MRC DiMeN Doctoral Training Partnership: A systems pharmacology approach to better understand variation in statin response using advanced computational methods

Cardiovascular disease is a leading cause of morbidity and mortality worldwide. Statins are amongst the most highly prescribed oral medications worldwide and their effectiveness at reducing cardiovascular events (e.g. heart attacks, strokes) has been widely demonstrated. Despite atorvastatin being the first choice statin in the UK, there is considerable variation in how individual patients respond, with many experiencing muscular side effects, new-onset diabetes and/or cardiovascular events whilst on atorvastatin treatment. Indeed, for a given dose of atorvastatin, there is at least a 45-fold variation in atorvastatin blood concentration levels between patients.

We need to better understand why people respond differently to atorvastatin in order to improve patient care and clinical outcomes. You will have access to clinical samples (blood and urine specimens) and data from nearly 1500 patients who were hospitalised following a heart attack and then followed up for at least one year afterwards. Data include demographic details, clinical information, drug levels and, importantly, the genotype at approximately 7 million single nucleotide polymorphisms (SNPs) across the genome, for each patient.

The aim of this exciting project is to identify and characterise novel genetic variants associated with atorvastatin drug levels and/or blood markers of insulin resistance, and then to relate these novel genetic variants to clinical outcomes (atorvastatin side effects and cardiovascular events). You will measure select markers of insulin resistance (adipokines, myokines, HBA1c) using stored blood samples, conduct computational analyses, and then functionally characterise any identified genes with potential causal links to drug response. You will also undertake metabolomic analysis in an unbiased manner. The associations will be further validated by using data from the UK Biobank.

Whilst there are good informatics and statistical techniques available to analyse SNP data, the ability to combine SNP data with other complex data types is more challenging. You will therefore work with the project’s industry partner, SAP, to use cutting-edge artificial intelligence techniques to identify the novel genomic variants from this Big Data.

The project will be based at the University of Liverpool’s MRC Centre for Drug Safety Science (CDSS), with the student spending additional time at SAP (see below). The CDSS was established in 2008 and is a world-class centre for the investigation of the fundamental mechanisms of clinically important adverse drug reactions (ADRs), with world-leading experts in drug metabolism, immunopharmacology and personalised medicine. Further information about the Centre can be found at: http://www.liv.ac.uk/drug-safety.

The project partner, SAP, is a world-leading data analytics company; they are the world’s third largest independent software manufacturer and employ more than 85,000 staff across 130 countries. SAP have a growing interest in using digital data to improve patient outcomes and support personalised healthcare, and there will be opportunity for you to spend time with SAP research teams in Berlin (Germany) or Stanford (USA).

This multidisciplinary project will provide you with training in cutting edge informatics techniques for the analysis of complex data sets, as well as provide a grounding in standard molecular and analytical laboratory skills, with world-leading support from both academic and industry scientists.