Retro Metabolic Design and Development of Potential Anti Cancer Drug Effective in Controlling TP53 Mutations Development of Soft Drug

Abstract

According to research reports, cancer is considered as the most common terminal disease, which takes the life of many, every year. In most of types of cancer, TP53 is identified as a common mutation. The p53 protein released from TP53 by transcription and translation acts as a tumour suppressor protein, and it generally prevents tumour growth and metastasis. The TP53 gene is sometimes referred to as quotthe guardian of the genomequot, as it supports gene repair and prevents the genes from mutation. The main factors influencing the variance and occurrence of cancer in people include genomics, epigenomics, metagenomics, environmental genomics, pharmacological genomics, mechanism genomics, diagnostic genomics, and toxicogenomics. While there are many genes linked to cancer, up-regulation of the TP53 gene is thought to be an endogenous enzymatic cause of other significant alterations. However, the propensity of the illness and the receptivity of the patients to therapeutic action varies from person to person, demanding for an individualized approach to drug design. The design of a novel TP53 anticancer drug using a Retrometabolic drug design strategy is presented in the current work, along with a thorough pharmacophoric analysis of the TP53 gene responsiveness towards cancer. A diagnostic system for early detection of TP53 mutations causing cancer from microscopic biopsy images is also introduced. Creating target models for major super populations (African, American, European, South Asian, and East Asian) is necessary for correlation analysis of the TP53 gene and the Covid 19 virus as well as because hereditary variations in the TP53 gene considerably enhance the chance of acquiring cancer. The target has been functionally prioritized by gene enrichment using ToppGene in conjunction with a machine-learning strategy that uses the fuzzy logic method. Since most biological systems are quite intelligent, it would be difficult to predict their operation completely using any algorithm. In order to further prioritize the...