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Objective <p>Lung 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.</p> Methods <p>This 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.</p> Results <p>For 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.</p> Conclusion <p>The 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月14日

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

摘要

Objective

Lung 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.

Methods

This 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.

Results

For 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.

Conclusion

The 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.