**2. Results and Discussion**

### *2.1. Optimization of Carvacrol-Loaded Niosomes (CVC-Ns)*

The formulation optimization of the CVC-Ns was carried out utilizing a Box–Behnken experimental design with three parameters and three levels. Different independent variables' individual and combined effects on the responses were assessed. Table 1 displays the minimum (−1), average (0), and maximum (+1) levels of the three factors (drug as A, surfactant as B, and cholesterol quantity as C). Table 1 displays the data of the evaluation of the 17 different compositions of CVC-loaded niosomes from the BBD design.

**Table 1.** Responses observed in BBD for development and optimization of CVC-Niosome formulations with a summary of parameters for responses (Y<sup>1</sup> , Y<sup>2</sup> , Y<sup>3</sup> ) by Design Expert Software.


A, drug (mg); B, surfactant (mg); C, cholesterol (mg); Y1, vesicle size (nm); Y2, PDI; Y3, EE (%).

As indicated in Table 2 and the above-mentioned equation, the CVC-loaded noisome vesicle size ranged from 180.23 to 206.99 nm. Figures 1A and 2 show 3D and contour plots that illustrate the effect of factors on size. The sizes of the niosomes slightly increase when the drug concentration (A) rises. The rise in the concentration of the drug (A) being trapped in the surfactant (B) and cholesterol (C) may be the cause of the size increase [43]. Our research found that the outcomes are consistent with previously published findings: the size increases as the drug concentration in the formulation increases [44]. As per the above equation, surfactant concentration had a negative impact on the size of the vesicles, as increasing the concentration of the surfactant reduces the size of the droplet until the concentration of the surfactant reaches a certain threshold, after which the size of the vesicle increases due to the formation of aggregates by the excess surfactant [45]. Cholesterol, on the other hand, is an important factor responsible for the formation of vesicles: as cholesterol levels rise, vesicle size decreases. This may be because cholesterol above a certain concentration can disrupt the bi-layered structure of vesicular membranes, resulting in drug loss from vesicles [40]. vesicle size ranged from 180.23 to 206.99 nm. Figures 1A and 2 show 3D and contour plots that illustrate the effect of factors on size. The sizes of the niosomes slightly increase when the drug concentration (A) rises. The rise in the concentration of the drug (A) being trapped in the surfactant (B) and cholesterol (C) may be the cause of the size increase [43]. Our research found that the outcomes are consistent with previously published findings: the size increases as the drug concentration in the formulation increases [44]. As per the above equation, surfactant concentration had a negative impact on the size of the vesicles, as increasing the concentration of the surfactant reduces the size of the droplet until the concentration of the surfactant reaches a certain threshold, after which the size of the vesicle increases due to the formation of aggregates by the excess surfactant [45]. Cholesterol, on the other hand, is an important factor responsible for the formation of vesicles: as cholesterol levels rise, vesicle size decreases. This may be because cholesterol above a certain concentration can disrupt the bi-layered structure of vesicular membranes, resulting in drug loss from vesicles [40].

14 5 90 5 199.94 0.279 81.55 15 10 120 5 181.08 0.26 90.01 16 10 90 7.5 191.08 0.288 76.08 17 15 150 5 205.54 0.271 83.17 A, drug (mg); B, surfactant (mg); C, cholesterol (mg); Y1, vesicle size (nm); Y2, PDI; Y3, EE (%).

As indicated in Table 2 and the above-mentioned equation, the CVC-loaded noisome

**Table 2.** Results of regression analysis of Responses Y<sup>1</sup> , Y<sup>2</sup> and Y<sup>3</sup> . **Table 2.** Results of regression analysis of Responses Y1, Y2 and Y3.

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**Figure 1.** *Cont*.

**Figure 1.** (**A**) Representation of 3D plots of independent variables impacting the dependent variable. Effect of drug conc. (A), surfactant conc. (B) and cholesterol (C) on the PDI (Y<sup>3</sup> ). (**B**) Representation of 3D plots of independent variables impacting the dependent variable (PDI). Effect of drug conc. (A), surfactant conc. (B) and cholesterol (C) on entrapment efficiency (%) (Y<sup>2</sup> ). (**C**) Representation of 3D plots of independent variables impacting the dependent variable (EE%). Effect of drug conc. (A), surfactant conc. (B) and cholesterol (C) on the vesicle size (Y<sup>1</sup> ).

**Figure 2.** The counterplots for responses for vesicle size, PDI and entrapment efficiency of CVC-Ns. **Figure 2.** The counterplots for responses for vesicle size, PDI and entrapment efficiency of CVC-Ns.

