Application of quality-by-design approach to optimize diallyl disulfide-loaded solid lipid nanoparticles

Abstract

The current work was carried out by the principles of quality-by-design approach to develop an optimized solid lipid nanoparticles (SLNs) formulation of diallyl disulfide (DADS) through systematic statistical study. And its antitumor activity of DADS was also evaluated on breast cancer cell lines. To understand the effect of formulation variables (critical parameters) on the responses (critical quality attributes) of SLN, a 3-factor, 3-level Box–Behnken design, was explored to predict the responses such as particle size (Y1) and % entrapment efficiency (EE) (Y2) when concentration of surfactant (X1), amount of lipid (X2), and volume of solvent (X3) were selected as independent variables. Particle size analysis revealed that all the batches were within the nanometer range. DADS was released from the SLN much more rapidly at pH 4.5 than at pH 7.4, which is a desirable characteristic for tumor-targeted drug delivery. The cytotoxicity, reactive oxygen species (ROS), determination revealed that the antitumor activity of DADS is enhanced with SLN compared to DADS-free drug, and apoptosis is the mechanism underlying the cytotoxicity. The present study indicated the remarkable potential of DADS-SLN in enhancing the anticancer effect of DADS in breast cancer cells in vitro.

Keywords:

• Box–Behnken design
• breast cancer
• design of experiment
• diallyl disulfide
• solid lipid nanoparticles

Introduction

Natural antioxidants, especially dietary flavonoids and polyphenolic compounds, have been well-documented for their prophylactic and therapeutic actions in chemical-induced carcinogenesis. They concentrate on various carcinogen bioactivating steps, which are essential for the covalent binding of the carcinogen to cellular DNA (Geetha et al. 2014). Allium species include garlic, onion, leeks, chives, and scallions which have great potential in treatment/prevention of cancers and cardiovascular diseases. Among them garlic has demonstrated antitumor, antihypertensive, and antihypercholemic actions (Lee et al. 2011). Epidemiological studies reported that dietary intake of allium products is inverse to the risk of many cancers. Even the epidemiological reports have interrelation with laboratory investigations. The beneficiary effects of the garlic are because of the presence of organosulfur compounds namely, allyl sulfides and flavonoids. Among them diallyl disulfide (DADS) is the principal organosulfur ingredient present in garlic as it shares the major portion about 60% garlic oil (Lawson and Wang 1993, Sengupta et al. 2004). In general, it is a hydrophobic organic compounds present in garlic which exhibited antitumorigenic action in vitro growth of breast, colon, lung and gastric cancer cell lines, and leukemia cell lines. These evidences represent DADS as an efficient cytotoxic drug (Druesne et al. 2004, Hong et al. 2000, Knowles and Milner 2001, Yuan et al. 2004). Despite its pronounced cytotoxic action, lipophilicity and short biological half-life of DADS bring up challenge to the formulator in designing a suitable drug delivery system. This became a major stumbling block to this wonder molecule for clinical translation. Aforementioned problems of DADS made us to encapsulate it in solid lipid nanoparticles (SLNs).

SLNs are attractive colloidal delivery systems which offer lower toxicity when compared to polymeric systems (Müller et al. 2000). They are robust, submicron size, high drug payload, and can entrap poor water-soluble drugs in lipid matrix. They ensure protection of drug from degradation, improve bioavailability, enable desired drug release pattern, and are flexible for large-scale production. In the past 15 years, research has been focused on SLNs for pharmaceutical and cosmetic applications (Wissing et al. 2004), as aforementioned promising features of SLNs make them as potential carriers for the drugs facing physicochemical problems.

Physicochemical properties of SLNs include particle size, entrapment efficiency (EE), drug release can be fine-tuned by optimization (Gidwani and Vyas 2016). Traditional formulation development of any dosage form needs screening and optimization which are univariate i.e., one-factor-at-a-time method. This univariate optimization calls up unwanted numerous runs which is laborious, expensive, time-consuming, and may develop an unreliable result (Ji et al. 2016). Interrelation between process parameters and product quality needs to be clearly understood. In particular, a design space needs to be established which will define multidimensional combinations and interactions of input variables and output variables (Hao et al. 2011). Statistical optimization methods like design of experiment (DOE) is one of the most efficient response surface methodologies to identify important process variables (independent factors) and determine the relation between factors on the outputs (dependent factors). This method proportionally minimizes time and experimentation runs, which turns the optimization systematic and organized through a grid search over the total factor space (Guideline 2009, Gohel and Amin 1998). Recent studies have demonstrated the efficiency of the statistical experimental design approach in developing a formulation by identifying the interconnection between independent and dependent factor in a formulation. Response surface methodology (RSM) is a statistical tool which simultaneously analyzes the process variables when the interactions are complex. Several studies have exhibited the potential of RSM to develop an optimized formulation in different pharmaceuticals (Liu et al. 2010). Many studies have developed optimized formulations encapsulating lipophilic drugs such as enalapril maleate (Singh et al. 2011), simvastatin (Gambhire et al. 2011), lutein (Liu and Wu 2010), sildenafil citrate (Ghasemian et al. 2013), etc., using factorial design and RSM.

The present research exploited Box–Behnken design, one of the RSM design which calls up lesser runs in a 3-factor experimental design than all other RSM designs and it is beneficial when many combinations are to be avoided. Box–Behnken design is preferable over central composite design as number of runs will be few when the numbers of factors considered are three. Box–Behnken design generates an independent quadratic equation which has no interconnection with factorial or fractional–factorial design (Box and Behnken 1960, Chopra et al. 2007). So, the present investigation focused on the development of DADS-encapsulated SLN by Box–Behnken design for treating breast cancer.

