In vivo antitumor effect of cromolyn in PEGylated liposomes for pancreatic cancer. J Control Release. 157(2), 190-195. Kushwah, V., Katiyar, S.S., Dora, C.P., Kumar Agrawal, A., Lamprou, D.A., Gupta, R.C., Jain, S., 2018. Co-delivery of docetaxel and gemcitabine by anacardic acid modified self-assembled albumin nanoparticles for effective breast cancer management. Acta Biomater. 73, 424-436. Lamarra, J., Rivero, S., Pinotti, A., 2016. Design of chitosan-based nanoparticles functionalized with gallic acid. Mater Sci Eng C Mater Biol Appl. 67, 717-726.

19

Mandal, B., Bhattacharjee, H., Mittal, N., Sah, H., Balabathula, P., Thoma, L.A., Wood, G.C., 2013. Core-shell-type lipid-polymer hybrid nanoparticles as a drug delivery platform. Nanomedicine. 9(4), 474-491. Mandal, B., Mittal, N.K., Balabathula, P., Thoma, L.A., Wood, G.C., 2016. Development and in vitro evaluation of core-shell type lipid-polymer hybrid nanoparticles for the delivery of erlotinib in non-small cell lung cancer. Eur J Pharm Sci. 81, 162-171. Martín-Banderas, L., Sáez-Fernández, E., Holgado, M.Á., Durán-Lobato, M.M., Prados, J.C., Melguizo, C., Arias, J.L., 2013. Biocompatible gemcitabine-based nanomedicine engineered by Flow Focusing for efficient antitumor activity. Int J Pharm. 443(1-2), 103-109. Panwar, P., Pandey, B., Lakhera, P.C., Singh, K.P. 2010. Preparation, characterization, and in vitro release study of albendazole-encapsulated nanosize liposomes. Int J Nanomedicine. 5, 101-108. Paolino, D., Cosco, D., Racanicchi, L., Trapasso, E., Celia, C., Iannone, M., Puxeddu, E., Costante, G., Filetti, S., Russo, D., Fresta, M., 2010. Gemcitabine-loaded PEGylated unilamellar liposomes vs GEMZAR®: Biodistribution, pharmacokinetic features and in vivo antitumor activity. J Control Release. 144(2), 144-150. Rangsimawong, W., Opanasopit. P., Rojanarata, T., Duangjit, S., Ngawhirunpat, T., 2016. Skin Transport of Hydrophilic Compound-Loaded PEGylated Lipid Nanocarriers: Comparative Study of Liposomes, Niosomes, and Solid Lipid Nanoparticles. Biol Pharm Bull. 39(8), 1254-1262. Sengel-Turk, C.T., Hascicek, C., 2017. Design of lipid-polymer hybrid nanoparticles for therapy of BPH: Part I: Formulation optimization using a design of experiment approach. J. Drug Deliv. Sci. Technol. 39, 16–27. Sercombe, L., Veerati, T., Moheimani, F., Wu, S.Y., Sood, A.K., Hua, S., 2015. Advances and Challenges of Liposome Assisted Drug Delivery. Front Pharmacol. 6(286), 1-13. Sharma, A., Sharma, U.S., 1997. Liposomes in drug delivery: progress and limitations. Int J Pharm. 154, 123 140. Stella, B., Arpicco, S., Rocco, F., Marsaud, V., Renoir, J.M., Cattel, L., Couvreur, P., 2007. Encapsulation of gemcitabine lipophilic derivatives into polycyanoacrylate nanospheres and nanocapsules. Int J Pharm. 344(1-2), 71-77.

