*3.1. Solubility*

The solubility of drug in lipid is necessary for the preparation of SLNs. The solubility of clarithromycin in solid lipids and surfactants was carried out by a modified method [25], as the normal equilibrium solubility method is not realistic for solid lipids. The lipid in which the drug is most soluble was selected, as low drug solubility in lipid can lead to a decrease in encapsulation efficiency and drug loading [14]. In addition, low drug lipid solubility may prevent binding of drug molecules to the lipid, and the drug may remain as free drug and thereby fail to provide sustained release as desired. From the data shown in Figure 1, it was concluded that the least amount of stearic acid was required to solubilize clarithromycin when compared with other lipids tested. In general, stearic acid has been widely used in ophthalmic drug delivery systems, and in particular in formulating SLNs [39]. Similarly, surfactants are also important components in SLNs, as they play a crucial role in the physicochemical properties, dissolution, permeation, and

stability of particles [40]. However, the high solubility of the drug in surfactant may cause leaching of the actives out of the solid lipid particles of the prepared formulations [41]. Therefore, a combination of surfactants was selected in such a manner that the drug would have minimum solubility in the surfactants. The applicability of the surfactant and cosurfactant in preparing single phase micro or nanoemulsions was described elsewhere in the literature [42]. Based on solubility criteria, the non-ionic surfactant Tween 80 was selected as the surfactant and Transcutol P as the cosurfactant for the preparation of various SLN formulations. Tween 80 is often used in various conventional ophthalmic formulations since it does not induce ocular sensitivity reactions [43]. Additionally, due to longer hydrocarbon chain length and larger polar head groups, Tweens may expand the zone of the emulsion region [42]. In addition, Transcutol P can enhance the corneal permeability of the drug through different transport mechanisms [44]. *Pharmaceutics* **2021**, *13*, x FOR PEER REVIEW 9 of 24

**Figure 1.** Solubility data showing the amount of lipid/surfactants required to solubilize 10 mg of clarithromycin. **Figure 1.** Solubility data showing the amount of lipid/surfactants required to solubilize 10 mg of clarithromycin.

The existence of several colloidal structures such as molecular solubilized emulsifier monomers, mixed micelles, supercooled melts, and drug nanoparticles are important points to consider in SLN formulation. Micelle‐forming surfactants are mainly present either on the lipid surface, in micelles or as monomers. The kinetics of drug redistribution between various coexisting colloidal species is very important since the dynamic phenomena are significant for drug stability and drug release [45]. The SLN system can be an efficient carrier system provided that the redistribution of drug between various colloidal forms are prevented. A wide range of drugs with varying lipophilicity has been The existence of several colloidal structures such as molecular solubilized emulsifier monomers, mixed micelles, supercooled melts, and drug nanoparticles are important points to consider in SLN formulation. Micelle-forming surfactants are mainly present either on the lipid surface, in micelles or as monomers. The kinetics of drug redistribution between various coexisting colloidal species is very important since the dynamic phenomena are significant for drug stability and drug release [45]. The SLN system can be an efficient carrier system provided that the redistribution of drug between various colloidal forms are prevented. A wide range of drugs with varying lipophilicity has been extensively investigated with respect to their incorporation into SLNs, which signifies the localization of drug within the solid lipid matrix [46]. The log P value of 3.16 for clarithromycin indicates that the drug is mostly located in the lipid matrix. Drug stabilization is a major

localization of drug within the solid lipid matrix [46]. The log P value of 3.16 for clarithromycin indicates that the drug is mostly located in the lipid matrix. Drug stabilization is a major formulation challenge in colloidal carriers such as SLN due to the enormous surface area and short diffusional pathways. High viscosity provided by lipids

The evaluation of fractional factorial screening batches of prepared SLN is shown in

Table 5. The prepared sixteen batches were evaluated for particle size, percent entrapment

actively decreases the diffusion coefficient of the drug inside the carrier system.

efficiency, and percent drug loading.

