Evidence map›Paper›PMID 41391063›Full record

ArticleDiscover oncology2025

Discovery of drug candidate to inhibit bronchogenic carcinoma genes biomarkers based on drug repurposing.

Bagher Khalvati, Kaveh Kavousi, Esmaeil Behmard, Amir Hosein Keyhanipour, Masoud Arabfard

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In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Bagher KhalvatiDepartment of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran.
Kaveh KavousiLaboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
Esmaeil BehmardSchool of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa, Iran.
Amir Hosein KeyhanipourComputer Engineering Department, Faculty of Engineering, College of Farabi, University of Tehran, Tehran, Iran. keyhanipour@ut.ac.ir.
Masoud ArabfardArtificial Intelligence in Health Research Center, Biomedicine Technologies Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran. arabfard@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveLung cancer is one of the most common and challenging cancers to treat. Advances in research have led to the development of targeted drug therapies that provide suitable options for patients with bronchogenic lung cancer. The aim of the current study is to inhibit genes that have been introduced as biomarkers for the diagnosis of bronchogenic lung cancer.

methodsThis study leverages computational methods to identify repurposable drugs targeting key biomarkers—MLKL, YWHAG, OAS3, TFRC, NXA2, CDK6, NTN1, CD59, RRAS2, and CYP51A1—for cancer treatment. As a primary screening tool, AutoDock Vina was employed for molecular docking to evaluate the binding affinity of existing drugs against these targets. Following the initial screening, molecular dynamics simulations were utilized to select the most stable and ideal drug candidates with specific inhibitory therapeutic properties from the pool identified by docking. This integrated workflow demonstrates an efficient path for discovering new therapeutic uses for existing drugs against a defined panel of cancer biomarkers.

resultsFor the ten genes under investigation, structurally reliable PDB entries were chosen as the basis for subsequent analyses. The herbal compound Dracorubin, along with 2,299 ligands collected from the PubChem database, was assessed. Molecular dynamics simulations indicated that Dracorubin sustained stable and meaningful inhibitory interactions with the proposed biomarkers.

conclusionThe study aimed to find new uses for existing drugs for Bronchogenic lung cancer. Using computational methods like molecular docking and dynamics, researchers prioritized compounds that bind strongly to target proteins. These candidate drugs, prioritize a computational lead requiring experimental validation of Bronchogenic lung cancer progression.

Indexed as

Biomarker inhibitionBronchogenic lung cancerDrug repurposing, plant-derived ligandsMolecular dynamics

Identifiers

PMID41391063
PMCPMC12819939

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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.