20

Tahir, N., Madni, A., Balasubramanian, V., Rehman, M., Correia, A., Kashif, P.M., Mäkilä, E., Salonen, J., Santos, H.A., 2017. Development and optimization of methotrexate-loaded lipid-polymer hybrid nanoparticles for controlled drug delivery applications. Int J Pharm. 533(1), 156-168. Trickler, W.J., Khurana, J., Nagvekar, A.A., Dash, A.K., 2010. Chitosan and glyceryl monooleate nanostructures containing gemcitabine: potential delivery system for pancreatic cancer treatment. AAPS PharmSciTech. 11, 392-401. van Rijt, S.H., Bein, T., Meiners, S., 2014. Medical nanoparticles for next generation drug delivery to the lungs. Eur Respir J. 44(3), 765-774. Vandana, M., Sahoo, S.K., 2010. Long circulation and cytotoxicity of PEGylated gemcitabine and its potential for the treatment of pancreatic cancer. Biomaterials. 31, 9340-9356. Varshosaz, J., Ghaffari, S., Khoshayand, M.R., Atyabi, F., Azarmi, S., Kobarfard, F., 2010. Development and optimization of solid lipid nanoparticles of amikacin by central composite design. J Liposome Res. 20(2), 97-104. Verma, S., Lan, Y., Gokhale, R., Burgess, D.J., 2009. Quality by design approach to understand the process of nanosuspension preparation. Int. J. Pharm. 377, 185–198. Vighi, E., Ruozi, B., Montanari, M., Battini, R., Leo, E., 2010. pDNA condensation capacity and in vitro gene delivery properties of cationic solid lipid nanoparticles. Int J Pharm. 389(1-2), 254-261. Xu, X., Khan, M.A., Burgess, D.J., 2012. Predicting hydrophilic drug encapsulation inside unilamellar liposomes. International Journal of Pharmaceutics. 423, 410-418. Yalcin, T.E., Ilbasmis-Tamer, S., Ibisoglu, B., Ozdemir, A., Ark, M., Takka, S., 2018. Gemcitabine hydrochloride-loaded liposomes and nanoparticles: comparison of encapsulation efficiency, drug release, particle size, and cytotoxicity. Pharm Dev Technol. 23(1), 76-86. Zhang, L., Chan, J.M., Gu, F.X., Rhee, J.W., Wang, A.Z., Radovic-Moreno, A.F., Alexis, F., Langer, R., Farokhzad, O.C., 2009. Self-assembled lipid-polymer hybrid nanoparticles: a robust drug delivery platform. ACS Nano. 2(8), 1696-1702. Zhang, L., Zhang, L., 2010. Lipid–Polymer Hybrid Nanoparticles: Synthesis, Characterization and Applications. Nano LIFE 01, 163–173.

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Table legends Table 1. Independent variables and their coded and actual levels in the experimental design Table 2. Central composite design (CCD) of three independent variables at different levels and encapsulation efficiency (%), mean particle size (nm) and cumulative drug release (%) as response Table 3. ANOVA table for 2FI model to estimate particle size with CCD Table 4. ANOVA table for 2FI model to estimate encapsulation efficiency with CCD Table 5. ANOVA table for 2FI model to estimate cumulative drug release with CCD Table 6. Polydispersity index and zeta potential of gemcitabine hydrochloride loaded LPHNs Table 7. Storage stability of gemcitabine hydrochloride loaded LPHNs (LPHN17) at different climatic conditions at different time interval

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Figure captions Fig. 1. Response surface plots indicating (A) the effect of PLGA 50:50/PLGA 65:35 mass ratio and SPC/polymer mass ratio on particle size, and (B) the effect of PLGA 50:50/PLGA 65:35 mass ratio and amount of DSPE-PEG on particle size. Fig. 2. Response surface plots indicating (A) the effect of PLGA 50:50/PLGA 65:35 mass ratio and SPC/polymer mass ratio on encapsulation efficiency, (B) the effect of PLGA 50:50/PLGA 65:35 mass ratio and amount of DSPE-PEG on encapsulation efficiency, and (C) the effect of SPC/polymer mass ratio and amount of DSPE-PEG on encapsulation efficiency. Fig. 3. Response surface plot for the effect of SPC/polymer mass ratio and amount of DSPE-PEG on the cumulative release. Fig. 4. Drug release profiles of the LPHNs prepared with (A) center points, (B) axial points and (C) factorial points. Fig. 5. TEM images of optimized LPHNs (LPHN17) (A) scale bar:200 nm (B) Scale bar: 500 nm

