One-Step LNP Characterization: The GeneVecto Advantage

Lipid nanoparticles (LNPs) have become the beating heart of modern nucleic-acid therapeutics. Far from being inert “bubbles,” LNPs are active components whose architecture and behavior shape potency, safety, and stability. If the LNP isn’t right, the clinical outcome won’t be either. That is why robust, decision-grade analytics for LNPs are no longer “nice to have”—they are the backbone of every serious mRNA, siRNA, and gene-editing program.

Yet most teams still juggle a patchwork of methods: DLS or NTA for size, fluorescent dye assays for encapsulation efficiency (EE%), zeta potential for surface charge, and LC–MS for lipid composition. Each lives on a different instrument, with its own sample prep quirks and analyst assumptions. The result? Biases creep in, minor populations slip by, and comparability across runs, sites, and scales gets harder just when regulators expect more.

GeneVecto changes that equation. It unifies absolute, calibration-free particle sizing and EE% determination in a single capillary electrophoresis (CE) assay—on instruments many labs already own—while our software automates analysis, reporting, and data integrity. The outcome is one-step LNP characterization that moves programs faster from discovery to GMP, with fewer handoffs and stronger dossiers.

Why LNP analytics are hard (and why that matters)

Heterogeneity & minor populations. Real LNP preparations aren’t monodisperse. They include small satellites (e.g., micelles or empty structures), main populations, and sometimes aggregates. Ensemble light-scattering methods over-emphasize large particles, so meaningful minorities can be masked. That becomes a problem for uptake, immunogenicity, and release testing. Authoritative technical notes and studies explain why intensity-weighted DLS inflates large species and how distribution conversions add assumptions.

Batch-to-batch variability. Particle size and distribution are recognized critical quality attributes (CQAs) for mRNA–LNPs. But when your primary assay is sensitive to matrix, ionic strength, or a few outlier particles, apparent “drift” can be methodological rather than real—confusing root-cause analysis and slowing scale-up.

Multi-component analytics. You must trend size, EE%, surface charge, and lipid composition over time and across manufacturing nodes. Those signals are interdependent, but today they live in silos—making contextual, site-to-site comparability and CMC authoring a slog.

Scale-up pressure. Small analytical biases at bench scale turn into big headaches under GMP. Regulators expect clear method validation and lifecycle controls; fragmented workflows raise questions and extend back-and-forth. ICH Q2(R2) and Q14 explicitly encourage robust validation and science-/risk-based analytical development—the approach GeneVecto operationalizes by design.

CE and TDA in plain terms

Capillary electrophoresis (CE) separates analytes in a narrow fused-silica capillary under an electric field. Detectors (UV/DAD or fluorescence) read tiny volumes with high resolution. It’s a natural fit for standardized, automation-friendly assays from early research through QC.

Taylor Dispersion Analysis (TDA) measures how an injected plug disperses as it flows through a capillary. The axial band-broadening contains the diffusion coefficient DD; via Stokes–Einstein, DD converts directly into hydrodynamic radius Rh. Because the physics is first-principles, TDA delivers absolute size without external particle standards and works natively in relevant matrices using nano- to picoliter injections. Tutorial reviews, monographs, and conference tutorials lay out the method and its operating window for nano-objects and sub-micron particles.

Meet GeneVecto: one assay, two CQAs, submission-ready

What it is. A device-agnostic CE toolkit that pairs TDA-based hydrodynamic sizing with a dye-based EE% readout in the same injection. The kit includes reagents, dynamic capillary coatings, reference materials, and method templates. The software layer automates analysis, enforces metadata capture, and produces audit-ready, eCTD-friendly outputs.

What it replaces. Instead of stitching together DLS/NTA for size and separate plate-based fluorescence for EE%, GeneVecto collapses the workflow to a single CE assay. EE% is determined by a well-understood dye assay paradigm (e.g., RiboGreen-type readouts distinguishing free vs total RNA after controlled lysis), now anchored to absolute size from TDA.

What you get. Calibration-free size distributions; % free RNA and EE%; automated acceptance criteria; immutable audit trail; and standardized consumables to minimize method drift. Results roll up to a central dashboard and integrate with LIMS/ELN.

The science backdrop (for the scientifically curious)

Most therapeutic LNPs co-assemble from four lipids around a nucleic-acid cargo: an ionizable cationic lipid (for complexation and endosomal escape), a helper phospholipid (often DSPC), cholesterol (fluidity and packing), and a PEG-lipid (stealth and circulation). The structural picture is not a classic liposome; rather, an electron-dense core forms around the nucleic acid with surface enrichment of helper lipids, cholesterol, and PEG-lipid. Recent reviews and primary studies summarize these roles and architectures in detail.

Two CQAs fall straight out of this structure–function nexus:

• Size & distribution drive biodistribution, manufacturability, and immunogenicity.

• Encapsulation efficiency (EE%) ties to dose accuracy and potency and is central to lot release.

Given their centrality, measuring size and EE% together—accurately and reproducibly—is the shortest path to confident decisions. That is precisely the GeneVecto design.

