A complete guide to understanding Lipid nanoparticles (LNP)

In addition to better encapsulation, according to the membrane phase transition theory, the use of the ionizable cationic lipid also allows for improved endosomal escape: upon entering the endosome, where the pH value drops, lipids get charged again, leading to a destabilization of the particle and thus an easier delivery of the nucleic acid to the cell.

Finally, the combination of the ionizable lipid with PEG, cholesterol, and a helper lipid allowed for the generation of a whole new kind of lipid-based nanoparticle with a unique structure and morphology. LNP are formed by a phospholipid, PEG and ionizable lipid layer surrounding a core in which ionizable lipids are entrapping the nucleic acids.
By adjusting the nature and ratios of each component, one can then fully regulate its interaction with the entrapped nucleic acid and thus tune the encapsulation efficiency, zeta potential… Making the balance of the composition a key challenge of the drug development.

Lipid nanoparticles formulation

All the current FDA-approved formulations are composed of 4 lipids (glycerophospholipids, cationic lipids, sterol lipids, and PEGylated lipids) each having a very specific role to encapsulate the oligonucleotides into aqueous phase. In addition to this, a selection of surfactants can be used to tailor their properties.

Lipid nanoparticles Synthesis methods

A wide varieties of methods are available for the synthesis of LNPs, each offering a different trade-off in terms of the characteristics of the generated LNPs, encapsulation efficiency and scale-up capabilities.
Two main categories of methods can be defined: The Top-Down/high energies methods, the Bottom-up methods, generally leveraging on nanoprecipitation.
The pros and cons of each method are summarized in the below table, and more details about them can be found in our LNP manufacturing review.

High energy 

High-energy methods, also know as Top-Down methods, such as high-pressure homogeneization and sonication are top to bottom approach using the high energy provided by the system to break down the particles into smaller ones. Their main advantage is to already be available and easily scalable. They can be used either alone or in combination with other methods. However, they provide very poor control of the generated NP and show very low encapsulation efficiency. The use of high energy also leads to high risks in terms of lipids and drug alteration during their synthesis.

Thin film hydration methods

Thin film hydration methods are solvent based methods where a solvent containing the lipid mix is vaporated. The remaining lipids are then mixed together with an antisolvent such as water containing the RNA, triggering nanoparticle assembly. They are relatively easy to use yet offer near no size control nor repeatability, poor encapsulation efficiency, and can hardly be scaled.

Manual methods

Manual methods such as manual mixing with pipette or in batch are also solvent based methods. However, those solvent-based processes are based on self assembly processes where the nanoparticles are formed by mixing between a solvent containing the lipids (usually ethanol) with an antisolvent (usually water) in excess, hence triggering the lipid nanoparticles nanoprecipitation. (see our review on nanoparticle self-assembly mechanism for more).
While manual approaches can be employed for basic proof-of-concept experiments, they suffer from significant limitations. The lack of precise control over mixing conditions results in minimal control over the final characteristics of the nanoparticles. This lack of consistency makes manual methods highly variable, unreliable, and unsuitable for repeatable or scalable processes.

Macrofluidics/T junctions

This approach, also based on nanoprecipitation, offers greater control over mixing conditions compared to manual methods. T-mixers, such as impingement jet mixers, are commonly used to achieve precise mixing. These systems are highly effective for large-scale production, such as in the manufacturing of COVID-19 vaccines.
However, their suitability for early-stage drug delivery processes—such as screening or in vivo studies—is limited due to the substantial liquid volumes required (at least several mL). Additionally, these methods share some drawbacks associated with high-energy techniques, particularly the high pressures involved, which can lead to potential degradation of RNA compounds.

Note that a purification step is required following synthesis to ensure solvent removal, hence lowered toxicity and better stability.

Microfluidics 

To overcome the main limitation of macrofluidics approaches, microfluidics manufacturing methods have been developed.
Thanks to its unique repeatability and capability to perfectly control mixing conditions, microfluidics permits the highest level of control and repeatability in terms of lipid nanoparticle manufacturing.
On top of that, the possibility to handle very small volumes with microfluidics at low pressure has made it the gold standard for LNP manufacturing methods all along the RNA therapies or vaccine development process. Learn more in our microfluidics synthesis methods review.
Typical performance achievable with microfluidics are size in the 50 to 200 nm range while being highly controllable, repeatable and homogeneous (PID <0.2). Encapsulation efficiency usually reaches 90% or above with such methods.