23

24

25

26

27

28

29

Table 1. Independent variables and their coded and actual levels in the experimental design Independent variables

X1 X2 X3

Levels

PLGA 50:50/PLGA 65:35 mass ratio (w/w) SPC/polymer mass ratio (%, w/w) Amount of DSPE-PEG (mg) Dependent variables

Y1 Y2 Y3

-1,682

-1

0

1

+1,682

0,16 3,18 1,59

0,5 10 5

1 20 10

1,5 30 15

1,84 36,82 18,41

Desired Outcomes Minimize Maximize In range

Particle size (nm) Encapsulation efficiency (EE%) Drug release (%)

30

Table 2. Central composite design (CCD) of three independent variables at different levels and encapsulation efficiency (%), mean particle size (nm) and cumulative drug Coded levels

Actual levels

PLGA 50:50/PLGA 65:35 ratio

SPC/polymer mass ratio

DSPE-PEG (mg)

PLGA 50:50/PLGA 65:35 ratio

SPC/polymer mass ratio

DSPE-PEG (mg)

Mean particle size (nm)

LPHN1

0

0

0

1,00

20,00

10,00

LPHN2

0

0

0

1,00

20,00

10,00

0

0

0

1,00

20,00

10,00

0

0

0

1,00

20,00

10,00

0

0

0

1,00

20,00

10,00

LPHN6

0

0

0

1,00

20,00

10,00

LPHN7

1,682

0

0

1,84

20,00

10,00

LPHN8

0

1,682

0

1,00

36,82

10,00

0

0

1,682

1,00

20,00

18,41

-1,682

0

0

0,16

20,00

10,00

0

-1,682

0

1,00

3,18

10,00

LPHN12

0

0

-1,682

1,00

20,00

1,59

LPHN13

-1

-1

-1

0,50

10,00

5,00

LPHN14

-1

-1

1

0,50

10,00

15,00

-1

1

-1

0,50

30,00

5,00

-1

1

1

0,50

30,00

15,00

1

1

1

1,50

30,00

15,00

1

1

-1

1,50

30,00

5,00

LPHN19

1

-1

1

1,50

10,00

15,00

LPHN20

1

-1

-1

1,50

10,00

5,00

221 ± 3 229 ± 4 225 ± 7 230 ± 9 226 ± 8 221 ± 5 233 ± 18 248 ± 29 269 ± 23 233 ± 20 213 ± 7 190 ± 9 185 ± 9 240 ± 9 224 ± 14 275 ± 24 237 ± 16 207 ± 13 251 ± 21 207 ± 7

