Interdisciplinary investigation of a large effect of sulfasalazine on HbA1c

The anti-inflammatory drug sulfasalazine, which is used in rheumatoid arthritis and inflammatory bowel disease, was reported earlier to suppress HbA1c in a small cohort of patients [1]. In preliminary studies, we have replicated this earlier finding in a much larger dataset of Tayside/Fife patients. This studentship will investigate this effect further, as it may be due either to anti-hyperglycaemic effects possibly suitable for drug repurposing, or alternatively confounding haematological effects. The interdisciplinary nature of the project will afford the student an outstanding opportunity to make high-impact findings relevant to clinical practice,through cross-validation of clinical and cell/molecular insights. To follow up the preliminary data, the student will use state-of-the-art national data hosted by the Scottish Diabetes Research Network epidemiology group. The additional power afforded by this database will be exploited to investigate the clinical impact of sulfasalazine on diabetes phenotypes in patients treated with these drugs. The study will include: replication of existing findings on HbA1c; haematological effects; effects on inflammatory markers (for example C-reactive protein) as well as changes in prescription of oral anti-hyperglycaemic medications or insulin. This element will comprise one third of the studentship. The remainder will be an appraisal of acute effects of sulfasalazine on diabetic and haematological responses, mainly using cell models. This will include the effect of the drug on haemolysis, inflammatory and metabolic signalling, insulin sensitivity, glucose uptake, hepatic glucose production and insulin secretion. In addition, investigation of analogues of the drug will enable the pharmacophore of any effects to be established, as sulfasalazine consists of fused 5-aminosalicyclic acid and sulfapyridine moieties, either or both of which could in principle contribute towards the effects.For the database element, the student will receive training from members of Prof Pearson’s team to extract the data and for the experimental studies they will be supervised by Dr Rena. All methodologies are established either by the supervisors or available through established collaborations[2, 3].

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