Evidence map›Paper›PMID 41406173›Full record

ArticlePloS one2025

An evaluation of Roluperidone as a promising repurposing candidate for Alzheimer's Disease: A Computational Investigation.

Rehnuma Tanjin, Md Al-Amin, Jannatul Mawa Etee, Ayesha Siddika, Ahmadullah Siddiki, Saiful Islam Mahi, Sharmin Nur Toma, Nafisa Akter, Md Helal Uddin, Neelima Akhter Bristy and 3 more

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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

13 authors.

Rehnuma TanjinDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.
Md Al-AminDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.ORCID https://orcid.org/0009-0009-1982-7001
Jannatul Mawa EteeDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.
Ayesha SiddikaDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.
Ahmadullah SiddikiApplied Chemistry and Chemical Engineering, Islamic University, Kushtia, Bangladesh.
Saiful Islam MahiDepartment of Biomedical Engineering, Islamic University, Kushtia, Bangladesh.
Sharmin Nur TomaDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.
Nafisa AkterDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.
Md Helal UddinDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.
Neelima Akhter BristyDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.
Samira Idris MowleeDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.
Elmu Kabir RafaDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.
Md Faruk HossenDepartment of Pharmacy, Islamic University, Kushtia, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is the most dominant and prevalent form of dementia. The therapeutic agents for AD are not sufficient. Drug repurposing (i.e., also called drug repositioning or therapeutic switching of drugs) could contribute to adding novel therapeutic agents in AD discovery pipeline. Blood-brain barrier (BBB) is a crucial factor, for brain's diseases related drug discovery. Since, CNS active compounds have BBB crossing property, in this study this category of compounds was re-evaluated as repurposing potential candidate for AD by integrated machine learning algorithm, cheminformatics analysis, molecular Docking and simulation-based approach. We builded three machine learning model such as Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) for the prediction of AD potential repurposing candidates. The SVM classification model performed better than others. The SVM classification model achieved an Area Under the Curve of the Receiver Operating Characteristics (ROC-AUC) of 0.81, along with higher precision, recall, and F1 scores. The support vector machine (SVM) was implemented to classify 500 CNS active compounds as AD drug potential and non-AD drug potential. Using the SVM model, 60 compounds were predicted as AD repurposing potential from 500 CNS active compounds. Structural similarity analysis of 60 compounds with Donepezil as a reference drug was performed using 5 different types of fingerprints such as 'substructure', 'extended', 'circular', 'EState', 'MACCS'. 9 compounds from them obtained as structurally most similar to the reference drug. After the molecular docking performance of 9 compounds into the active site & peripheral anionic site of human acetylcholinesterase (hAChE), it was revealed that Roluperidone' had binding affinity of -12 kcal/mol, and 'Napitane' had binding affinity of -11.9 kcal/mol whereas the reference drug Donepezil had a binding affinity of -11.8 Kcal/mol. Molecular dynamics simulation revealed that Roluperionde had better binding integrity to hAChE. This study laid out computational reinvestigation of 500 CNS active drugs for therapeutic switching to AD, and 'Roluperidone' is found as an AD repurposing potential candidate. However, in-vitro and in-vivo studies are further needed to fully elucidate the compound's potential as AD repurposing drugs.

Indexed as

Alzheimer DiseaseDrug RepositioningBlood-Brain BarrierDonepezilHumansMachine LearningMolecular Docking SimulationSupport Vector MachineDonepezil

Identifiers

PMID41406173
PMCPMC12711050

What Socratic 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 Socratic graph.