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

the FMEA analysis allows the information on risk to be stored for future use. In this manner, it is important for the larger organization in which the turnover results in the loss of institutional memory. The outcomes of FMEA analysis are to define the RPN and further computation of RPN.

TABLE 2.2. Risk Estimation Matrix (REM) for Qualitative Analysis of Risk Constructed After Assigning Low, Medium, and High Risk(s) Levels to Each Material Attributes and Process Parameters of Topical Ophthalmic Emulsions

Critical Quality Attributes (CQAs)	REM for Qualitative Analysis of Risk Assigned to
Volume of Castor Oil	Amount of Chitosan	Amount of Poloxamer 407	Premixing Time	Homogenization Time	Homogenization Speed	Probe Sonication Time
Mean particle size	High	Medium	Medium	Medium	High	High	Medium
Polydispersity index	Low	Low	Medium	Medium	Medium	High	Medium
Zeta potential	Low	High	Low	Low	Low	Low	Low

TABLE 2.3. Summary of Failure Mode and Effect Analysis (FMEA) Demonstrating Risk Priority Number (RPN) Scores for Various Materials and Process Variables Affecting the Critical Quality Attributes (CQAs) Such As Mean Particle Size (MPS), Polydispersity Index (PDI), and Zeta Potential (ZP)

Failure Modes	Detection (D)	Occurrence (O)	Severity (S)	RPN (=DOS)	Consequences on CQAs
Volume of castor oil (ml)	5	5	8	200	MPS
Amount of chitosan (mg)	6	5	7	210	MPS and ZP
Amount of poloxamer (mg)	5	5	5	125	MPS and PDI
Premixing time (min)	5	7	7	245	MPS and PDI
Homogenization time (min)	5	4	6	120	MPS and PDI
Homogenization speed (rpm)	5	6	7	210	MPS and PDI
Probe sonication time (min)	2	3	3	18	MPS and PDI

Figure 2.4. Ishikawa fish‐bone diagram made with the help of Minitab 18 software showcasing the potential cause–effect relationship among the product and process variables for topical ocular emulsions.

Based on the REM analysis, an elaborative risk assessment was carried out by assigning ordinal scores to each MA and PP. Table 2.3 displays the details of MAs and PPs employed during FMEA analysis together with their computed RPN scores, which collectively explain their effect and plausible/possible consequences on CQAs of topical ocular emulsions. To discriminate the high‐risk factors against the low‐risk factors, a critical cutoff value of RPN that was fixed for ocular emulsions is 100 or above. With lone exception of probe sonication time (which shows the RPN scores of 18 only), the factors such as amount of chitosan, amount of poloxamer, homogenization time, homogenization speed, premixing time, and volume of castor oil possessed the high RPN scores. The factors associated with high RPN scores were finally subjected to factor screening study by employing the Taguchi design.

Table 2.4 shows the selective list of various designs used for optimization and screening of CPPs of o/w nanosized emulsions.

       2.5.1.2. Factor Screening Study

The principles of factor sparsity are applied for the factor screening studies in which only a few of the factors among the numerous ones are identified to explain the major proportion of the experimental variation in the final product (Negi et al. 2015). Whereas the active or influential variables were responsible for the major variability, remaining all other factors were termed as inactive, less influential, or simply the noise variables. Based on Eq. (2.1), the CMAs/CPPs that produced high RPN values were finally subjected to factor screening studies to quantitatively estimate the risk associated with the formulation and process variables of topical ophthalmic emulsions. A 7‐factor 8‐run Taguchi design was employed for screening the formulation and process variables of the ophthalmic emulsions. The design matrix was prepared using Design‐Expert^® (version 11.1.0.1, Stat‐Ease Inc., Minneapolis, MN, USA) software. Table 2.5 enlists the Taguchi design matrix selected for the preparation of topical ophthalmic emulsions along with the description of their respective low and high levels. A total of eight trial formulations were thus prepared as per the screening design and evaluated for the identified/selected CQAs [mean particle size (MPS), polydispersity index (PDI), and zeta potential (ZP)]. The analysis of the design‐generated data was performed by fitting it to the first‐order linear model obviating the interaction effect(s), while analyzing coefficients for each of the factors. The Half‐normal and Pareto charts were used for