TY - JOUR
AU - Wang, Zhuanhua
AB - Abstract The self-nanoemulsifying drug delivery system has shown many advantages in drug delivery. In this study, a self-nanoemulsifying drug delivery system of buckwheat flavonoids was prepared for enhancing its antioxidant activity and oral bioavailability. A nanoemulsion of buckwheat flavonoids was developed and characterized, and its antioxidant, in vitro release, and in vivo bioavailability were determined. The nanoemulsion was optimized by the central composite design response surface experiment, and its particle size, polymer dispersity index (PDI), zeta potential, morphology, encapsulation efficiency, and stability were evaluated. The antioxidant activity was tested by measuring its 2,2-diphenyl-1-picrylhydrazyl scavenging activity, hydroxyl radical scavenging activity, and superoxide anion scavenging ability. In vitro release of buckwheat flavonoids nanoemulsion showed a higher cumulative release than the suspension, and the release fitting model followed the Ritger–Peppas and Weibull models. The effective concentration of the nanoemulsion was evaluated in vivo using a Wistar rat model, and the area under the plasma concentration-time curve of the buckwheat flavonoids nanoemulsion was 2.2-fold higher than that of the buckwheat flavonoid suspension. The Cmax of the nanoemulsion was 2.6-fold greater than that of the suspension. These results indicate that the nanoemulsion is a promising oral drug delivery system that can improve the oral bioavailability to satisfy the clinical requirements. buckwheat flavonoids, self-nanoemulsifying drug delivery system, nanoemulsion, antioxidant activity, bioavailability Introduction Buckwheat (genus: Fagopyrum; family: Polygonaceae), an important medicinal and edible herb, contains many bioactive compounds, including flavonoids, phenolic compounds, triterpenoids, amino acids, volatile compounds, etc. [1]. Buckwheat flavonoids contain a common benzo-g-pyrone structure and a polyphenolic structure consisting of a 15-carbon basic skeleton (C6-C3-C6). The herb has various pharmacological activities, including antioxidant, anticancer, anti-inflammatory, and other activities [2–4]. Though buckwheat flavonoids possess diverse activities, their medicinal and edible values are limited due to the low solubility, low oral bioavailability, and poor systemic absorption. So, the clinical administration of buckwheat flavonoids requires a reasonably effective drug delivery system. Recently, the self-nanoemulsifying drug delivery system (SNEDDS), a spontaneous emulsification method, has attracted increased attention [5–7], which can improve the oral bioavailability of hydrophobic drugs and bioactive food components [8]. The system is composed of various components, including oil phase, surfactant, cosurfactant, and water phase. The system has a variety of molding mechanisms, and the ultra-low or negative surface tension is considered as the main reason behind the formation of the nanoemulsion, and appropriate phase composition is important for the stability of nanoemulsion [9,10]. The diffusion of the cosurfactant from the organic phase into the aqueous phase may also be an important mechanism for the formation of the nanoemulsion [11,12]. The small particle sizes in a drug delivery system offer many promising advantages, including smaller surface tension and greater stability. The smaller particle size offers a greater interfacial surface area for drug absorption, and the solubility and bioavailability are enhanced. For example, a SNEDDS of resveratrol with the droplet size of 50 nm exhibited better antioxidant capacity and less toxicity than free resveratrol [13]. SNEDDS of nintedanib (a poorly soluble molecule) significantly increased its area under the plasma concentration-time curve (AUC) [14]. Therefore, the self-nanoemulsifying drug delivery system can improve the bioactivity of encapsulated components, which can also potentially improve the medicinal and edible values of buckwheat flavonoid. To enhance the antioxidant activity and oral bioavailability of buckwheat flavonoids, a SNEDDS of buckwheat flavonoids was prepared in this study. The preparation and optimization were based on the central composite design response surface experiment, and particle size, zeta potential, and encapsulation efficiency were determined. The study specifically focused on evaluating the antioxidant activity and comparing the oral bioavailability of the nanoemulsion with that of suspension in vivo. Materials and Methods Materials Buckwheat flavonoids (purity >95%) were procured from Xi’an Tianxiang Bioengineering Co., Ltd (Xi’an, China). Rutin was purchased from the National Institute for the Control of the Pharmaceutical and Biological Products (Beijing, China). 