*3.2. Evaluation of Fractional Factorial Screening Batches of SLN*

**Homogenization Speed (RPM)**

**Sonication Time (Min)** **Amount of Lipid (mg)**

**Batch No.**

> formulation challenge in colloidal carriers such as SLN due to the enormous surface area and short diffusional pathways. High viscosity provided by lipids actively decreases the diffusion coefficient of the drug inside the carrier system. F10 6000 5 150 3:7 1 134 0.18 87.3 26.9 F11 6000 5 150 5:5 3 119 0.14 83.7 27.9 F12 12,000 5 200 3:7 1 130 0.14 85.7 21.4 F13 6000 5 200 5:5 1 124 0.12 84.5 21.1

**Particle Size (nm)** **Polydispersity Index**

**Entrapment Efficiency (%)**

**Drug Loading (%)** 

#### *3.2. Evaluation of Fractional Factorial Screening Batches of SLN* F14 12,000 10 200 5:5 1 397 0.29 75.2 18.9

F8 12,000 5 150 3:7 3 113 0.16 87.9 29.3 F9 12,000 10 150 5:5 3 413 0.29 49.9 15.9

*Pharmaceutics* **2021**, *13*, x FOR PEER REVIEW 10 of 24

**Table 5.** Evaluation of fractional factorial design batches.

F1 12,000 5 200 5:5 3 115 0.15 84.9 21.2 F2 6000 10 200 5:5 3 357 0.32 69.9 17.5 F3 12,000 10 200 3:7 3 334 0.40 72.4 18.1 F4 6000 10 150 5:5 1 276 0.26 79.1 26.4 F5 6000 5 200 3:7 3 113 0.12 85.8 21.5 F6 12,000 5 150 5:5 1 124 0.18 92.4 30.8

**Surfactant Ratio**

**Independent Variables with Actual Values Dependent Variables**

**Surfactant Concentration (%)**

The evaluation of fractional factorial screening batches of prepared SLN is shown in Table 5. The prepared sixteen batches were evaluated for particle size, percent entrapment efficiency, and percent drug loading. F15 6000 10 150 3:7 3 284 0.22 77.1 25.7 F16 12,000 10 150 3:7 1 397 0.26 74.9 24.9 3.2.1. Effect on Particle Size

#### 3.2.1. Effect on Particle Size The particle size diameter of the SLN dispersions prepared was between 113 nm to

The particle size diameter of the SLN dispersions prepared was between 113 nm to 413 nm, as shown in Table 5. From the ANOVA study, it was observed that particle size was significantly (*p* < 0.05) affected by sonication time (min), and it was also confirmed from the Pareto chart (Figure 2a) that t-value of sonication time was above the Bonferroni critical limit, so it had a more significant effect on SLN size. The contour (Figure 2b) and 3D surface plot (Figure 2c) showed that as sonication time decreased from 10 min to 5 min, the size of SLNs also decreased from 413 to 113 nm. This is possible because the sonication can break down the large emulsion drops into tiny particles [25]. On the other hand, the polydispersity index values noticed were in the range of 0.12–0.40 (Table 5). 413 nm, as shown in Table 5. From the ANOVA study, it was observed that particle size was significantly (*p* < 0.05) affected by sonication time (min), and it was also confirmed from the Pareto chart (Figure 2a) that t‐value of sonication time was above the Bonferroni critical limit, so it had a more significant effect on SLN size. The contour (Figure 2b) and 3D surface plot (Figure 2c) showed that as sonication time decreased from 10 min to 5 min, the size of SLNs also decreased from 413 to 113 nm. This is possible because the sonication can break down the large emulsion drops into tiny particles [25]. On the other hand, the polydispersity index values noticed were in the range of 0.12–0.40 (Table 5).

Entrapment efficiency refers to the quantity of clarithromycin that was entrapped either within the solid matrix or adsorbed on the surface of nanoparticles. It was quantified by analyzing the amount of drug present in the solid pellets as well as in the

**Figure 2.** Pareto chart (**a**), contour plot (**b**), and 3D surface plot (**c**) representing the effect on particle size of fractional factorial design. **Figure 2.** Pareto chart (**a**), contour plot (**b**), and 3D surface plot (**c**) representing the effect on particle size of fractional factorial design.

