The results of in vitro drug release were analyzed using model dependent approach. Various kinetic models—zero order, first order, Higuchi, Hixson Crowell and Korsmeyer-Peppas, and Weibull models—were applied to obtain the drug release mechanism from the Chitosan nanoparticles [17–19]. 3. Result and Discussions 3.1. Particle Sizes Particle

sizes of respective find more batches are shown in Table 1. Particle size was varied in the range of 180.5 (CN3)nm to 383.3 (CN6). The drug loaded nanoparticles exhibited relatively narrow particle size distribution as indicated by relatively low PDI values in the range of 0.202 to 0.472. Low PDI Inhibitors,research,lifescience,medical values also indicate the relative homogenous nature of the dispersion. 3.2. Morphology Morphology of chitosan nanoparticles under scanning Inhibitors,research,lifescience,medical electron microscope (SEM) is shown in Figure 1. SEM micrograph shows that the Chitosan nanoparticles have regular and uniform spherical shapes. It also shows that there is only little aggregation between the prepared Chitosan nanoparticles. Figure 1 Scanning electron microscope image of Chitosan nanoparticles. 3.3. Drug Encapsulation Efficiency

and Drug Loading Percentage of drug encapsulation efficiency and percentage of drug loading for respective batches are shown in Inhibitors,research,lifescience,medical Table 1. Higher drug encapsulation efficiency and drug loading were observed for the batch CN8, and CN5 has the lowest drug encapsulation efficiency and drug loading. 3.4. Statistical Analysis of Data A statistical design was utilized in order to derive the relationship between the response variables and the independent variables. Inhibitors,research,lifescience,medical Table 1 shows the independent Inhibitors,research,lifescience,medical factors and response values of respective batches. The statistical evaluation of the results was carried out by Design Expert software. The analysis of variance (ANOVA) results (P value) of the effect

of the variables on particles size, percentage of drug encapsulation efficiency, and percentage of drug loading can be seen in following full-model polynomial equation: Y1=249.61+31.99X1(P<0.0001)−22.89X2(P<0.0001)+38.39X3(P<0.0001)−8.66X1X2(P<0.0001)+10.76X1X3(P<0.0001)−12.86X2X3(P<0.0001)−8.14X1X2X3(P<0.0001),Y2=29.84+9.92X1(P<0.0001)−2.48X2(P<0.0001)+4.41X3(P=0.0105)−1.77X1X2(P= 0.0551)+1.28X1X3(P=0.1539)+3.61X2X3(P<0.0007)+1.93X1X2X3(P=0.0389),Y3=30.56+8.40X1(P<0.0001)−2.82X2(P=0.0008)+3.89X3(P<0.0084)−1.21X1X2(P=0.2164)+1.67X1X3(P=0.0941)+4.02X2X3(P<0.0006)+1.59X1X2X3(P=0.1108). else (6) The terms of full-model polynomial equation having insignificant P value (P > 0.05) have negligible contribution to obtained dependent variables and thus are omitted to get reduced model equation.