Design of experiment (DoE) as a quality by design (QbD) tool to optimise formulations of lipid nanoparticles for nose-to-brain drug delivery

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) similar to|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.

Automated summary

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.