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Lipid-based lyotropic liquid crystals, also called “liquid crystalline nanoparticles” (LCNPs), are a new type of nanocarrier systems that arise from the combination of lyotropic liquid crystals and lipid-based nanoparticles. The special benefits and enormous popularity of LCNPs can be better utilized. One systematic approach that can be used in formulation development is QbD. Applying QbD to the formulation of LCNPs will offer several distinct benefits, including improved understanding of the product and process, process flexibility within the design space, application of more effective and efficient control strategies, simple transfer from bench to bedside, and more robust product. The use of QbD in the formulation of LCNPs has been investigated in this work.Case studies have been used to provide a thorough explanation of each of the QbD components, which include the quality target product profile, critical quality attributes, critical material attributes, critical process parameters, quality risk management, experiment design, and control strategy for the production of LCNPs. The current study will give the reader a foundation for QbD-driven formulation of LCNPs from a regulatory perspective and assist them in understanding the specifics of applying QbD to the formulation of LCNPs
Keywords:
Liquid crystalline nanoparticles Quality by design ,Relative risk-based matrix analysis , Quality risk management , Design of experiments
Cite Article:
"A risk-based industrial approach to the formulation and optimization of liquid crystalline nanoparticles (LCNPs) using quality by design (QbD)", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.49 - 57, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401009.pdf
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000205244
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator