Selecting a preparation method is not a generic step in lipid nanoparticle (LNP) development — it is a decision driven first by the physical and chemical identity of the payload. mRNA, self-amplifying RNA (saRNA), small interfering RNA (siRNA), plasmid DNA (pDNA), circular RNA (circRNA), proteins, peptides, and small molecules each interact with the lipid membrane, the ionizable lipid headgroups, and the aqueous core in fundamentally different ways. How a payload partitions during solvent-to-buffer exchange determines whether it ends up encapsulated inside the particle, loosely adsorbed on the surface, or lost to the continuous phase.
The practical consequence is direct: a protocol that achieves 95% encapsulation efficiency (EE%) for a 21-base-pair siRNA duplex may deliver only 40% EE% for a 9-kilobase plasmid, because the larger cargo requires different mixing energetics, a different lipid-to-payload ratio, and often a pre-condensation step that smaller payloads do not need. Four payload properties dominate every method decision that follows in this guide:
Size and molecular weight: larger cargoes need tighter mixing control and higher N/P ratios to remain entrapped.
Charge density: anionic nucleic acids complex electrostatically with protonated ionizable lipids, whereas proteins and peptides respond unpredictably to cationic surfaces.
Hydrophobicity: it dictates whether the payload sits in the bilayer, in the aqueous core, or stays free in solution.
Solvent and shear sensitivity: proteins denature in ethanol-rich environments and impose hard limits on which mixing platforms are even compatible with the payload.
BOC Sciences organizes its LNP encapsulation services around payload identity first, then refines the process around scale and quality targets. The table below summarizes the payload properties that drive method selection throughout this article.
Table 1. Payload Properties That Drive LNP Preparation Method Selection.
| Payload Type | Typical Size | Charge Behavior | Preferred Particle Location | Primary Method Driver |
| mRNA | 1–5 kb | Anionic backbone | Ionizable lipid core | Fast, controlled mixing |
| saRNA | 9–14 kb | Anionic, high charge density | Ionizable lipid core | High N/P + gentle shear |
| siRNA / ASO | 15–25 nt | Anionic, compact | Ionizable lipid core | Cost-efficient high-throughput mixing |
| pDNA | 5–15 kb | Rigid anionic polymer | Condensed core | Pre-condensation before lipid coating |
| Protein / peptide antigens | 1–100 kDa | Amphiphilic, solvent-sensitive | Aqueous core or bilayer anchor | Solvent-free or gradient loading |
| Small molecules | <1 kDa | LogP-dependent | Bilayer (hydrophobic) or core (hydrophilic) | Partition-driven loading strategy |
All LNP preparation methods share one mechanism: a lipid phase dissolved in a water-miscible organic solvent is rapidly combined with an aqueous buffer, the ionizable lipids protonate, and particles self-assemble around the payload. What differs between platforms is how fast and how uniformly the two phases mix, which controls supersaturation, nucleation rate, and ultimately particle size distribution.
Lipids are dissolved in chloroform or methanol, the solvent is evaporated to a dry film, and the film is hydrated with an aqueous buffer containing the payload. The resulting multilamellar vesicles are sized by extrusion through polycarbonate membranes. The method is gentle, equipment-light, and keeps the payload in aqueous media throughout, which makes it the default choice for solvent-sensitive proteins. Its trade-offs are low throughput, batch variability, and limited nucleic acid encapsulation efficiency.
The lipid-ethanol phase is injected into a stirred aqueous buffer, and spontaneous solvent displacement forms the particles. The approach scales easily and suits hydrophilic small molecules, but its slow, uncontrolled mixing rarely achieves the EE% and polydispersity index (PDI) required for nucleic acid programs. Many teams use it as a feasibility screen during early LNP formulation development before committing to continuous-flow platforms.
Lipid and aqueous streams meet at a defined angle inside intersecting channels, giving reproducible, continuous-flow solvent exchange with simple hardware. T-junction mixers are adequate for siRNA, antisense oligonucleotides (ASOs), and hydrophilic small molecules, where intermediate mixing intensity is acceptable. For large nucleic acids, the mixing length is usually insufficient to reach sub-0.15 PDI values.
