Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System

Abstract

Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.

Effects of Doxorubicin Delivery by Nitrogen-Doped Graphene Quantum Dots on Cancer Cell Growth: Experimental Study and Mathematical Modeling

“…Based on these experimental studies, we developed a novel mathematical model of cell growth in the presence of the therapeutic that could accurately portray the rate of cancer cell growth after treatment with DOX or DOX-N-GQDs. While the logistic model neglects many of the biological details of the cancer growth process [ 55 ], it has few parameters, thus allowing it to focus its parameter estimation on the effect of the treatment. Modeling suggested a 10% increase in the maximum efficacy of drug treatment when DOX was delivered by N-GQDs.…”

AI in Medical Imaging Informatics: Current Challenges and Future Directions

“…diffusing chemical ingredients, the tissue-multicellular extent of different cell-regions and the macroscopic scale of the tumor volume. The interconnection of the different levels is considered great challenge of in-silico models, through coupling of blood flow, angiogenesis, vascular remodeling, nutrient transport and consumption, as well as movement interactions between normal and cancer cells [219].…”

“…Distinct spatial and temporal scales have been considered, such as the subcellular scale of molecular pathways and gene expressions, the microscopic-cellular level of individual cell’s behavior and phenotypic properties, the microenvironmental scale of the diffusing chemical ingredients, the tissue-multicellular extent of different cell-regions and the macroscopic scale of the tumor volume. The interconnection of the different levels is considered great challenge of in-silico models, through coupling of blood flow, angiogenesis, vascular remodeling, nutrient transport and consumption, as well as movement interactions between normal and cancer cells [ 219 ].…”

Integrative lymph node-mimicking models created with biomaterials and computational tools to study the immune system

“… (1) Cancer diseases biology [ 120 , 121 ]; (2) Immunology and immunotherapy [ [122] , [123] , [124] , [125] , [126] , [127] , [128] ]; (3) Virus infection [ 129 ]; (4) Pharmacodynamics [ 130 ]; (5) Organ disease [ 131 ]; (6) Vascular network [ 132 ] Hybrid multiscale model Integrative model combining several kinds of computational models. (1) Vascular network [ [133] , [134] , [135] ]; (2) Cancer and therapy [ 113 , [136] , [137] , [138] , [139] ]; (3) Disease model [ 140 ]; (4) Immune system (cell and organ) [ 141 ] Lattice model e Model established on a 2D/3D lattice (or grid), as opposed to the continuum of spatial or spatio-temporal coordinates (off-lattice model). (1) Predict protein structure [ 142 ]; (2) Tissue differentiation [ 143 ]; (3) Cell migration [ 102 , 116 ]; (4) Tumor growth [ 144 ] Petri net formalism Directed bipartite graph with two types of elements- places and transitions, to describe discrete-event dynamical systems.…”