Evidence map›Paper›PMID 41137527›Full record

ArticleBiotechnology and bioengineering2026

Characterization of Sterilizing-Grade Membranes/Prefilters and Application to mRNA-LNP Process Development.

Kevork Oliver Messerian, Anton Zverev, Jack F Kramarczyk, Andrew L Zydney

Abstract read
In one paragraph

Article in Biotechnology and bioengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Kevork Oliver MesserianDepartment of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA.
Anton ZverevModerna Inc., Cambridge, Massachusetts, USA.
Jack F KramarczykModerna Inc., Cambridge, Massachusetts, USA.
Andrew L ZydneyDepartment of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA.ORCID 0000-0003-1865-9156

Funding

This study was supported by Moderna. Inc. (PSU-2021).
6 · The paper itself

Abstract

Sterile filtration is an essential step in the production of lipid nanoparticles (LNPs) used in mRNA vaccines and therapeutics. The overlap between the particle size of the LNPs (typically 50-200 nm) and the pore size of the sterilizing-grade membranes (rated at 0.2 µm) complicates the design and operation of the sterile filtration process. The objective of this study was to characterize the pore size distribution, fouling behavior, and capacity of different sterilizing-grade membranes and prefilters for LNP filtration. Mercury intrusion porosimetry revealed significant variability in pore size distributions across several sterilizing-grade membranes despite their consistent 0.2 µm rating, with LNP filtration capacity strongly correlated with the experimentally observed mean pore size. Dual-layer membranes, including the 0.8/0.2 µm Sartopore 2 XLG, significantly enhanced LNP filtration capacity and reduced fouling due to their integrated prefilter layer. Prefilter membranes with pore sizes in the 400-800 nm range provided the greatest enhancement in LNP filtration capacity. These findings highlight that for a given LNP formulation, the filtration capacity is strongly influenced by sterilizing-grade filter selection and specifically by the membrane pore size distribution and morphology, which are generally not published by filter manufacturers.

Indexed as

FiltrationLipidsMembranes, ArtificialNanoparticlesRNA, MessengerSterilizationLiposomesParticle SizeLipid NanoparticlesLipidsLiposomesMembranes, ArtificialRNA, Messengerlipid nanoparticle (LNP)mercury intrusion porosimetrymRNA vaccineprefiltrationsterile filtration

Identifiers

PMID41137527
PMCPMC12699132

What OpenQuestion holds

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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.