Quercetin was purchased from Sigma-Aldrich. Grape seed oil under the Aceites Borges brand name and Palmtop canola oil is obtained from a local Palembang supermarket. Croduret 50-SS from Croda, PEG-400, and aquadest were purchased from Bratachem.

SNE was prepared by dissolving quercetin with carrier oil using vortex followed by ultrasonication for 5 minutes at room temperature. Surfactants and co-surfactants are added to the oil-quercetin solution. The homogeneous mixture was placed in a rotary shaker (25–30 °C for 12 hours) and allowed to stand again for 12 hours (Ogino et al. 2021; Shiyan et al. 2022).

The experimental design for screening the constituent components of SNE was carried out using the FrFD 2^4-1 approach. The formulation design was determined by factors including the type of oil (A; canola oil and grape seed oil), the concentration of surfactants (B; %), the concentration of co-surfactants (C; %), and oil concentration (D; %). The FrFD approach uses two levels (upper limit +1 and lower limit -1) in a certain portion. The category choice is used for A and numeric factors for B, C, and D in preparing the formula design. Canola and grape seed oil use a lower limit of 14% and an upper limit of 20%, respectively. The croduret concentration range uses a lower limit of 30% and an upper limit of 60%. Co-surfactant PEG 400 uses a lower limit range of 10% and an upper limit of 30%. The main responses observed and measured consisted of droplet size (R₁; d.nm), polydispersity index (R₂), zeta potential (R₃; mV), electrophoretic mobility (R₄; µmcm/Vs), emulsification time (R₅; seconds), viscosity (R₆; mPa.s) and drug load (R₇; mg/mL). The complete design and data of the eight experiment runs are shown in Table 1.

The data obtained were also analyzed using a chemometric approach with the PCA and CA methods. The PCA-CA method was processed using Minitab 17 series software (Minitab, State College, PA, USA). Evaluation at this stage is not part of modeling and prediction optimization, but evaluation of 8 runs and the correlation between responses (Kartini et al. 2020; Shiyan et al. 2021).

The optimum droplet diameter, polydispersity index (PDI), and zeta potential of SSQ-SNE formula were measured using a particle size analyzer Zetasizer Nano ZSP (Malvern Panalytical, UK) by applying the dynamic light scattering (DLS-PSA) method. Data was collected in triplo (n=3) and presented in the form of mean ± standard deviation. The data processing used Zetasizer 7.12 (Malvern Panalytical) software which helped the analysis run, in order to obtain results in the form of particle size (d.nm), PDI, zeta potential (mV) and electrophoretic mobility (µmcm/Vs).

Emulsification is essentially the process of dispersing SSQ-SNE in aqueous media to form a nanoemulsion. A total of 1 mL of SSQ-SNE is dropped into 500 mL of media. The dispersing process is conditioned at 37 °C on the magnetic stirrer with a stirring rate of 120 rpm. Observations were made on time it took from the start of the drop until the nanoemulsion was formed. Visual observations were made by looking at the nanoemulsion efficiency, transparency, phase separation, and quercetin droplets. The nanoemulsion formed was characterized by the complete dissolution of SSQ-SNE in the medium (Shiyan et al. 2021). SSQ-SNE viscosity measurement uses an Oswald viscometer in mPa.s units (Yadav et al. 2014). The quantity of quercetin contained in SNE was measured by centrifugation at 3500 rpm for 30 minutes. The precipitate formed is weighed as quercetin, which does not enter the system.

A total of 100 µL of SSQ-SNE was emulsified into 10 mL of aqua pro injection. Clarity (transmittance; %) was determined using a Genesys 10S UV-Vis spectrophotometer (Thermo Scientific, USA) at a wavelength of 650 nm and the blank solution is purified water.

Stability tests for SSQ-SNE and nanoemulsions using heating-cooling and freezing methods in selected formulas. Centrifugation studies were carried out at 3500 rpm for 30 minutes, and visual observations were made to confirm phase separation, precipitation, instability, cracking, or cream formation (Jumaryatno et al. 2018).

