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

Each of these elements is essential for product development, manufacturing, and quality assurance. Through parallel evolution of QbD and PAT, it is now well established that developing a robust formulation and manufacturing process requires a thorough understanding of inter‐relationships between material attributes (MAs), processing parameters (PPs), and dependent product quality attributes at each stage of product development. In this regard, pharmaceutical, chemical, and engineering industries, research institutions, and regulatory agencies worldwide have significantly contributed through joint collaborations, workshops, conference presentations, and publications. More recently, a 5‐year pilot QbD program (March 2011–April 2016) was launched between the FDA and the EMEA (EMA 2017).

      In this pilot program, the subject matter experts from both agencies had thoroughly exchanged their viewpoints to allow a joint evaluation of QbD elements in an effort to harmonize agencies’ expectation for regulatory submissions worldwide. As a result of this pilot program, both the FDA and the EMEA have mutually agreed on several pertinent topics and level of details required in a regulatory submission (EMA 2014).

These include risk assessments related to process and product design, DOEs, and design space, which are briefly summarized below. In QbD‐based product development, the MAs and PPs are initially identified from prior scientific knowledge using risk assessment techniques. One of commonly utilized risk assessment tools is failure mode effect analysis (FMEA). In this approach, a risk priority number (RPN) is calculated for each factor that can impact the CQAs of the product. The RPN is calculated by multiplying numerical rankings (e.g., 1–5 or 1–10) ascribed to three risk components (severity of harm, probability of occurrence, and detectability) associated with each factor, i.e.,

(2.1)

      The RPN values can be presented in a tabular format or as a Pareto plot for a quantitative display of relative risk rank order. It needs to be emphasized that all the MAs and PPs that can potentially affect CQAs are considered as part of the initial risk analysis; however, only a subset of these attributes and parameters are selected for development studies as warranted by the outcome of risk analysis (Badawy et al. 2016).

      This QbD element has originated from the Pareto principle. As a rule of thumb, about 80% of the problems originate from roughly 20% of the factors identified, that is why Pareto concept is sometimes referred to as 80/20 rule (Orloff 2011).

      Subsequently, the combinations and interactions of identified subset of MAs and PPs are studied as part of the product development through DOE, which is regarded as a “toolkit” component of a QbD approach. The collective outcome from formulation and process design DOEs is utilized in finalizing the list of critical material attributes (CMAs) and CPPs, thereby establishing a design space and an overall control strategy. Based on current ICH Q8(R2), a design space is defined as the “multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” [ICH Q8(R2) guideline 2009].

A generalized scheme representing the evolution of QbD and multidimensional combination and interaction of critical input variables (CMAs and CPPs) on CQAs of an output product is given in Flowchart 2.2. CMAs of input materials may include physical, chemical, or microbiological attributes of APIs, excipients, and other components (e.g., purified water, solvent) that are used in formulating a quality product.

2.5.1. Case Study for Optimizing Systematically a Formula to Make O/W Nanosized Emulsions

      The case study initially starts with the risk assessment plan for o/w nanosized emulsions by utilizing Ishikawa fish‐bone diagram and RPN score.

       2.5.1.1. Initial Quality Risk Assessment Studies

To find out the influence of CMAs and/or CPPs (independent variables) on CQAs (dependent variables) of topical ophthalmic emulsions, the initial risk assessment studies were performed. By employing the Minitab 18 software (M/s Minitab Inc., Philadelphia, PA, USA), an Ishikawa fish‐bone diagram was constructed to ascertain the potential cause–effect relationship among the product and process variables. Prioritization studies were carried out to select the CMAs/CPPs with high risk by constructing the Risk Estimation Matrix (REM) for qualitative analysis of risk by assigning low‐, medium‐, and high risk(s) levels to each MA and/or PP of topical ophthalmic emulsions (Table 2.2). Furthermore, the quantitative estimation of risk(s) and detection of the plausibility of failure modes associated with the emulsions were assessed with the help of the FMEA (Table 2.3). The rank order scores, ranging between 1 and 10 each, were allotted to the CMAs/CPPs (independent variables) for indicating severity, detectability, and occurrence of risks. The FMEA defines the RPN according to the formula already shown in Eq. (2.1) (Fahmy et al. 2012).

Flowchart 2.2. Evolution of QbD and multidimensional combination and interactions of critical input variables (CMAs and CPPs) on critical response variables (CQAs) for the preparation of o/w nanosized emulsions.

      [Adapted from Montgomery (2013) and Yu et al. (2014).]

      The parameter D is the ease that a failure mode can be detected because the more detectible a failure mode is, the less risk it presents to product quality. For D, the rank 1 is considered as easily detectable, 5 as moderately detectable, and 10 as hard to detect. The parameter O is the occurrence probability or the likelihood of an event occurring. For O, the rank 1 is considered as unlikely to occur, 5 as 50 : 50 chance of occurring, and 10 as likely to occur. The parameter S is a measure of how severe of an effect a given failure mode would cause. For S, the rank 1 is considered as no effect, 5 as moderate effect, and 10 as severe effect. Using this procedure, the REM carried out for qualitative analysis of risk associated with each MA and/or PP.

The Ishikawa fish‐bone diagram constructed for topical ophthalmic emulsions is simply portraying the cause–effect relationship among the factors that potentially affect the final product CQAs (shown in Fig. 2.4). The parameters outlined in the Ishikawa fish‐bone diagram assisted in the identification of the failure modes, i.e., the modes through which a system, process step, or piece of equipment might fail. Table 2.2 illustrates the REM carried out for qualitative analysis of risk associated with each MA and/or PP. The REM suggested that factors such as amount of chitosan, speed and time of homogenization, and volume of castor oil were found to be high risk, while the factors like the amount of poloxamer, times for premixing, and probe sonication were associated with medium risk. An in‐house exercise employing extensive brain storming among the diverse research group members as well as the existing literature reports were used for prioritizing the factors or parameters and allotting the scores for RPN computation. Using FMEA, the modes of failure can be prioritized for risk management purposes according to their seriousness of their consequences (effects), how rottenly they occur, and how easily they can be detected. Through this information, the variables that are needed to be further studied and controlled were found out or short‐listed. In addition, the process of doing the FMEA analysis within a larger organization facilitates systematic gathering of current knowledge inside the organization. Furthermore, with the help of knowledge