2,2-Diphenyl-1-picrylhydrazyl (DPPH) was purchased from Phygene Life Sciences Co., Ltd (Fuzhou, China). The reagents used in high-performance liquid chromatography (HPLC) were chromatographically pure, and the other chemicals and reagents and solvents were of analytical grade. Preparation and experimental design Buckwheat flavonoids nanoemulsion was prepared through self-nanoemulsification. Briefly, PEG-40 hydrogenated castor oil (surfactant) and propylene glycol (cosurfactant) were thoroughly mixed with a magnetic stirrer at room temperature, and castor oil was added to the mixture. Subsequently, the buckwheat flavonoids were added into the admixture and stirred, until they were dissolved completely. Then, distilled water was added dropwise into the system until the nanoemulsion was formed. The experimental design was carried out by the central composite design (CCD) response surface experiment [15, 16]. Based on the solubility study and the pseudo-ternary phase diagram, three factors of the oil phase (A), surfactant and cosurfactant (Smix, B), and water phase (C) were used as the independent variables, and the particle size (Y1) and encapsulation efficiency (Y2) were considered as the dependent variables. Design-Expert 8.0.6 software was used to analyze the experimental data, and the three-dimensional response surface graphs were plotted. The fitted model was expressed by the coefficient of R, and its statistical significance was determined by the F-test. Characterization of nanoemulsion The particle size and zeta potential of the nanoemulsion were analyzed using the dynamic light scattering method with a Malvern Zetasizer (Malvern Instruments Ltd, Malvern, UK). The morphology of nanoemulsion was observed under a transmission electron microscope (JEM-1011; JEOL, Tokyo, Japan) at 200 kV accelerating voltage. Encapsulation efficiency was calculated using the formula: encapsulation (%) = Ws/Wt×100% (Ws and Wt are the amounts of the supernatant and total drug, respectively). The nanoemulsion was dispersed in methanol and then ultrasonicated at 37°C for 30 min. The mixture was centrifuged at 5000 g for 10 min, and the supernatant was analyzed by HPLC (Agilent 1200 LC; Agilent Tech Instrument Co, Santa Clara, USA) equipped with a C18 column (Agilent Zorbax, 250 mm×4.6 mm, 5 μm). A mixture of methanol and acetic acid (50:50) aqueous solution (0.5%) was used as the mobile phase at a flow rate of 1 ml/min. The stability of the nanoemulsion was tested at different temperatures (−40°C, 4°C, 25°C, and 37°C) for 30 days. The nanoemulsion was also examined for the phase separation by centrifuging at 3000 g and 10,000 g. The nanoemulsion was diluted 10- and 100-folds with distilled water to investigate the particle size, zeta potential, and stratification of the system. DPPH scavenging activity The DPPH (0.2 mM, 1 ml) reagent was used for determining the antioxidant activity of the buckwheat flavonoid nanoemulsion (0.2, 0.4, 0.6, 0.8, and 1.0 mg/ml, 1 ml) [17, 18]. DPPH ethanol solution and nanoemulsion were mixed and kept in the dark for 30 min at room temperature, and the absorbance was measured at 517 nm. The mixtures of DPPH and distilled water were used as the controls. Meanwhile, the suspension and the ascorbic acid were also examined using the above method. The DPPH scavenging activity was calculated by the following equation: $$\begin{equation*}{\rm{DPPH\ scavenging\ activity}}\left( {\rm{\% }} \right) = {{{A_c} - {A_s}} \over {{A_c}}} \times 100{\rm{\% }}\end{equation*}$$ where As and Ac are the absorbance of sample and control, respectively. Hydroxyl radical scavenging activity The hydroxyl radical scavenging activities of buckwheat flavonoid nanoemulsion and suspension and the ascorbic acid solution samples were measured as previously reported [19, 20]. In brief, FeSO4 solution (2.5 mM, 1 ml) was added to the samples and then H2O2 (2.5 mM, 1 ml) and salicylic acid (2.5 mM, 1 ml) were added successively. The temperature of the mixture was adjusted to 37°C for 60 min. After completion of the reaction, the hydroxyl radical was measured by monitoring the absorbance at 510 nm. Meanwhile, distilled water in stead of the sample was used as the control. The scavenging activity of hydroxyl radicals was calculated with the following equation: $$\begin{equation*}{\rm{Hydroxyl\ radical\ scavenging\ activity}}\left( {\rm{\% }} \right) = {{{A_c} - {A_s}} \over {{A_c}}} \times 100{\rm{\% }}\end{equation*}$$ where As and Ac are the absorbance of sample and control, respectively. Superoxide anion scavenging activity Pyrogallic acid (25 mM, 10 µl) was added to 3 ml of Tris-HCl buffer (pH 8.2). The absorbance of the mixture was determined every 30 s for 4 min at 325 nm. According to the absorbance, the auto-oxidation rate of pyrogallic acid