Doxorubicin is a chemotherapeutic drug with well-known cardiotoxic side effects. However, decades of research have yielded a piece-wise picture of cardiotoxicity that remains to be integrated. Computational modelling provides a framework for analysing mechanisms that collectively govern cardiac function, and for investigating the impact of drug exposure on specific physiological parameters in heart failure.
We developed a multi-scale computational model of the heart to simulate features of the cardiac cycle that are measured in the routine clinical treatment of cancer patients. The model implements mechanisms ranging from the cellular to the whole heart level, reproducing 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 literature data.
We compared representative models fitted to (1) doxorubicin-treated heart-failure patients, (2) heart-failure patients not receiving chemotherapy, and (3) healthy controls. Left-ventricular cardiac anatomy (chamber dimensions, wall thickness) and ejection fraction were characterised by echocardiography. Hemodynamic measurements yielded maximum ejection pressures and heart rates. Biopsies taken from heart-failure patients provided measurements of the collagen volume fraction and underwent a proteomic analysis by mass spectrometry. Based on these data, we used mechanical simulations to identify emerging correlations between the micro- and tissue-scale phenotypes and global cardiac functionality between the cohorts.
The simulation results suggest that doxorubicin-unrelated heart failure is characterised on average by a 2- to 3-fold stiffness increase, a decrease in cellular contraction by ~0-20%, and ventricular dilation (10-30%). Doxorubicin-related heart failure is similar but shows stronger bias toward reduced contraction (10-30%) and less dilation (0-20%). Ultimately, identifying these dominant mechanisms may help improve diagnosis, therapies, and at-risk patient stratification.