Mechanistic investigations of structure-function interplay as causal

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BackgroundHypertrophic cardiomyopathy (HCM) is one of the most common genetic heart diseases and a leading cause of sudden cardiac death in the young. Acute myocardial ischaemia, where there is a sudden shortage of blood supply to the heart muscle, is widely acknowledged as a contributor to lethal arrhythmias in HCM, yet the assessment of ischaemia remains absent from clinical guidelines. This is in part because the interactions that ischaemia has with other disease features in HCM are poorly understood, which limits attempts to characterise arrhythmic risk, affects the diagnosis of clinically significant ischaemia on electrocardiography (ECG), and constrains the development of novel antiarrhythmic pharmacologic therapies.Aims and ObjectivesThe research aims to investigate the role of myocardial ischaemia in HCM using multiscale cardiac electrophysiology modelling and simulation, to inform risk stratification, ECG diagnosis and antiarrhythmic therapy. The objectives are to:(i) investigate the mechanisms by which ischaemia interacts with other disease features in HCM (ionic remodelling and fibrosis) to modulate arrhythmic risk(ii) investigate how ischaemia manifests on the ECG in HCM in comparison to normal changes that occur with exercise, to improve the diagnosis of ischaemia on stress ECG(iii) investigate the effects of ranolazine (an antianginal drug) on arrhythmic risk in HCM, to identify patient subgroups likely to benefit from treatmentNovelty of Research MethodologyThe research includes ischaemia as a novel factor in simulations of HCM ventricles. Ischaemia, as a dynamic process, has variable severity at a cellular level and spatial extent. Accounting for this complexity, alongside existing variability in the severity of ionic remodelling and fibrosis in HCM, and intrinsic population electrophysiological variability, imposes significant scientific computing challenges. These challenges are addressed using a novel GPU cardiac electrophysiology solver. Additionally, the research uses novel methods to combine patch-clamp, perfusion and ECG data to inform and validate the computational models, constraining the investigations to clinically relevant cases. This paves the way for more accurate 'digital twin' simulations, which with the application of drug therapy to the HCM population of models, moves towards in silico clinical trials for antiarrhythmic drug development.Alignment with EPSRC strategiesThe proposed research extends the current portfolio of EPSRC funded research in the areas of Mathematical Biology and Non-linear Systems. It aligns closely with Healthcare Technologies Grand Challenges for Optimising Treatment ('technologies for timely and accurate diagnosis, stratification, predictive modelling, and real-time, evidence-based decision making') and the Cross-Cutting Capabilities on Novel Computational and Mathematical Sciences ('in silico modelling and simulation').CollaboratorsCollaborations are established with Prof. Betty Raman (Oxford Centre for Clinical Magnetic Resonance Research) for access to perfusion imaging data, Prof. Raffaele Coppini (University of Florence) for unique access to human HCM cellular measurements, Prof. Rafael Sachetto (Federal University of São João del-Rei) for technical expertise, and Prof. Hugh Watkins (Radcliffe Department of Medicine) and Prof. Iacopo Olivotto (Careggi University Hospital) for clinical HCM expertise.