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Calculating Apparent p <i>K</i> <sub>a</sub> Values of Ionizable Lipids in Lipid Nanoparticles

2024· article· es· W4405243425 on OpenAlexaff
Nicholas B. Hamilton, Steve Arns, Mee Shelley, Irene Bechis, John C. Shelley

Bibliographic record

VenueMolecular Pharmaceutics · 2024
Typearticle
Languagees
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsAcuitas Therapeutics (Canada)
Fundersnot available
KeywordsChemistryNanoparticleNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide 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 p K a value of the lipid as formulated in an LNP. We have developed a structure-based, computational methodology for the prediction of the apparent p K a value of ionizable lipids within LNPs and have tested it using the lipid formulations in the mRNA LNP COVID-19 vaccines COMIRNATY and Spikevax, and the siRNA LNP therapeutic Onpattro. The calculation was also applied to Lipid A, a variant of the ionizable lipid used in COMIRNATY.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.298
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2024
Admission routes1
Has abstractyes

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