Microfluidic mixers subdivide the streams into micrometer-scale lamellae and complete mixing within milliseconds — fast enough to decouple nucleation from particle growth. The four leading architectures are hydrodynamic flow focusing (HFF), which compresses the lipid stream between aqueous sheath flows; staggered herringbone micromixers (SHM), which induce chaotic advection through grooved walls; bifurcating mixers, which split and recombine the streams; and asymmetric recirculating-channel mixers, which promote repeated flow division and secondary circulation. These platforms consistently produce particles below 100 nm with PDI under 0.10 and anchor BOC Sciences' microfluidic LNP production services.
When batch volumes exceed a few hundred milliliters, impingement jet mixers collide two high-velocity streams head-on, achieving microfluidic-grade mixing times at liter-per-minute flow rates. Confined impingement jet and tangential (T-T) designs preserve the narrow size distributions of microfluidic chips while delivering the volumetric output needed for late-stage supply, making them the standard scale-up path for mRNA and saRNA programs.
Table 2. Comparison of Major LNP Preparation Methods.
| Method | Mixing Principle | Typical Size / PDI | Scalability | Best-Suited Payloads |
| Thin-film hydration + extrusion | Film hydration, mechanical sizing | 50–200 nm / 0.2–0.3 | Low, batch | Proteins, peptides, hydrophobic drugs |
| Ethanol injection | Bulk solvent displacement | 60–150 nm / 0.2–0.4 | High, batch | Hydrophilic small molecules, screening |
| T-/Y-junction mixing | Continuous-flow channel impingement | 50–120 nm / 0.15–0.25 | Moderate, continuous | siRNA, ASO, hydrophilic small molecules |
| Microfluidic (HFF, SHM, recirculating-channel) | Millisecond laminar/chaotic micromixing | <100 nm / <0.10 | Low to moderate, continuous | mRNA, saRNA, circRNA, RNP, co-encapsulation |
| Impingement jet / T-T mixing | High-velocity stream collision | 60–100 nm / <0.15 | High, continuous | mRNA, saRNA at manufacturing scale |
Even with the right platform, a handful of critical process parameters (CPPs) decide whether EE%, size, PDI, and payload integrity meet specification. Optimization at this stage is rarely about switching methods; it is about refining the operating window of the chosen method. BOC Sciences applies design-of-experiments (DoE) screening across the parameters below as part of its LNP process optimization services.
Flow rate ratio (FRR): the aqueous-to-ethanol ratio controls lipid dilution at the moment of solvent exchange; higher FRR (3:1 to 5:1) generally yields smaller particles and higher nucleic acid EE%.
Total flow rate (TFR): higher TFR shortens residence time in the mixing zone, making particle formation more reproducible and tightening PDI. The two parameters interact — a high-FRR, high-TFR window is the typical starting point for nucleic acid payloads, while fragile protein payloads may require reduced TFR to limit shear.
The N/P ratio — protonatable nitrogens on the ionizable lipid per phosphate on the nucleic acid backbone — is the dominant stoichiometric lever for nucleic acids. Raising N/P increases EE% up to a plateau, after which excess lipid adds cost, elevates surface charge, and can accelerate clearance in vivo. Because the optimal window differs for every cargo and lipid combination, lipid ratio optimization typically screens N/P from 3 to 12 to locate the EE% inflection point for each program.
Buffer pH: mixing at pH 4–5, below the ionizable lipid pKa, keeps headgroups cationic so anionic cargoes complex efficiently; subsequent exchange to physiological pH neutralizes the particle surface.
Counterions and ionic strength: citrate, acetate, and Tris buffers influence colloidal stability and EE% differently, and divalent cations can bridge anionic surfaces into aggregates. Systematic LNP buffer screening identifies the buffer/counterion pair that preserves both EE% and stability for a given formulation.
Temperature: most protocols run at 4–25 °C; lower temperatures protect labile payloads but slow lipid dissolution.
