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.
 Eyres et al. (2021) Gastroenterology. 2021:S0016-5085(21)00682-X.
 Pham et al. (2021). Nat. Mach. Intell. 3, 247–257
 Kay et al (2017). PLoS Comp Biol. 2017; 13(2): e1005400.
 MacLean et al. (2015) PNAS. 2015; 112(9): 2652-2657.
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.
Personal GitHub Repository
Saunus JM. et al, (2022), npj Breast Cancer, 8
Pribyl AL. et al, (2021), ISME Communications, 1
Saunus J. et al, (2021)
Nicolau DV. and Hasson A., (2020)