Dynamic Light Scattering Method Using Derived Count Rate to Accurately Quantify Lipid Nanoparticle Concentration─A Double-Blind Study

Mar 11, 2026ACS nano

Using Light Scattering to Accurately Measure Lipid Nanoparticle Amounts in a Double-Blind Study

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Abstract

The predictive model for lipid nanoparticle (LNP) concentrations demonstrated an error of only ∼4% compared to theoretical values in a double-blind validation.

Derived count rate (DCR) from dynamic light scattering provides a new approach for LNP concentration analysis.
A comprehensive map correlating DCR, LNP size, and concentration was established using model LNP batches.
The method showed consistent high precision and accuracy across formulators with varying expertise.
DCR measurements may offer a simple and widely accessible alternative to traditional, costly nanoparticle analysis techniques.
Validation involved independently formulated LNP batches with randomized compositions and unknown concentrations.

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The rapid advancement of nanomedicine, particularly underscored by the clinical success of lipid nanoparticle (LNP)-based vaccines, has intensified the need for simple, accurate, and widely accessible analytical techniques. A critical yet often overlooked parameter in nanoparticle development is the precise determination of the nanoparticle concentration. Conventional methods such as nanoparticle tracking analysis and electron microscopy are costly, low-throughput, and unavailable in most laboratories, creating a significant bottleneck in nanomedicine research and quality control. We investigated the potential of derived count rate (DCR), a scattered intensity parameter inherent to dynamic light scattering (DLS) when using a Zetasizer Nano ZS (Malvern Panalytical Ltd.)─an instrument available in virtually all nanomedicine laboratories─as a quantitative tool for LNP concentration analysis. A comprehensive DCR-concentration-size map was established using model LNP batches with varying concentrations. This data was used to derive a model correlating DCR, LNP size, and concentration. The model's predictive power was rigorously validated through a double-blind study involving independently formulated LNP batches with randomized compositions and unknown concentrations, done by formulators of varying expertise. The predictive model demonstrated an excellent fit. In the double-blind validation, the model predicted LNP concentrations with an error of only ∼4% compared to the theoretical values. Crucially, the method proved robust and user-independent, showing consistent high precision and accuracy across beginner, junior, and senior formulators. This study establishes DCR from standard DLS measurements as a simple, accurate, and highly accessible method for LNP quantification, bypassing the need for specialized equipment.