Experimental data-driven tumor modeling for chemotherapy


Mathematical models of tumor growth in response to chemotherapy are crucial for therapy optimization and outcome. We create a relatively simple tumor growth model describing the antitumor effect of pegylated liposomal doxorubicin (PLD) validated with real experimental data obtained in a genetically engineered mouse model of breast cancer. We use formal reaction kinetics to describe the pathophysiological phenomena using differential equations, and carry out parametric identification based on experiments using a mixed-effect model with stochastic approximation expectation maximization. The model gives a sufficient fit to describe tumor growth and pharmacokinetic data, and a satisfactory fit for the complex case, i.e., tumor response to chemotherapy. The results showed that identification of certain subsystems is easy using experimental data even if it is not specifically designed for identification. However, the identification of the complex pathophysiological phenomena may require experiments specially designed for identification purposes. Copyright (C) 2020 The Authors.