was evaluated by the slope of the absorbance-time curve. Then the samples (0.2, 0.4, 0.6, 0.8, and 1.0 mg/ml) were measured following the above method, and the scavenging rate of superoxide anion radical was calculated by the following formula [21]: $$\begin{equation*}{\rm{Superoxide\ radicalscavenging\ activity}}\left( {\rm{\% }} \right) = {{{V_c} - {V_s}} \over {{V_c}}} \times 100{\rm{\% }}\end{equation*}$$ where Vs and Vc are the scavenging rate of sample and control, respectively. In vitro release experiment In vitro release experiment was carried out by dynamic dialysis of nanoemulsion [22]. The nanoemulsion and suspension of buckwheat flavonoids were poured into activated dialysis bags (molecular weight cutoff, 8 kDa) and immersed in 50 ml of phosphate buffer saline (PBS; pH 7.4) at 37°C±0.5°C under continuous stirring at 100 rpm. Then, 2 ml of each sample was withdrawn at different time points (0.25, 0.5, 1, 2, 4, 8, 12, 24, 36, and 48 h), and an equal volume of PBS was replenished. The samples were examined by ultraviolet spectroscopy (UV 1200; Shanghai Meipuda Instrument Co., Shanghai, China) at 507 nm using NaNO2-Al(NO3)3 colorimetric method, and the standard curve was A=0.0149C+0.1008 (r=0.9997). The cumulative release (Q) was calculated by the following formulas: $$\begin{equation*}{\rm{Cumulative\ release }}\left( {\rm{\% }} \right) = \left( {{{{{{C}}_{{n}}}{{V}} + {{{V}}_{{i}}}\mathop \sum \nolimits_{{{i}} = 1}^{{{n}} - 1} {{{C}}_{{i}}}} \over {{m}}}} \right) \times 100{\rm{\% }}\end{equation*}$$ where Ci and Cn are the concentration at different time points, V and Vi are the volumes of 50 ml PBS and sample (2 ml), respectively, and m is the initial amount of drug (10 mg). In vivo experiment The Wistar rats (200±20 g) were obtained from the Experimental Animal Center of Shanxi Medical University (Taiyuan, China), and all experimental procedures were approved by the Institutional Animal Care and Use Committee of Shanxi Medical University. The animals were maintained under a 12/12 h light/dark cycle at 25±2°C and relative humidity of 60%±10%. The animals were provided free access to water and food (food was provided until 12 h before the experiment). The buckwheat flavonoid nanoemulsion and suspension were administered intragastrically at a dose of 60 mg/kg to the test and control groups [23,24]. The blood samples (0.5 ml) were withdrawn from the fundus venous plexus into heparinized tubes at 0.083, 0.25, 0.5, 1, 2, 4, 8, 12, 24, and 48 h after oral administration of the drugs. The plasma was separated by centrifuging the blood sample at 10,000 g for 8 min. Then, the morin hydrate (internal standard) was added to 100 µl of plasma, vortexed for 3 min, and 300 µl of methanol was added subsequently and mixed for 5 min. The mixtures were centrifuged at 10,000 g for 8 min to precipitate the protein aggregates. The supernatant was dried, re-dissolved in 100 µl of methanol, and then centrifuged at 10,000 g for 5 min. The supernatants were analyzed by HPLC using a mobile phase containing methanol:acetic acid aqueous solution (0.5%; 50:50) at a flow rate of 1 ml/min. The analysis of samples and internal standard was not influenced by endogenous impurities. The standard curve, y=15,677 x+8.328 (r=0.9990), was plotted with the ratio of peak areas of the standard and internal standard as ordinate against the standard concentration as abscissa. The recovery rate was 95%–105%, and the relative standard deviation (RSD) was less than 3%; the RSD of precision was less than 3%, and the RSD of reproducibility was less than 2%. Statistical analysis The data were expressed as the mean±SD. The statistical analyses were conducted using the Student’s paired t-test. The difference between the mean was considered statistically significant at P<0.05. The pharmacokinetic parameters were calculated with the Statistics software program DAS 2.0 (Mathematical Pharmacology Professional Committee of Shanghai, Shanghai, China). The statistical analysis of the standard curve was carried out by OriginPro 8.0 software (OriginLab Corporation, Northampton, USA). Results Optimization of preparation of nanoemulsion using central composite design response surface experiment The three factors of the oil phase, surfactant to cosurfactant, and water phase were used as independent variables, and the particle size and encapsulation efficiency were considered as dependent variables; the nanoemulsion optimization was performed by the Design-Expert software. F-test and P value were used to analyze the statistical significance of the regression models, and the analysis of variance for the response surface models is shown in Table1, and the multiple regression equations from the design expert software were as follows: Table 1. Statistical analysis of