Experimental

Materials and methods

DADS was purchased from Alfa Aesar, India. Palmitic acid was obtained from SD-Fine chemicals limited, Mumbai, India. Acetonitrile used was HPLC grade purchased from Merck, India. Sulforhodamine B (SRB) was obtained from Sigma-Aldrich, Bangalore, India. Poloxamer 188 (F68) was purchased from Sigma (St. Louis, MO). Primary antibodies against Bax, Bcl-2, Bad, caspases-9, and caspases-3 were procured from Cell Signaling Technology, Inc., San Diego, CA and Santa Cruz Biotechnology, Santa Cruz, CA. The secondary antibodies, horse radishperoxidase (HRP)-conjugated rabbit-anti-mouse IgG, and goat-anti-rabbit IgG were obtained from Santa Cruz Biotechnology. All the chemicals used were extra pure of analytical grade.

Cell and culture conditions

MCF-7 breast cancer cell lines and MCF-10A healthy breast cell lines were purchased from National Center for Cell Science (Pune, India). The cell lines were maintained as a continuous culture in Dulbecco’s modified Eagle’s medium (DMEM; Sigma-Aldrich, Inc.), supplemented with 10% fetal bovine serum (FBS; Himedia, Mumbai, India), 100 U/mL penicillin, and 100 μg/mL streptomycin. Cells were grown in a humidified atmosphere of 5% CO₂ at 37 °C. Media were replenished every 3 days.

Preformulation studies

Drug–lipid interaction study by Fourier transform infra-red (FTIR) spectral analysis

The interaction between DADS and palmitic acid was determined using FTIR scanning between 4000 and 400 cm⁻¹ (FT-IR Spectrometer, Perkin Elmer) using KBr pellet method; the IR spectra of DADS, palmitic acid, DADS-SLN, and the physical mixture of both the ingredients were recorded.

Preparation of DADS-SLNs

The DADS-SLNs were prepared by solvent diffusion method as reported elsewhere but with slight modification (Cho et al. 2014, Hu et al. 2005, Mohanty et al. 2015). Briefly, palmitic acid and DADS were dissolved completely in ethanol in a water bath at 70 °C. The resultant organic solution was quickly dispersed into 50 mL of an aqueous phase containing surfactant under continuous mechanical agitation at 400 rpm in a water bath at 70 °C for 5 min. The obtained pre-emulsion (melted lipid droplet) was subsequently transferred into an ice bath to solidify the lipid droplets then cooled to room temperature till SLN dispersion was obtained. The SLN dispersion was purified by dialysis against distilled water for 12 h to remove water-soluble impurities (organic solvents and nonadsorbed surfactants) and subsequently centrifuged (7000 rpm, 5 min) to remove large lipid particles and precipitate-free DADS. The final dispersion was freeze-dried.

Experimental design

For the present study, a 17-run, 3-factor, 3-level Box–Behnken design was employed to optimize the SLN formulation using Design-Expert software (Version 7.1.6. Stat-Ease, Inc., Minneapolis, MN). The design is suitable for exploring quadratic response surfaces and constructing second-order polynomial models. This cubic design is characterized by center-point replicates and a set of points present at the mid-point of each edge of the multidimensional cube that circumscribes the region of interest. Region of interest helps to investigate the main effects, interaction effects, and quadratic effects of the formulation ingredients and to optimize the formulation. Design matrix consisting of 17 experimental runs was constructed.

The computer-generated nonlinear quadratic model equation of the design is as follows:

where, A₀: the intercept representing the arithmetic average of all of 17 runs;

A₁, A₂, A₃, A₄, A5 A₆, A7, A₈, and A_9: the regression coefficients estimated from the observed experimental values of responsevariable Y;

X₁, X₂, and X₃: the coded levels of the independent variables;

X₁X_2, X₂X_3, and X₁X_3: the linear interaction terms;

X₁², X₂², and X₃²: quadratic terms;

Factors evaluated in this design matrix were the percentage of surfactant (X1), amount of lipid (X2), and volume of solvent (X3) as the independent variables which were represented by −1, 0, and +1, analogous to the low, middle, and high values, respectively as described in Table 1. The dependent variables are particle size and EE with constraints applied shown in Table 1. The responses obtained after the preparation of these 17 formulations were filled in the design.

Table 1. Variables and their levels in Box–Behnken design.

	Levels
Factor	Low (−1)	Medium (0)	High (+1)
Independent variables
Percentage of surfactant (%) X1	1.00	1.5	2.00
Amount of lipid (mg) X2	50	75	100
Volume of solvent (mL) X3	2	3	4
Constraints
Dependent variables
Particle size Y1	Minimize
EE Y2	Maximize

Determination of particle size, zeta potential, and morphology

The particle size, zeta potential, and polydispersity index were measured by photocorrelation spectroscopy with a Zetasizer 3000HAS (Malvern Instruments Ltd, Malvern, UK). Before measurement, the samples were suspended in deionized water. All measurements were carried out at 25 °C and performed in triplicate. The morphology of DADS-SLNs was analyzed using scanning electron microscopy (TEM, JEM-200 CX, JEOL, Tokyo, Japan; Venkata Siddhartha et al. 2014).