Lipid concentration: organic-phase concentrations of 5–25 mg/mL set throughput and influence viscosity-driven mixing artifacts.
Solvent: ethanol is standard for its water miscibility and easy removal, yet LNP solvent screening sometimes identifies alcohol blends that improve EE% for difficult payloads, and adjacent excipient screening refines the helper-lipid and cholesterol environment around the payload.
Nucleic acids share an anionic phosphate backbone that complexes with protonated ionizable lipids during solvent exchange, but their physical dimensions span two orders of magnitude — from a 21-bp siRNA duplex to a 14-kb saRNA replicon. That size spread, together with cargo-specific stability issues, is what separates the recommended platforms below. BOC Sciences structures its nucleic acid LNP encapsulation services around these cargo subclasses.
Modified mRNA (1–5 kb) is the most extensively encapsulated nucleic acid cargo, and the combination of moderate size, high charge density, and shear sensitivity makes microfluidic mixing the industry default. Hydrodynamic flow focusing and staggered herringbone micromixer platforms routinely deliver 90–98% EE%, 60–90 nm particles, and PDI below 0.10 at N/P 6 and FRR 3:1. The ionizable lipid anchor and pKa deserve as much attention as the mixing hardware — matched pairs are the subject of ionizable lipid optimization.
Table 3. mRNA Encapsulation — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | Microfluidic HFF or SHM mixing | Avoid bulk ethanol injection for development or release-grade material |
| N/P ratio | 6 (screen 3–12) | Excess lipid raises surface charge without improving EE% beyond the plateau |
| FRR / TFR | 3:1 / 10–15 mL/min | Higher TFR tightens PDI; reduce TFR if RNA fragmentation is observed |
| Aqueous buffer | 10–50 mM citrate, pH 4.0 | Dialyze to pH 7.4 buffer promptly after mixing |
| Expected EE% / size | 90–98% / 60–90 nm | Confirm by dye-exclusion assay; verify poly(A) tail integrity post-encapsulation |
saRNA replicons are roughly an order of magnitude larger than mRNA, so encapsulation demands higher N/P ratios (8–12) for complete electrostatic complexation and tight 70–90 nm size control for uniform biodistribution. Because long replicons are shear-sensitive, Dean flow LNP production is an attractive alternative to HFF: it delivers comparable mixing intensity while handling the higher viscosity of saRNA-loaded streams. Membrane rigidity also matters more at this cargo size, which is why helper lipid optimization and cholesterol optimization are standard workstreams, and why encapsulation efficiency optimization runs in parallel rather than after formulation lock.
Table 4. saRNA Encapsulation — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | Microfluidic SHM or Dean flow mixing | Confirm replicon integrity by gel or capillary electrophoresis after mixing |
| N/P ratio | 8–12 | Insufficient complexation leaves free saRNA that inflates apparent EE% readings |
| FRR / TFR | 3:1 / 8–12 mL/min | Moderate TFR limits shear fragmentation of the long replicon |
| Lipid composition | Increased helper lipid and cholesterol fraction | Stabilizes the larger core but monitor impact on endosomal escape |
| Expected EE% / size | 85–95% / 70–90 nm | Tighter size window than mRNA due to dose-sparing self-amplification |
siRNA duplexes and single-stranded ASOs are the smallest and most cost-tolerant nucleic acid payloads, so the optimization goal shifts from maximizing EE% toward throughput and cost per batch. T-junction or impingement jet mixing at moderate flow rates routinely exceeds 95% EE%, and the low payload mass per particle allows parallel screening of many sequences. The same platforms serve both duplex and single-stranded chemistries, with adjustments for the modified backbones common in ASO programs; broader platform guidance is covered under LNP systems for RNA delivery.