3.2.2. Effect on Entrapment Efficiency

*Pharmaceutics* **2021**, *13* , 523


#### 3.2.2. Effect on Entrapment Efficiency *Pharmaceutics* **2021**, *13*, x FOR PEER REVIEW 11 of 24

Entrapment efficiency refers to the quantity of clarithromycin that was entrapped either within the solid matrix or adsorbed on the surface of nanoparticles. It was quantified by analyzing the amount of drug present in the solid pellets as well as in the aqueous phase of the nanoparticle dispersion. It was concluded from the Pareto chart (Figure 3a) that the sonication time had a substantial influence on the entrapment efficiency. The majority of the formulations possessed high entrapment efficiency of >70%, which also revealed the role of surfactant concentration and ratio, amount of lipid, and homogenization speed on drug incorporation. The selection of suitable stabilizing agents, such as surfactants, and their concentration can have a major impact on the entrapment efficiency of SLN dispersions [45]. Surfactants not only present on the lipid surface but also exists as micelles in the aqueous phase. Micelles and mixed micelles are well known to solubilize the drugs and thus act as an alternate drug localization site. In the current study, sonication likely resulted in more interaction between the particles in lipid and aqueous phases due to the ultrasonic shock waves created by the cavitation forces in the liquid, as described in the literature [47]. These intense forces would have probably caused a rapid transfer of clarithromycin to the core of lipid matrix and subsequently increased entrapment percentage at an optimum sonication time. The contour plot presented in Figure 3b and 3D surface plot in the Figure 3c also confirmed that as sonication time increased (from 5 min to 10 min), the percent entrapment efficiency decreased. This reduction in entrapment efficiency could be due to the extension of sonication time, which in turn would have resulted in greater disintegration of agglomerates, which results in leaking of drug particles from lipid vesicles and thereby decreases the percent entrapment efficiency [48]. However, when the surfactant concentration decreased, a minor improvement in the percent entrapment efficiency was noticed. This could be due to the solubilizing property of surfactant [49] and the existence of an optimum surfactant level, which helped the clarithromycin to stay within the lipid particles and improve the entrapment efficiency of the drug [50]. aqueous phase of the nanoparticle dispersion. It was concluded from the Pareto chart (Figure 3a) that the sonication time had a substantial influence on the entrapment efficiency. The majority of the formulations possessed high entrapment efficiency of >70%, which also revealed the role of surfactant concentration and ratio, amount of lipid, and homogenization speed on drug incorporation. The selection of suitable stabilizing agents, such as surfactants, and their concentration can have a major impact on the entrapment efficiency of SLN dispersions [45]. Surfactants not only present on the lipid surface but also exists as micelles in the aqueous phase. Micelles and mixed micelles are well known to solubilize the drugs and thus act as an alternate drug localization site. In the current study, sonication likely resulted in more interaction between the particles in lipid and aqueous phases due to the ultrasonic shock waves created by the cavitation forces in the liquid, as described in the literature [47]. These intense forces would have probably caused a rapid transfer of clarithromycin to the core of lipid matrix and subsequently increased entrapment percentage at an optimum sonication time. The contour plot presented in Figure 3b and 3D surface plot in the Figure 3c also confirmed that as sonication time increased (from 5 min to 10 min), the percent entrapment efficiency decreased. This reduction in entrapment efficiency could be due to the extension of sonication time, which in turn would have resulted in greater disintegration of agglomerates, which results in leaking of drug particles from lipid vesicles and thereby decreases the percent entrapment efficiency [48]. However, when the surfactant concentration decreased, a minor improvement in the percent entrapment efficiency was noticed. This could be due to the solubilizing property of surfactant [49] and the existence of an optimum surfactant level, which helped the clarithromycin to stay within the lipid particles and improve the entrapment efficiency of the drug [50].

**Figure 3.** Pareto chart (**a**), contour plot (**b**), and 3D surface plot (**c**) representing the effect on percent entrapment efficiency of fractional factorial design. **Figure 3.** Pareto chart (**a**), contour plot (**b**), and 3D surface plot (**c**) representing the effect on percent entrapment efficiency of fractional factorial design.

