Evidence map›Paper›PMID 40929887›Full record

ArticleBiomaterials2026

Investigating the functional contributions of phospholipids in selective organ targeting lipid nanoparticles.

Erick D Guerrero, Amogh Vaidya, Julien Santelli, Zeru Tian, Gabriela A Pazzi, Daniel J Siegwart

Abstract read
In one paragraph

Article in Biomaterials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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.

Erick D GuerreroDepartment of Biomedical Engineering, Program in Genetic Drug Engineering, Department of Biochemistry, Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Amogh VaidyaDepartment of Biomedical Engineering, Program in Genetic Drug Engineering, Department of Biochemistry, Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Julien SantelliDepartment of Biomedical Engineering, Program in Genetic Drug Engineering, Department of Biochemistry, Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Zeru TianDepartment of Biomedical Engineering, Program in Genetic Drug Engineering, Department of Biochemistry, Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Gabriela A PazziDepartment of Biomedical Engineering, Program in Genetic Drug Engineering, Department of Biochemistry, Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Daniel J SiegwartDepartment of Biomedical Engineering, Program in Genetic Drug Engineering, Department of Biochemistry, Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA. Electronic address: Daniel.Siegwart@UTSouthwestern.edu.

Funding

UT Southwestern Medical Center Simmons Comprehensive Cancer CenterP30CA142543 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Kathryn Ann O'Donnell · 2010 to 2026
$53.7M
UT Southwestern NORCP30DK127984 · NIDDK · UT SOUTHWESTERN MEDICAL CENTER · PI Jeffrey M Zigman · 2022 to 2026
$7.4M
Defining the molecular interactions within nanoparticles that enable delivery of long nucleic acidsR01EB025192 · NIBIB · UT SOUTHWESTERN MEDICAL CENTER · PI SIEGWART, DANIEL JOHN · 2018 to 2025
$3.1M
Multiplexed nanoparticle delivery to increase CRISPR/Cas gene editing for enhanced cancer therapyR01CA269787 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Daniel John Siegwart · 2022 to 2026
$1.9M
NCI NIH HHS P30 CA142543NCI NIH HHS R01 CA269787NIBIB NIH HHS R01 EB025192NIDDK NIH HHS P30 DK127984
6 · The paper itself

Abstract

Modular lipid nanoparticles (LNPs) are a promising platform to deliver mRNA to various tissues and cells. Optimization of LNPs for hepatic and extrahepatic tissues often involves substitution of helper lipids or addition of novel lipids not found in conventional four-component LNPs. Among the lipids that comprise LNPs, the functional contributions of phospholipids (PLs) in selective organ targeting (SORT) LNPs remain poorly understood. In this study, we systematically evaluate the roles of PLs within SORT LNPs. Our results demonstrate that PL enrichment enhances cellular transfection efficiency by increasing membrane fusion and endosomal escape. In vivo, we observe that PL-containing SORT LNPs significantly increase protein expression following intramuscular administration in mice, whereas moderate PL inclusion is optimal for intravenous delivery. Cryo-electron microscopy reveals that PL modulation induces distinct morphological rearrangements in LNP structure, which may influence the selective adsorption of plasma proteins, an essential factor in endogenous targeting mechanisms. These findings highlight the fundamental role of PLs in supporting intracellular delivery and guiding organ-specific biodistribution through protein corona formation. A deeper understanding of the structural and functional impact of lipid components, especially PLs, will be crucial for the rational design of next-generation mRNA delivery systems with improved efficacy and precision.

Indexed as

LipidsNanoparticlesPhospholipidsAnimalsCryoelectron MicroscopyHumansMiceMice, Inbred C57BLRNA, MessengerTissue DistributionTransfectionLipidsPhospholipidsRNA, MessengerEndogenous targetingEndosomal escapeHelper lipidsLipid nanoparticlesmRNA deliveryPhospholipidsSelective organ targeting (SORT)

Identifiers

PMID40929887
PMCPMC12869451

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.