The morphology of nanoemulsion globules or droplets was identified using a transmission electron microscope (TEM). The TEM instrumentation used was JEM 2100 (Jeol, Tokyo, Japan). The interaction of SNE constituent components was identified using Fourier transform infrared spectrophotometry-attenuated total reflectance (FTIR-ATR) Nicolet iS5 (Thermo Scientific, USA). Spectra readings were carried out on SSQ-SNE, quercetin material, oil (canola and grape seed), surfactant (croduret 50-SS), and co-surfactant (PEG 400). IR spectra readings were carried out at a wavenumber between 4000 cm^-1 to 500 cm^-1.

The FrFD approach to screening provides a more effective and efficient measure. Statistical data from the fitting model on all evaluated responses are presented in Table 3. The droplet size model has an R² value of more than 0.7, with an adjusted R² value of 0.9985. Predicted R² is 0.9929, and the difference between adjusted R² and predicted R² is less than 0.2. The value of adequate precision reinforces the model if the value is more than 4. The polydispersity index also shows an adequate response in modeling. The fitting model is included in the right criteria for predicting the selected or optimal formula by considering the value of R², adjusted R², predicted R², adequate precision, and press (Pratiwi et al. 2019; Shiyan et al. 2019).

Zeta potential is an essential parameter in determining the best formula for SSQ-SNE. The results of the fitting of the model for zeta potential, R² value 0.9837, adjusted R² 0.9428, predicted R² 0.7385, and adequate precision 15.72. Overall, the statistical evaluation of each parameter or response is very suitable for use in prediction. Based on the fitting model results, all responses have the same model, namely reduced 2FI. Based on ANOVA analysis, the equation and model for the response to droplet size (R₁) showed significant results p<0.05. Each of the SNE constituent components, namely the type of oil (A), the concentration of croduret (B), the concentration of PEG-400 (C), and the concentration of oil (D), affect the increase in the size of the resulting droplets. The type of oil (A) and the interaction between the type of oil and the concentration of Croduret as a surfactant (AB) can increase the droplet size diameter. Oil as a carrier will interact and dissolve the active substance in a certain amount so that the interaction of oil with surfactants will increase the droplet size. The concentration of PEG as co-surfactant (C), the interaction of oil type with PEG concentration (AC), and type of oil with oil concentration (AD) can reduce droplet size.

The effect of the SNE components on droplet size was evidenced from the experimental results (Table 1) and evaluation of the FrFD-chemometric combination. Experiments on the eight runs resulted in varying diameters from 26.74 ± 0.51 nm to 266.53 ± 12.01 nm. Large droplet size is strongly influenced by the interaction of the type of oil and croduret as a surfactant. The relationship between the response (R₁) and the independent variables or factors (A, B, C, and D) can be seen in the 3D surface plot of Fig. 2A. The appearance of SSQ-SNE and nanoemulsion from eight trial runs is presented in Table 2 and Fig. 1. The effect of the PDI nanoemulsion response components can be seen in equation 4 (Table 4). The dominant factors that influence the PDI value are the type of oil (A), PEG-400 concentration (C), and oil concentration (D), AC interaction, and AD interaction. The components of the factors mentioned above can affect the PDI of the resulting nanoemulsion. Based on ANOVA analysis, the p-value is less than 0.05, indicating a significant model.

Figure 1. 

Visual appearance of SSQ-SNE and nanoemulsion of eight FrFD 2^4-1 runs, (A) SNE, (B) nanoemulsion.

Figure 2. 

Graph of the 3D model surface plot of the evaluated responses, (A) droplet size (B) Polydispersity index, (C) zeta potential, (D) electrophoretic mobility, (E) emulsification time, (F) viscosity, (G) drug load.