variance for the central composite design response surface results Source . Y1 . . Y2 . Sum of squares Mean square F value P Sum of squares Mean square F value P Model 3443.87 382.65 17.62 <0.0001 5713.32 634.81 18.41 <0.0001 A-A 3.34 3.34 0.15 0.7030 325.31 325.31 9.43 0.0118 B-B 752.56 752.56 34.65 0.0002 505.28 505.28 14.65 0.0033 C-C 850.95 850.95 39.19 <0.0001 304.10 304.10 8.82 0.0141 AB 10.63 10.63 0.49 0.5002 0.015 0.015 <0.0001 0.9836 AC 90.72 90.72 4.18 0.0682 38.06 38.06 1.10 0.3182 BC 384.75 384.75 17.72 0.0018 2.13 2.13 0.062 0.8087 A2 481.86 481.86 22.19 0.0008 4461.71 4461.71 129.38 <0.0001 B2 408.60 408.60 18.82 0.0015 236.48 236.48 6.86 0.0257 C2 455.31 455.31 20.97 0.0010 49.92 49.92 1.45 0.2566 Residual 217.16 21.72 344.85 34.48 Lack of fit 160.60 32.12 2.84 0.1383 233.04 46.61 2.08 0.2197 Pure error 56.56 11.31 111.81 22.36 Cor total 3661.03 6058.16 Source . Y1 . . Y2 . Sum of squares Mean square F value P Sum of squares Mean square F value P Model 3443.87 382.65 17.62 <0.0001 5713.32 634.81 18.41 <0.0001 A-A 3.34 3.34 0.15 0.7030 325.31 325.31 9.43 0.0118 B-B 752.56 752.56 34.65 0.0002 505.28 505.28 14.65 0.0033 C-C 850.95 850.95 39.19 <0.0001 304.10 304.10 8.82 0.0141 AB 10.63 10.63 0.49 0.5002 0.015 0.015 <0.0001 0.9836 AC 90.72 90.72 4.18 0.0682 38.06 38.06 1.10 0.3182 BC 384.75 384.75 17.72 0.0018 2.13 2.13 0.062 0.8087 A2 481.86 481.86 22.19 0.0008 4461.71 4461.71 129.38 <0.0001 B2 408.60 408.60 18.82 0.0015 236.48 236.48 6.86 0.0257 C2 455.31 455.31 20.97 0.0010 49.92 49.92 1.45 0.2566 Residual 217.16 21.72 344.85 34.48 Lack of fit 160.60 32.12 2.84 0.1383 233.04 46.61 2.08 0.2197 Pure error 56.56 11.31 111.81 22.36 Cor total 3661.03 6058.16 Open in new tab Table 1. Statistical analysis of variance for the central composite design response surface results Source . Y1 . . Y2 . Sum of squares Mean square F value P Sum of squares Mean square F value P Model 3443.87 382.65 17.62 <0.0001 5713.32 634.81 18.41 <0.0001 A-A 3.34 3.34 0.15 0.7030 325.31 325.31 9.43 0.0118 B-B 752.56 752.56 34.65 0.0002 505.28 505.28 14.65 0.0033 C-C 850.95 850.95 39.19 <0.0001 304.10 304.10 8.82 0.0141 AB 10.63 10.63 0.49 0.5002 0.015 0.015 <0.0001 0.9836 AC 90.72 90.72 4.18 0.0682 38.06 38.06 1.10 0.3182 BC 384.75 384.75 17.72 0.0018 2.13 2.13 0.062 0.8087 A2 481.86 481.86 22.19 0.0008 4461.71 4461.71 129.38 <0.0001 B2 408.60 408.60 18.82 0.0015 236.48 236.48 6.86 0.0257 C2 455.31 455.31 20.97 0.0010 49.92 49.92 1.45 0.2566 Residual 217.16 21.72 344.85 34.48 Lack of fit 160.60 32.12 2.84 0.1383 233.04 46.61 2.08 0.2197 Pure error 56.56 11.31 111.81 22.36 Cor total 3661.03 6058.16 Source . Y1 . . Y2 . Sum of squares Mean square F value P Sum of squares Mean square F value P Model 3443.87 382.65 17.62 <0.0001 5713.32 634.81 18.41 <0.0001 A-A 3.34 3.34 0.15 0.7030 325.31 325.31 9.43 0.0118 B-B 752.56 752.56 34.65 0.0002 505.28 505.28 14.65 0.0033 C-C 850.95 850.95 39.19 <0.0001 304.10 304.10 8.82 0.0141 AB 10.63 10.63 0.49 0.5002 0.015 0.015 <0.0001 0.9836 AC 90.72 90.72 4.18 0.0682 38.06 38.06 1.10 0.3182 BC 384.75 384.75 17.72 0.0018 2.13 2.13 0.062 0.8087 A2 481.86 481.86 22.19 0.0008 4461.71 4461.71 129.38 <0.0001 B2 408.60 408.60 18.82 0.0015 236.48 236.48 6.86 0.0257 C2 455.31 455.31 20.97 0.0010 49.92 49.92 1.45 0.2566 Residual 217.16 21.72 344.85 34.48 Lack of fit 160.60 32.12 2.84 0.1383 233.04 46.61 2.08 0.2197 Pure error 56.56 11.31 111.81 22.36 Cor total 3661.03 6058.16 Open in new tab $$\begin{align*} {\rm{Y}_1} &= 22.43 - 0.58\rm{A} + 7.42\rm{B} + 7.89\rm{C} + 1.15AB - 3.37\rm{AC}\\[2.5pt] &\quad - 6.93\rm{BC} + 7.49{\rm{A}^2} + 5.31{\rm{B}^2} + 5.60{\rm{C}^2} (\rm{r} = 0.9699,\\[2.5pt] &\quad P \lt 0.0001, {\rm lack\ of\ fit} = 0.1383 )\\[2.5pt] {\rm{Y}_2} &= 93.23 + 5.72\rm{A} + 6.08\rm{B} + 4.72\rm{C} + 0.04\rm{AB} + 2.18\rm{AC}\\[2.5pt] &\quad - 0.52\rm{BC}-22.79{\rm{A}^2}-4.04{\rm{B}^2}-1.86{\rm{C}^2} ( \rm{r} = 0.9711,\\[2.5pt] &\quad P \lt 0.0001, {\rm Lack\ of\ fit} = 0.2197 \end{align*}$$ The model of the regression equations of the particle size was significant with P value <0.05, P value of the lack of fit of the particle size was 0.1383, and the coefficient of R was fine. The predicted R2 (0.8236) reasonably agreed with the adjusted R2 (0.9420). The adequate precision was measured by the signal-to-noise ratio. A ratio greater than 4 was desirable. The ratio of 11.044 indicated an adequate signal. This model could be used to navigate the design space. The model of the regression equations of the encapsulation efficiency was very significant with P value <0.0001, the P value of lack of fit of the encapsulation efficiency was 0.2197, and the coefficient of R was fine. The predicted R2 of 0.8622 reasonably agreed with the adjusted R2 of 0.9443. The ratio of adequate precision was 13.856, and this