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Record W4411399363 · doi:10.1016/j.omtm.2025.101518

Rational design of lipid nanoparticles for enabling gene therapies

2025· review· en· W4411399363 on OpenAlex
Cedric A. Brimacombe, Jayesh A. Kulkarni, Miffy H. Y. Cheng, Kevin An, Dominik Witzigmann, Pieter R. Cullis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMolecular Therapy — Methods & Clinical Development · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsFPInnovationsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNanoMedicines Innovation Network
KeywordsRational designNanoparticleComputational biologyNanotechnologyChemistryBiologyMaterials science

Abstract

fetched live from OpenAlex

Lipid nanoparticle (LNP) technology is increasingly enabling RNA-based gene therapies that can potentially be used to treat most diseases. Further, these LNP RNA therapeutics can be designed and manufactured in a matter of weeks, allowing personalized medicines that can be produced in a time frame relevant to individuals suffering from terminal diseases. Here, we focus on the rational design principles that have successfully enabled LNP small interfering RNA (siRNA) formulations to silence pathogenic genes in the liver and LNP mRNA formulations to express therapeutic proteins for vaccines and gene therapies. These principles have evolved from over 50 years of research into the physical properties and functional roles of lipids in membranes as well as experience gained developing LNP systems for delivery of small molecule drugs. It is expected that these rational design principles will be successful in enabling most forms of gene therapies.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.196
GPT teacher head0.485
Teacher spread0.290 · 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