Work

Computational Screening of FDA‐Approved Hepatitis C Drugs for Inhibition of VEGFR2 in Liver Cancer

Year: 2024

Type: article

Abstract: Liver cancer (LC) is one of the most common tumours and the leading cause of cancer‐related death globally. Amidst the problems associated with existing treatments, such as hepatotoxicity, recurrence, drug resistance, and other adverse effects, researchers are under pressure to find alternatives. Towards a comprehensive rationalisation of the search for new anti‐LC drugs among approved ones, we employed an in‐silico approach to accelerate the selection of the most efficacious LC drugs. The FDA‐approved hepatitis C virus (HCV) drugs were docked with the LC protein using the AutoDock Vina software. Compared to the control compound, two FDA‐approved HCV drugs (DB09102 and DB09027) were selected based on their binding energies and interactions with the target protein, which showed comparable binding energies. Furthermore, these compounds were then subjected to molecular dynamic simulation, principle component analysis, and MMGBSA using the AMBER20 software, and the results showed stable complexes compared to the control complex. All things considered, this study will help the scientific community and society find a novel drug to treat LC.

Source: ChemistrySelect

DOI: 10.1002/slct.202402683

Authors: Miah Roney, Abdul Rashid Issahaku, Nasir Tufail, Anke Wilhelm, Mohd Fadhlizil Fasihi Mohd Aluwi

Institutions: Universiti Malaysia Pahang Al-Sultan Abdullah, University of the Free State, University of Allahabad

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funder: Universiti Malaysia Pahang