*3.6. Design Space*

*3.6. Design Space* Since it is difficult to predict the influence of SLN composition and preparation technique on the particle size and percent entrapment efficiency, systematic approaches such as quality by design (QbD) were applied to comprehensively analyze and characterize the design space of the formulation. Using design expert software, design space was obtained by selecting the required range for each dependent variable to obtain the optimized batch. The optimized batch was selected by adding flags to the design space at the design point, which showed that the best response was selected as the optimized batch, and it was further evaluated. Figure 9 shows the overlay plot forthe optimized SLN batch (CL10) with low particle size (147 nm), higher entrapment efficiency (83.6%), and good drug loading (26.5%), and its corresponding value of X1 (amount of lipid in mg) was 149.64 and X2 (sonication time in min) was 5.8. Based on these values, the optimized batch was prepared (composition in Table 7). The optimized batch was further evaluated, and the comparison of values (overlay plot‐predicted and practical‐observed) of dependent Since it is difficult to predict the influence of SLN composition and preparation technique on the particle size and percent entrapment efficiency, systematic approaches such as quality by design (QbD) were applied to comprehensively analyze and characterize the design space of the formulation. Using design expert software, design space was obtained by selecting the required range for each dependent variable to obtain the optimized batch. The optimized batch was selected by adding flags to the design space at the design point, which showed that the best response was selected as the optimized batch, and it was further evaluated. Figure 9 shows the overlay plot for the optimized SLN batch (CL10) with low particle size (147 nm), higher entrapment efficiency (83.6%), and good drug loading (26.5%), and its corresponding value of X1 (amount of lipid in mg) was 149.64 and X2 (sonication time in min) was 5.8. Based on these values, the optimized batch was prepared (composition in Table 7). The optimized batch was further evaluated, and the comparison of values (overlay plot-predicted and practical-observed) of dependent variables is shown in Table 8. It was revealed that the predicted values and observed values were similar to each other. The representative size distribution curve and zeta potential distribution of CL10 are depicted in Figure 10.

variables is shown in Table 8. It was revealed that the predicted values and observed values were similar to each other. The representative size distribution curve and zeta

potential distribution of CL10 are depicted in Figure 10.

*Pharmaceutics* **2021**, *13*, x FOR PEER REVIEW 17 of 24

**Table 8.** Evaluation of optimized solid lipid nanoparticle batch (CL10).

\* All values are expressed as mean ± S.D; *n* = 6.

**Table 7.** Composition of optimized solid lipid nanoparticle batch (CL10) based on design space.

**Ingredients Quantity** Stearic acid 149.64 mg Tween 80 0.3 mL Transcutol P 0.3 mL Water 20 mL Ethanol 5 mL Homogenization speed 9000 rpm Sonication time 5.8 min

**Parameter Predicted Value Observed Value \***

Entrapment efficiency (%) 83.6 81.3 ± 4.6 Drug loading (%) 26.5 27.1 ± 3.9 Particle size (nm) 147 157 ± 42.4 Polydispersity index 0.12 0.13 ± 0.02 Zeta potential (mV) −18.4 −17.2 ± 3.1 In vitro drug release (%) 87 89.4 ± 4.5

**Figure 9.** Overlay plot of optimized solid lipid nanoparticle batch using design space. **Figure 9.** Overlay plot of optimized solid lipid nanoparticle batch using design space.


**Table 7.** Composition of optimized solid lipid nanoparticle batch (CL10) based on design space.

**Table 8.** Evaluation of optimized solid lipid nanoparticle batch (CL10).


\* All values are expressed as mean ± S.D.; *n* = 6.
