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

TABLE 2.4. Selective List of Various Designs Used for Optimization and Screening of CPPs of O/W Nanosized Emulsion [As Per Design‐Expert® (version 11.1.0.1, Stat‐Ease Inc., Minneapolis, MN, USA) Software]

Design for
Optimization	Screening
Box‐Behnken Central compositeFace centeredOrthogonal quadraticPractical (k > 5)SphericalRotatable (k < 6) Miscellaneous3‐Level fractionalHybridPentagonalHexagonal MixtureSimplex latticeSimplex centroid Split‐plotCentral compositeOptimal (custom) Supersaturated	MiscellaneousIrregular res VPlackett–BurmanTaguchi OA RandomizedMin‐run characterizeMin‐run screenMultilevel categoricOptimal (custom)Regular two‐level Split‐plotMultilevel categoricOptimal (custom)Regular two‐level

       2.5.1.3. Factor Screening Studies by Taguchi Design

To establish the orthogonal effects between formulation or process variables and selected CQAs, the Taguchi design is mainly employed. This design helps not only in filtering the most significant critical variables from the insignificant ones but also in optimizing further the formula for the development of topical ophthalmic emulsions. Figure 2.5a–c depict the Pareto charts for screening of influential formulation and process variables as per Taguchi design using selected CQAs. The standard t limit and Bonferroni limit are depicted as a black line and a red line, respectively, in the Pareto chart. Similarly, the positive and negative effects of each formulation and process variables on each CQAs are also depicted using yellow and blue box in the Pareto chart, respectively.

TABLE 2.5. Taguchi Design Matrix Portraying the Layout of Various Experimental Runs for Factor Screening of Topical Ophthalmic Emulsions

Critical Material Attributes (CMAs) and Critical Process Parameters (CPPs, also Called as Independent Variables) with Their Code	Levels	Critical Quality Attributes (CQAs, also Called as Dependent Variables)
Low (−1)	High (+1)
A:Castor oil (ml)	1	2	Mean particle size (MPS, nm) Polydispersity index (PDI) Zeta potential (ZP, mV)
B:Chitosan (mg)	6	18
C:Poloxamer (mg)	75	100
D:Premixing time (min)	10	15
E:Homogenization time (min)	15	20
F:Homogenization speed (min)	15,000	17,000
G:Probe sonication time (min)	5	10
Run	A	B	C	D	E	F	G
1	1	−1	1	−1	1	−1	1
2	−1	1	1	−1	−1	1	1
3	−1	−1	−1	−1	−1	−1	−1
4	1	−1	1	1	−1	1	−1
5	1	1	−1	−1	1	1	−1
6	−1	1	1	1	1	−1	−1
7	−1	−1	−1	1	1	1	1
8	1	1	−1	1	−1	−1	1

      If a particular formulation and process variables showed an effect that exceeds the standard t limit in the Pareto chart, then the variables produced a significant effect on the CQAs. On the other side, any of the formulation and process variables showing the effect that is lower than the standard t limit will be considered to produce a nonsignificant influence on the CQAs. In a similar manner, the significances of the formulation and process variables are also determined by the Bonferroni limit in the Pareto chart.

      The formulation and process variable such as homogenization speed was found to exceed either t value limit or Bonferroni limit for the MPS (Fig. 2.5a). This indicates that this formulation and process variable might produce the most significant influence on MPS. Although the homogenization speed was found to produce a significant influence on MPS, it did not influence the PDI and ZP values. Taking the insignificant influence of homogenization