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Expression kinetics of nucleoside-modified mRNA delivered in lipid nanoparticles to mice by various routes

Journal of Controlled Release(2015)10.1016/j.jconrel.2015.08.007Source: DataRank Database

Expression kinetics of nucleoside-modified mRNA delivered in lipid nanoparticles to mice by various routes is a research paper published in Journal of Controlled Release (2015). On theSindex it has a DataRank of 1.0. It has been cited 1,015 times.

N/A
1.0DataRank · unranked
1.0
Open Access1015 citations · base score 6.9
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

In recent years, in vitro transcribed messenger RNA (mRNA) has emerged as a potential therapeutic platform. To fulfill its promise, effective delivery of mRNA to specific cell types and tissues needs to be achieved. Lipid nanoparticles (LNPs) are efficient carriers for short-interfering RNAs and have entered clinical trials. However, little is known about the potential of LNPs to deliver mRNA. Here, we generated mRNA-LNPs by incorporating HPLC purified, 1-methylpseudouridine-containing mRNA comprising codon-optimized firefly luciferase into stable LNPs. Mice were injected with 0.005-0.250mg/kg doses of mRNA-LNPs by 6 different routes and high levels of protein translation could be measured using in vivo imaging. Subcutaneous, intramuscular and intradermal injection of the LNP-encapsulated mRNA translated locally at the site of injection for up to 10days. For several days, high levels of protein production could be achieved in the lung from the intratracheal administration of mRNA. Intravenous and intraperitoneal and to a lesser extent intramuscular and intratracheal deliveries led to trafficking of mRNA-LNPs systemically resulting in active translation of the mRNA in the liver for 1-4 days. Our results demonstrate that LNPs are appropriate carriers for mRNA in vivo and have the potential to become valuable tools for delivering mRNA encoding therapeutic proteins.

Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

FAIR Checklist

Context only (not used in score)
Findable (1/2)
  • Has DOI
Accessible (1/2)
  • Open Access
Interoperable (0/2)
    Reusable (0/3)

      FAIR checklist signals are shown for context only and do not affect DataRank scoring.

      DataRank Breakdown

      Base Score 100%Citation Network 0%

      Base Score Contribution

      1.0

      From this paper's citation signal

      Citation Network Contribution

      0

      Citation network not refreshed for this result

      This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

      Learn more about DataRank methodology →
      Why this DataRank?

      DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

      Base score B(p)
      log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
      Network N(p)
      Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
      Damping factor d = 0.85
      DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
      Self-citations excluded
      Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

      Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

      Read the full methodology →

      Authors (9)

      Steven Tuyishime,Hiromi MuramatsuORCID,Barbara L. MuiORCID,Ying K. TamORCID,Thomas D. Madden