Computational Modeling of Drug Delivery to Solid Tumors: A Pilot Study Based on a Real Image

Abstract This study uses a multi-scale computational model to assess drug delivery to a solid tumor and to predict the treatment’s efficacy. A geometric model of the tumor and the capillary network are obtained by extracting the geometry through processing a real image. Next, the equations related to intravascular and interstitial flows as well as drug transport in the tissue are solved by considering real conditions as well as details such as drug binding to cells and cellular uptake. The study results show values of about 34.71% and 5.27% for the fraction of killed cells (FKCs) for tumor cells and normal tissue cells, respectively. Examining eight different modes for inlet and outlet pressures of parent vessels shows that the difference between the maximum and minimum FKCs is about 7.37%, and the side effects for all modes are almost the same. For this geometry, a comparison of two tumor shapes illustrates that circular tumors are more easily eradicated than elliptical ones. Evaluating tumor treatments based on the fraction of surviving cells (FSCs) shows that after eight sequential treatment stages at 15-day intervals, 7.78% of tumor cells remain, and about 16% of healthy tissue cells have been damaged by the treatment. Reducing the size of larger tumors is found to be much easier than reducing the size of smaller ones; therefore, the best way to eliminate small tumors is to use adjuvant therapy. The proposed approach can help drug designers decide on new drugs by considering treatment outcomes, and can also help oncologists plan the best treatment for each patient by evaluating treatment responses.