Lipid nanoparticle characterization

As described above, the physiochemical attributes of LNPs need to be precisely controlled throughout their synthesis to reach the targeted area and efficiently deliver their payload to the cells.

While there is currently no regulatory framework for nanomedicines a set of general characterization guidelines is available.

The most important physiochemical parameters are: LNP size and size distribution (characterized by the polydispersity index or PDI), morphology as well as their Zeta potential. Drug loading and encapsulation efficiency are also important parameters to be considered to evaluate the overall LNP encapsulation effectivness.

In addition to these, drug makers in the pharmaceutical industry should also investigate LNP biodistribution all throughout their clinical trials to ensure medicine’s efficiency.

Zeta potential

The Zeta potential is a measure of the magnitude of the electrostatic field close to the particle's surface.
A too-neutral LNP (low Zeta potential) will lead to an increased aggregation during manufacturing, as the charge will not repel each other. On the other hand, a too-high charge at the surface will lead to toxicity as described previously. For this reason, a zeta potential in the order of -30 mV is generally considered optimal.

Zeta potential is characterized using a Zeta Potential Analyzer

Surface morphology

Microscopy methods such as Transmission electron microscopy (TEM or Cryo-TEM) or Atomic Force Microscopy (AFM) are generally used to characterize the lipid nanoparticle morphology, each offering a different compromise. AFM allows for a 3D mapping of the particle, while TEM offers a view of the internal composition of the LNP.

As introduced in the figure above, the next steps will then depend on the type of RNA encapsulated: mRNA will be translated into proteins by the ribosomes in the cytoplasm. SiRNA will combine with other proteins to form a RISC complex, which will bind with a complementary mRNA, inducing its cleavage.
For CRISPR, both the guide RNA and the Cas9 protein are encapsulated and delivered to the cell for gene editing

LNP functionalization

In the previous step, we have described how LNP manages to efficiently release its encapsulated material within the cell. However, an efficient drug not only requires efficient delivery of material but also requires the delivery of the right amount of drug/RNA at the right location.
2 main strategies can be used for nanoparticle targeting: active and passive.

Passive targeting

Passive targeting relies on the careful optimization of the lipid nanoparticle physicochemical parameters (size, EE%, PDI) to improve the accumulation of nanoparticles in some location.
In the example of tumor cells, the careful selection of nanoparticle size allows for an improved cellular uptake by the target cancer cells rathan than regular cells. Additionally, the enhanced vascularization of the tumor leads to a longer retention time within the tumor itself, leading to more extravasation and thus accumulation of the lipid nanoparticle at the location. This process has been widely studied and is known as the enhanced permeability and retention (EPR) effect.However, not all types of diseases show this effect, which can also vary from model to model. 

Active targeting

Active targeting relies on the functionnalization of the LNP by a ligand to bind to a specific surface. Those targeting ligands located on the surface of the NP can recognize specific receptors on disease cells, and pathogens… and conjugate with them.
The decoration can be achieved either by the mean of targeting peptides, but also carbohydrate ligands, or even antibodies. This permits a very specific interaction with the tissues, and thus precise delivery of the payload to the cells.

Active targeting assembly methods

There exist 2 main ways to fabricate functionalized lipid nanoparticles.
The first one called One-pot assembly requires a pre-step where the target ligand is bonded to one of the lipids, which is then used to prepare the LNP using the standard process described in the synthesis method section. Though easy to use, this process shows 2 major drawbacks as most of the ligand can get trapped within the LNP and not at its surface, making it much less efficient. Also, ligands being very fragile, they can very easily be altered during production.

Considering those limitations, ligands are usually post-inserted after their synthesizes.
In this context, they are coated with a lipid with a functional group, which is then put in contact and binds with a targeting ligand. This has the advantage of having the ligands only located on the surface of the LNP but requires a more complex synthesis process.