Oil-in-Water Nanosized Emulsions for Drug Delivery and Targeting. Tamilvanan Shunmugaperumal

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СКАЧАТЬ the quantitative comparison between the theoretical prediction and obtained experimental values was used by plotting the predicted versus observed value curve to validate/verify the chosen face‐centered CCD model. The predicted error is the difference between the experimental value and the predicted value divided by predicted value.

TABLE 2.7. Low and High Levels of Critical Material Attributes (CMAs) and Critical Process Parameters (CPPs, also Called as Independent Variables) Along with the Goals of Critical Quality Attributes (CQAs, also Called as Dependent Variables) Used for Making Face‐Centered Central Composite Design (CCD) Throughout the Initial Pilot Screening Study to Prepare Topical Ophthalmic Emulsions

Screened CMAs and CPPs	Quantity	Levels
Low	High
X 1: Castor oil (ml)	1, 1.5, 2	1	2
X 2: Chitosan (mg)	6, 12, 18	6	18
X 3: Poloxamer (mg)	75, 87.5, 100	75	100
CsA (% w/w)	0.05 or 0.1
Glycerin (g)	2.25
Double distilled water (ml)	Upto 100
Selected CQAs		Goal
R 1 : Mean particle size		Minimize
R 2 : Polydispersity index		Minimize
R 3 : Zeta potential		Maximize

      Note: Italics numbers and texts indicate constant quantity of independent variables while the quantity of other independent variables were altered.

       2.5.1.5. Systematic Optimization Using Face‐Centered CCD

A Box–Wilson central composite design, commonly termed as CCD, is frequently utilized to build a second‐order polynomial for the response variables (CQAs) in RSM without using a complete full factorial design of experiments. There are two main varieties of CCD, namely, face‐centered CCD and rotatable CCD. Due to its simplicity, regions of interest, and operability, the face‐centered CCD was chosen in the present study. As per the face‐centered CCD, the optimal composition and the experimental conditions to prepare the topical ophthalmic emulsions were fixed (Table 2.7). Although the goal of ZP is fixed at maximum (Table 2.7) by considering the previous observation that a stable cationic nanosized emulsion should contain the ZP value that ranges from +25 to +45 mV (Tamilvanan et al. 2010), the stable stability of emulsion is not increasing with the increase of ZP's values. With the help of polynomial regression equation, the factor–response relationship was examined for the response function (Yi) using the generalized response surface model [as given in Eq. (2.2)]. In Eq. (2.2), the terms X₁, X₂, and X₃ indicate the three different factors (CPPs, independent variables) such as amounts of castor oil, chitosan, and poloxamer, respectively. While the term a₀ represents intercept (a constant), the linear (first‐order), quadratic (second‐order), and interactive polynomial coefficients are given as a₁, a₂, and a₃ (in general, a_i), a₁₁, a₂₂, and a₃₃ (in general, a_ii), and a₁₂, a₁₃, and a₂₃ (in general, a_ij), respectively.

(2.2)

The estimated model equations (both coded and actual), regression coefficients, R², adjusted R², regression (P‐value and F‐value), and standard deviation related to the effect of the three CPPs (independent variables) are presented in Table 2.8. Although the actual model equation contains the levels specified in the original units for each CPP, this equation should not be used to determine the relative impact of each CPP (factor). Therefore, the coded model equation is used for identifying the relative impact of the CPPs by comparing the regression coefficient values of each factor. Nevertheless, a positive value in the regression equation represents an effect that favors optimization due to synergistic effect, while a negative value indicates an inverse relationship or antagonistic effect between the factor and the responses (Woitiski et al. 2009). It should be mentioned that nonsignificant (p < 0.005) linear terms (main CCD effect) were included in the final reduced model if quadratic or interaction terms containing these variables were found to be significant (p < 0.05). In the present study, the response surface analysis demonstrated that the second‐order polynomial used for MPS has a higher coefficient of determination (R² = 0.8186) compared to ZP value (R² = 0.6525) and PDI value (R² = 0.7188). The obtained coefficient of determination showed that >82% of the response variation of the MPS, PDI, and ZP values could be described by response surface models as the function of the CPPs. It was observed that the lack of fit had no indication of significant (p < 0.05) for the final reduced model, therefore providing the satisfactory fitness of the RSM to the significant independent variables (factors) effect. From Table 2.8, it was observed that two independent variables (A and B) exhibited a positive effect on the response of ZP (R₃). Both MPS (R₁) and PDI (R₂) showed a positive effect for all of the tested three independent variables (A, B, and C). The interaction coefficients with more than one factor, or higher order terms in the regression equation, represent the interaction between terms or the quadratic relationship, respectively, which suggest a nonlinear relationship between factors and responses (Motwani et al. 2008). Therefore, a different degree of response than it is originally predicted by regression equation may be expected from the independent variables as they are varied at different levels or more than one factor or variables is changed simultaneously (Woitiski et al. 2009). In the present face‐centered CCD modeling, all the responses (MPS, PDI, and ZP or R₁, R₂, and R₃) were affected by the interaction of independent variables and hence displaying a quadratic relationship. The interaction effects between B and C СКАЧАТЬ