BioModels

This model is built by COPASI 4.24(Build 197), based on paper: Mathematical Approach to Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment. Author: James M. Greene, Jana L. Gevertz, Eduardo D. sontag Abstract: PURPOSE:Drug resistance is a major impediment to the success of cancer treatment. Resistance is typically thought to arise from random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests that progression to drug resistance need not occur randomly, but instead may be induced by the treatment itself via either genetic changes or epigenetic alterations. This relatively novel notion of resistance complicates the already challenging task of designing effective treatment protocols. MATERIALS AND METHODS:To better understand resistance, we have developed a mathematical modeling framework that incorporates both spontaneous and drug-induced resistance. RESULTS:Our model demonstrates that the ability of a drug to induce resistance can result in qualitatively different responses to the same drug dose and delivery schedule. We have also proven that the induction parameter in our model is theoretically identifiable and propose an in vitro protocol that could be used to determine a treatment's propensity to induce resistance.

• Mathematical Approach to Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment.
• Greene JM, Gevertz JL, Sontag ED
• JCO clinical cancer informatics , 4/ 2019 , Volume 3 , pages: 1-20 , PubMed ID: 30969799
Affiliation: Rutgers University, New Brunswick, NJ.
Abstract: PURPOSE:Drug resistance is a major impediment to the success of cancer treatment. Resistance is typically thought to arise from random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests that progression to drug resistance need not occur randomly, but instead may be induced by the treatment itself via either genetic changes or epigenetic alterations. This relatively novel notion of resistance complicates the already challenging task of designing effective treatment protocols. MATERIALS AND METHODS:To better understand resistance, we have developed a mathematical modeling framework that incorporates both spontaneous and drug-induced resistance. RESULTS:Our model demonstrates that the ability of a drug to induce resistance can result in qualitatively different responses to the same drug dose and delivery schedule. We have also proven that the induction parameter in our model is theoretically identifiable and propose an in vitro protocol that could be used to determine a treatment's propensity to induce resistance.