#### 3.2.3. Effect on Drug Loading 3.2.3. Effect on Drug Loading

From the ANOVA study and Pareto chart (Figure 4a), it was observed that the amount of lipid and sonication time had significant effects on percent drug loading, as the t‐value was above the limit. The batches with 150 mg of lipid showed good drug entrapment and faster solidification of the nanoparticles. The contour (Figure 4b) and 3D From the ANOVA study and Pareto chart (Figure 4a), it was observed that the amount of lipid and sonication time had significant effects on percent drug loading, as the t-value was above the limit. The batches with 150 mg of lipid showed good drug entrapment and faster solidification of the nanoparticles. The contour (Figure 4b) and 3D surface

surface response plot (Figure 4c) show that drug loading increased as the sonication time decreased. However, no significant effect in drug loading was noticed with

response plot (Figure 4c) show that drug loading increased as the sonication time decreased. However, no significant effect in drug loading was noticed with homogenization speed, surfactant concentration, or surfactant ratio. *Pharmaceutics* **2021**, *13*, x FOR PEER REVIEW 12 of 24

**Figure 4.** Pareto chart (**a**), contour plot (**b**), and 3D surface plot (**c**) representing the effect on percent drug loading of fractional factorial design. **Figure 4.** Pareto chart (**a**), contour plot (**b**), and 3D surface plot (**c**) representing the effect on percent drug loading of fractional factorial design.

#### *3.3. The 32 Full Factorial Optimization Design 3.3. The 3<sup>2</sup> Full Factorial Optimization Design*

Based on the studies of fractional factorial screening design and data analysis, it was observed that out of five independent variables (including formulation and process variables), two variables (amount of lipid and sonication time) were considered forfurther optimization. Homogenization speed, surfactant ratio, and surfactant concentration had low or insignificant effects, and hence they were kept constant. A total of nine batches (CL1–CL9) were prepared (Table 6) and were evaluated for particles size, percent entrapment efficiency, and percent drug loading. Based on the studies of fractional factorial screening design and data analysis, it was observed that out of five independent variables (including formulation and process variables), two variables (amount of lipid and sonication time) were considered for further optimization. Homogenization speed, surfactant ratio, and surfactant concentration had low or insignificant effects, and hence they were kept constant. A total of nine batches (CL1– CL9) were prepared (Table 6) and were evaluated for particles size, percent entrapment efficiency, and percent drug loading.


**Table 6.** Evaluation of 32 full factorial design batches. **Table 6.** Evaluation of 3<sup>2</sup> full factorial design batches.

*3.4. Evaluation of Design Batches of SLN* 3.4.1. Effect on Particle Size *3.4. Evaluation of Design Batches of SLN*

The effect of amount of lipid and sonication time on particle size was studied, and 3.4.1. Effect on Particle Size

the relevant contour plot and 3D surface response plot are shown in Figure 5a,b, respectively. From the plots, it was observed that as the lipid amount increased from 150 mg to 175 mg, there were increases in particle size (for sonication time 4 and 6 min). This was due to the distribution of sonication energy in the dispersion containing a higher lipid amount dispersion being weaker than in that with the lower lipid amount, which was The effect of amount of lipid and sonication time on particle size was studied, and the relevant contour plot and 3D surface response plot are shown in Figure 5a,b, respectively. From the plots, it was observed that as the lipid amount increased from 150 mg to 175 mg, there were increases in particle size (for sonication time 4 and 6 min). This was due to the distribution of sonication energy in the dispersion containing a higher lipid amount

dispersion being weaker than in that with the lower lipid amount, which was responsible for more efficient increases in the particle size [25]. However, the same was not observed in the case of 125 mg to 150 mg due to the effect of sonication time. In the case of sonication time, as it increased from 4 to 6 min, particle size decreased, as mentioned earlier, due to more sonication energy reducing the size of the nanoemulsion droplets [25]. However, by further increasing the tie from 6 to 8 min, particle size increased. This was due to more sonication energy, smaller droplet agglomerates, and increases in the size. As per this study, it was observed that a lipid amount of 150 mg and a sonication time of 6 min could be considered as the optimum value. The below polynomial equation (Equation (1)) shows the main, interactive, and polynomial effects of amount of lipid and sonication time on particle size. droplets [25]. However, by further increasing the tie from 6 to 8 min, particle size increased. This was due to more sonication energy, smaller droplet agglomerates, and increases in the size. As per this study, it was observed that a lipid amount of 150 mg and a sonication time of 6 min could be considered as the optimum value. The below polynomial equation (Equation (1)) shows the main, interactive, and polynomial effects of amount of lipid and sonication time on particle size. Particle size = +150.41 − 21.17 × X1 + 50.67 × X2 − 14.95 × X1 X2 + 19.13 × X11 + 139.23 × X22 (1) The reduced equation by considering the significant term/s (*p* < 0.05) for particle size can be written as Equation (2): particle size = +150.41 − 21.17 × X1 + 50.67 × X2 + 139.23 × X22 (2)