Determination of drug entrapment efficiency and drug loading

The amount of DADS in SLN was determined by HPLC. About 10 mg of prepared SLN was dissolved in 10 mL of 1:1 mixture of methanol and sodium dodecyl sulfate. Above mixture was placed in a super filter tube, and then centrifuged by a Sigma-3k30 Centrifuges (Sigma-Aldrich, Seelze, Germany) at 14000 rpm for 10 min at the temperature of 32 °C. The ultrafiltrate was extracted by methanol, and filtered with a 0.45-mm filter. The drug concentration in ultrafiltrate was determined as the content of the free drug (Hu et al. 2005). The drug content in the supernatant after centrifugation was measured by HPLC method using an mobile-phase delivery pump (LC-20 AD; Shimadzu, Japan) at a flow rate of 1.0 mL min⁻¹, a photodiode array detector (SPDM20A; Shimadzu, Japan) set at 240 nm, a 20-μL loop (Rheodyne), and Phenomenex Gemini C₁₈ (250 mm × 4.6 mm) were used. The mobile phase consisted of acetonitrile and water (75:25, v/v). The encapsulation efficiency and loading capacity were calculated by the following equations:

In vitro release studies

The in vitro release behavior of DADS and DADS–SLN was detected by performing dialysis bag diffusion method. Accurately weighed sample equivalent to 1.0 mg of DADS and DADS-SLN was dispersed in 10 mL of Phosphate buffer saline (PBS) and then placed into a pre-swelled dialysis bag with molecular weight cutoff (MWCO) 8–12 kDa (Dialysis membrane-150, HiMedia, Mumbai, India). The bag was individually suspended in 100 mL of PBS pH 4.5 and PBS pH 7.4, at 37 °C in water bath at 100 rpm. At predetermined time intervals, 2-mL sample of the medium was taken and replaced with the same amount of fresh medium. Sink condition was maintained throughout the release period. The amount of DADS released was determined by an HPLC. Data obtained in triplicate were analyzed graphically (Vivek et al. 2014).

In vitro cytotoxicity studies

The cytotoxicity of DADS and DADS-SLN was tested in MCF-10A and MCF-7 cell lines using the SRB assay (Vichai and Kirtikara 2006). Briefly, cells were seeded in a 96-well plate at a density of (2 × 10⁴/well) viable cells per well and incubated for 24 h to allow cell attachment. Then, the cells were treated with serial concentrations of DADS and DADS-SLN at 37 °C. The cells were rinsed with cell medium three times and further incubated at 37 °C for 72 h, followed by fixing of cells with cold trichloroacetic acid, washing, and drying in the air. The fixed cells were then stained with 0.4% SRB dye for 30 min, and the excess dye was washed by 1% acetic acid. After bound dye dissolved in 10-mM Tris base solution, the absorbance was determined using a Thermo scientific multiscan FC microplate photometer at the wavelength of 540 nm. The data were expressed as the percentages of viable cells compared to the survival of control group (cells treated with medium) and presented as mean ± SD (n = 3).

Measurement of reactive oxygen species

Reactive oxygen species (ROS) generation was measured by means of the probe 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA) method with some modification (Wang and Joseph 1999). DCFH-DA is a nonfluorescent permeant molecule that passively diffuses into cells, where it gets hydrolyzed to DCFH as acetates get cleaved by intracellular esterases, and gets entrapped within the cell. In the presence of intracellular ROS such as hydrogen peroxide and other relevant peroxides, DCFH oxidizes to a high fluorescent DCF. Thereby, the DCF fluorescence intensity is proportional to the quantum of hydrogen peroxide generated intracellularly (Zang et al. 2001). Briefly, MCF-7 and MDA-MB-231 cells (2 × 10⁴/well) were seeded into 96-well plate and allowed to attach for 24 h. Then, the medium was replaced with fresh medium and treated with different concentrations of DADS and DADS-SLN (1.562, 3.125, 6.25, 12.5, 25, 50, and 100 μM/well) using blank-SLNs as the respective controls and incubated for 24 h at 37 °C in CO₂ incubator. After treatment, the cells were harvested, washed twice with PBS, resuspended in serum-free medium, and incubated with 10-μM DCFH-DA for 1 h at 37 °C. Thus, after 1-h incubation, fluorescence was monitored at an excitation wavelength of 502 nm and an emission wavelength of 520 nm using Infinite 200 PRO multimode reader (Tecan, India). Results were expressed as fluorescence intensity versus dose. As a positive control, cells were treated with H₂O₂ and processed for ROS detection.

In vitro cellular uptake and internalization of nanoparticles

MCF-7 cells were seeded on 8-well cover-glass chamber under standard conditions and left for overnight incubation. Subsequently, cells were washed and treated with free Nile Red (0.3 μg). Cells were washed thrice after incubation for 2 h and then fixed and stained with ER-Tracker™ Green and 4, 6-diamidino-2-phenylindole (DAPI). Cells were imaged under confocal laser scanning microscopy (Carl Zeiss LSM710, Germany) to determine the cellular uptake and internalization of SLN with ER (Sharma et al. 2015, Zhang et al. 2015).

Apoptosis analysis

Annexin V/propidium iodide (PI) dual-staining method is a sensitive assay for quantitative determination of apoptotic cells (Schutte et al. 1998). MCF-7 cells were seeded in 6-well plate at a density of 2 × 10⁴ cells/well and incubated for 24 h for adhesion. Afterward, old culture media was replaced with fresh culture media, and cells were treated with DADS (8 μM), DADS-SLN (8 μM), and B-SLNs for 24 h. Later, the cells were washed thrice by PBS and stained with Annexin V (2.5 μl) and PI (2.5 μl) at 37 °C for 30 min. The cells were then analyzed by flow cytometry (BD FACS Canto TM, BD Biosciences) for measuring the proportion of apoptotic cells.