Table 5. siRNA and ASO Encapsulation — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | T-junction mixing or impingement jet mixing | Microfluidics optional — small duplexes tolerate slower mixing |
| N/P ratio | 4–8 | Lower than large-RNA programs; excess lipid mainly adds cost |
| FRR / TFR | 3:1–5:1 / moderate TFR | High FRR favors small, uniform particles for extrahepatic delivery |
| Payload chemistry | 2'-O-methyl, phosphorothioate ASOs supported | Heavily modified backbones change apparent charge density — re-titrate N/P |
| Expected EE% | >95% | Verify with duplex-specific dyes; free-payload removal is fast at this size |
Supercoiled pDNA is large, rigid, and prone to kinking during solvent exchange, making it the most demanding nucleic acid cargo. The workable route is pre-condensation: the plasmid is compacted with spermine, protamine, or a short polyamine into a nanometric core, which is then coated with lipid during mixing. N/P ratios of 12–20 are typical, and bulk ethanol injection or impingement jet mixing often outperforms fine-channel microfluidics because the pre-formed condensate is less sensitive to micromixing detail. Platform details and hybrid lipid/polymer designs are described on the pDNA LNP delivery page.
Table 6. pDNA Encapsulation — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | Pre-condensation + ethanol injection or impingement jet mixing | Direct encapsulation of naked pDNA rarely exceeds 50% EE% |
| Condensing agent | Spermine, protamine, or low-MW polyamine | Titrate condensate size — oversize cores block complete lipid coating |
| N/P ratio | 12–20 | Reflects both condensing amine and ionizable lipid nitrogens |
| Topology control | Monitor supercoiled fraction before and after processing | Shear and solvent exposure relax supercoils and reduce expression |
| Expected EE% / size | 60–85% / 80–150 nm | Wider size distribution is accepted for pDNA programs |
circRNA lacks the free 3'-OH required for exonuclease digestion, which underpins its durable protein expression, but the circular junction is vulnerable to hydrolysis if mixing generates localized hotspots or prolonged solvent exposure. circRNA encapsulation therefore favors moderate mixing intensity (FRR 2:1–3:1) and rapid buffer exchange after formation. miRNA mimics and inhibitors behave much like ASOs — compact, anionic, and compatible with the same moderate-intensity platforms — with sequence-specific tuning described under miRNA LNP delivery.
Table 7. circRNA and miRNA Encapsulation — Recommended Method Profile.
| Aspect | circRNA | miRNA |
| Preparation method | Gentle microfluidic mixing (HFF, low TFR) | T-junction or microfluidic mixing |
| N/P ratio | 6–8 | 4–8 |
| FRR / TFR | 2:1–3:1 / 6–8 mL/min | 3:1 / moderate |
| Structural risk | Junction hydrolysis from solvent hotspots | Passenger-strand displacement during loading |
| Expected EE% | 85–95% | >90% |
Cas9 or Cas12a ribonucleoproteins (RNPs) are large protein–RNA assemblies that do not follow the anionic-payload model: they carry net positive or heterogeneous surface charge and aggregate on contact with cationic lipids. Workable protocols either pre-complex the RNP with an anionic helper (modified guide RNA or an anionic lipid additive) to normalize its surface charge, or encapsulate the RNP with a negatively biased lipid composition under gentle microfluidic mixing. Because each nuclease and guide combination behaves differently, CRISPR RNP LNP delivery programs almost always begin with a short method-development phase rather than a fixed protocol.
Table 8. CRISPR RNP Encapsulation — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | Gentle microfluidic mixing after charge modulation | Direct mixing with cationic lipids causes visible aggregation |
| Charge strategy | Anionic lipid additive or extended guide-RNA scaffold | Normalize RNP surface charge before lipid contact |
| Mixing intensity | Low TFR, room temperature or below | Preserve nuclease conformation and guide-RNA binding |
| Analytics | Post-encapsulation nuclease activity assay | EE% alone does not confirm the RNP remains functional |
| Expected EE% | 50–80% | Lower than naked-RNA payloads; activity recovery is the true metric |
BOC Sciences matches the preparation platform to your nucleic acid payload — microfluidic, Dean flow, impingement jet, or hybrid pre-condensation routes — and optimizes N/P, FRR, and buffer conditions to your EE%, size, and stability targets.