Where conventional methods trip up (and how GeneVecto fixes it)

• Size by DLS/NTA.
• DLS is fast and sensitive, but it reports intensity-weighted distributions—so a handful of larger particles can dominate the signal. Transformations to volume or number require assumptions (e.g., refractive index, shape) that don’t always hold. NTA offers number-weighted distributions but can be affected by matrix and user choices. Consensus white papers and FAQs from leading vendors and standards groups dissect these pitfalls. GeneVecto’s TDA derived size bypasses external size standards and intensity weighting to provide an absolute Rh, useful both as a primary readout and as an orthogonal check on light-scattering results.
• EE% by fluorescence dyes.
• Fluorescent assays (e.g., RiboGreen-type) are widely used to quantify free vs total RNA. However, detergent choice and lysis conditions matter, and conventional plate workflows add transfer steps. By integrating the dye readout directly into the CE run and enforcing standardized reagents and method parameters, GeneVecto reduces operator-dependent variability.
• Fragmented ownership.
• Size on one instrument, EE% on another, composition on a third—different teams, different SOPs. GeneVecto consolidates the two decision-critical CQAs on CE (hardware your lab likely already has), while the software ingests zeta-potential results and LC–MS lipidomics for context in one workspace.
• Submission readiness.
• ICH Q2(R2) and Q14 expect clear validation and lifecycle narratives; regulators and auditors look for data integrity, audit trails, and traceability. GeneVecto’s software implements the controls aligned with 21 CFR Part 11 and European expectations for computerized systems, and its outputs map naturally to eCTD Module 3.



How the one-step platform works

• One platform → two answers. A defined plug enters the capillary under an electric field. TDA interprets the axial dispersion to compute DD and Rh. In the same assay, the dye assay quantifies free vs total RNA, enabling EE% calculation without shuttling samples between instruments.

• Minimal prep, no external size standards. Because TDA is physics-based, you don’t calibrate with beads to “teach” the instrument a size scale.

• Tiny samples, dual-mode detection. Nano- to picoliter injections and UV/fluorescence detection support scarce materials and stability time points.

• Standardized consumables. Dynamic capillary coatings and pre-formulated reagents reduce run-to-run drift and aid transfer across instruments and sites.

• Regulatory-ready outputs. Every run auto-captures method version, reagent lot numbers, capillary history, and instrument settings; the system produces a locked PDF report plus raw data, method files, and a tamper-evident audit trail aligned with Part 11 expectations.

What you can measure (and how it all fits)

• Particle size & distribution (primary CQA).
• Absolute hydrodynamic radius Rh from TDA gives a calibration-free anchor for size. Use it to validate or cross-check DLS/NTA, resolve small but meaningful shifts during DOE, and trend distribution width during stability.
• Zeta potential (contextual CQA).
• Surface charge correlates with colloidal stability and protein corona formation. While GeneVecto doesn’t replace ELS, the platform ingests zeta-potential results (with buffer and ionic strength metadata) and trends them alongside size and EE%. (ELS specifics are well-documented in the literature; GeneVecto is the hub that aligns these readouts with CE-TDA.)
• Encapsulation efficiency & payload content.
• The dye readout quantifies free versus total nucleic acid to compute EE%. Integrating this inside the CE workflow reduces manual handoffs and harmonizes method parameters across runs.
• Lipid composition (imported).
• Targeted lipidomics by LC–MS remains the method of choice to confirm ionizable/helper/cholesterol/PEG-lipid ratios and track degradants. GeneVecto’s software imports those results so you can correlate composition shifts with CE-TDA size and EE%. (Typical LC–MS workflows are well established in pharma method suites.)

Take-home: DLS/NTA remain useful for quick surveillance, ELS for charge, and LC–MS for composition—but anchor your size and EE% decisions on absolute CE-TDA to standardize across sites and phases.

Workflow & turnaround

• Load & run. Place vial or plate on the CE autosampler, select the GeneVecto method, and inject once.
• Built-in QC. System suitability checks (e.g., reference materials) and control flagging run in the background.
• Auto-analysis. The software fits the TDA profile, computes EE%, applies acceptance criteria, and packages outputs (report, raw data, method, audit trail).
• Trend at scale. Dashboards chart mean size, distribution width, % free RNA, pass/fail rates, and stability trajectories across lots, sites, and operators.
• Hand off to CMC. Outputs map cleanly into eCTD Module 3 narratives; validation and lifecycle documentation align with ICH Q2(R2)/Q14.

What makes it different (in one glance)

Applications across the development lifecycle

Early-phase R&D. Iterate fast on ionizable lipid ratios, PEG-lipid %, buffers, and mixing conditions. One injection returns absolute size and EE%, so you can run tight loops and chase promising formulations while discarding noisy artifacts. (Reviews continue to emphasize size and payload content as primary quality drivers in LNP programs.)

Clinical translation. Comparability work becomes practical: consistent, calibration-free sizing plus EE% across lots and sites, with automated method capture and audit trails. That aligns with what IND reviewers expect for analytical control strategies and lifecycle clarity per ICH Q2(R2)/Q14.

