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.