Evidence map›Paper›PMID 37498794›Full record

ArticleThe journal of physical chemistry. B2023

Ionizable Amino Lipids Distribution and Effects on DSPC/Cholesterol Membranes: Implications for Lipid Nanoparticle Structure.

Sepehr Dehghani-Ghahnaviyeh, Michael Smith, Yan Xia, Athanasios Dousis, Alan Grossfield, Sreyoshi Sur

Abstract read
In one paragraph

Article in The journal of physical chemistry. B, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Article
  2. Personalized neoantigen mRNA vaccines for pancreatic cancer: the role of lipid nanoparticle delivery systems.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026
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  17. Computational approaches to lipid-based nucleic acid delivery systems.The European physical journal. E, Soft matter · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sepehr Dehghani-GhahnaviyehModerna, Inc., Cambridge, Massachusetts 02139, United States.
Michael SmithModerna, Inc., Cambridge, Massachusetts 02139, United States.
Yan XiaModerna, Inc., Cambridge, Massachusetts 02139, United States.
Athanasios DousisModerna, Inc., Cambridge, Massachusetts 02139, United States.
Alan GrossfieldDepartment of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, New York 14642, United States.ORCID 0000-0002-5877-2789
Sreyoshi SurModerna, Inc., Cambridge, Massachusetts 02139, United States.ORCID 0000-0001-7780-2640

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lipid nanoparticles (LNPs) containing ionizable aminolipids are among the leading platforms for the successful delivery of nucleic-acid-based therapeutics, including messenger RNA (mRNA). The two recently FDA-approved COVID-19 vaccines developed by Moderna and Pfizer/BioNTech belong to this category. Ionizable aminolipids, cholesterol, and DSPC lipids are among the key components of such formulations, crucially modulating physicochemical properties of these formulations and, consequently, the potency of these therapeutics. Despite the importance of these components, the distribution of these molecules in LNPs containing mRNA is not clear. In this study, we used all-atom molecular dynamics (MD) simulations to investigate the distribution and effects of the Lipid-5 (apparent p

Indexed as

COVID-19NanoparticlesCholesterolCOVID-19 VaccinesHumansLipid BilayersLiposomesRNA, MessengerRNA, Small InterferingCholesterolCOVID-19 VaccinesLipid BilayersLipid NanoparticlesLiposomesRNA, MessengerRNA, Small Interfering

Identifiers

PMID37498794
PMCPMC10424244

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.