A Cell-Free Kinetic Analysis of Ionizable Lipid Hydrolysis
Bibliographic record
Abstract
The prolonged retention of ionizable lipids within the body limits the repeated dosing of lipid nanoparticles (LNPs) for nucleic acid delivery. While most ionizable lipids are primarily metabolized in the liver via the enzymatic hydrolysis of ester bonds, elimination half-lives can range from several hours to days. The development of compounds that undergo rapid biodegradation remains a major engineering challenge in the absence of standardized biodegradability assessments in the early stages of drug discovery. Here, we analyze and compare the hydrolysis kinetics of well-known ionizable lipids (ALC-0315, DLin-MC3-DMA, LP-01, L319, and SM-102) using optimized cell-free reactions monitored by 1 H NMR. Unlike conventional analytical techniques, these NMR-based methods are universal and suitable for high-throughput screening. We demonstrate that enzyme-catalyzed and base hydrolysis reactions can predict whether ionizable lipids undergo fast or slow liver elimination, as our results are in alignment with prior pharmacokinetic studies. Furthermore, we show that the hydrolysis kinetics of ionizable lipids vary by several orders of magnitude depending on steric effects. This study provides a framework to expedite the discovery of rapidly degradable ionizable lipids, with implications for improving the therapeutic index of LNP-based drugs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".