Abstract 5337: An in silico platform for characterizing ADC bystander effects

Abstract

Abstract Introduction The “bystander effect” is the hypothesized process by which neighboring cancer cells are killed when free drug is released into the tumor environment by dying cells. The extent to which an antibody drug conjugate (ADC) elicits the bystander effect can have significant impact on the magnitude and dynamics of efficacy. Understanding the bystander effect though experimental methods (e.g. video-microscopy) are challenging; however, when supplemented with in silico modeling, mechanistic details can be elucidated. The purpose of this work is to develop a spatial-temporal in silico platform describing tumor cell progression in the presence of an ADC in order to quantify the bystander effect. Methods The model employs spatial-stochastic methods to simulate the time evolution of cell death resulting from ADCs and freely diffusing payload based on following parameters: drug to antibody ratio (DAR), linker stability, antigen concentration, ADC cell membrane permeability, ADC-antigen internalization, payload permeability, and payload potency. Specifically, a 2-D rectangular grid of tumor cells, ADCs, and payload are numerically implemented through binary pixel occupation of the grid. Governing rules are then assigned for: mobility for freely diffusing ADCs and payload, reaction rate for ADC binding to antigen-expressing cells, rate of cell death dependent on payload concentration, and internalization rate of payload into non-antigen expressing cells. The simulation is run until stopping criteria are met, e.g. all cells are killed. Baseline parameters can be optimized by fitting the model to in vitro experimental video-microscopy data. Results Experimental conditions were simulated with the model to characterize the influence of the aforementioned ADC and biological parameters on the bystander effect. Conclusions By incorporating the spatial aspect of ADC mechanics, spatially dependent metrics can be formulated to quantify the bystander effect. The model allows for wide adjustability of the ADC design parameters as well as relevant biological parameters. Thus, by understanding the relative parameter sensitivities, the model can explore design optimization of ADCs that maximize the bystander effect. Through such optimization, the magnitude and dynamics of drug exposure at the site of action can be controlled to maximize efficacy and improve the therapeutic window. Citation Format: Jackson Burton, Shu-Wen Teng, Christopher J. Zopf, Ryan Nolan, Arijit Chakravarty. An in silico platform for characterizing ADC bystander effects. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5337. doi:10.1158/1538-7445.AM2015-5337