School of Education

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Browsing School of Education by Author "Murugesan, Anbu Megala"

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    In Silico Drug Design in Human Apotosis Inducing Factor(AIF) in Lund Cancer
    (Journal of Emerging Technologies and Innovative Research (JETIR), 2025) Murugesan, Anbu Megala
    The malignant condition known as lung cancer is characterized by unchecked growth in the lung's tissues or cells. This aberrant growth develops into a tumour known as a carcinoma. If it is not appropriately or quickly treated, it may metastasize to other areas of the body. In multicellular organisms, apoptosis is a process of programmed cell death in which the cell goes through a sequence of biochemical events that promote cell development, eliminate undesirable cells, preserve tissue integrity, and stop the spread of cancer. It is the cell's method of carrying out a controlled suicide. The cell shrinks, develops blebs, and breaks apart its DNA during apoptosis. Apoptotic cell mutations can result in tissue damage, tumour growth, unchecked cell division, and neurodegenerative diseases. causing apoptosis. Mutations in apoptotic cells can lead to uncontrolled cell proliferation, tumor development, tissue damage, and neurodegenerative disorders. Apoptosis-inducing factor (AIF) is a mitochondrial protein involved in both caspase-dependent and caspase-independent apoptosis pathways. AIF was initially characterized as a cell death mediator and plays an important role in lung cancer. Coiled-Coil-Helix, domain containing Protein 4, or CHCHD4, is involved in oxidative stress regulation and mitochondrial health maintenance. CHCHD4 plays a role in the cellular reaction to damage by interacting with proteins in the inner membrane of the mitochondria. CHCHD4 may have an impact on lung cancer cell survival, particularly in the presence of oxidative stress, which is typical of cancer cells. Computer-aided drug design (CADD), also known as in silico drug design, is a computational method that uses bioinformatics tools to find molecules that resemble drugs. The biological and physicochemical characteristics of possible drug candidates are analysed and predicted with the aid of these tools. Because it offers tools and techniques for analyzing vast amounts of biological data, forecasting drug-target interactions, modelling protein structures, and simulating molecular interactions, bioinformatics is essential to in-silico drug design. This research could pave the way for more effective therapies targeting mitochondrial functions and cell death pathways in cancer, bridging the gap between theoretical research and practical application in drug discovery to improve patient outcomes.
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    In-Silico Approach for Potential Drug Target in Human Mutant Complex with Nadph and AG-881 Inhibitor
    (Journal of Emerging Technologies and Innovative Research (JETIR), 2025) Murugesan, Anbu Megala
    The drug discovery has undergone a significant transformation with the advent of computational approaches, enabling rapid identification and evaluation of potential therapeutic candidates. In-silico methods such as molecular docking, structural stability analysis, and toxicity prediction have become essential tools for exploring drug-target interactions. This study employs a computational approach to investigate potential drug targets in a human mutant complex bound to NADPH and AG-881 (Vorasidenib) inhibitor, with a particular focus on two promising compounds: Tovorafenib and Curcumin. Mutations in key regulatory proteins often lead to structural and functional alterations, which can contribute to the progression of various diseases, including cancer and metabolic disorders. Understanding how these mutations influence drug binding is crucial for designing effective targeted therapies. AG-881 is known for its selective inhibition of mutant enzymes, but alternative compounds such as Tovorafenib and Curcumin could provide new therapeutic avenues. Tovorafenib, a kinase inhibitor, has demonstrated efficacy in targeting oncogenic mutations, particularly in RAF kinase-driven malignancies, while Curcumin, a naturally occurring polyphenol, exhibits potent anti-inflammatory, antioxidant, and anticancer properties. Despite these promising attributes, the binding efficiency, stability, and toxicity profiles of these compounds in the context of the studied mutant complex remain unexplored. To evaluate their potential, molecular docking was performed using CB-Dock, an automated docking tool that predicts ligand binding sites and ranks interactions based on binding affinity. Structural stability analysis was conducted using PyMOL, where root mean square deviation (RMSD) calculations were used to assess conformational changes and the stability of the protein-ligand complexes. Additionally, the toxicity profiles of Tovorafenib and Curcumin were predicted using toxicity identification tools, which evaluates hepatotoxicity, carcinogenicity, mutagenicity, and overall drug-likeness. This study aims to determine the binding affinities of Tovorafenib and Curcumin with the mutant complex, analyze their impact on structural stability, and compare their toxicity profiles. By integrating these computational techniques, we aim to provide insights into the feasibility of repurposing these compounds for potential therapeutic applications. The results of this study could contribute to the ongoing efforts in precision medicine by identifying promising drug candidates that warrant further experimental validation through in-vitro and in-vivo studies. Leveraging in-silico approaches for drug discovery enhances our ability to identify novel treatments efficiently, reducing the time and cost associated with traditional drug development methods [1, 2, 3, 4 ,5].