LPHN5

LPHN9 LPHN10 LPHN11

LPHN15 LPHN16 LPHN17 LPHN18

Axial points

LPHN4

Factorial points

LPHN3

Centre points

Formulation code

release (%) as response

31

Table 3. ANOVA table for 2FI model to estimate particle size with CCD

Source Model X1 X2 X3 X1X2 X1X3 X2X3 Residual Lack of fit Pure Error Cor total

Sum of squares 9373 35,4 1034 7167 968 128 40,5 251 178 73,3 9625

df

Mean square

F value

6 1 1 1 1 1 1 13 8 5 19

1562 35,4 1034 7167 968 128 40,5 19,3 22,3 14,7

80,8 1,83 53,5 371 50,0 6,62 2,09

p-value Prob > F <0,0001* 0,1989 <0,0001* <0,0001* <0,0001* 0,0232 0,1716

1,52

0,3352

32

significant

not significant

Table 4. ANOVA table for 2FI model to estimate encapsulation efficiency with CCD

Source Model X1 X2 X3 X1X2 X1X3 X2X3 Residual Lack of fit Pure Error Cor total

Sum of squares 584 22,9 149 17,1 147 243 5,28 14,2 10,2 3,95 599

df

Mean square

F value

6 1 1 1 1 1 1 13 8 5 19

97,4 22,9 149 17,1 147 243 5,28 1,09 1,27 0,79

89,5 21,1 137 15,8 135 223 4,85

p-value Prob > F <0,0001* 0,0005 <0,0001* 0,0016 <0,0001* <0,0001* 0,0463

1,61

0,3108

33

significant

not significant

Table 5. ANOVA table for 2FI model to estimate cumulative drug release with CCD

Source Model X1 X2 X3 X1X2 X1X3 X2X3 Residual Lack of fit Pure Error Cor total

Sum of squares 1567 1231 0,36 0,73 16,2 20,5 298 76,4 60,6 15,8 1643

df

Mean square

F value

6 1 1 1 1 1 1 13 8 5 19

261 1231 0,36 0,73 16,2 20,5 298 5,88 7,57 3,16

44,4 210 0,061 0,12 0,277 3,49 50,7

p-value Prob > F <0,0001* <0,0001* 0,8083 0,7304 0,1203 0,0846 <0,0001*

2,40

0,1751

34

significant

not significant

Table 6. Polydispersity index and zeta potential of gemcitabine hydrochloride loaded LPHNs

Formulation Polydispersity code index LPHN1 0,198 ± 0,020

Zeta potential (mV) -24,5 ± 0,8

LPHN2

0,178 ± 0,036

-18,1 ± 0,9

LPHN3

0,129 ± 0,044

-20,5 ± 0,7

LPHN4

0,146 ± 0,036

-18,3 ± 0,7

LPHN5

0,198 ± 0,020

-22,2 ± 0,7

LPHN6

0,178 ± 0,036

-23,3 ± 1,3

LPHN7

0,172 ± 0,061

-17,0 ± 2,5

LPHN8

0,286 ± 0,042

-19,5 ± 1,1

LPHN9

0,203 ± 0,026

-17,9 ± 0,7

LPHN10

0,196 ± 0,041

-18,1 ± 0,9

LPHN11

0,190 ± 0,081

-20,5 ± 1,0

LPHN12

0,188 ± 0,033

-23,6 ± 0,8

LPHN13

0,195 ± 0,027

-19,8 ± 2,0

LPHN14

0,156 ± 0,062

-21,0 ± 0,9

LPHN15

0,168 ± 0,019

-23,7 ± 0,8

LPHN16

0,284 ± 0,080

-16,7 ± 1,5

LPHN17

0,219 ± 0,048

-18,1 ± 0,9

LPHN18

0,173 ± 0,024

-18,0 ± 1,3

LPHN19

0,245 ± 0,036

-18,5 ± 2,7

LPHN20

0,178 ± 0,013

-22,3 ± 0,9

35

Table 7. Storage stability of gemcitabine hydrochloride loaded LPNs (LPHN17) at different climatic conditions at different time interval Time

Storage condition

Initial 3 Months

6 Months

9 Months 12 Months

4°C 25°C 40°C 4°C 25°C 40°C 4°C 25°C 4°C 25°C

Particle size (nm) 216 ± 10 226 ± 9 254 ± 7 280 ± 12 253 ± 22 280 ± 18 336 ± 54 257 ± 17 285 ± 28 264 ± 14 334 ± 13

Polydispersity Zeta potential index (mV) 0,155 ± 0,033 -20,6 ± 1,3 0,162 ± 0,030 -16,4 ± 1,1 0,272 ± 0,026 -18,7 ± 0,8 0,349 ± 0,041 -15,7 ± 0,7 0,232 ± 0,027 -23,7 ± 0,5 0,283 ± 0,036 -23,0 ± 0,5 0,648 ± 0,061 -20,8 ± 1,1 0,232 ± 0,017 -17,5 ± 0,4 0,249 ± 0,008 -17,2 ± 0,6 0,269 ± 0,038 -22,6 ± 1,5 0,379 ± 0,056 -17,3 ± 0,7

36

Retention rate (%) 100 94,4 ± 0,3 72,2 ± 0,6 54,4 ± 0,5 90,8 ± 0,5 66,3 ± 0,8 36,3 ± 0,7 88,4 ± 1,4 65,1 ± 1,3 82,5 ± 2,8 60,8 ± 2,8