Western blot analysis

Using the cell protein isolation kit, the cytosolic fractions were prepared according to the manufacturer’s protocol. After the 24-h treatment with DADS and DADS-SLN, the cells were lysed in RIPA buffer containing 1X protease inhibitor cocktail. Proteins (30 μg/lane) were electrophoresed in 10% SDS-PAGE, and then transferred to PVDF membranes. Then, the membranes were blocked using 5% TBST solution (w/v) nonfat milk for 2 h. Followed by overnight incubation of the membranes at 4 °C with primary antibodies anti-Bcl-2, anti-Bax, anti-Bad, anti-caspase-3, and anti-caspase-9. Afterward, washed with TBST buffer, the PVDF membranes were incubated for 1 h with the secondary antibody, horseradish peroxidase-conjugated goat anti-rabbit IgG. Using chemi-luminescence reagents (ECL Kit) the antibody-bound proteins were detected (Vivek et al. 2014).

Statistical analysis

All the experiments were performed in triplicate, and results were expressed as mean ± SD (n = 3). To analyze the data statistically, Graphpad Prism 5.0 (San Diego, CA) software was used. Data were evaluated by Student’s t-test, and value of P < 0.05 was considered statistically significant.

Results and discussion

Preformulation studies

Drug–lipid compatibility study by FTIR spectral analysis

The drug–lipid compatibility was studied by FT-IR spectroscopy and depicted. The IR spectrum of the pure drug (Figure 1) revealed the absorption bands at 3082.35 cm⁻¹ (=C–H stretch), 2979.16 cm⁻¹ (C–H stretch), and 721.40 cm⁻¹ (C–H rock). These are the characteristic peaks of DADS. For Palmitic acid, IR absorption peaks were recorded at 2917.43 cm⁻¹ (C–H stretch), 2660.89 cm⁻¹ (O–H stretch), 1692.59 cm⁻¹ (C = O stretch), and 933.58 cm⁻¹ (O–H stretch). The peaks appeared by the DADS-SLN were observed at 3082.35 cm⁻¹, 2955.04 cm⁻¹, 2656.07 cm⁻¹, 1705.13 cm⁻¹, 918.15 cm⁻¹, and 720.44 cm⁻¹, respectively indicating vibrations of = C–H stretch, C–H stretch, O–H stretch, C = O stretch, O–H stretch, and C–H rock have not altered. The IR spectrum of drug–lipid physical mixture showed that palmitic acid did not affect DADS signature bands. Compatibility studies carried out for the DADS-SLN showed no disappearance of the peaks or peak shifts, indicating that the drug is compatible with ingredients in the SLN formulation.

Figure 1. FT-IR spectra of (A) DADS, (B) palmitic acid, (C) physical mixture of DADS and palmitic acid, (D) DADS-SLN.

Statistical analysis of experimental data by Design-Expert^® software

The responses of the experimental design were statistically analyzed using Design-Expert^® Software (Version 7.1.6. Stat-Ease, Inc., Minneapolis, MN). It provided ample-sized valuable information and proclaimed the utility of statistical design for carrying out experiments. The selected independent variables including the concentration of surfactant, amount of lipid, and volume of solvent played crucial role in the preparation and stabilization of SLN. And these formulation variables have influenced the observed responses for particle size (PS) and EE. Polynomial equations indicate the main effect, and interaction effects were determined based on assessment of statistical parameters such as multiple correlation coefficient, adjusted multiple correlation coefficient, and the predicted residual sum of squares generated by Design-Expert software. The statistical validation of the polynomial equations was generated by ANOVA provision available in the software. Thereby, the optimized values of the variables were generated as per the obtained experimental data using the Design-Expert software, based on the constrained criterion of desirability presented in Table 2.

Table 2. Responses obtained for studied parameters from experimental batches.

Run	Factor 1: concentration of surfactant (%)	Factor 2: amount of lipid (mg)	Factor 3: volume of organic solvent (mL)	Response 1: particle size (nm)	Response 2: eEE (%)
1	1.00	75	2.00	129	55.85
2	1.50	50	4.00	112.1	56.75
3	1.50	75	3.00	135.2	65.17
4	2.00	75	4.00	121.8	74.68
5	1.50	100	4.00	131.35	73.28
6	1.50	100	2.00	171.2	80.77
7	2.00	50	3.00	102.8	54.33
8	1.00	75	4.00	114.7	48.78
9	1.50	75	3.00	134.2	65.9
10	1.50	75	3.00	133.9	65.5
11	1.50	75	3.00	134.7	63.11
12	1.00	100	3.00	135.4	70.02
13	1.00	50	3.00	101.6	34.8
14	1.50	50	2.00	109.2	39.44
15	1.50	75	3.00	133	65.54
16	2.00	100	3.00	152.4	75.99
17	2.00	75	2.00	141.3	59

Perturbation plots and response surface analyses were plotted in three-dimensional model graphs for depicting the effects of the predetermined factors on the response of the EE and drug loading are shown in Figure 2 based on the model polynomial functions, to determine change in the response surface. These plots infer the role of each variable on each response which can be perceived by the developed celluloid.

Figure 2. Perturbation graph for effect of individual factor on response Y1 (globule size) and response Y2 (zeta potential).

Analysis of responses

All the responses observed for the 17 formulations prepared were fitted into the experimental design provided by the Design-Expert software.

Response Y1 (particle size).

The ratio of maximum to minimum for response Y1 was 1.68 which required no power transformation. Transformation of response plays vital role in data analysis. Transformation is essential if the error (residuals) is a function of magnitude of response (predicted values). The thumb rule of power transformation in responses is necessary. The thumb rule of power transformation responses is when ratio of maximum to minimum response is greater than 10 transformations is not required, whereas less than 3, transformation is required. The selection of model for analyzing the responses was carried out on the basis of sequential model sum of squares, lack of fit, and model summary statistics. The Prob > F value of P < 0.0001, low standard deviation, high R², and lower predicted residual error sum of square (PRESS) value suggested quadratic model for Y1 response. ANOVA of the data confirms that model was significant (Model Prob > F less than 0.05). The Model F value for response y1 was 818.66, which implies model is significant. ANOVA identifies concentration of surfactant, amount of lipid, and volume of solvent as significant model terms that affect the Y1 response (P < 0.05). Lack of fit F value for Y1 was 0.96 which infers that lack of fit was not significant relative to the pure error. The multiple regression terms were also analyzed. The predicted R² and adjusted R² values for response y1 were 0.9928 and 0.9978, respectively. The predicted R² value was found to be in reasonable agreement with adjusted R² value, which indicates that model has predicted the responses well. This indicates the model is good fit. Adequate precision for response y1 was 109.54. Adequate Precision determines the signal-to-noise ratio. A ratio greater than 4 is desirable. This quadratic model can be utilized to navigate the design space.

