In this paper, we established a multiscale mechanistic model for studying drug delivery, biodistribution, and therapeutic effects of cancer drug therapy in order to identify optimal treatment strategies. Due to the specific characteristics of cancer, our proposed model focuses on drug effects on malignant solid tumor and specific internal organs as well as the intratumoral and regional extracellular microenvironments. At the organ level, we quantified drug delivery based on a multicompartmental model. This model will facilitate the analysis and prediction of organ toxicity and provide important pharmacokinetic information with regard to drug clearance rates. For the analysis of intratumoral microenvironment which is directly related to blood drug concentrations and tumor properties, we constructed a drug distribution model using diffusion-convection solute transport to study temporal/spatial variations of drug concentration. With this information, our model incorporates signaling pathways for the analysis of antitumor response with drug combinations at the extracellular level. Moreover, changes in tumor size, cellular proliferation, and apoptosis induced by different drug treatment conditions are studied. Therefore, the proposed multi-scale model could be used to understand drug clinical actions, study drug therapy-antitumor effects, and potentially identify optimal combination drug therapy. Numerical simulations demonstrate the proposed system's effectiveness. 1. Introduction Cancer disease remains a leading cause of high morbidity and mortality in both adults and children. Despite extensive efforts to discover and develop effective drugs, very few promising candidates currently exist in the development pipeline. Traditional methods for developing and testing new drugs usually involve a series of controlled experiments on groups of selected healthy volunteers to establish the relationship between dose and therapeutic effects for a given drug candidate. Traditional drug development methods are time and resource intensive and carry substantial risk for adverse effects. Hence a more efficient approach is urgently needed. Fortunately, cancer research has undergone dramatic changes recently. One of the most important changes is the application of computational modeling for pharmacokinetics/pharmacodynamics (PK/PD) analysis [1, 2]. PK model can describe or predict the time course of drug concentration in different body compartments like blood and heart, and so forth. In contrast, PD models focus on time course of drug effects at the site of action (Figure
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