Untargeted metabolomic study of synergistic herbal and/or synthetic anticancer combinations against breast cancer cell lines
Breast cancer is the most common cancer diagnosed in Australia and accounts for a quarter of all cancer cases in females in worldwide. Though there are continuous advances in treatments, breast cancer is fourth among cancers causing death and still increasing. Drug combinations of known drugs are favoured as treatment for cancer; however, despite experimental advances in automation, these are the result of intensive trial-and-error studies which can be lengthy and resource intensive.
Utilising the genomic information of cancer cells and chemo-informatics of various drugs, Muhammad's PhD project aims to develop and validate a novel deep learning model to predict drug combinations with optimal synergistic efficacy. The outcomes of this research will contribute to the will not only save time, effort and resources dedicated in the blind screening for drug combinations, it will also provide insight and deeper understanding of synergistic mechanisms.
Muhammad has completed a Bachelor of Pharmacy and a Master’s in pharmacognosy from Cairo University. He has experience as a research and teaching assistant in the Pharmacognosy department, Faculty of Pharmacy, Cairo University, Egypt as well as a research assistant in University of Canberra in Australia.
Professor Chun Guang Li; Dr Allan Torres; Professor Dennis Chang; Assistant Professor Ibrahim Radwan