Evidence map›Paper›PMID 42326525›Full record

ArticleResearch square2026

PEG-associated efficacy loss in lipid nanoparticles persists across formulation modifications for mRNA delivery.

Mariah L Arral, Namit Chaudhary, Katherine C Fein, Alexandra N Newby, Saigopalakrishna S Yerneni, Jilian R Melamed, Samuel T LoPresti, Daria M Strelkova Petersen, Kathryn A Whitehead

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In one paragraph

Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Mariah L ArralCarnegie Mellon University.
Namit ChaudharyCarnegie Mellon University.
Katherine C FeinCarnegie Mellon University.
Alexandra N NewbyCarnegie Mellon University.
Saigopalakrishna S YerneniCarnegie Mellon University.
Jilian R MelamedCarnegie Mellon University.
Samuel T LoPrestiCarnegie Mellon University.
Daria M Strelkova PetersenCarnegie Mellon University.
Kathryn A WhiteheadCarnegie Mellon University.

Funding

Engineering Smart Protein Coronas to Advance Theraputic mRNA DeliveryF32EB029345 · NIBIB · CARNEGIE-MELLON UNIVERSITY · PI MELAMED, JILIAN R · 2019 to 2021
$128k
NIBIB NIH HHS F32 EB029345
6 · The paper itself

Abstract

Lipid nanoparticles (LNPs) are clinically proven mRNA delivery vehicles, and repeated administration is often required to maintain therapeutic or prophylactic benefit. However, a single dose can provoke adaptive immune responses against polyethylene glycol (PEG), a polymeric excipient on the LNP surface. In this event, resultant anti-PEG antibodies neutralize subsequent LNP doses, causing efficacy loss and limiting therapeutic utility. Here, we investigated whether altering the LNP formulation between initial and subsequent doses could mitigate PEG-associated efficacy loss. Following the administration of an LNP known to elicit anti-PEG antibodies, we re-dosed with LNPs formulated with varied PEG-lipid chemistry, PEG concentration, mRNA cargo, and administration route. None of these adjustments prevented efficacy loss, which consistently correlated with elevated anti-PEG immunoglobulin M (IgM) and, in some cases, immunoglobulin G (IgG). Reduced PEG concentration diminished immunogenicity but also potency, and intraperitoneal dosing elicited the strongest responses upon repeat injection. Further, LNPs that elicit anti-PEG antibodies also impaired the subsequent delivery of an FDA-approved MC3 formulation, suggesting that anti-PEG immune responses can compromise any PEGylated LNP formulation. Together, these findings demonstrate that PEG-associated efficacy loss cannot be resolved through formulation optimization alone. Instead, they suggest that the ionizable lipid's engagement of innate immune receptors is the primary determinant of repeat dosing success.

Indexed as

immune responsesLipid nanoparticlesmRNA deliveryPEG

Identifiers

PMID42326525
PMCPMC13278322

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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.