The anthracycline family of chemotherapeutic drugs have well-known cardiotoxic side effects. However, decades of research have yielded a piece-wise picture of cardiotoxicity that remains to be integrated. Data-driven computational modelling provides a framework for simulating and analysing the mechanisms that collectively govern cardiac function, and hence for investigating the impact of drug exposure on specific physiological parameters in drug-induced heart failure.
We developed a multi-scale computational model of the heart to simulate features of the cardiac cycle that are readily measurable as part of the routine clinical treatment of cancer patients. The model implements mechanisms ranging from the cellular to the whole heart level, to reproduce cardiac behaviour under physiological conditions. An externally imposed signal generates contraction forces throughout the tissue, eliciting a viscoelastic deformation of the anatomy and the ejection of blood into the circulation. The model parameters are amenable to fitting using direct measurements or data available in the literature.
We compared heart-failure patients receiving anthracycline treatment, with healthy controls. For both groups, cardiac anatomy (left-ventricular (LV) cavity dimensions, wall thickness) and LV ejection fraction were characterised using echocardiography measurements. Hemodynamic measurements yielded maximum ejection pressures and heart rates. Biopsies taken from the heart-failure patients provided measurements of collagen volume fraction and underwent a proteomic analysis by mass spectrometry. We used the mechanical simulations to identify emerging correlations between the micro- and tissue-scale phenotypes and global cardiac functionality between the cohorts.
Simulation results suggest that a tissue-stiffness increase by 150% reproduces the phenotype changes between average healthy and cardiotoxic patients. This relative increase is comparable to the observed increase in collagen fraction, consistent with a passive mechanical cause of heart failure. Using proteomics analyses of the patient biopsies, we then consider the variations in relevant protein abundances in the light of the collagen hypothesis.