Design of experiments in the optimization of nanoparticle-based drug delivery systems.

Abstract

Design of experiment (DoE) is a powerful statistical technique used for variable screening and optimization. It is based on the simultaneous variation of multiple factors with the objective of finding the configuration of parameters that optimizes one or more outputs of interest, while using the minimal number of experimental runs required for testing, resulting very cost and time-efficient. Despite the high potential offered by this approach for innovation and process optimization, DoE is still only marginally applied in the field of nanomedicine and often its rationale application and analysis result is difficult to grasp by many. In this review, we discuss some of the latest applications of DoE in the formulation of nanovectors used for drug delivery across many different applications. First, we introduce general principles of DoE to the reader, which are indispensable to understand the works we report. Then, we give particular attention to the process variables, the specific designs, and the readouts used for process analysis and optimization for different classes of nanovectors. Finally, we try to delve into the current shortcomings of DoE application and possible future directions that could be employed to further improve the information that can be derived from this approach.