Agent-based modeling demonstrates how target-independent processes supplement killing by antibody-drug conjugates in cancer therapy
Fig 3
Independent calibration of payload potency and baseline T cell activity.
Experimental data from Rios Doria et al. [8] (top row) were used to train the simulations (bottom) on payload efficacy and T cell effects. (A) The cell doubling time was fit to match the untreated animal model tumor growth, and pharmacodynamic parameters were fit to efficacy data using an anti-EphA2 antibody with either a Tubulysin or PBD payload in a nude mouse model that lacks T cell effects. SimADC was able to capture the magnitude and dose response of these effects. (B) For T cell killing, the growth rate in a nude mouse host versus immunocompetent syngeneic host was compared. The baseline (non-activated) T cell killing rate was fit to account for the slower growth rate when intratumoral T cells are present. Error bars represent standard deviation; each simulation point is the result of 300 simulation runs (100 simulations run in triplicate).