Evidence map›Paper›PMID 42380585›Full record

Articlenpj drug discovery2025

Understanding the molecular insights of marketed liposomal drugs using molecular dynamics simulations of reduced scale coarse-grained models.

Ruifeng Wang, Jieying Zang, Hao Zhong, Yunsen Zhang, Defang Ouyang

Abstract read
In one paragraph

Article 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. 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

5 authors.

Ruifeng Wang *State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, 999078, China.
Jieying Zang *State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, 999078, China.
Hao ZhongState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, 999078, China.
Yunsen ZhangState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, 999078, China.
Defang OuyangState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, 999078, China. defangouyang@um.edu.mo.

Funding

Macau Science and Technology Development Fund 0071/2024/RIA1University of Macau Multi-Year Research Grant MYRG-GRG2023-00077-ICMS-UMDF
6 · The paper itself

Abstract

The complexity of the internal structure of liposome remains insufficiently understood, posing challenges to the rational design and development of liposomal formulations. In this study, we employed coarse-grained molecular dynamics simulation to investigate the structural details of the commercially available nine liposomal formulations, with five passive loading systems (Arikayce Kit®, Visudyne®, Ambisome®, Mepact®, and Exparel®) and four active loading systems (Doxil®, Myocet®, Onivyde®, and Vyxeos®). In five passive loading liposome systems, protonation of drug molecules significantly reduces their binding to phospholipid membranes and even drastically alters the morphology and vesicle size in multivesicular liposomes. However, the orientation of drug molecules in lipid membranes is independent of protonation, with hydrophobic part inward and hydrophilic part outward. In four active loading systems, the presence of ions within the liposome cavity promotes the self-aggregation of drugs, thereby enhancing drug retention and release profiles. This study provided valuable molecular insights for marketed liposomes formulation.

Identifiers

PMID42380585
PMCPMC13267066

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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.