High-Throughput Quantification and Characterization of Dual Payload mRNA/LNP Cargo via Deformulating Size Exclusion and Ion Pairing Reversed Phase Assays
Bibliographic record
Abstract
Therapeutic drugs and multivalent vaccines based on the delivery of mRNA via lipid nanoparticle (LNP) technologies are expected to dominate the biopharmaceutical industry landscape in the coming years. Many of these innovative therapies include several nucleic acid components (e.g., nuclease mRNA and guide RNA) posing unique analytical challenges when monitoring the quantity and quality of each individual payload substance in the formulated LNP. Current methods were optimized for single payload analysis and often lack resolving power needed to investigate nucleic acid mixtures. Ion pairing reversed phase (IP-RP) and size exclusion chromatography (SEC) are increasingly being used to characterize nucleic acids. Here, we studied their application for payload quantification in formulated LNP drug-like products. Using a detergent to disrupt the LNPs, the liberated payloads can be separated on an octadecyl RP column using a fast gradient. Reproducible results were obtained as lipids, and surfactants were efficiently eluted using a high organic solvent wash protocol. Alternatively, we also established an online SEC disruption analysis of the mRNA/LNPs wherein an alcohol and detergent containing a mobile phase was applied. Such conditions universally deformulated all tested LNP samples, indicating that a 5 min-long SEC separation can be used as a high-throughput platform method. In both approaches, the measurements facilitate a multiattribute analysis. Apart from quantitation, the characterization of specific impurities is achieved: IP-RP reveals mRNA-lipid adducts, while SEC informs on size variants, which in turn reduces a laboratory's analytical workload. These easy-to-adopt LC-based assays are expected to fortify the analytical toolbox for emerging gene therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".