model was adequate. According to the results of regression analysis, three-dimensional response surface diagrams of the relationship between independent variables and dependent variables were drawn (Fig. 1). Among the main factors affecting the particle size, the water phase (C) showed the greatest effect, followed by the surfactant and cosurfactant (B) and the interaction items (B and C). The particle size of nanoemulsion was increased with the Smix and the water phase, and the effect of the oil phase (A) on the particle size was not very significant (Fig. 1A−C). As shown in Fig. 1B, the particle size was increased with the increasing values of the Smix and water phase simultaneously, which implied that the interaction effects of B and C were positively significant. Among the main factors of the encapsulation efficiency, the quadratic of oil phase showed a significant effect, and other factors were not significant. Among the main factors affecting the encapsulation efficiency, the surfactant and cosurfactant showed the greatest effect, and the encapsulation efficiency of nanoemulsion was increased with the increase in the surfactant and cosurfactant (Fig. 1D,E). The oil phase and water phase also affected the encapsulation efficiency significantly (Fig. 1D−F). The encapsulation efficiency was increased first and then decreased with the increase in the oil phase, and the encapsulation efficiency was enhanced when the water phase was increased. Figure 1. Open in new tabDownload slide The response surface diagrams of particle size and encapsulation efficiency    (A–C) The response surface diagrams of particle size (diameter). (D–F) The response surface diagrams of encapsulation efficiency (E). From the design response surface experiment, we evaluated the predictive optimum formulation composition of oil (16.5%), Smix (38.7%), and water (44.8%). Three batches of samples were prepared in parallel according to the optimized conditions. The predicted and experimental values of the particle size were 23.72 nm and 23.16±0.25 nm, respectively, and the statistical prediction error was − 2.36%. The predicted and experimental values of the encapsulation efficiency were 93.67% and 98.15%±0.19%, respectively, and the statistical prediction error was 4.78%. The model fitted well with the actual situation. Physiochemical characterization of nanoemulsion Particle size and zeta potential are two important properties of nanoemulsion. Size of the droplets of buckwheat flavonoids nanoemulsion was 23.22±0.13 nm (Fig. 2A), PDI was 0.22±0.06, and zeta potential was − 20.92±0.27 mV (Fig. 2B), as determined using the dynamic light scattering method with a Malvern Zetasizer. Fig.2C shows that the nanoemulsion appeared as a pale yellow clear liquid, and the morphology of the droplets was nearly spherical or spherical, as observed under the transmission electron microscope (Fig. 2D), and the particle size distribution was uniform. The encapsulation efficiency of the nanoemulsion was 98.35%±0.04%. After the storage at different temperatures for 30 days, the particle size and the zeta potential exhibited no significant change. No noticeable creaming was observed after centrifuging at 3000 g and 10,000 g for 10min. Figure 2. Open in new tabDownload slide Characterization of nanoemulsion    (A) Particle size and size distribution of nanoemulsion. (B) Zeta potential of nanoemulsion. (C) Appearance of nanoemulsion. (D) Transmission microscopy image of nanoemulsion with bar length 200 nm. Antioxidant activity study of nanoemulsion The antioxidant capacity of buckwheat flavonoids nanoemulsion was assessed by DPPH scavenging activity, hydroxyl radical scavenging activity, and superoxide anion scavenging activity. DPPH exhibits the maximum absorption at 517 nm owing to its stable nitrogen-containing free radicals, and its purple color turns yellow when the free radicals are scavenged by antioxidants. The antioxidant capacity of the nanoemulsion was evaluated and compared with that of the suspension using ascorbic acid as a positive control. As shown in Fig. 3A, the DPPH scavenging activity of nanoemulsion was increased with the concentration from 0.2 to 1.0 mg/ml, and the IC50 values of the nanoemulsion were 0.52 mg/ml. The DPPH radical scavenging activity curve of the suspension was also gradually increased, but at the concentration of 1.0 mg/ml, its scavenging activity was 37.1%, which was significantly lower than that of the nanoemulsion (81.92%). The results suggested that the nanoemulsion possessed potent DPPH free radical scavenging capacity. Figure 3. Open in new tabDownload slide The antioxidant activities of the nanoemulsion and suspension    The ascorbic acid was used as the positive control, and the nanoemulsion and suspension were the test