INTEGRATING COMPUTER AIDED DRUG DESIGN AND NEXT GENERATION SEQUENCING TO OPTIMIZE TARGETED THERAPIES FOR PI3KA H1047R MUTANT IN CANCER

Cancer is a genetic disease that arises from genetic or epigenetic mutations defined by the abnormal growth of cells in the body, and is also responsible for one of the highest burdens of mortality worldwide. The genes that contribute to cancer development can generally be divided into two groups, the oncogenes and the tumour suppressor genes. The H1047R mutation in the PI3Kα catalytic subunit (PIK3CA) is one of the most common oncogenic alterations seen in multiple cancers, promoting abnormal PI3K signalling and adding to therapeutic resistance. In this research we will pursue precision therapeutics targeting the cryo EM structure of the cancer specific PI3Kα H1047R mutant via computer aided drug design (CADD) to aid structure based virtual screening and optimization. The 3D structural analysis and visualization are performed using structural bioinformatics tools to identify binding interactions and conformational dynamics. Multiple sequence alignment will be conducted with Clustal Omega to consider the evolutionary conservation of the H1047 residue in PI3K homologs to help localize mutation hotspots and druggable pockets. In this structural and sequence context, web server docking, a cavity detection based docking tool, will be used for blind virtual screening of small molecule libraries to identify new inhibitors with good binding affinity towards the target mutant protein. This integrative computational pipeline is fundamentally based on structural biology and molecular modelling, has the goal of finding and optimizing novel small molecule inhibitors with improved specificity against the PI3Kα H1047R mutant and the future of personalized cancer therapeutics.