The quadratic equation generated in term of coded factors for responses Y1 is given in Equation (1)

The regression equation indicates the quantitative effect of all the three formulation variables (X₁, X₂, and X₃). X₁, X₂, and X₃ represent the main effects influencing the response Y1. X₁ X₂, X₁ X₂, X₂ X₃, X₁², X₂², and X₃² are the interaction terms phrases with second-order factors which stand for the nonlinear relationship between the response and the variable infer particle size changes when two variables were changed simultaneously. Positive sign and negative sign indicate the synergistic and antagonistic effects, respectively on the response Y1. Response Y1 shows positive relationship with variables X₁, X₂ and negative relationship with variable X₃.

Perturbation graphs were plotted to determine the factors that influence the response (Figure 2). A steep slope or curvature in a factor shows that response is sensitive to change in that factor, whereas relatively flat line shows insensitivity over the response. Among two or more factors, a perturbation plot is capable to determine whether the factor has most affect over the response. For response y1, Factor A shows noticeable slope; Factor B shows steep slope; and Factor C shows slight bend. It indicates that concentration of lipid and surfactants was most important for determining the particle size. For response y2, Factor A and Factor B show steep curvature, whereas Factor C shows slight bend. It indicates that lipid concentration was important for determining EE.

It is evident that concentration of surfactant increased the particle size significantly. During the emulsification, high shearing droplet size usually gets reduced and also droplet tends to form aggregate in order to reduce their surface energy. But existence of surfactant molecule stabilizes the emulsion by developing a thick protective layer around the droplet to avoid aggregation. The particle size response was increased, when the surfactant concentration increased from the lowest to the middle level (−1 to 0) and later it get declined. The increase in surfactant concentration up to certain extent showed reduction of surface tension between lipid and aqueous phase thereby leading to particle separation and increase in surface area. A saturation point exists beyond the optimum surfactant concentration where the affinity between the lipid and aqueous phase explored completely which did not show the decrease of particle size. The strong positive coefficient of X₂ indicates that particle size is directly proportional to lipid concentration. This was in agreement with the report of Muller–Goymann (Schubert and Müller-Goymann 2003).

The mathematical relationship between the independent variables and the responses is expressed using the response surface plots. The interaction effects of X1 and X2 were analyzed by making X3 at constant level; the interaction effects of X1 and X3 and their effect were analyzed by keeping X2 at constant level; and the effect of X2 and X3 and their interaction when X1 was kept at fixed level on response Y1 are shown in Figure 3(A–C), respectively. Simultaneous increase in the concentrations of X₁ and X₂ demonstrated positive effect (coefficient of X₁X₂= + 3.95) on the particle size as can be seen from Figure 3(A) while X₁ and X₃ were increased simultaneously the particle size was found to be increased significantly (coefficient of X₁ X₃ = −1.30, Figure 3B). Insignificant effect on particle size was observed when X2 and X3 were changed simultaneously. Increase in the concentrations of X₂ and X₃ has less significant effect (coefficient of X₂ X₃= −10.69) on the particle size as shown in Figure 3(C) where X₂ showed positive relationship but X₃ has no significant effect over the particle size. Particle size is the deciding factor which has influence over the drug loading, drug release, bioavailability, and efficacy. Designing nanoparticles within a narrow size distribution will be a challenge if emulsion cannot be formed with a narrow droplet size distribution. Nanoparticles undergo cellular internalization by endocytosis; here particle size has inverse relationship on cellular uptake and influence the drug bioavailability.

Figure 3. Response surface plot showing the effect of (A) lipid concentration and surfactant concentration on Y1, (B) organic solvent and surfactant concentration on Y1, and (C) organic solvent and lipid concentration on Y1.

Response Y2 (entrapment efficiency).

Power transformation is not required for the Y2 response as the ratio of maximum to minimum was 2.32. Quadratic model was suggested for the Y2 response for analyzing it on the basis of sequential model sum of squares, lack of fit, and model summary statistics. The Prob > F value of P < 0.0001, low standard deviation, high R² and lower predicted residual error sum of square (PRESS) values . By the ANOVA of the data, the model was found significant (Model Prob > F less than 0.05). The Model F value for response Y2 was 285.06, which defines the model was significant. Concentration of surfactant, amount of lipid, and volume of solvent were significant model terms that influence the Y2 response (P < 0.05).

Lack of fit F value for Y1 was 0.51 which implies that lack of fit was not significant relative to the pure error. The predicted R² and adjusted R² values for response y1 were 0.9849 and 0.9938, respectively. This indicates that predicted R² value and adjusted R² value are in good agreement indicating a good fit. Adequate precision for response Y2 was 61.503 indicates that it is an adequate signal.

The quadratic equation generated in term of coded factors for responses Y2 is given in Eq. (2)

The regression equation of response Y2 showed positive relationship with all the three formulation variables. As evidenced by the strong positive regression coefficient for X2 in Equation (2), amount of the lipid is the major factor governing drug EE. This could be related to presence of the long-chain fatty acids. DADS being an oily drug may enhance the imperfections in the lipid matrix and thereby assist drug encapsulation. It has a pronounced hydrophobic character that may account for the higher association with the lipid and further enhance the drug encapsulation in the lipid matrix.

