The BHF-Turing Cardiovascular Data Science Awards (Second Call): Integration of time and genetic instrument dependent causal methods for the construction of a metabolomics network for the assessment of drug compounds (joint funding with The Alan Turing Institute)
Development of a new drug is a long, expensive process. The majority are rejected during clinical trials because they are deemed unsafe or ineffective. A tool that could accurately predict the biological effects of a new drug early in its development could save significant time and money. Such a tool would need to mathematically model the complex network of molecules that could play a part in the development of diseases. The metabolome is all of the small molecules, called metabolites, in a biological sample – including sugars, amino acids and hormones. Changes in the metabolome occur in in many diseases and can also be influenced by genetics and drug treatments. A group of data scientists and cardiovascular experts, led by Dr Fotios Drenos and Professor Tomaso Aste, will model the network of metabolites, and any changes that are related to type 2 diabetes and coronary heart disease, in order to evaluate and predict the effect of drugs on these conditions. The scientists aim to (1) model relationships between a number of metabolites to see how a change in one affects all of the others; (2) use large-scale genetic data to model how genes influence these metabolites; and (3) combine these models with the known links between the metabolome, diabetes, and heart disease, and the known effects of drugs used to treat them. This research could be used to accelerate drug development for the prevention or treatment of coronary heart disease and type 2 diabetes.
Project details
| Grant amount | £31,090 |
|---|---|
| Grant type | Chairs & Programme Grants |
| Application type | Special Project |
| Start Date | 01 January 1900 |
| Duration | 1 year |
| Reference | SP/19/5/34806 |
| Status | In Progress |