On the other hand, the polydispersity index values noticed were in the range of 0.12–

not observed in the case of 125 mg to 150 mg due to the effect of sonication time. In the case of sonication time, as it increased from 4 to 6 min, particle size decreased, as mentioned earlier, due to more sonication energy reducing the size of the nanoemulsion

Particle size = +150.41 − 21.17 × X<sup>1</sup> + 50.67 × X<sup>2</sup> − 14.95 × X<sup>1</sup> X<sup>2</sup> + 19.13 × X<sup>11</sup> + 139.23 × X<sup>22</sup> (1) 0.37 (for sonication times of 4 and 6 min; Table 6), suggesting particles were monodispersed [51], while they were polydispersed when the sonication time was 6 min.

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

**Figure 5.** Contour plot (**a**) and 3D surface plot (**b**) representing the effect on particle size for full factorial design batches. **Figure 5.** Contour plot (**a**) and 3D surface plot (**b**) representing the effect on particle size for full factorial design batches.

3.4.2. Effect on Entrapment Efficiency Figure 6a,b shows the contour plot and 3D surface plot on the effect of the amount of The reduced equation by considering the significant term/s (*p* < 0.05) for particle size can be written as Equation (2):

$$\text{particle size} = +150.41 - 21.17 \times \chi\_1 + 50.67 \times \chi\_2 + 139.23 \times \chi\_{22} \tag{2}$$

decreased from 6 min to 8 min. This was due to higher sonication, which increased the mixture viscosity (with increased lipid concentration) and increased drug solubility within the lipid core and thereby increased the entrapment efficiency [52]. In this study, at lower sonication time (4 min), as the lipid amount increased, entrapment efficiency On the other hand, the polydispersity index values noticed were in the range of 0.12–0.37 (for sonication times of 4 and 6 min; Table 6), suggesting particles were monodispersed [51], while they were polydispersed when the sonication time was 6 min.

#### increased from 79.6% to 81.9%. The reverse condition was observed in cases of higher sonication time (8 min); as the lipid amount increased, the percent entrapment efficiency 3.4.2. Effect on Entrapment Efficiency

decreased from 74.9% to 65.7%. However, at medium sonication time (6 min), near to a significant difference was observed in the entrapment efficiency, initially increasing from 76.9% to 87.2% and then decreasing to 79.4%, with respect to the lipid amount (150 mg). The batch showed good entrapment efficiency of about 87.2%. In addition, the polynomial Figure 6a,b shows the contour plot and 3D surface plot on the effect of the amount of lipid and sonication time on percent entrapment efficiency. It was observed that as sonication time increased, percent entrapment efficiency increased up to 6 min and then decreased from 6 min to 8 min. This was due to higher sonication, which increased the mixture viscosity (with increased lipid concentration) and increased drug solubility within the lipid core and thereby increased the entrapment efficiency [52]. In this study, at lower sonication time (4 min), as the lipid amount increased, entrapment efficiency increased from 79.6% to 81.9%. The reverse condition was observed in cases of higher sonication time (8 min); as the lipid amount increased, the percent entrapment efficiency decreased from 74.9% to 65.7%. However, at medium sonication time (6 min), near to a significant

difference was observed in the entrapment efficiency, initially increasing from 76.9% to 87.2% and then decreasing to 79.4%, with respect to the lipid amount (150 mg). The batch showed good entrapment efficiency of about 87.2%. In addition, the polynomial equation confirmed that the effect of sonication time was more significant on percent entrapment efficiency than the amount of lipid. Percent entrapment efficiency = +83.19 − 0.73 × X1 − 5.10 × X2 − 2.87 × X1X2 − 3.03 × X11 − 5.63 × X22 (3) The reduced equation by considering the significant term (*p* < 0.05) for percent entrapment efficiency can be written as Equation (4): percent entrapment efficiency = − 5.10 × X2 (4)

