Dynamic Light Scattering Method Using Derived Count Rate to Accurately Quantify Lipid Nanoparticle Concentration─A...

Area of Science:

• Nanomedicine
• Analytical Chemistry
• Biotechnology

Background:

• Advancements in nanomedicine, especially lipid nanoparticle (LNP)-based vaccines, necessitate accessible analytical methods.
• Accurate nanoparticle concentration determination is crucial but often challenging with existing techniques.
• Conventional methods (nanoparticle tracking analysis, electron microscopy) are expensive and low-throughput.

Purpose of the Study:

• To investigate derived count rate (DCR) from dynamic light scattering (DLS) as a quantitative tool for LNP concentration.
• To develop and validate a model correlating DCR, LNP size, and concentration.
• To establish a simple, accurate, and accessible method for LNP quantification.

Main Methods:

• Established a DCR-concentration-size map using model LNP batches.
• Derived a predictive model correlating DCR, LNP size, and concentration.
• Validated the model through a double-blind study with independently formulated LNP batches and formulators of varying expertise.

Main Results:

• The derived model accurately predicted LNP concentrations.
• Double-blind validation showed an average prediction error of approximately 4% compared to theoretical values.
• The method demonstrated robustness and user independence across different expertise levels.

Conclusions:

• Derived count rate (DCR) from standard DLS measurements is a viable, simple, and accurate method for LNP quantification.
• This approach bypasses the need for specialized and costly equipment.
• The DCR method enhances accessibility for nanomedicine research and quality control.