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

variations in particle size without a distinct distribution shape (Müllar 1990; Cegnar et al. 2004). The experimental CPPs involved in the preparation of topical ophthalmic emulsions may influence the performance of CQAs (PDI). All of the studied CPPs (amounts of castor oil, chitosan, and poloxamer) produced a direct relationship with PDI values observed. An increase in CPPs amounts increases the PDI values (Fig. 2.6d–f). Higher is the amount of castor oil, lesser is the oil breakdown frequency during emulsification promoting the formation of larger sized oil droplets and thus the PDI to attain a higher value. Increasing the amounts of these two emulsifying agents (chitosan and poloxamer) either alone (Fig. 2.6d and e) or in combination (Fig. 2.6f) also increased the PDI values. For instance, the PDI value of >0.6 was noted when the interaction between chitosan and castor oil occurred (Fig. 2.6d). But the interaction between poloxamer and castor oil yielded the PDI value of ~0.6 (Fig. 2.6e). However, the combined interaction of two emulsifying agents produced the PDI value of <0.6 (Fig. 2.6f). These effects are possibly due to the progressive increase in the apparent viscosity of the emulsion, which ultimately provides a higher flow resistance in the batch emulsification process (Müllar 1990). In consequence, this condition increased the coalescence rate resulting in a large particle size to form. Moreover, the large particles with inadequate emulsifier film coverage tend to coalesce faster than small particles. This phenomenon contributed to the high PDI value. Figure 2.6g–i demonstrates that by increasing the chitosan or castor oil concentration, the ZP value increases. In contrast, a biphasic manner, i.e., an initial increase followed by decrease in the ZP values, was observed with an increase in poloxamer concentration (Fig. 2.6h and i). It should be added that the present topical ophthalmic emulsions were stabilized by both electrostatic and steric mechanisms due to the chitosan and poloxamer emulsifier combination. Whatever the strong repulsive Coulomb force occurred between the protonated chitosan molecules must be counterbalanced by the week van der Waals attraction forces or the steric hindrance effect of poloxamer. That is why the biphasic attitude was seen for the influence of poloxamer concentration on ZP values (Tamilvanan 2009).

       2.5.1.7. Optimization of Responses for Formulation of CsA‐Loaded Nanosized Emulsion

By using graphical optimization, an overlay plot (Fig. 2.7g) showing the obtained design space (highlighted) was produced by the Design‐Expert^® software. From the overlay plot, the design space representing desired amounts of CPPs and the selected CQAs (R₁:MPS, R₂:PDI, and R₃:ZP) values of topical ophthalmic emulsions was found out. Within the design space, the optimized formula was selected that consists of minimum MPS and PDI values but maximum ZP value. The CPPs amounts found in the optimized formula are 1.39 ml castor oil, 6 mg chitosan, and 75 mg poloxamer. Similarly, the CQAs values established for the optimized formula within the design space are 260.173 nm, 0.275 and 25.85 mV, respectively, for MPS, PDI, and ZP.

      To substantiate further the established optimized formula for topical ophthalmic emulsion within the design space, the predictability of chosen face‐centered CCD model is at first corroborated by evaluating the randomly selected six different formulae along with the optimized formula for the actual CQAs values and comparing them with the predicted CQAs values. The diagnostic plot of actual versus predicted CQAs (R₁:MPS, R₂:PDI, and R₃:ZP) values are shown in Fig. 2.7a–f. Interestingly, the actual versus predicted plots for all of the CQAs were shown the r² value of greater than 0.9 indicating the establishment of the closeness between the values and hence conformed/justified the predictability of chosen model within the design space (Fig. 2.7b, d, and f). The value of randomness of scatter and deviations was found to be within ±4% in residual versus predicted value plot (Fig. 2.7a, c, and e). Furthermore, the chosen model also showed an overall mean percent error value of 0.30 ± 0.13%. In addition, an adequate precision should measure the signal‐to‐noise ratio, and its value of greater than 4 is desirable. As per the selected face‐centered CCD model, the signal‐to‐noise ratio values shown by Design‐Expert^® software for MPS, PDI, and ZP were 8.701, 6.415, and 4.6524, respectively. This directly indicates that the adequate precision is produced and therefore this model can be used to navigate the design space to find out the optimized formula for topical ophthalmic emulsion.

2.6. CONCLUSION

While narrating the importance of selecting the components of emulsion like oil, emulsifying agents, tonicity‐adjusting agent, etc., the emulsion formulator should also consider the application of Design Expert^® software to optimize a formula for making the final emulsion. The non‐exhaustive and selected list of excipients used to make emulsions suitable for regulatory approval is briefly discussed in this chapter in conjunction with a case study of how to optimize a formula by applying the ICH Q8(R2), Q9, and Q10 guidelines. The QbD approach using the Design Expert^® software could also further be substantiated via artificial intelligence (AI) and machine learning (ML) as proposed recently by Ghate et al. (2019). The AI and ML approach applied on the optimization and selection of emulsion formula is considered to reduce the amount of excipients, formulation preparation time, and thus the possible toxicity reduction due to high excipient concentration. Hence, the AI and ML approach is one of the welcome additions in the emulsion‐making technology to get cleared rapidly the regulatory hurdles.

Figure 2.7. Derived plots obtained from 3D‐response surface model: residual versus predicted plot (a, c, e), actual versus predicted plot (b, d, f), and overlay plot (g).

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