Evidence map›Paper›PMID 42198221›Full record

ReviewPharmaceutics2026

Emerging Non-Conventional Approaches in mRNA-LNP Formulation for Therapeutic Applications.

Yitian Zhang, Gabriel Linaje-Ferrel, Juan Manuel Rocha Angel, Oindrila Banik, Earu Banoth, Amine A Kamen, Naresh Yandrapalli, Ayyappasamy Sudalaiyadum Perumal

Abstract readReview
In one paragraph

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

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

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

8 authors.

Yitian ZhangDepartment of Bioengineering, Faculty of Engineering, McGill University, Montreal, QC H3A 0C3, Canada.
Gabriel Linaje-FerrelDepartment of Bioengineering, Faculty of Engineering, McGill University, Montreal, QC H3A 0C3, Canada.
Juan Manuel Rocha AngelDepartment of Bioengineering, Faculty of Engineering, McGill University, Montreal, QC H3A 0C3, Canada.ORCID 0009-0005-7972-3221
Oindrila BanikOBMS Lab, Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela 769008, Odisha, India.ORCID 0000-0001-6188-6990
Earu BanothOBMS Lab, Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela 769008, Odisha, India.ORCID 0000-0001-6824-4675
Amine A KamenViral Vectors and Vaccines Bioprocessing Group, Department of Bioengineering, McGill University, Montreal, QC H2X 1Y4, Canada.ORCID 0000-0001-9110-8815
Naresh YandrapalliDepartment of Synthetic Biology, Gebaude B2.2, University of Saarland, 66123 Saarbrücken, Germany.ORCID 0000-0002-1516-6674
Ayyappasamy Sudalaiyadum PerumalDepartment of Bioengineering, Faculty of Engineering, McGill University, Montreal, QC H3A 0C3, Canada.ORCID 0000-0002-1360-9152

Funding

NIT-PhD scholarship NAOptica Travel Lecturship NA
6 · The paper itself

Abstract

Lipid nanoparticles (LNPs) have become the cornerstone of nucleic acid delivery platforms, particularly in RNA-based vaccines and therapeutics. However, the conventional methods of LNP production, which are primarily reliant on microfluidic mixing of aqueous and organic solvent phases, pose limitations in terms of mRNA stability, residual organic contamination, scalability, cost, and environmental impact. These limitations prompted a renewed search for non-conventional strategies with the promise of improving mRNA-LNP encapsulation approaches. These emerging approaches aim to address key bottlenecks, including mRNA hydrolysis-driven degradation, high production losses, and complex downstream purification. Moreover, the ability to decouple LNP synthesis from mRNA encapsulation could enable streamlined, modular manufacturing workflows and customizable payload delivery, including single- or multiple-mRNA payloads, thereby expanding the therapeutic scope of LNPs. This review offers an early insight into the design principles and scalability potential of emerging non-conventional LNP encapsulation approaches, including solvent-free and microfluidics-free methodologies, and pre-built LNP workflows. We also examine trends in emerging LNP encapsulation tools, including high-shear mixing, sonication, membrane contraction, and other approaches. Finally, we extrapolate the suitability of the methods for scale-up approaches and their economic implications based on the process information.

Indexed as

coacervationencapsulationhot homogenizationlipid nanoparticleslyophilizationmachine learningmicrofluidicsmRNAmRNA-LNPnon-conventional methodssiRNAthin film hydrationvaccine development

Identifiers

PMID42198221
PMCPMC13210782

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.