samples. The statistical significance was analyzed by the Student’s paired t-test with P<0.05 as the minimum level of significance, P<0.01 as the minimum level of very significance, and P<0.001 as the minimum level of extreme significance. (A) DPPH radical scavenging activity. (B) Hydroxyl radical scavenging activity. (C) Superoxide anion scavenging activity. Hydroxyl radicals are the most active free radicals, capable of lipid peroxidation and destruction of the biomacromolecules in cells. The buckwheat flavonoid nanoemulsion exhibited potent hydroxyl radical scavenging activity, and its activity was increased in a concentration-dependent manner (Fig. 3B). As a positive control, ascorbic acid displayed a strong free radical scavenging ability, and at the concentration of 1.0 mg/ml, the hydroxyl radical-scavenging activities of ascorbic acid, nanoemulsion, and suspension were 91.21%, 79.55%, and 40.45%, respectively. The scavenging ability of the nanoemulsion to hydroxyl radicals was significantly stronger than that of the suspension. Pyrogallic acid is spontaneously oxidized under weak alkali conditions to form superoxide free radicals and intermediates, and its color varies with superoxide radical content. As shown in Fig. 3C, the superoxide anion scavenging ability of the nanoemulsion and suspension was increased in a concentration-dependent manner; at the concentration of 1.0 mg/ml, its maximum scavenging activity was 56.80%, which was obviously better than that of the nanoemulsion (29.15%). Compared with the DPPH and hydroxyl radicals scavenging abilities, superoxide anion scavenging abilities of nanoemulsion and suspension were significantly lower, indicating a moderate superoxide anion scavenging activity. Cumulative release and model fitting of nanoemulsion The in vitro release of the nanoemulsion was investigated using the dialysis method, and the drug release in the gastrointestinal tract was simulated in vitro. A dialysis tube with a biofilm structure was placed in the buffer in a thermostatic bath at 37°C for simulating gastrointestinal fluid, and the drug digestion and peristalsis in the gastrointestinal tract were simulated by magnetic stirring. The in vitro release of the buckwheat flavonoids nanoemulsion and suspension were continuously monitored for 48 h. The cumulative release of the suspension was 5.17% in 15 min, and only 15.14% drug was released in 48 h because of its poor solubility (Fig. 4). The nanoemulsion released 25.09% in 15 min, 42.56% in 1 h, and the cumulative release was increased gradually until 88.16% of the drug was released in 48 h. The results showed that the cumulative release efficiency of the nanoemulsion was 5-folds higher than that of the suspension, and the effect of drugs in vitro was greatly improved. Figure 4. Open in new tabDownload slide The cumulative release curves of the nanoemulsion and suspension The cumulative release curves was in 8 h and in 48 h through a dialysis membrane (molecular weight cutoff, MWCO, 8 kDa) at 37±0.5°C under stirring at 100 rpm. Further studies on the release model fitting of the buckwheat flavonoids nanoemulsion were performed to understand the release pattern. Various dynamic models were fitted to the release curve, and the correlation coefficients (R2), residual sum of squares (RSS), and Akaike information criterion (AIC) were calculated (Table2). The drug release model was closer to Weibull (R2 = 0.9947, RSS = 18.6194, AIC = 33.2369) and Ritger–Peppas (R2 = 0.9723, RSS = 113.4027, AIC = 51.3095) models. Table 2. Dynamic model fitting of the cumulative release curve and the correlation coefficients (R2) Model . Equation . R2 . RSS . AIC . Zero order Q = 1.1147 t + 44.6680 0.7441 1095.2628 73.9875 First order ln (1 − 0.01Q) = − 0.0359 t + 4.0195 0.9129 694.1201 69.4264 Higuchi Q = 9.0552 t1/2 + 32.8700 0.9062 401.4697 63.9513 Weibull lnln [100/100−Q)] = 0.3613 ln t − 0.6594 0.9947 18.6194 33.2369 Hixcon–Crowell (100 − Q)1/3 = − 0.0368 t + 3.8092 0.8631 793.3247 70.7623 Ritger–Peppas ln Q = 0.2246 ln t + 3.6786 0.9723 113.4027 51.3095 Model . Equation . R2 . RSS . AIC . Zero order Q = 1.1147 t + 44.6680 0.7441 1095.2628 73.9875 First order ln (1 − 0.01Q) = − 0.0359 t + 4.0195 0.9129 694.1201 69.4264 Higuchi Q = 9.0552 t1/2 + 32.8700 0.9062 401.4697 63.9513 Weibull lnln [100/100−Q)] = 0.3613 ln t − 0.6594 0.9947 18.6194 33.2369 Hixcon–Crowell (100 − Q)1/3 = − 0.0368 t + 3.8092 0.8631 793.3247 70.7623 Ritger–Peppas ln Q = 0.2246 ln t + 3.6786 0.9723 113.4027 51.3095 Notes: Q means cumulative release at time t. Open in new tab Table 2. Dynamic model fitting of the cumulative release curve and the correlation coefficients (R2) Model . Equation . R2 . RSS . AIC . Zero order Q = 1.1147 t + 44.6680 0.7441 1095.2628 73.9875 First order ln (1 − 