Drug ranking using machine learning
By using machine learning to rank drugs, we could revolutionise cancer treatment through offering truly personalised medicine.
Selecting the best drug for individual cancer patients is complicated. Every tumour and patient is different – so people can react dramatically differently to the same treatments.
Invented by Dr Pedro Cutillas, DRUML is a methodology for building and integrating ML models, using ensembles of proteomic, phosphoproteomic and transcriptomic features to generate lists of ranked drugs based on their efficacy.
DRUML can predict drug rankings within a cancer cell population without needing to compare to samples – a crucial requirement for the clinical implementation of machine learning and a core aim of precision medicine.
Using large-scale proteomics and phosphoproteomics in machine has never been systematically applied before.
Recent advances in proteomic techniques and a greater number of drug response profiles means we can now feed this data into machine learning models of drug response – advancing the field of precision medicine and bringing hope to the millions of people diagnosed with cancer around the world each year.
A patent has been filed and we’re actively looking for partners to license this technology to develop commercially.
Contact
Monika Hamilton PhD CLP – monika.hamilton@qmul.ac.uk