The mathematical relationship between the independent variables and the responses is also expressed using the response surface plots as shown in (Figure 4A–C). It can be concluded from these figures that all the three variables are positively influencing the response Y2, and their effects are significant. The negative regression coefficient for simultaneous increase of X₁ and X₂ indicated that the surfactant concentration and the amount of lipid had an inverse relationship with the drug EE. Pluronic^® F-68 is a surface modifying agent which can influence porosity of lipid material. This may render diffusion of encapsulated drug to the external aqueous phase and thus may account for the reduced drug encapsulation. Increasing X₂ and X₃ simultaneously significantly influenced the EE. And even the same positive response observed when there is simultaneous increase of X₂ and X_3.

Figure 4. Response surface plot showing the effect of (A) lipid concentration and surfactant concentration on Y2, (B) organic solvent and surfactant concentration on Y2, and (C) organic solvent and lipid concentration on Y2.

Optimization and validation

The desirability criterion was probed using Design-Expert software to acquire the optimized formulation. The optimum formulation developed on the basis of set criteria of minimal particle size and maximum EE. Thereby, a new batch of SLN with the predicted value of the formulation factors was prepared to validate the optimization protocol. The composition of optimized formulation was 2.23% (w/v) surfactant concentration, 64.15 mg of lipid, and 4 mL of solvent which fulfill the requirements of optimization. The optimized formulation showed 108.112 ± 0.57 nm (Figure 5A) and EE 71.806 ± 0.14% which were in good agreement with the predicted values.

Figure 5. (A) Particle size distribution of DADS-SLN and (B) scanning electron microscopic image of DADS-SLN.

Physicochemical characterization of solid lipid nanoparticles

SEM studies revealed that DADS-SLN were almost uniform-sized, mono-dispersed spherical shaped. It was observed majority of SLN showed slight rough surface morphology (Figure 5B). Zeta potential and drug loading of DADS-SLN were −7.7 mv and 34.72333 ± 1.000417.

In vitro drug release

Excessive glycolytic rate, high lactic acid production, and insufficient drainage by convective transport, H + ions accumulate in the tumor tissue. So, pH shift occurs toward the high acidic values, pronouncedly in bulky and/or low-flow tumors (Vaupel et al. 1989). Beside, extracellular acidosis in solid-growing tumors develops to a chemo-resistant phenotype due to increased p-glycoprotein activity (Sauvant et al. 2008). Ideal anticancer drug delivery system should release the cargo efficiently at the tumor site while less release at the normal cells. Such a smart release would ultimately result an improved cytotoxic efficacy against tumors. Since, cancer cells develop more acidic microenvironment delivery system having distinct release profile close to the physiological pH would be an invaluable approach for anticancer chemotherapy. To demonstrate the pH variation between cellular exterior (pH 7.4) and intracellular lysosome (pH 4.5), drug release was investigated at different pH environment. The drug release profile of SLN showed controlled release of DADS from lipid nanoparticles. As clearly seen in (Figure 6), higher release rate of DADS was achieved at lower pH, with the present system. DADS is a weak acid and alkaline in nature which is due to the pair of sulfide groups and it exhibits higher solubility at lower pH. Thereby, the encapsulated DADS in the SLN have a high tendency to enter into the release medium of lower pH. The favored release in acidic environment would show higher release rate of DADS in tumor cells; still more in the resistant cell lines will elevate therapeutic potential to the delivery system. Interestingly, the burst release of both SLN in pH 5.0 and 7.4 was not observed.

Figure 6 In vitro DADS release profiles from SLN at neutral condition (pH 7.4) and acidic conditions (pH 4.5) at 37 °C. Each point represents the mean ± SEM, and P < 0.05 was considered to be statistically significant.

In vitro cytotoxicity study

The in vitro cytotoxic activity of DADS, DADS-SLN, and blank-SLN was evaluated by the SRB assay; dose-dependent and time-dependent cell viability is shown in Figure 7(A and B). DADS, DADS-SLN-exhibited negligible cytotoxicity in MCF-10A cell lines (Figure 7C), and even blank SLN have no cytotoxic effect in MCF-7 cells which confirms the safety of the nanoparticles.

Figure 7. Antiproliferative activity study. (A) Dose-dependent cytotoxicity: MCF-7 cells were treated with different concentrations of DADS, DADS-SLN, and Blank-SLN. The extent of cell viability was measured after 24 h by performing SRB assay. Data are represented as mean ± SD (n = 3). *P < 0.05, DADS-SLN versus DADS. (B) Time-dependent cytotoxicity: MCF-7 cells were treated with different concentrations of DADS, DADS-SLN, and Blank-SLN. (C) Dose-dependent cytotoxicity: MCF-10A cells were treated with different concentrations of DADS, DADS-SLN, and Blank-SLN. The extent of growth inhibition was measured after predetermined time points of 1, 3, 6, 12, 24, and 48 h by performing SRB assay. Data are represented as mean ± SD (n = 3).

