Design and identification of bioreductive and pharmacogenomic P4 anti cancer drugs to control tumor hypoxia

Abstract

Cancer prevails as a life threatening disease despite lot of upcoming therapeutic interventions. The major hindrance found in the cancer treatment is development of Hypoxia. It has been found that the Tumour Micro Environment (TME) of the patient continually changes over the course of cancer progression. Moreover, the TME for each patient is found to be unique keeping specific variant features (Single Nucleotide Variant-SNV) for the mutations. An early diagnosis of the hypoxic condition would help in taking the appropriate precautions and the necessary treatment on time. The hypoxic condition is primarly caused by the mutated HIF, regulated by the ARNT signalling. However, several other mutations are directly or indirectly involved in the condition. The individual variation in the responsible mutations make the target proteins unique, demanding a pharmacogenomic approach in studying the variation of the effect of known drugs among populations, analysing proneness of the disease and designing customized drugs for each population. The computational techniques have been used to list out few pharmacogenomic and potential Preventive, predictive, participatory and personalized P4 molecules. Methodology: By combining genomes, epigenomics, metagenomics, and environmental genomics, the pharmacogenomic technique has been used to identify the genetic markers associated with tumor hypoxia development. The study includes all of the widespread mutations connected to hypoxia. Diagnosis of hypoxic regions have been elucidated with the help of deep learning by using CNN based Cifar-10 model. With the identified genes responsible for hypoxia, functional enrichment and gene prioritization was carried out with a fuzzy logic algorithm using Toppgene platform. The Bio-molecular networking technique was used to study the Protein-protein interaction (PPI) and interaction based prioritization. The control molecules were identified by repurposing of all known drug by using Protein drug interaction (PDI) network. The Single..