The polynomial equation (Equation (3)) of percent entrapment efficiency is as

equation confirmed that the effect of sonication time was more significant on percent

*Pharmaceutics* **2021**, *13*, x FOR PEER REVIEW 14 of 24

entrapment efficiency than the amount of lipid.

follows:

**Figure 6.** Contour plot (**a**) and 3D surface plot (**b**) representing the effect on percent entrapment efficiency for full factorial design batches. **Figure 6.** Contour plot (**a**) and 3D surface plot (**b**) representing the effect on percent entrapment efficiency for full factorial design batches.

3.4.3. Effect on Drug Loading The polynomial equation (Equation (3)) of percent entrapment efficiency is as follows:

From Figure 7a,b, it was observed that as the amount of lipid increases, the percent drug loading decreases. This is due to the fixed drug amount in the formulation, as the Percent entrapment efficiency = +83.19 − 0.73 × X<sup>1</sup> − 5.10 × X<sup>2</sup> − 2.87 × X1X<sup>2</sup> − 3.03 × X<sup>11</sup> − 5.63 × X<sup>22</sup> (3)

> drug concentration remained the same regardless of the increased lipid concentration that caused a decrease in the drug:lipid ratio with increasing lipid concentration [25]. For sonication time, also as time increased, there was a minor decrease in the percent of drug The reduced equation by considering the significant term (*p* < 0.05) for percent entrapment efficiency can be written as Equation (4):

$$\text{percent entanglement efficiency} = -5.10 \times \text{X}\_2\tag{4}$$

equation (Equation (5)) is as follows: 3.4.3. Effect on Drug Loading

Percent drug loading = +26.32 − 4.62 × X1 − 1.63 × X2 (5) The reduced equation is the same, as both factors significantly affect percent drug loading. From Figure 7a,b, it was observed that as the amount of lipid increases, the percent drug loading decreases. This is due to the fixed drug amount in the formulation, as the drug concentration remained the same regardless of the increased lipid concentration that caused a decrease in the drug:lipid ratio with increasing lipid concentration [25]. For sonication time, also as time increased, there was a minor decrease in the percent of drug loading. The polynomial equation showing a negative sign for both variables indicates that the variables have inverse relations with percent drug loading. The polynomial equation (Equation (5)) is as follows:

$$\text{Percent drug loading} = +26.32 - 4.62 \times \text{X}\_1 - 1.63 \times \text{X}\_2 \tag{5}$$

**Figure 7.** Contour plot (**a**) and 3D surface plot (**b**) representing the effect on percent drug loading for full factorial design batches. **Figure 7.** Contour plot (**a**) and 3D surface plot (**b**) representing the effect on percent drug loading for full factorial design batches.

In summary, it can be concluded that for particle size, sonication time is the key variable, and with 6 min of sonication time, the targeted particle size can be achieved. In terms of percent entrapment efficiency, again sonication time of 6 min would be highly effective, while with increases in the amount of lipid beyond a certain limit, there would be no further increase in percent entrapment efficiency. At last, considering the percent drug loading, the amount of lipid with a significant effect was 125 mg and 150 mg on targeted drug loading and sonication time at 4 min to 6 min. Thus, an overall conclusion from the design study is that around 150 mg amount of lipid and 6 min of sonication time would give the required particle size, percent entrapment efficiency, and percent drug loading. *3.5. In Vitro Drug Release* The reduced equation is the same, as both factors significantly affect percent drug loading. In summary, it can be concluded that for particle size, sonication time is the key variable, and with 6 min of sonication time, the targeted particle size can be achieved. In terms of percent entrapment efficiency, again sonication time of 6 min would be highly effective, while with increases in the amount of lipid beyond a certain limit, there would be no further increase in percent entrapment efficiency. At last, considering the percent drug loading, the amount of lipid with a significant effect was 125 mg and 150 mg on targeted drug loading and sonication time at 4 min to 6 min. Thus, an overall conclusion from the design study is that around 150 mg amount of lipid and 6 min of sonication time would give the required particle size, percent entrapment efficiency, and percent drug loading.

