Modeling of Antitumor Drug Pharmacodynamics Using Genetic algorithm

Available mathematical models of tumor growth and the effect of anticancer treatments in animals are still of limited practical use within the drug industry because of complex andcontradictory aspects. A simple and effective model would facilitate the preclinical development of oncology drugs owing toprediction capabilities. Another challenge of this field is to estimate the model parameters according to laboratory experimental data. Simplicity of mathematical models often causesa major impediment in fitting the models to experimental data. In this paper, a minimal Pharmacokinetic-Pharmacodynamic (PKPD)model is studied to analyze the effects of antitumor drugs using empirical data obtained from the experimentations on 5groups of laboratory mice: one group untreated and 4 groups treated with four different antitumor drugs which are intravenouslyadministered. Tumor growth and process of cell death are here modeled by three differential equations obtained from a compartmental model. The related compartmental model is thenapplied to experimental data supposing an exponential decaying model for drug concentration. Using Genetic algorithms, theparameters of model have been optimally estimated. According to simulations, the proposed model copes with the interpretation ofcontradictory effects of CpG drug. Besides, the parameters of immunological system are uniquely obtained in the presence of different anticancer drugs. Proposed model parameters may be used in the prediction of an eventual treatment of mixing profile

کلیدواژه ها:

drug ، tumor growth ، model ، mice ، genetic algorithm