0.01Q) = − 0.0359 t + 4.0195 0.9129 694.1201 69.4264 Higuchi Q = 9.0552 t1/2 + 32.8700 0.9062 401.4697 63.9513 Weibull lnln [100/100−Q)] = 0.3613 ln t − 0.6594 0.9947 18.6194 33.2369 Hixcon–Crowell (100 − Q)1/3 = − 0.0368 t + 3.8092 0.8631 793.3247 70.7623 Ritger–Peppas ln Q = 0.2246 ln t + 3.6786 0.9723 113.4027 51.3095 Model . Equation . R2 . RSS . AIC . Zero order Q = 1.1147 t + 44.6680 0.7441 1095.2628 73.9875 First order ln (1 − 0.01Q) = − 0.0359 t + 4.0195 0.9129 694.1201 69.4264 Higuchi Q = 9.0552 t1/2 + 32.8700 0.9062 401.4697 63.9513 Weibull lnln [100/100−Q)] = 0.3613 ln t − 0.6594 0.9947 18.6194 33.2369 Hixcon–Crowell (100 − Q)1/3 = − 0.0368 t + 3.8092 0.8631 793.3247 70.7623 Ritger–Peppas ln Q = 0.2246 ln t + 3.6786 0.9723 113.4027 51.3095 Notes: Q means cumulative release at time t. Open in new tab Pharmacokinetic parameters and bioavailability of nanoemulsion The efficacy of the buckwheat flavonoids nanoemulsion was further verified by monitoring the buckwheat flavonoids plasma concentrations in vivo after intragastric administration to the test and control groups. The samples were analyzed by HPLC. The curves of the mean plasma concentration versus time for buckwheat flavonoids nanoemulsion and suspension are shown in Fig. 5. The pharmacokinetic parameters were calculated with the Statistics 2.0 software program (DAS 2.0), and the results are shown in Table3. Figure 5. Open in new tabDownload slide The plasma concentration-time curves of nanoemulsion and suspension    The nanoemulsion and suspension were administered intragastrically to the Wistar rats (200±20 g) at a dose of 60 mg/kg. Data are shown as the mean±SD. On the right side, the plasma concentration-time curves of the first 8 h are displayed. Table 3. The pharmacokinetic parameters of nanoemulsion and suspension in rats’ plasma after intragastrical administration at a dose of 60 mg/kg Parameter . Unit . Nanoemulsion . Suspension . AUC (0-t) ng/ml⋅h 1621.26±141.60 734.48±109.81 AUC (0-∞) ng/ml⋅h 1905.50±203.08 863.90±272.36 MRT (0-t) h 13.16±0.98 14.90±1.47 MRT (0-∞) h 22.68±3.74 26.26±8.55 t1/2z h 19.66±3.26 14.60±8.31 Tmax h 0.90±0.22 1.60±0.55 Cmax ng/ml 156.15±8.14 59.89±6.49 Parameter . Unit . Nanoemulsion . Suspension . AUC (0-t) ng/ml⋅h 1621.26±141.60 734.48±109.81 AUC (0-∞) ng/ml⋅h 1905.50±203.08 863.90±272.36 MRT (0-t) h 13.16±0.98 14.90±1.47 MRT (0-∞) h 22.68±3.74 26.26±8.55 t1/2z h 19.66±3.26 14.60±8.31 Tmax h 0.90±0.22 1.60±0.55 Cmax ng/ml 156.15±8.14 59.89±6.49 Open in new tab Table 3. The pharmacokinetic parameters of nanoemulsion and suspension in rats’ plasma after intragastrical administration at a dose of 60 mg/kg Parameter . Unit . Nanoemulsion . Suspension . AUC (0-t) ng/ml⋅h 1621.26±141.60 734.48±109.81 AUC (0-∞) ng/ml⋅h 1905.50±203.08 863.90±272.36 MRT (0-t) h 13.16±0.98 14.90±1.47 MRT (0-∞) h 22.68±3.74 26.26±8.55 t1/2z h 19.66±3.26 14.60±8.31 Tmax h 0.90±0.22 1.60±0.55 Cmax ng/ml 156.15±8.14 59.89±6.49 Parameter . Unit . Nanoemulsion . Suspension . AUC (0-t) ng/ml⋅h 1621.26±141.60 734.48±109.81 AUC (0-∞) ng/ml⋅h 1905.50±203.08 863.90±272.36 MRT (0-t) h 13.16±0.98 14.90±1.47 MRT (0-∞) h 22.68±3.74 26.26±8.55 t1/2z h 19.66±3.26 14.60±8.31 Tmax h 0.90±0.22 1.60±0.55 Cmax ng/ml 156.15±8.14 59.89±6.49 Open in new tab In our study, the AUC for the nanoemulsion was 1621.2 ±141.60 ng/ml·h, which was 2.2-fold higher than that of the control suspension (734.48±109.81 ng/ml·h). The Cmax (the maximum plasma drug concentration) of the nanoemulsion was 156.15±8.14 ng/ml, which was 2.6-fold higher than that of the suspension (59.89±6.49 ng/ml). The Tmax (the maximum plasma drug concentration) of the nanoemulsion reached peak concentrations faster than the suspension within 0.90±0.22 h, which might be related to the good solubility of buckwheat flavonoids in the nanoemulsion. The nanoemulsion of buckwheat flavonoids exhibited significantly higher AUC than the suspension (P<0.05). Discussion The CCD response surface experiment is a multivariate statistical method for optimization analysis [25], and it provides an idea of two more probability values, which can lead to a more comprehensive, intuitive, and accurate design. CCD was used in this study to determine the optimum conditions for nanoemulsion, which led to suitable particle size and encapsulation efficiency. The model effectively described and predicted the responses of the particle size and encapsulation efficiency with the changes in the constitution conditions in given experimental ranges. The particle size of nanoemulsion increased with the Smix and water phase, and the effect of oil phase was not very significant. The surfactant and cosurfactant showed the greatest effects, and the oil phase and water phase also affected the encapsulation efficiency. The results of these experiments