Scale-up & manufacturing. Track CQAs most sensitive to process changes (e.g., aqueous:organic ratio, total flow rate in microfluidics). Use rule-based alerts and trending to catch drift early. European guidance now under development specifically calls out size, polydispersity, RNA content/integrity, and encapsulation among key quality elements for mRNA vaccines—exactly what GeneVecto foregrounds.

Modalities. The platform’s CQAs map across mRNA vaccines, siRNA drugs (e.g., patisiran as a field-defining example), and emerging CRISPR LNP payloads, enabling one analytics backbone for multiple pipelines.

Regulatory and data-integrity alignment (so QA can breathe)

• Validation & lifecycle. GeneVecto ships with protocols and datasets mapped to ICH Q2(R2) (specificity, accuracy, precision, range, robustness) and a development rationale consistent with ICH Q14’s science- and risk-based approach.
• Computerized systems. Architecture, change control, access management, and audit-trail behavior follow FDA’s current thinking on 21 CFR Part 11; records are attributable, contemporaneous, and tamper-evident.
• Submission bundles. Each run yields a locked report plus raw data and method/audit files—organized for eCTD Module 3 and comparability packages.
• European context. We track the EMA’s draft guideline on mRNA vaccine quality (public consultation in 2025) and EDQM pharmacopoeial texts, keeping outputs aligned with evolving expectations.

Case study (illustrative): seeing what DLS missed

Context. A formulation team varied PEG-lipid and ionizable lipid ratios. DLS showed a single peak around ~90 nm (PDI ~0.2). They suspected micro-populations but lacked a tool to disentangle them.

What GeneVecto ran. A standard CE instrument with GeneVecto reagents and method template. CE–TDA returned absolute sizes and the in-run dye assay delivered EE%. CE mobility separated fractions with slightly different surface chemistries; TDA quantified Rh for each fraction. (This CE–TDA coupling is well described in the literature.)

What emerged. Three fractions from one sample:

• Main LNP: ~79 ± 2 nm (high EE%)
• Close neighbor: ~92 ± 3 nm (slightly lower EE%)
• Small satellites: ~12 ± 2 nm (consistent with micelles/empties)

Why DLS missed it. Intensity weighting masked number-minor species; slight differences in surface chemistry that CE resolves appear as a single ensemble in DLS. A calibration-free Rh per fraction enabled targeted process changes to suppress the satellites and tighten the 79/92 nm split—improving batch consistency and release confidence.

Future-ready: high-throughput and ML-powered development

Lights-out loops. High-throughput microfluidic synthesis of LNP libraries is maturing. Pairing autosampled CE–TDA (via GeneVecto) with Bayesian optimization or active-learning pipelines creates a closed loop: design → make → measure → learn. Reviews emphasize microfluidics’ control over size/PDI and scalability; GeneVecto provides the standardized CQAs to train models that genuinely generalize.

Data you can trust. ML only works as well as the labels you feed it. Absolute size and harmonized EE%—with metadata capture and audit trails—are exactly the kind of high-fidelity signals models need. ICH Q14 explicitly encourages enhanced, data-rich analytical development; GeneVecto turns that philosophy into an operational reality.

Why pharma and biotech teams should care

• Accelerate development. One CE–TDA assay returns the two CQAs you tune most—size and EE%—so you converge on viable formulations in fewer cycles, with less rework from conflicting assays.

• Reduce operational complexity. Replacing multi-assay, multi-team workflows with a single, automated run minimizes sample transfers and transcription—classic sources of error and deviation.

• De-risk scale-up. Calibration-free sizing and standardized consumables improve cross-site comparability and make tech transfer cleaner.

• Be inspection-ready. Outputs are designed for ICH Q2(R2)/Q14 narratives and Part 11 expectations. That makes reviews smoother and deviations easier to defend.

• Future-proof analytics. The same workflow scales from feasibility screens to QC release; data structures are ready for high-throughput and ML-assisted development.

Conclusion

GeneVecto turns LNP characterization from a fragmented chore into a single, standardized CE–TDA workflow. In one assay, you get absolute, calibration-free particle size and encapsulation efficiency, wrapped in automated analysis, audit-ready reporting, and a data hub that aligns zeta potential and LC–MS results without the usual copy-paste gymnastics. Because it runs on CE platforms you likely already have, it drops into your lab with minimal disruption and scales with you from early formulation to GMP release.

What this means for your program:

• Faster decisions. Lock formulations and processes sooner; shorten paths to IND and comparability.
• Regulatory confidence. Validation and lifecycle controls aligned with ICH Q2(R2)/Q14; outputs designed for eCTD Module 3; audit trails aligned with Part 11.
• Operational simplicity. One method, one team, one report—less variability, fewer transcription errors.
• Scalable manufacturing. Consistent CQAs across sites and scales; tighter process control and cleaner tech transfer.

Future-ready. High-throughput-friendly today; ML-driven optimization tomorrow, powered by standardized, trustworthy data.