Could 3D models of cancer enhance drug screening?

Virginia Brancato1, Joaquim Miguel Oliveira2, Vitor Manuel Correlo2

  • 13B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics, University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Parque de Ciência e Tecnologia, Zona Industrial da Gandra, 4805-017, Barco, Guimarães, Portugal; ICVS/3B's-PT Government Associate Laboratory, Braga, Guimarães, Portugal.

Biomaterials
|January 10, 2020
PubMed

Insights

Three dimensional in vitro cancer models offer a more accurate preclinical testing platform than traditional 2D cultures and animal models. These advanced models improve drug efficacy and toxicity predictions, accelerating cancer drug development.

Area of Science:

  • Oncology
  • Biotechnology
  • Drug Discovery

Background:

  • Current 2D cell cultures and animal models inadequately represent cancer's complexity and tumor microenvironment.
  • Existing methods fail to accurately predict anti-cancer drug efficacy, toxicity, and metastatic progression.
  • Significant resources are lost in drug discovery due to low success rates in predicting clinical outcomes.

Purpose of the Study:

  • To review state-of-the-art three-dimensional (3D) in vitro cancer models for preclinical drug testing.
  • To highlight the potential of 3D models in improving the predictability of drug response and toxicity.
  • To discuss strategies for standardizing 3D models for reliable preclinical trials and personalized cancer care.

Main Methods:

  • Review of current literature on 3D in vitro cancer models.
  • Analysis of the advantages of 3D models over 2D cultures and animal models.
  • Discussion on the integration of different 3D approaches for standardization and reliability.

Main Results:

  • 3D in vitro cancer models better recapitulate tumor microenvironment complexity and cellular interactions.
  • These models demonstrate improved prediction of drug sensitivity and toxicity compared to traditional methods.
  • Standardization challenges remain but coupling different 3D approaches offers a promising solution.

Conclusions:

  • 3D in vitro models are crucial for enhancing the accuracy of preclinical cancer drug testing.
  • Adoption of standardized 3D models can accelerate the development of effective and personalized cancer therapies.
  • Further research into standardization and integration of 3D systems is needed to optimize cancer drug development pipelines.