Mechanistic Modelling of Multitargeted Therapeutics
Pancreatic cancers remain intractable, resulting in high mortality. Thus, there is an urgent need for innovative ways to tackle this disease. Aberrant epigenetics is a key “Hallmark of cancer" that cancer cells use to drive de-differentiation of normal cells into an embryonic squamous-like state which favours uncontrolled proliferation.
We recently identified the epigenetic mechanism defining these phenotypic changes and found that a combination of biologically-targeted agents (Metformin and Vitamin C) could restore epigenetic control and revert squamous pancreatic cancer cells to a more regulated differentiated state . Although we observe a shift towards normal cell epigenetics, this drug combination is not fully optimised to drive the phenotypic shift. Therefore, we aim to use an innovative data-driven artificial intelligence (AI) approach developed by BedrockBio based on existing algorithms, to delineate novel molecules that can demonstrate improved and robust re-normalisation of epigenetic status and pancreatic differentiation, in order to offer new treatments for this hitherto intractable cancer.
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My key interests in research lie in multidisciplinary computational sciences. My Honours thesis was in Computation by Biological Means (biological alternative computing).
I am currently involved primarily in computational mathematics, bioinformatics and machine learning. The applications of are namely computational medicine (oncology), computational biology and protein/cheminformatics.
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