A dynamical model of oncotripsy by mechanical cell fatigue: selective cancer cell ablation by low-intensity pulsed ultrasound

The method of oncotripsy, first proposed in Heyden & Ortiz (Heyden & Ortiz 2016 J. Mech. Phys. Solids 92, 164–175 (doi:10.1016/j.jmps.2016.04.016)), exploits aberrations in the material properties and morphology of cancerous cells in order to ablate them selectively by means of tuned low-intensity pulsed ultrasound. We propose the dynamical model of oncotripsy that follows as an application of cell dynamics, statistical mechanical theory of network elasticity and ‘birth–death’ kinetics to describe the processes of damage and repair of the cytoskeleton. We also develop a reduced dynamical model that approximates the three-dimensional dynamics of the cell and facilitates parametric studies, including sensitivity analysis and process optimization. We show that the dynamical model predicts—and provides a conceptual basis for understanding—the oncotripsy effect and other trends in the data of Mittelstein et al. (Mittelstein et al. 2019 Appl. Phys. Lett. 116, 013701 (doi:10.1063/1.5128627)), for cells in suspension, including the dependence of cell-death curves on cell and process parameters

A dynamical model of oncotripsy by mechanical cell fatigue: selective cancer cell ablation by low-intensity pulsed ultrasound

The method of oncotripsy, first proposed in Heyden & Ortiz (Heyden & Ortiz 2016 J. Mech. Phys. Solids 92, 164–175 (doi:10.1016/j.jmps.2016.04.016)), exploits aberrations in the material properties and morphology of cancerous cells in order to ablate them selectively by means of tuned low-intensity pulsed ultrasound. We propose the dynamical model of oncotripsy that follows as an application of cell dynamics, statistical mechanical theory of network elasticity and ‘birth–death’ kinetics to describe the processes of damage and repair of the cytoskeleton. We also develop a reduced dynamical model that approximates the three-dimensional dynamics of the cell and facilitates parametric studies, including sensitivity analysis and process optimization. We show that the dynamical model predicts—and provides a conceptual basis for understanding—the oncotripsy effect and other trends in the data of Mittelstein et al. (Mittelstein et al. 2019 Appl. Phys. Lett. 116, 013701 (doi:10.1063/1.5128627)), for cells in suspension, including the dependence of cell-death curves on cell and process parameters