#### The release of clarithromycin from SLNs was carried out using a dialysis membrane *3.5. In Vitro Drug Release*

in Franz diffusion cells. Figure 8 shows the cumulative percentage release of clarithromycin SLNs at specified time intervals from CL1 to CL9. Figure 8 signifies that the drug release profiles of various formulations showed similar patterns of initially slow and then progressively increasing over the time and was higher than 70% in 8 h for all SLNs tested. It was reported that SLN can retard the drug release when the chosen drug has a higher melting point compared to the lipid matrix [46]. It was also disclosed that other factors such as interactions between drug–lipid and surfactant–lipid, as well as solubility of drug in the lipid can play a major role in the drug release from SLNs [53]. Thus, one of the possible explanations for the observed drug release profile could be due to the large differences in the melting point of clarithromycin and stearic acid used. In addition, such release is also possible due to the higher lipid viscosity contributed by the drug–lipid interaction combined with slow diffusion of clarithromycin from the waxy matrix. Amongst all the designed formulations, CL9 had the lowest particle size (154 nm), higher entrapment efficiency (87.2%), and good drug loading (29.1%), which were in well‐ defined ranges targeted for SLN formulation. The CL9 formulation also exhibited higher drug release (~80% in 8 h). However, the release of clarithromycin in solution (control) was quick, and the whole drug was released in 1 h. The mechanism of clarithromycin release kinetics from CL9 batch was studied using standard mathematical models. Goodness of fit models were selected by evaluating the r2 value, sum of squares of residuals, and Fischer ratio in order to avoid errors in the prediction of the release The release of clarithromycin from SLNs was carried out using a dialysis membrane in Franz diffusion cells. Figure 8 shows the cumulative percentage release of clarithromycin SLNs at specified time intervals from CL1 to CL9. Figure 8 signifies that the drug release profiles of various formulations showed similar patterns of initially slow and then progressively increasing over the time and was higher than 70% in 8 h for all SLNs tested. It was reported that SLN can retard the drug release when the chosen drug has a higher melting point compared to the lipid matrix [46]. It was also disclosed that other factors such as interactions between drug–lipid and surfactant–lipid, as well as solubility of drug in the lipid can play a major role in the drug release from SLNs [53]. Thus, one of the possible explanations for the observed drug release profile could be due to the large differences in the melting point of clarithromycin and stearic acid used. In addition, such release is also possible due to the higher lipid viscosity contributed by the drug–lipid interaction combined with slow diffusion of clarithromycin from the waxy matrix. Amongst all the designed formulations, CL9 had the lowest particle size (154 nm), higher entrapment efficiency (87.2%), and good drug loading (29.1%), which were in well-defined ranges targeted for SLN formulation. The CL9 formulation also exhibited higher drug release (~80% in 8 h). However, the release of clarithromycin in solution (control) was quick, and the whole drug was released in 1 h. The mechanism of clarithromycin release kinetics from CL9 batch was studied using standard mathematical models. Goodness of fit models were selected by evaluating the r<sup>2</sup> value, sum of squares of residuals, and Fischer ratio in order to avoid errors in the prediction of the release mechanism [29]. The data indicated higher

r <sup>2</sup> value (0.9736), least squares of residuals value (121.54), and Fischer ratio value (17.36) with Weibull model kinetics. Furthermore, the diffusion exponent value (*n* < 0.5) implied the Fickian diffusion mechanism of clarithromycin from CL9. value (121.54), and Fischer ratio value (17.36) with Weibull model kinetics. Furthermore, the diffusion exponent value (*n* < 0.5) implied the Fickian diffusion mechanism of clarithromycin from CL9.

mechanism [29]. The data indicated higher r2 value (0.9736), least squares of residuals

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**Figure 8.** Comparison of percentage of clarithromycin release from solid lipid nanoparticles (CL1– CL9) and drug solution (control). Data represented are mean ± SD (*n* = 6). **Figure 8.** Comparison of percentage of clarithromycin release from solid lipid nanoparticles (CL1–CL9) and drug solution (control). Data represented are mean ± S.D. (*n* = 6).