were close to the predicted values from the optimization analysis, suggesting that the multiple regression models are reasonable and reliable. The nanoemulsion was obtained by the CCD experiment, and the nanoemulsion with small particle sizes displayed a smaller surface tension and greater stability. The smaller particle size can lead to a greater interfacial surface area for drug absorption, and the solubility and bioavailability are enhanced [26]. The zeta potential of the nanoemulsion is caused by the adsorption of charged molecules on its surface, which depends up on the types of emulsifier and the conditions of the medium, and this charge can cause molecular repulsions between dispersed phase droplets, so the zeta potential can improve the stability of the nanoemulsion [27–29]. In our study, buckwheat flavonoid nanoemulsion displayed a particle size within the abovementioned range, suggesting that our nanoemulsion system was stable. Flavones possess antioxidant properties, but buckwheat flavonoid nanoemulsion showed significant antioxidant activity compared with suspensions in the three test methods because nanoemulsion technology can enhance the solubility and release of insoluble or poorly soluble drugs. A higher amount of drug might be responsible for the enhanced antioxidant potential [30]. The results of this study suggest that the SNEDDS is a feasible strategy to improve the antioxidant application, which can be potentially used as a natural antioxidant. Drug release is the basis for all biological functions, including absorption, distribution, metabolism, and excretion. Drug release model fitting was performed to understand the release pattern [31]. As shown in Table 2, the best fitting parameters (R2=0.9947, RSS=18.6194, AIC=33.2369) were derived from the Weibull model, and the in vitro drug release from the nanoemulsion followed this distribution, suggesting that the in vitro release process is continuous and dynamic, simulating the human digestive tract environment. It also indicated that the drug release has a sustained release trend. Ritger–Peppas (R2=0.9723, RSS=113.4027, AIC=51.3095) might be similar to the drug released pattern, and its release mechanism might follow the Fickian diffusion (n<0.45) during the drug release process of the nanoemulsion. In our results, the in vitro release of the buckwheat flavonoids from the nanoemulsion was more advantageous than the suspension, which provided a potential possibility for the application of nanoemulsion flavonoids. Bioavailability, an important criterion for evaluating the effect of drugs in vivo, is usually assessed by three parameters: Cmax, Tmax, and AUC. AUC is the most reliable indicator of bioavailability. It is directly proportional to the amount of prototype medicine that enters the body. This study demonstrated that the buckwheat flavonoid nanoemulsion is absorbed 2.2-fold higher than the suspension, as the drug particles in the nanoemulsion are relatively small and more drugs can be released. The absorption of a poorly water-soluble drug is often limited by its insufficient dissolution in the gastrointestinal tract [32]. The small droplets of the drug-loaded nanoemulsion can pass through the membrane directly and get absorbed easily [33]. A self-nanoemulsifying drug delivery system of persimmon leaf extract was shown to accelerate the drug absorption, and the drug was not precipitated in the gastrointestinal tract [34]. Thus, the main problems of poor solubility and weak absorption of flavonoids were solved through the self-nanoemulsifying technology, and the oral bioavailability was significantly improved [35]. Nanoemulsion can be used as an effective carrier to improve the therapeutic effect of water-insoluble drugs. In summary, this study was based on a previous experiment for developing buckwheat flavonoids into a nano-drug delivery system [36]. We constructed a drug delivery system and investigated its characteristics in vitro and the drug delivery to animals in vivo, which provided a theoretical basis for the application of buckwheat flavonoids in clinical practice. 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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2020. Published by Oxford University Press on behalf of the Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences.
TI - Preparation, characterization, and evaluation of antioxidant activity and bioavailability of a self-nanoemulsifying drug delivery system (SNEDDS) for buckwheat flavonoids
JF - Acta Biochimica et Biophysica Sinica
DO - 10.1093/abbs/gmaa124
DA - 2020-12-08
UR - https://www.deepdyve.com/lp/oxford-university-press/preparation-characterization-and-evaluation-of-antioxidant-activity-sDITgEjfoI
SP - 1265
EP - 1274
VL - 52
IS - 11
DP - DeepDyve
ER -