Type of oil (A), the concentration of croduret (B), the concentration of PEG-400 (C), and concentration of oil (D) affect the increase in electrophoretic mobility of the resulting SNE. Electrophoretic mobility can be decreased by the interaction of oil types with PEG-400 (AC) concentrations. The emulsification time can be decreased by the interaction of oil type with PEG-400 (AC) concentration. The interaction of oil type with oil concentration (AD) increases the emulsification time. The interaction between the type of oil and the concentration of croduret (AB) causes an increase in viscosity. The interaction between types of oil with a concentration of PEG-400 (AC) can reduce viscosity. Drug load is strongly influenced by the type of oil (A) and the concentration of PEG 400 (C).

The interaction of oil types with Croduret and PEG-400 for each response is shown in Fig. 3. The use of croduret with high grape seed oil concentrations will produce smaller droplet sizes than interactions with canola oil (Fig. 3A). The PDI was smaller when the PEG-400 concentration was higher, whether the interaction was in grape seed or canola oil. The lower PEG-400 concentration gave a greater PDI, especially its interaction with grape seed oil. The increase in the croduret concentration could increase the zeta potential, interacting with grape seed oil and canola oil (Fig. 3C). The interaction between grape seed oil and the higher PEG-400 concentration increased the level of electrophoretic mobility. However, the interaction of canola oil and high concentrations of PEG-400 resulted in lower mobility (Fig. 3D). The emulsification time will be longer at the interaction of low concentration PEG-400 with canola oil, while the interaction with grapeseed oil still results in a faster emulsification time (Fig. 3E). The interaction between canola oil and high concentrations of croduret resulted in a high SNE viscosity (Fig. 3F). A high drug load was obtained at canola oil interaction with low concentrations of PEG-400 (Fig. 3G).

Figure 3. 

Graph of interactions between factors on the evaluated response, (A) droplet size (B) Polydispersity index, (C) zeta potential, (D) electrophoretic mobility, (E) time of emulsification, (F) viscosity, (G) drug load.

The response data from the SSQ-SNE formulas that have been obtained were analyzed using a chemometric approach with the principal component analysis (PCA) and cluster analysis (CA) methods. The multivariate approach using PCA aims to simplify variables by reducing data from a large number of interrelated variables without changing existing information (Cui et al. 2021; Shiyan et al. 2021; Kim et al. 2022). CA technique is a method based only on information found in data that describes relationships and objects or is based on similar characteristics of these objects. CA analysis forms and separates groups with the closest relationship in more detail to provide more accurate information (Iaboni et al. 2020; García del Moral et al. 2021).

Fig. 4B is a score plot that shows the run formula grouping into 5 clusters. The score plot classifies the samples based on the run composition function and the resulting response (Talekar et al. 2019; Hong et al. 2021). Multivariate analysis was successful in grouping the runs at different distances from each other. The distance between runs or samples shows the similarity of characteristics. The further distance between the runs indicates little similarity in traits or characteristics (Shiyan et al. 2020; Setyawan et al. 2021). The dendrogram in CA can group the same variables and have bonds in one group based on the value of closeness (similarity) (Szentmiklóssy et al. 2020). The characteristic similarity index is depicted in dendrogram form in Fig. 4C. Each run is classified based on its similarity. Run 1 and 2 have a closeness with a value of 97.82%; run 4 and 6 have a closeness of 96.94%; 7 and 8 have a value of 85.56%. The proximity of each run was also evidenced by a similar FTIR-ATR spectra pattern (Fig. 6A).

Figure 4. 

The results of a chemometric analysis using the PCA-CA approach, (A) scree plot, (B) score plot, (C) dendrogram, (E) loading plot.