MCF-7 cells treated with DADS, DADS-SLN, and blank-SLN exhibited cytotoxicity at various concentrations (1.562, 3.125, 6.25, 12.5, 25, 50, and 100 μM). It is evident that DADS, DADS-SLN, and FA-DA-SLN exhibited dose-dependent cytotoxic action. DADS-SLN had exhibited lower cytotoxic action when compared with FA-DA-SLN which might be due to the efflux of the diffused drug in the cytoplasm by P-glycoprotein (P-gp) pumps. FA-DA-SLN might be internalized into cells via receptor mediated endocytosis and have no link with P-gp efflux. This leads to sustained presence of drug inside cells which exhibit high cytotoxic action. The effect of time-dependent cytotoxic action of DADS, DADS-SLN, and Blank-SLN was also studied (Figure 7C). There was no significant difference in the effect between DADS and DADS-SLN until 12-h treatment, but at 24 h, 48 h, and 72 h DADS-SLN was significant effect than DADS indicating the sustained release effect of SLNs (Figure 7B). The IC₅₀ concentrations of the DADS and DADS-SLN from the dose-dependent study were used for further in vitro studies.

Measurement of reactive oxygen species (ROS)

The intracellular ROS was determined with different concentrations of 1.562, 3.125, 6.25, 12.5, 25, 50, and 100 μM of DADS and DADS-SLN. The results were expressed as RFU of fluorescent DCF and proportional increase in RFU infers elevated ROS generation. As shown in Figure 8(A), dose-dependent increase of ROS production was observed when MCF-7 cells treated with 1.562–100 μM of DADS. When compared with DADS, DADS-SLN has exhibited enhanced fluorescent intensity. In time-dependent study, DADS-SLN showed significantly higher ROS generation compared to DADS at 24 and 48 h time points (Figure 8B). These results were in agreement with the results of previous study of time-dependent cytotoxic study.

Figure 8. (A) Effect of dose on generation of ROS by DADS and DADS-SLN in MCF-7 cells after 24 h treatment was determined by ROS assay using DCF-DA. Data as mean ± SD (n = 3). *P < 0.05, DADS versus DADS-SLN. (B) Effect of time of treatment on generation of ROS by DADS and DADS-SLN. MCF-7 cells were treated with three formulations (10-μM DADS), and generation of ROS was determined at predetermined time points of 1,3, 6, 12, 24,48, and 72 h. Data as mean ± SD (n = 3). *P < 0.05, DADS versus DADS-SLN.

In vitro cellular uptake and internalization of nanoparticles

The cardinal characteristic feature of chemotherapeutic for the cancer treatment is intra-cellular uptake. Endocytosis is considered as key cellular uptake pathway for nanoparticles in tumors. Thereby, cellular uptake of SLN in cancer cells was investigated to illustrate the internalization mechanism. For the precise observation, the nuclei were stained with DAPI (blue dye); the endoplasmic reticulum and actin were stained with ER-Tracker™ Green (green dye); and the red fluorescence from Nile red labels the localization of lipid nanoparticles. The co-localization of SLN with ER was visualized as yellow spots due to overlapping green and red fluorescence. Row 1 exhibits passive or less targeting effect of DADS, and Row 2 exhibits the enhanced internalization of DADS-SLN (Figure 9). These results demonstrated the enhanced cellular uptake of the SLN as compared with the naïve drug. These images suggest receptor-mediated endocytosis could be the mechanism for the cellular uptake.

Figure 9. Confocal microscopic images of free Nile Red and SLN. Images taken after 2 h of treatment and stained with ER-Tracker™ Green (green for endosplamic reticulum) and DAPI (blue for nucleus). Nile Red represents red color. The overlap of ER-Tracker™ Green and Nile Red is visualized as yellow-color spots. (For color images refer online version).

Apoptosis analysis

Annexin V labels the extrinsic phosphatidyl serine (PS) with fluorescence; this is an essential mark in differentiating early apoptotic cells from live cells. In addition, another fluorescent dye PI labels by the permeation into necrotic cells and it could not internalize into live cells. So the four variants cells can be projected into four quadrants. Necrotic cells/mechanically injured cells were stained with PI appear in the first quadrant (Q1), late apoptotic cells were stained with PI, and Annexin V appear in the second quadrant (Q2); early apoptotic cells were stained with Annexin V appear in the third quadrant (Q3), and healthy cells not stained with PI and Annexin V appear in the fourth quadrant (Q4). Apoptosis was analyzed by the percentage of the gated events as shown in (Figure 10), the percentages of early apoptotic and late apoptosis cells in the control were 0.7% and 0.8%, while that of the DADS-SLN were 3.3% and 55.6%, which were higher than the DADS (2.61% and 12.42%) and the blank-SLN (1.4% and 0.9%), thus increased apoptosis was observed after treatment at the concentration of 8 μM for 24 h. However, the percentage of apoptotic cells in DADS-SLN was higher which indicates that DADS slightly induced cell apoptosis, thus DADS-SLN significantly apoptosis effect in comparison with naïve DADS. Blank-SLNs exhibited negligible apoptotic action on MCF-7 cells; this mild cytotoxicity might be attributed due to the excipients. Thereby, nanoencapsulation of DADS could enhance the anticancer effect whose mechanism was mainly associated with the enhanced induction of apoptosis.

Figure 10. Quantitative apoptotic measurement in MCF-7 cells with treatment of control, DADS, DADS-SLN, and Blank-SLN. (A) Control (B) Blank-SLN (C) DADS (D) DADS-SLN. Results of dose-dependent apoptosis are expressed as plot of Annexin V-FITC versus PI, and representative values are shown.

Western blot analysis

Apoptotic signaling pathway is controlled by several complex molecules which involve in the expression changes of distinct proapoptotic and antiapoptotic proteins. Mitochondria-mediated apoptosis includes upregulation of caspase activation which is controlled by Bcl-2 family proteins. The Bcl-2 family of proteins includes antiapoptotic proteins (Bcl-2, Bcl-xL and Mcl-1), as well as a number of pro-apoptotic molecules (Bax, Bad and Bim), whereas overexpression of the antiapoptotic protein Bcl-2, blocks mitochondrial outer membrane permeabilization, and inhibits apoptosis (Elumalai et al. 2012, Gillings et al. 2009, Tait and Green 2010). DADS has been well-documented in inducing the intrinsic apoptosis pathway (Lee et al. 2011, Lei et al. 2008). The molecular mechanisms of apoptosis by the DADS and DADS-SLN were explored in the MCF-7 cells by Western blot. When compared with the DADS, DADS-SLN exhibited upregulation of pro-protein expressions of Bax, Bad, caspase-9, and caspase-3 and downregulation of antiapoptotic proteins such as Bcl-2 (Figure 11). This suggests that DADS-SLN induces cell apoptosis through intrinsic signaling pathway.

