Evidence map›Paper›PMID 42380479›Full record

Reviewnpj drug discovery2025

Challenges and opportunities in computational studies for lipid nanoparticle development.

Younghoon Oh, Sean K Bedingfield, Severin T Schneebeli, Jianing Li, Arezoo M Ardekani, Kyle J Colston, Scott P Brown

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in npj drug discovery, 2025. 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. 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

7 authors.

Younghoon OhEli Lilly and Company, Lilly Seaport Innovation Center, Boston, MA, USA. oh_younghoon@lilly.com.
Sean K BedingfieldEli Lilly and Company, Lilly Seaport Innovation Center, Boston, MA, USA.
Severin T SchneebeliDepartment of Industrial & Molecular Pharmaceutics and James Tarpo Jr. and Margaret Tarpo Department of Chemistry, Purdue University, West Lafayette, IN, USA.
Jianing LiBorch Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, USA.
Arezoo M ArdekaniDepartment of Mathematics, School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA.
Kyle J ColstonDepartment of Industrial & Molecular Pharmaceutics and James Tarpo Jr. and Margaret Tarpo Department of Chemistry, Purdue University, West Lafayette, IN, USA.
Scott P BrownEli Lilly and Company, Lilly Seaport Innovation Center, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lipid nanoparticles (LNPs) are essential carriers for genetic medicines, yet optimizing their design remains challenging due to numerous parameters. Computational methods-including molecular dynamics (MD), computational fluid dynamics (CFD), and machine learning (ML)-offer molecular insights and predictive power. This perspective highlights recent advances, ongoing challenges, and the need for multiscale modeling frameworks and standardized experimental datasets to systematically explore LNP design space and improve the efficacy of next-generation formulations.

Identifiers

PMID42380479
PMCPMC13267057

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.