Density Matching Multi-wavelength Analytical Ultracentrifugation to Measure Drug Loading of Lipid Nanoparticle Formulations
Bibliographic record
Abstract
Previous work suggested that lipid nanoparticle (LNP) formulations, encapsulating nucleic acids, display electron-dense morphology when examined by cryogenic-transmission electron microscopy (cryo-TEM). Critically, the employed cryo-TEM method cannot differentiate between loaded and empty LNP formulations. Clinically relevant formulations contain high lipid-to-nucleic acid ratios (10-25 (w/w)), and for systems that contain mRNA or DNA, it is anticipated that a substantial fraction of the LNP population does not contain a payload. Here, we present a method based on the global analysis of multi-wavelength sedimentation velocity analytical ultracentrifugation, using density matching with heavy water, that not only measures the standard sedimentation and diffusion coefficient distributions of LNP mixtures, but also reports the corresponding partial specific volume distributions and optically separates signal contributions from nucleic acid cargo and lipid shell. This makes it possible to reliably predict molar mass and anisotropy distributions, in particular, for systems that are heterogeneous in partial specific volume and have low to intermediate densities. Our method makes it possible to unambiguously measure the density of nanoparticles and is motivated by the need to characterize the extent to which lipid nanoparticles are loaded with nucleic acid cargoes. Since the densities of nucleic acids and lipids substantially differ, the measured density is directly proportional to the loading of nanoparticles. Hence, different loading levels will produce particles with variable density and partial specific volume. An UltraScan software module was developed to implement this approach for routine analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".