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Navigating Cancer Treatment: A Journey from 2D to 3D Cancer Models and Nanoscale Therapies

Kaur N, Savitsky MJ, Scutte A, Mehta R, Dridi N, Sogbesan T, Savannah A, Lopez D et al.

ACS pharmacology & translational science, 2025

9 citations

Abstract

Cancer remains one of the most significant global health challenges, characterized by an increasing incidence and mortality rate worldwide. In vitro models play a significant role in the initial investigations of cancer biology, drug screening, and therapeutic development. However, although widely used, conventional 2D cell cultures fail to replicate the complex tumor microenvironment, leading to discrepancies in drug response and therapeutic efficacy. This perspective explores the transition from 2D to 3D culture models, highlighting their advantages, limitations, and impact on cancer research. Various 3D culture approaches, including scaffold-based systems, hydrogels, 3D-printed models, microfluidics, and organ-on-a-chip technologies, are discussed in terms of their relevance to cancer modeling and drug testing. Additionally, the review also highlights the integration of theranostic nano- and microparticles in cancer treatment, focusing on their application in drug delivery and interactions with 3D tumor spheroids and organoids. A comparative analysis of uptake mechanisms and interactions between particles and 3D models is presented along with advanced techniques for probing nanoparticle behavior and drug screening in these models. By bridging the gap between in vitro assays and clinical outcomes, 3D culture systems integrated with nanotechnology offer promising tools for improving cancer therapeutics.

How strong is the evidence?

Evidence Hub reads this as a review, done in cell cultures (in vitro), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

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