Abstract
Nanotechnology has made great strides in developing targeted drug delivery systems over
the past few decades. These systems have garnered attention for their unique biological properties
and ability to deliver drugs in a stable and sustainable manner. Despite these advances, there are still
concerns about quality, efficacy, and safety. Many fabrication techniques still need to be refined to
address the complex structures and non-standard manufacturing processes that can impact the quality
of drug delivery systems. Recently, optimization techniques such as Quality by Design (QbD) have
gained popularity in the pharmaceutical industry. QbD is a structured approach that addresses many
technological and trait-related issues by providing a deep understanding of the product and its
operations. This review examines the current state of QbD in the design of various nano-drug
delivery systems, including lipid nanoparticles, lipid carriers, nano micelles, beaded drug delivery
systems, nanospheres, cubosomes, and novel cosmeceuticals. Various mathematical models and
statistical tests have been used to identify the parameters that influence the physical characteristics of
these nanosystems. Critical process attributes such as particle size, yield, and drug entrapment have
been studied to assess risk factors during development. However, critical process parameters are
often identified through trial and error. This review highlights common material attributes and
process parameters that affect the quality of nano-drug delivery systems. Hence, this survey has disclosed
the various material attributes and process parameters, quality variables of different nano-drug
systems. QbD designs such as Central drug composite, Design of experiment, D-optimal Design,
Box-Benkhen Design, and Face center Design in optimizing the nanosystems have also been added.
Conclusively, QbD optimization in nano drug delivery systems is expected to be a time-honored
strategy in the forthcoming years.
Keywords:
Quality by design, nano drug delivery systems, process variables, response methodology, status, application.
Graphical Abstract
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