RESUMEN
OBJECTIVES: The vasculature of tumours imposes certain barriers that transport of anti-cancer drugs must overcome. Here follows an account of development of a general computational model that describes the mechanisms of drug transport to a solid tumour, with an emphasis on modelling the vasculature using solute transport concepts. MATERIALS AND METHODS: Investigation into the biological parameters that enhance/prevent anticancer drug transport to the tumour provides a means to evaluate the effects of these parameters on the treatment process. Sensitivity analysis of these provides useful insights concerning anticancer drug transport mechanisms from the vasculature to the solid tumour for a non-specified drug and non-specified solid tumour by revealing the conditions that promote or prevent effective drug transport. The effect of the vasculature on transport efficiency is studied using a parametric analysis of some of the transport and biological parameters. Understanding the various transport mechanisms provides a basis to evaluate the effectiveness of the drug treatment a priori. RESULTS: It was found that increases in the capillary hydrostatic pressure, diffusive permeability coefficient and hydraulic conductivity all result in a decrease in tumour size. Similarly, decreases in the interstitium hydrostatic pressure and filtration constant result in a decrease in tumour size. Dependence of the change in the tumour size to changes in these parameters is non-linear. These results demonstrate the potential of the integrated computational model of the tumour and its vasculature to estimate efficacy of a particular treatment process. Regardless of the dependency of the outcome on the assumed model parameters and the assumed kinetics, mathematical models of this type can provide more explanation on the issues related to the transport barriers, the efficacy of the treatment, and the development of effective anticancer drugs. A case study also is presented to demonstrate the model's flexibility to accommodate a two-cell-glioma population.