Computational fluid dynamics modeling and simulation of nanoparticle-tumor interaction: Systematic literature review

Cancer therapy mediated by nanoparticles is gaining recognition for shifting the paradigm of targeted and/or personalized cancer therapy. Despite the great promise, only a few nanoformulations have been clinically approved due to the complexities that limit effective and efficient nanodrug development. Moreover, the preparation of cancer nanodrug has not yet been optimized for clinical approval in patient treatment. Computational fluid dynamics (CFD) is a new technique that simulates and analyzes fluid flows and their interactions with surfaces using computer algorithms and numerical analysis. This simulation and modeling tool provides a distinct advantage in understanding tumor-host mechano-biology and mechanisms that help identify the main factors affecting the transport of tumor-targeting nanoagents. Taking these factors into consideration, the advent of computational fluid dynamics simulation and modeling represents a shift in the optimization of cancer nanoagents' fluidics. This review briefly introduces the fluid mechanism along with its principles and foundations relating to cancer drug delivery. Key components of tumor microenvironments relating to temperature, flow velocity, fluid pressure, and tumor rheology, as well as physicochemical properties of nanoparticles modulating fluid mechanics, were discussed. It also includes a thorough examination of the advantages and challenges of using nanoformulations such as liposomes, polymers, and extracellular matrix in exploring the progress made in computational fluid dynamics simulation to study the mechanism of nanoparticle delivery and interactions with cancerous tumors. The convergence of Machine Learning algorithms and CFD simulation in tumor-nanodrug interactions. The application of ML algorithms provides high predictive accuracy of nanodrug delivery that can benefit cancer biomedicine research by predicting how flow affects drug efficacy. The future of the ML-CFD is detailed to include imaging and 3D-CFD simulations to increase the credibility of these models and advancement to translational clinical research. This review concluded by urging collaborative efforts for a multiscale approach by biomedical engineers and scientists, as well as oncologists, to develop a modeling framework that advances precision medical care for effective cancer treatment. Standardization of the model and approaches, together with nanoparticle synthesis, is recommended to advance this research to the translational and clinical stage.