Oil-in-Water Nanosized Emulsions for Drug Delivery and Targeting. Tamilvanan Shunmugaperumal
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Название: Oil-in-Water Nanosized Emulsions for Drug Delivery and Targeting
Автор: Tamilvanan Shunmugaperumal
Издательство: John Wiley & Sons Limited
Жанр: Химия
isbn: 9781119585251
isbn:
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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