Evidence map›Paper›PMID 39548203›Full record

ArticleScientific reports2024

Structure-based discovery of a new type of scaffold compound as binding competitors for protein-bound Uremic Toxins.

Ping Wang, Shasha Liu, Shengtian Zhao, Yan Wang

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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
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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

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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

4 authors.

Ping Wang *Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, 264003, P.R., China.
Shasha Liu *Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, 264003, P.R., China.
Shengtian ZhaoDepartment of Urology, Binzhou Medical University Hospital, Yantai, 256603, P.R., China. zhaoshengtian@sdu.edu.cn.
Yan WangInstitute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, 264003, P.R., China. yanw@mail.hust.edu.cn.

Funding

National Natural Science Foundation of China 81970578Natural Science Foundation of Shandong Province ZR2023QH059Scientific Research Fund of Binzhou Medical University 50012304415
6 · The paper itself

Abstract

Protein-bound uremic toxins (PBUTs) are the main cause of uremia, but traditional hemodialysis is ineffective in removing them because of their strong ability to bind to human serum albumin (HSA), highlighting the need for new treatments. In this study, first, structure-based docking was used to screen a diverse library of 200,376 virtual compounds against the active sites I and II. After two rounds of docking screening, 3944 candidate molecules were obtained. Second, 23 candidate molecules were obtained after ADMET prediction and toxicity analysis. Five candidate molecules were finally obtained after visual analysis and MM-PBSA calculations. We subsequently assessed their competitive displacement efficiency through a microdialysis experiment, and the results revealed that ZINC000008791789, ZINC000012297018, and ZINC000012296493 are promising binding competitors for PBUTs, as they have higher dialysis efficiency than the optimal displacer LA, approximately double the dialysis efficiency. The other two molecules, ZINC000031161007 and ZINC000004090361, although less efficient than LA, still outperformed the control group. Notably, four of them shared the same molecular scaffold, and three of them contained a flavonoid group. These findings provide a foundation for the development of more effective PBUT binding competitors, potentially benefiting uremia patients in the future.

Indexed as

Molecular Docking SimulationProtein BindingUremic ToxinsBinding, CompetitiveBinding SitesDrug DiscoveryHumansRenal DialysisSerum Albumin, HumanUremiaSerum Albumin, HumanUremic ToxinsBinding competitorsCKDMolecular dynamics simulationProtein-bound uremic toxinsVirtual screening

Identifiers

PMID39548203
PMCPMC11568142

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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.