Proteins and peptides differ from nucleic acids in two structural ways that change the entire strategy: they are amphiphilic rather than uniformly anionic, and most denature above roughly 10% residual ethanol. The preparation route must therefore either avoid organic solvent (thin-film hydration), remove it very quickly (high-FRR continuous mixing), or load the payload after particle formation (gradient-driven remote loading). These options, and their application to vaccine antigens, are the focus of BOC Sciences' work on LNP-based vaccine delivery.
Recombinant protein antigens are best handled by thin-film hydration followed by extrusion, because the antigen stays in aqueous buffer throughout particle formation. Where throughput matters, remote loading is the alternative: pre-formed particles are exposed to a pH or ammonium sulfate gradient that drives the protein across the bilayer into the interior. This gradient route — central to protein encapsulation in LNPs — reaches EE% above 70% for antigens that tolerate mildly acidic interiors, with no solvent exposure at all.
Table 9. Protein Antigen Encapsulation — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | Thin-film hydration + extrusion, or gradient remote loading | Avoid ethanol-injection routes for solvent-sensitive antigens |
| Process temperature | 4 °C or below for conformational epitopes | Thermal drift during extrusion is a common denaturation source |
| Protein-to-lipid ratio | 1:20 to 1:50 (w/w) | Higher ratios raise apparent loading but invite aggregation |
| Loading verification | Detergent-disruption assay | Distinguish surface-adsorbed from truly encapsulated protein |
| Expected EE% | 40–80% | Gradient loading typically outperforms passive hydration |
Peptides of 8–30 residues are small enough that passive aqueous-core encapsulation leaks badly — most material partitions into the bilayer or escapes during downstream purification. The two dominant retention strategies are lipophilic anchoring, in which the peptide is conjugated to cholesterol or a lipid tail before particle formation, and electrostatic retention, in which cationic lipid content holds anionic peptides at the particle surface. Peptide encapsulation in LNPs usually combines one of these retention chemistries with microfluidic mixing for size control.
Table 10. Peptide Antigen Encapsulation — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | Microfluidic mixing with retention chemistry | Passive encapsulation alone gives poor peptide retention |
| Retention strategy | Cholesterol/DSPE anchor conjugation, or cationic-lipid surface retention | Anchor choice affects release kinetics and epitope accessibility |
| Epitope orientation | Keep the antigenic sequence free of the conjugation site | Anchoring at a key residue can mask the epitope |
| Surface charge | Moderate cationic content if using electrostatic retention | Excess cationic lipid increases aggregation and off-target binding |
| Expected retention | 60–90% (anchor-dependent) | Measure both total association and post-dialysis retention |
Subunit vaccines depend on antigen and adjuvant reaching the same antigen-presenting cell, and co-encapsulating both in one particle is the most reliable way to guarantee that co-delivery. Microfluidic mixing is preferred because its narrow size distribution keeps antigen-to-adjuvant ratios consistent across the particle population, which bulk mixing cannot ensure. The formulation challenge is accommodating two payloads with different charge and partition behavior at once — the reason co-encapsulation of multiple payloads is run as a dedicated development workstream rather than an off-the-shelf protocol.
Table 11. Subunit Antigen Co-Encapsulation — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | Microfluidic co-encapsulation (HFF or SHM) | Narrow PDI preserves antigen:adjuvant ratio across the population |
| Ratio control | Define antigen:adjuvant molar ratio as a release attribute | Ratio drift between batches confounds potency readouts |
| Lipid composition | Balance ionizable and helper lipids for both species | Adjuvant charge can compete with antigen for ionizable lipid |
| Stability testing | Monitor both payloads independently during storage | Antigen and adjuvant can leak at different rates |
| Expected EE% | 70–90% (both payloads) | Accept lower single-payload EE% to keep the ratio on target |
From solvent-free thin-film hydration to gradient remote loading and adjuvant co-encapsulation, BOC Sciences selects the preparation route that protects your antigen's conformation while meeting EE%, size, and stability targets.
