Translational Models for Glioblastoma: Revolutionizing Drug Development and Personalized Medicine through Clinical

Gaeun Lee1, Yu Jin Kim2,3, Sharon Jeeho Ham4

  • 1Department of MetaBioHealth, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea.

Theranostics
|April 17, 2026
PubMed

Insights

Developing advanced glioblastoma (GBM) models, including microfluidic and 3D bioprinted platforms, is crucial for overcoming therapeutic resistance. These innovative systems enhance translational accuracy and accelerate drug discovery for this aggressive brain tumor.

Area of Science:

  • Neuro-oncology
  • Biomedical Engineering
  • Translational Medicine

Background:

  • Glioblastoma (GBM) is a highly aggressive brain tumor with limited treatment options.
  • Conventional preclinical models fail to accurately replicate the complex GBM microenvironment and therapeutic resistance mechanisms.
  • There is a critical need for advanced models that better predict clinical outcomes.

Purpose of the Study:

  • To review recent advances in glioblastoma (GBM) model development.
  • To highlight the potential of microfluidic and 3D bioprinting platforms for GBM research.
  • To discuss the integration of in vitro and in vivo systems for enhanced translational accuracy.

Main Methods:

  • Focus on microfluidic GBM chip models and 3D bioprinted platforms.
  • Summarize conventional 2D cultures, organoids, and animal models (syngeneic, xenograft, GEMMs).
  • Discuss the integration of microengineering, biomaterials, and patient-derived cells.

Main Results:

  • Microfluidic and 3D bioprinting enable controlled reconstruction of the GBM microenvironment.
  • These advanced models offer improved biological relevance and resemblance to in vivo tumor behavior.
  • In vivo systems remain essential for evaluating pharmacokinetics, immune responses, and toxicity.

Conclusions:

  • Advanced GBM models, including microengineered in vitro and in vivo systems, are vital for improving drug discovery and personalized therapies.
  • These platforms enhance translational accuracy by better mimicking human disease.
  • Rational selection and integration of GBM models are key for optimizing next-generation therapeutics.