Surface Response Based Modeling of Liposome Characteristics in a Periodic Disturbance Mixer

Area of Science:

• Nanotechnology and Materials Science
• Biomedical Engineering
• Chemical Engineering

Background:

• Liposome nanoparticles (LNPs) are critical for drug, gene, and imaging agent delivery.
• Precise control over LNP physicochemical properties (size, distribution, zeta potential) is essential for optimal functionality.
• Micromixer technology offers superior control over LNP characteristics compared to traditional methods.

Purpose of the Study:

• To investigate the influence of Total Flow Rate (TFR) and Flow Rate Ratio (FRR) on LNP characteristics using a custom-designed Periodic Disturbance Micromixer (PDM).
• To develop a statistical model predicting LNP physicochemical properties based on TFR and FRR.
• To demonstrate the practical utility of the developed model for targeted LNP production.

Main Methods:

• Fabrication and utilization of a novel Periodic Disturbance Micromixer (PDM).
• Application of Design of Experiments (DoE) and Response Surface Methodology (RSM) for statistical modeling.
• Systematic variation of TFR and FRR to assess their impact on LNP size, Polydispersity Index (PDI), and zeta potential.

Main Results:

• TFR and FRR were found to effectively control LNP size within the 52 nm to 200 nm range.
• FRR demonstrated a significant impact on PDI, with a threshold around 2.6 separating highly monodisperse from less monodisperse populations; TFR had no significant effect on PDI.
• Zeta potential was observed to be independent of both TFR and FRR.

Conclusions:

• A predictive model was successfully developed, enabling the pre-determination of experimental conditions for producing LNPs within a desired size range.
• The study validates the PDM's capability for controlled LNP fabrication.
• The established model enhances the efficiency and predictability of LNP manufacturing for advanced delivery applications.