ABSTRACT
Introduction
The nose-to-brain route has been widely investigated to improve drug targeting to the central nervous system (CNS), where lipid nanoparticles (solid lipid nanoparticles – SLN and nanostructured lipid carriers – NLC) seem promising, although they should meet specific criteria of particle size (PS) <200 nm, polydispersity index (PDI) <0.3, zeta potential (ZP) ~|20| mV and encapsulation efficiency (EE) >80%. To optimize SLN and NLC formulations, design of experiment (DoE) has been recommended as a quality by design (QbD) tool.
Areas covered
This review presents recently published work on the optimization of SLN and NLC formulations for nose-to-brain drug delivery. The impact of different factors (or independent variables) on responses (or dependent variables) is critically analyzed.
Expert opinion
Different DoEs have been used to optimize SLN and NLC formulations for nose-brain drug delivery, and the independent variables lipid and surfactant concentration and sonication time had the greatest impact on the dependent variables PS, EE, and PDI. Exploring different DoE approaches is important to gain a deeper understanding of the factors that affect successful optimization of SLN and NLC and to facilitate future work improving machine learning techniques.
Article highlights
Using the QbD approach as a tool for optimizing formulations is crucial to achieving the increasingly high-quality standards of pharmaceutical development.
The nose-to-brain route could solve the problem of the low bioavailability of drugs that aim to reach the central nervous system, since it is a direct route to the brain that bypasses the need for molecules to cross the blood–brain barrier.
The development of effective formulations for the nose-to-brain route involves the use of strategies that allow the physiological barriers of the nasal cavity to be circumvented (e.g. enzymatic activity and mucociliary clearance), such as the encapsulation of drugs in lipid nanoparticles.
Specific features of lipid nanoparticles are fundamental to their effectiveness and should be optimized when developing a formulation, such as: particle size, polydispersity index, zeta potential, and encapsulation efficiency.
DoE can help researchers find the best combination of components and process parameters to obtain lipid nanoparticle formulations with the appropriate characteristics for the nose-to-brain route.
A review of published work in this area can guide researchers in defining independent and dependent factors, predicting the relationship between them, and choosing the best type of DoE.