Small molecules are classified by partition behavior: hydrophobic molecules prefer the bilayer interior, hydrophilic molecules prefer the aqueous core, and that single distinction dictates solvent choice, loading strategy, and release profile. Both classes are supported within BOC Sciences' work on LNP systems for drug delivery, using the class-specific routes below.
Hydrophobic drugs (logP > 3) dissolve in the lipid phase and load during particle formation rather than after. Solvent emulsification-evaporation forms a drug-lipid emulsion that collapses into particles as solvent is removed, while thin-film hydration suits shear-sensitive compounds. Both routes are covered by hydrophobic payload encapsulation workflows, which frequently use solid lipid nanoparticle matrices to immobilize the drug and control release. Surface-localized drug causing burst release is the main failure mode, and PEG-lipid optimization is the standard countermeasure.
Table 12. Hydrophobic Small Molecule Encapsulation — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | Solvent emulsification-evaporation or thin-film hydration | Match method to compound shear sensitivity |
| Matrix choice | Solid lipid core for crystallization-prone drugs | Amorphous dispersions prevent drug crystallization on storage |
| Drug-lipid compatibility | Screen lipid identity against drug logP and melting point | Poorly matched pairs phase-separate during storage |
| Burst release control | PEG-lipid surface passivation | Surface drug releases within minutes of administration |
| Expected loading | 1–10% (w/w), EE% often >90% | High EE% is easy; maintaining amorphous state is the challenge |
Hydrophilic drugs (logP < 1) partition poorly into lipid and must be loaded passively during solvent exchange — ethanol injection at high FRR — or actively across a transmembrane gradient. Active loading exploits the protonation equilibrium of weak bases or acids to drive accumulation into the particle interior and routinely exceeds 90% EE% for ionizable compounds. Both routes, and the counterion choices that stabilize the loaded state, are detailed under hydrophilic payload encapsulation.
Table 13. Hydrophilic Small Molecule Encapsulation — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | Ethanol injection (passive) or pH-gradient active loading | Active loading only applies to ionizable weak bases/acids |
| Gradient design | Ammonium sulfate or citrate interior, pH 7.4 exterior | Gradient collapse during storage causes payload leakage |
| Counterion selection | Match counterion to drug pKa | Wrong counterion forms precipitates inside the particle |
| Leakage monitoring | Accelerated stability with free-drug assay | Leakage, not initial EE%, usually limits shelf life |
| Expected EE% | 50–70% passive; >90% active | Active loading also concentrates drug beyond passive limits |
TLR and STING agonists are small-molecule adjuvants that must arrive in the same endosome as the antigen to shape the immune response. Microfluidic co-encapsulation gives precise control of the antigen-to-agonist ratio at the single-particle level and applies mixing conditions gentle enough for cyclic dinucleotides and other shear-labile agonists. The formulation logic — ratio control, independent stability monitoring of both payloads, and lipid compositions that serve two chemistries at once — mirrors the co-encapsulation practices described above for subunit vaccines.
Table 14. TLR/STING Agonist Co-Delivery — Recommended Method Profile.
| Aspect | Recommendation | Key Considerations |
| Preparation method | Microfluidic co-encapsulation with antigen | Ensures synchronized endosomal delivery of both species |
| Agonist chemistry | Route by logP — bilayer partition vs. aqueous core | Imidazoquinolines and cyclic dinucleotides behave differently |
| Ratio control | Fix antigen:agonist molar ratio per particle population | Off-ratio batches over- or under-stimulate |
| Mixing intensity | Moderate TFR for shear-labile agonists | STING agonists degrade under high-shear conditions |
| Expected EE% | 70–90% (agonist) | Track agonist EE% separately from antigen EE% |
BOC Sciences selects emulsification, thin-film, passive injection, or pH-gradient active loading based on your molecule's logP and pKa — then optimizes the lipid matrix against crystallization, burst release, and long-term leakage.
BOC Sciences provides end-to-end LNP preparation support organized around payload type rather than a fixed platform, so the recommended method can evolve with the cargo and the project stage — from microliter-scale screening batches to multi-liter continuous production.
