Lipid-based nanoparticles (LNPs) are becoming an essential platform in pharmaceutical research and manufacturing. They’re used to improve the delivery of mRNA, enhance the absorption of poorly water-soluble drugs, and carry genetic material in gene therapies. What sets LNPs apart is their biocompatibility, biodegradability, and their ability to encapsulate and protect active pharmaceutical ingredients (APIs) as they travel through the body.
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LNPs play a central role in targeted drug delivery, increasing therapeutic efficacy while minimizing side effects. Prominent applications include cancer therapies and nucleic acid-based vaccines, such as the mRNA vaccines developed for COVID-19.
To ensure safe and effective performance, the manufacturing processes of LNPs must be tightly controlled. One of the most critical attributes is particle size, which strongly affects biological uptake, stability, and clinical outcome. This makes accurate and preferably real-time size characterization a key component of modern nanomedicine production.
Overview of LNP Types
Lipid-based nanoparticles consist of several distinct types (Figure 1), each with its own structure and applications:
- Liposomes: Spherical vesicles composed of one or more phospholipid bilayers surrounding an aqueous core. These structures can encapsulate both hydrophilic and hydrophobic compounds and are widely used in drug delivery, diagnostics, and cosmetics.
- Lipid Nanoemulsions (LNEs): Submicron oil-in-water emulsions stabilized by surfactants. They are used for both parenteral nutrition and as carriers for lipophilic drugs or vitamins in pharmaceuticals, food, and cosmetics.
- Lipid Nanoparticles: Designed for the delivery of genetic material such as RNA or DNA. Unlike liposomes, LNPs often lack a clear bilayer structure and are typically composed of cationic lipids. They can be surface-modified (e.g., with PEG) to enhance circulation time and targeting.
- Solid Lipid Nanoparticles (SLNs): Nanocarriers made from lipids that are solid at room temperature, forming a crystalline matrix that traps active molecules.
- Nanostructured Lipid Carriers (NLCs): A next-generation SLN system combining solid and liquid lipids to improve drug loading and reduce crystallization-related limitations.
Figure 1. Overview of the different classes of lipid-based nanoparticles. Image Credit: InProcess-LSP
The Role of Particle Size
Particle size is one of the most critical characteristics influencing the biological fate of LNPs. Optimal uptake by cells via endocytosis generally requires sizes below 100 nm. Oversized particles are more likely to be cleared by the immune system or pose health risks (e.g., embolism from large droplets), while undersized ones may have reduced drug loading efficiency.
A narrow and consistent size distribution also improves reproducibility, reduces side effects, and supports predictable dosing. This makes precise control of particle size both during development and at production scale a major priority for quality and safety.
Manufacturing Methods
The production of LNPs typically involves either low-energy or high-energy methods:
- Low-energy methods rely on phase transitions and solubility differences (e.g., solvent diffusion, ethanol injection).
- High-energy methods use mechanical forces such as shear, ultrasound, or high pressure to break down and homogenize formulations (e.g., high-pressure homogenization (HPH), ultrasonication, or microfluidization).
Each method includes several critical process parameters (CPPs) such as pressure, temperature, and formulation composition that directly impact the particle size and overall quality. For instance, homogenization pressure and ethanol-to-water ratios must be carefully optimized to achieve and maintain desired particle characteristics.
Process Analytical Technology (PAT)
To manage this complexity, the US FDA promotes the use of Process Analytical Technology (PAT). PAT is part of the Quality by Design (QbD) framework and emphasizes real-time monitoring and control of critical quality attributes during production.
For nanomedicines, particle size is a key quality attribute. However, inline measurement of particle size during manufacturing has long been a bottleneck. Traditional methods, such as standard Dynamic Light Scattering (DLS), electron microscopy, or analytical centrifugation, are typically performed off-line and require dilution and sample preparation, making them time-consuming and poorly suited for real-time process control.
The NanoFlowSizer: Expanding the Limits of Inline Sizing
Traditional DLS can only be used for clear or lightly turbid samples, limiting its usefulness in real production environments. The NanoFlowSizer, using Spatially Resolved Dynamic Light Scattering (SR-DLS), overcomes this limitation.
Standard DLS can handle only the transparent samples (shown on the left). SR-DLS extends this range significantly, enabling accurate measurements even in turbid, milky suspensions without dilution or sample preparation.
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Advanced Measurement Features:
- Multiple (1000+) intensity time traces are recorded simultaneously
- Depth-resolved scattering data is collected across ~3 mm of the sample
This allows SR-DLS to isolate the light fluctuations caused by particle motion (Brownian motion), even under flow conditions.
Figure 2. The NanoFlowSizer measures light signals at different depths and uses a special analysis method to determine particle sizes, even in cloudy (turbid) and flowing liquids. Image Credit: InProcess-LSP
This Approach Offers Several Key Advantages:
- Real-time particle size measurements within seconds
- Accurate results for concentrated and flowing suspensions
- No need for dilution or sample extraction
- Suitable for both batch and continuous manufacturing processes
- Non-invasive integration into production lines
- Inline monitoring over a wide flow range from milliliters to 250 L per hour
Application Examples
Liposome Characterization
In one example, concentrated liposome dispersions were measured in flow using a micro flow cell. Particle size and polydispersity remained stable across a range of flow rates, confirming that SR-DLS is insensitive to flow velocity and does not require prior calibration. Flow speed data automatically extracted from the scattering signal supports robust analysis under various conditions.
Figure 3. (a) NanoFlowSizer with a micro flow cell module. (b) Measured size and size variation of liposomes at different flow rates. (c) Flow speed inside the micro flow cell at different depths, measured at the same time as particle size. Image Credit: InProcess-LSP
Nanoemulsion Production
In one example, the NanoFlowSizer was integrated into an HPH process (Figure 4) to produce a lipid nanoemulsion (5 % sunflower oil, 1 % Tween). Droplet size was monitored in real time through three pressure stages (400, 600, 800 bar). The inline data closely matched offline measurements, confirming the system’s accuracy.
The immediate feedback during processing enabled rapid optimization of homogenization conditions and ingredient composition, supporting efficient process development.
![(a) Schematic of the homogenization setup and circuit employed for LNE production. (b) Inline data of the droplet size and polydispersity index (indicating spread in size) for the three different stages. The initial emulsion at]()
Figure 4. (a) Schematic of the homogenization setup and circuit employed for LNE production. (b) Inline data of the droplet size and polydispersity index (indicating spread in size) for the three different stages. The initial emulsion at t=0 was homogenized at 200 bar. Image Credit: InProcess-LSP
Conclusion
Lipid-based nanoparticles play a vital role in modern drug delivery, especially in the context of complex biologics and personalized medicine. Their manufacturing processes demand precise and consistent control of particle size.
The NanoFlowSizer finally makes it possible to measure particle size inline, in real-time, and without interrupting the process, marking a major step forward in the efficient, reliable production of nanoparticle-based medicines.
By making real-time particle sizing easy and accurate, this technology supports better product quality, faster development, and lower costs, helping innovative therapies reach patients more efficiently.
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Acknowledgment
This article is based on materials originally authored by Dr. R. Besseling, Director of Science and Technology and Managing Director at InProcess-LSP.
This information has been sourced, reviewed, and adapted from materials provided by InProcess-LSP.
For more information on this source, please visit InProcess-LSP.