Drug-loaded nanoparticles for cancer therapy: A high-throughput multicellular agent-based modeling study

Abstract

Interactions between biological systems and engineered nanomaterials have become an important area of study due to their application in medicine. In particular, the opportunity to apply nanomaterials for cancer diagnosis and treatment presents a challenge due to the complex biology of this disease, which spans multiple time and spatial scales. A systems-level analysis from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated system parameters and patient response would be of benefit. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, inheritance of NPs by daughter cells at cell division, cell pharmacodynamic response to intracellular drug levels, and overall drug effect on tumor growth. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on tumor growth. In particular, through the exploration of NP inheritance at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dose of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by heritable NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.