Each new program starts with a DoE-driven screen of lipid composition, N/P ratio, FRR, TFR, and buffer conditions, delivered as a defined operating window against EE%, size, PDI, and stability targets. Screening is accelerated by structured lipid library screening, which evaluates ionizable lipid candidates in parallel rather than sequentially.
Once a laboratory-scale method is locked, formulation teams transition it to impingement jet or T-T mixing platforms for volume production, with single-batch capacity reaching tens of liters. Scale-up work is anchored by critical quality attribute and QC testing to confirm that size, PDI, EE%, and payload integrity transfer intact, and by full LNP manufacturing capabilities for commercial supply.
Every batch passes through LNP characterization covering size, PDI, zeta potential, and morphology, with payload-specific assays for EE% and integrity. Storage performance is established through LNP stability studies and payload retention testing, while downstream cleanup relies on validated free payload removal. Formulations that underperform are escalated to dedicated LNP encapsulation troubleshooting.
Table 15. BOC Sciences LNP Preparation Services by Payload Type.
| Service | Recommended Platform | Scope of Service | Inquiry |
| Lipid Nanoparticles for mRNA Delivery | Microfluidic HFF / SHM | mRNA encapsulation with EE%, size, PDI, and stability targets defined per program | Inquiry |
| Lipid Nanoparticles for saRNA Delivery | SHM / Dean flow, high N/P | Large-replicon encapsulation with shear-controlled mixing and integrity analytics | Inquiry |
| Lipid Nanoparticles for siRNA Delivery | T-junction / impingement jet | High-throughput, cost-efficient silencing payload encapsulation | Inquiry |
| Lipid Nanoparticles for Gene Delivery | Pre-condensation + jet mixing | pDNA and gene cargo encapsulation via hybrid lipid/polymer strategies | Inquiry |
| LNP-Based Protein Delivery | Thin-film hydration / remote loading | Solvent-free protein encapsulation preserving conformational integrity | Inquiry |
| LNP-Based Peptide Delivery | Microfluidic + anchor chemistry | Peptide retention via lipophilic anchoring or electrostatic strategies | Inquiry |
| Lipid Nanoparticles for Antigen Delivery | Microfluidic co-encapsulation | Antigen and adjuvant co-delivery with per-particle ratio control | Inquiry |
| Lipid Nanoparticles for Small Molecule Delivery | Emulsification / pH-gradient loading | Class-specific loading routes for hydrophobic and hydrophilic drugs | Inquiry |
| Lipid Nanoparticles for Co-Delivery | Microfluidic co-encapsulation | Dual-payload formulations with independent EE% and ratio analytics | Inquiry |
| Method Development for LNP Encapsulation | DoE-driven platform screen | Method selection and operating-window definition for novel payloads | Inquiry |
| LNP Process Scale-Up Services | Impingement jet / T-T mixing | Lab-to-pilot-to-production transfer with CQA comparability testing | Inquiry |
| Efficiency Testing for LNP Encapsulation | Payload-specific analytics | EE%, retention, and integrity assays across all payload classes | Inquiry |
LNP preparation method selection is inseparable from payload identity. mRNA thrives on microfluidic HFF or SHM mixing at N/P 6; saRNA needs higher N/P ratios and shear-controlled platforms such as Dean flow mixers; siRNA and ASOs reach high EE% in cost-efficient T-junction or impingement jet systems; pDNA requires pre-condensation before lipid coating; circRNA and miRNA demand gentle processing to protect structural integrity; and CRISPR RNPs depend on charge modulation before mixing. Protein, peptide, and antigen payloads follow a solvent-avoidance logic built on thin-film hydration, remote loading, and lipophilic anchoring, while small molecules split into emulsification and pH-gradient routes according to their partition behavior. Each recommendation is a starting point — the final operating window comes from disciplined CPP screening and payload-specific analytics. BOC Sciences supports that full journey, from method screening and formulation optimization through scale-up, characterization, and troubleshooting, so the right preparation method for each payload type can be selected, validated, and industrialized without redesigning the underlying science.