*2.2. Design Validation*  To develop the ideal formulation with the lowest globule size, PDI, and highest en-According to the equation below, the size's interaction terms is displayed by the following equation:

trapment efficiency, BBD was used. Table 3 lists the predicted values for all responses and Vesicles size = +166.43 + 2.60A <sup>−</sup> 1.89 B <sup>−</sup> 0.367 C <sup>−</sup> 0.380 AB <sup>−</sup> 1.0723.40 AC + 1.39 BC + 19.61 A<sup>2</sup> + 2.59 B<sup>2</sup> + 6.36 C<sup>2</sup> .

variables derived from the design. Therefore, CVC-N formulations based on the runs, process variables, and responses as shown in Table 2 were created, and the experimental findings attained using the formulations were compared to the expected responses. The fact that the experimental and predicted numbers were nearly closed supports the validity of the optimization process [48]. All seventeen trials' PDI values ranged between 0.259 and 0.294. Figures 1B and 2 show 3D and contour plots that illustrate the effect of factors on PDI. The experimental data indicated that the remainder of the independent variable's surfactant and cholesterol had a negative effect on PDI, indicating that PDI decreased with increasing surfactant concentration until a certain level, as shown in the equation below.

```
Table 3. In vitro drug release kinetics of CVC-loaded niosomes. 
PDI = +0.2614 + 0.001 A − 0.0020 B − 0.0036 C − 0.0003 AB + 0.001 AC − 0.001 BC +0.0101 A2 + 0.004 B2 + 0.018 C2
```
**Kinetic Models** *X***-Axis** *Y***-Axis CVC-Ns (R2)**  Zero-order Fraction of drug released Time 0.9268 First order Log % drug remaining Time 0.9878 Higuchi matrix Fraction of drug release Square root time 0.9933 Cholesterol is an essential factor responsible for vesicle formation: as cholesterol levels rise, vesicle size decreases, resulting in a decrease in PDI. This may be because cholesterol above a certain concentration can disrupt the bi-layered structure of vesicular membranes, resulting in drug loss from vesicles [45].

Korsmeyer–Peppas Log fraction of drug released Log time 0.9927 The following equation displays the EE interaction terms:

EE = +29.68 <sup>−</sup> 0.3700 A + 0.4338 B <sup>−</sup> 1.48 C <sup>−</sup> 0.5750 AB <sup>−</sup> 0.2540 AC + 0.8075 BC <sup>−</sup> 2.83 A<sup>2</sup> <sup>−</sup> 3.92 B<sup>2</sup> <sup>−</sup> 7.60 C<sup>2</sup>

*2.3. Optimized Composition*  The optimized formula obtained from BBD for formulating CVC-loaded niosomes, drug (9.76 mg), surfactant (131.02 mg), and cholesterol (4.62 mg). Optimized formulation According to the above-mentioned equation, the developed CVC-niosomes had an entrapment efficiency that ranged from 76.08 to 90.41% (Table 2). As per the above equation, the drug concentration increases, and the entrapment efficiency decreases. Figures 1C and 2 show 3D and contour plots that illustrate how factor variables influence the efficiency of encapsulation. The encapsulation effectiveness of the independent factors varied significantly. The effectiveness of the niosome's encapsulation is favourably impacted by the factor surfactant (B). The encapsulation efficiency increases with a rise in surfactant concentration due to the formation of multiple layers of surfactant molecules around the vesicles and resulting in extra space for incorporating the drug [46]. Moreover, the use of surfactant tween 80 also helped in enhancing the solubilization of the drug molecule by incorporating them [47]. To obtain stable niosomes, cholesterol is the most common additive added to the formulation. It stabilizes bilayers, prevents leakage, and slows the permeation of solutes contained within the aqueous interior of these vesicles. In the present study, the above equation indicates that cholesterol has had a negative effect on entrapment efficiency. The entrapment efficiency of the drug in niosomes was decreased by increasing the cholesterol concentration from 2.5 to 7.5 mg. This may be because cholesterol above a certain concentration can disrupt the bi-layered structure of the vesicular membranes, resulting in drug loss from the vesicles [45].

### *2.2. Design Validation*

To develop the ideal formulation with the lowest globule size, PDI, and highest entrapment efficiency, BBD was used. Table 3 lists the predicted values for all responses and variables derived from the design. Therefore, CVC-N formulations based on the runs, process variables, and responses as shown in Table 2 were created, and the experimental findings attained using the formulations were compared to the expected responses. The fact that the experimental and predicted numbers were nearly closed supports the validity of the optimization process [48].

**Kinetic Models** *X***-Axis** *Y***-Axis CVC-Ns (R<sup>2</sup> )** Zero-order Fraction of drug released Time 0.9268 First order Log % drug remaining Time 0.9878 Higuchi matrix Fraction of drug release Square root time 0.9933 Korsmeyer–Peppas Log fraction of drug released Log time 0.9927

**Table 3.** In vitro drug release kinetics of CVC-loaded niosomes.
