Calculating Apparent pKa Values of Ionizable Lipids in Lipid Nanoparticle
Bibliographic record
Abstract
Creating new ionizable lipids for use in lipid nanoparticles (LNPs) is an active field of research. One of the critical properties for selecting suitable ionizable lipids is the apparent pKa value of the lipid as formulated in an LNP. We have developed a structure-based, computational methodology for the prediction of the apparent pKa value of ionizable lipids within LNPs. This methodology has been validated for the three most successful ionizable lipids to date which are present in the mRNA LNP COVID-19 vaccines COMIRNATY® (Pfizer/BioNTech) and Spikevax® (Moderna), and the siRNA LNP therapeutic Onpattro® (Alnylam). The calculation was also applied to Lipid A, a variant of the ionizable lipid used in COMIRNATY®. We believe that this new technology permits systematic computational prescreening of ionizable lipids to select the most promising candidates for synthesis and experimental testing, accelerating the formulation improvement process and reducing costs.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".