The loading plot aims to determine the variable of a sample or formula that most contributes to forming the principal component (PC) values. The contribution of the sample variables to the loading plot can be seen from a distance used. Data analysis using the PCA loading plot depicts the angle that shows a correlation between the responses of all formulas. The responses of R₃ and R₄, which form an adjacent angle (less than 45°), indicate a positive correlation. The electrophoretic mobility (R₄) of the droplets will increase with the high zeta potential (R₃) value. A negative correlation occurs between R₂ and R₃, which forms an angle close to 180°. A high polydispersity index (R₂) can reduce the zeta potential (R₃). The angle between the two vectors that are close to 90° indicates no correlation between responses.

The best formula for screening can be predicted by the model obtained from the FrFD. The most critical stage in prediction is to determine the level of importance and goals of each response. The target droplet size is 50 nm, with an important level value of 5. The polydispersity index has a lower limit of 0.33 and an upper limit of 0.54 with an in-range target and an important level value of 3. Zeta potential in the FrFD2^4-1 experiment produces a range of 16.64–28.40 mV. Considering this response is related to stability, the prediction stage uses a target of 25 mV and the value of importance 4. Electrophoretic mobility in the in-range target with a level of importance of 3 is positively correlated with zeta potential. The emulsification time and viscosity were determined with minimum targets with importance values of 5 and 4. Considering the super saturable-SNE formulated, the target set for drug load must be a maximum with a level of importance of 4.

The SNE components selected were grape seed as the oil phase, croduret as a surfactant, and PEG-400 as a co-surfactant with concentrations of 19.6%, 60%, and 16.6%, respectively. The desirability value is an essential indicator in determining the selected formula mixture in the SSQ-SNE formulation. The desirability at the prediction stage obtained a value of 0.751. High desirability values (close to 1) indicate the ability of the FrFD design to produce perfect predictions and proper screening procedures (Shiyan et al. 2019; Indrati et al. 2020). The desirability value provides an overview of the similarity between the predicted value and the actual observation. The composition of SSQ-SNE selected in FrFD 2^4-1 obtained optimum results with a droplet size of 112.84 d.nm, a polydispersity index of 0.487, a zeta potential of 25 mV, mobility of 1.932 µmcm/Vs, an emulsification time of 8.60 seconds, a viscosity of 364.72 mPa.s, and drug load of 27.64 mg/mL.

The interaction analysis of constituent materials used FTIR instrumentation based on vibrations in each SNE component (Pratiwi et al. 2020; Shiyan et al. 2022). The spectral patterns on the SNE constituent components of quercetin, canola oil, grapeseed oil, croduret 50-SS, and PEG-400 are presented in Fig. 6. The spectral patterns of the eight runs on FrFD at first glance look similar, but in a more detailed evaluation, the intensity at the peak is different. SNE has a typical peak at wavenumbers 3300–3600 cm^-1, 2800–3500 cm^-1, 2200–2400 cm^-1, and a fingerprint area of 500–1800 cm^-1. The spectral pattern of the selected SSQ-SNE can be observed in Fig. 6B with the ratio of the components used. Quercetin spectra (Fig. 6B) have typical peaks that widen in 3000–3600 cm^-1. The peak was lost in the SSQ-SNE spectra (Fig. 6B). Based on the FTIR-ATR spectra pattern and droplet morphology of TEM, quercetin was successfully incorporated into the oil globule system (SNE). Theoretically, verification is carried out by evaluating changes of spectral patterns in each component and the SNE.

Figure 6. 

Profile of FTIR-ATR spectra, (A) overlay spectra of eight runs on FrFD, (B) SSQ-SNE using selected formulas and constituent components.

Physical stability is carried out to determine the maximum storage time leading to separation of the emulsion phase (creaming or cracking). Heating cooling was chosen as an accelerated thermodynamic stability test method because, with a short time, the kinetic stability of SNE could be known through the phase separation that occurred. Observations on the stability of SNE and nanoemulsions were carried out visually to see their clarity, physical changes such as creaming, cracking, and the formation of deposits. The stability testing results using the heating-cooling and free-thaw method showed that the selected SNE and nanoemulsion formulas remained stable (Table 5). SNE and nanoemulsions show no phase separation (Fig. 5C–E).