Figure 11. Mitochondrial-mediated apoptosis induced by DADS-SLN in comparison with DADS-treated MCF-7 cells confirmed by Western blot analysis of apoptotic-related protein expressions.

Discussion

Potential of DADS as a powerful antioxidant (Na et al. 2012, Wu et al. 2005) and anticancer agent (Lee et al. 2011, Liao et al. 2009, Yang et al. 2006) is very well-documented. Significant efficacy gets hindered by some limitations like low oral bioavailability (Germain et al. 2002). Another important limitation of DADS is shorter biological half-life (Ankri and Mirelman 1999, Lemar et al. 2002). So, these drawbacks turn as rationale to develop drug delivery system. Hence, in the present study, we proposed solid lipid nanoparticulate formulation for DADS to overcome the aforementioned limitations. IR analysis has revealed that there were no drug-lipid interactions in the physical mixture and DADS-encapsulated SLN. It is necessary to identify the drug–lipid interactions as it may affect the EE and stability of the SLN. Experimental designs allowed statistical optimization of the SLN by evaluating the most important physicochemical parameters on observed responses and investigating the relationship between factors by the perturbation plots and response surface plots. Box–Behnken design was successfully utilized to statistically optimize the formulation parameters and to evaluate the main interaction and quadratic effects of the independent variables on the particle size and EE. Particle size of the SLN is quintessential for the cellular uptake and EE of the SLN influence the stability and release point of view. Shorter biological half-life limitation can be prevented by encapsulating inside the SLNs which exhibit sustained release. In vitro release study revealed that SLNs has exhibited sustained drug release behavior until 48 h. The cytotoxicity action of DADS, DADS-SLN, and blank-SLN was studied using the SRB assay both in dose- and in time-dependent manner. It was observed that DADS-SLN has significant cytotoxic action in both the cell lines which might be due to the increased intracellular uptake of DADS-SLN by endocytosis. In time course cytotoxicity study, DADS-SLN showed prominent effect of time of treatment on their cytotoxic action compared to DADS. This was attributed to the sustained release behavior (and thereby increased retention time) of DADS-SLNs in the intracellular site corroborating the in vitro release data. DADS-SLN showed more remarkable difference in cytotoxic action after 12 h compared to DADS. The increased cytotoxic action of DADS-SLN was observed in both dose- and time-dependent study. Poloxamer can be the one of the reason for the reduction in IC₅₀ as aforementioned that lipid material might have been destructed by enzymes and it has no action anymore. It has ability to inhibit P-glycoprotein (P-gp) and multidrug resistance (MDR) mechanisms in tumor cells. Under this circumstance, the drug accumulation occurs in the tumor cells (Yan et al. 2010). Also, MDR mechanisms can be inhibited by Polaxamer, which results in DTX accumulation inside the cell. Petersen et al. reported that Poloxamer can improve adsorption of SLN by coming in contact with cell membrane and will enable endocytotic pathways (Petersen et al. 2011). DADS is known to induce apoptosis by elevating the generation of ROS (Wu et al. 2005). DADS-SLN showed prominent increase in ROS generation in both dose-dependent and time-dependent study. By the cellular uptake investigation, SLN exhibited enhanced internalization with the treated cells. Further, SLNs were found co-localized within endoplasmic reticulum which may be involved in inducing specific cellular responses (Paulo et al. 2011). Apoptosis analysis by flow cytometry affirmed that enhanced apoptosis which may be attributed due to nanoencapsulation of DADS, sustained release of DADS from lipid shell, and elevated cellular uptake. Further, the apoptosis induction by intrinsic signaling pathway of DADS-SLN was confirmed by the Western blot analysis. By the present investigation, we conclude that proposed drug delivery system of DADS is biocompatible, biodegradable and exhibits sustained release behavior. The elevated efficacy of DADS against MCF-7 breast cancer cells affirms the potential of proposed drug delivery in the treatment of breast cancer. The aforementioned results, from present investigation, suggest that the developed DADS-SLN can be exploited for active targeting of breast cancer and other cancers like brain, prostate, etc.

Funding information

The authors acknowledge grant support from JSS University, Mysore (REG/DIR(R)/URG/54/2011-12/1883). Mr. Siddhartha Venkata Talluri also gratefully acknowledges the support of the JSS-URF fellowship from JSS University.

Abbreviations
DADS	=	diallyl disulfide
SLN	=	solid lipid nanoparticles
PS	=	particle size
EE	=	entrapment efficiency
DOE	=	design of experiment
RSM	=	response surface methodology
SRB	=	sulforhodamine B
DCFH-DA	=	dichlorodihydrofluorescein diacetate
FTIR	=	Fourier transform infra-red
ROS	=	reactive oxygen species
P-gp	=	P-glycoprotein
MDR	=	multidrug resistance

Acknowledgements

The authors are thankful to Pindiprolu SS Kiran, post-graduate scholar, JSS College of Pharmacy, Udhagamandalam for giving valuable time and suggestions in preparation of this manuscript. We would like to acknowledge Gattefosse foundation, USA for gifting lipids used in the formulation.

Disclosure statement

All the authors have no competing interests.