Towards personalized medicine with a three-dimensional micro-scale perfusion-based two-chamber tissue model system

Liang Ma1, Jeremy Barker, Changchun Zhou

  • 1Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.

Biomaterials
|March 21, 2012
PubMed

Insights

A novel 3D-microPTC system models liver metabolism and brain cancer, enhancing anticancer drug cytotoxicity testing. This platform improves prediction of drug efficacy and dosage for personalized medicine.

Area of Science:

  • Biomedical Engineering
  • Pharmacology
  • Oncology

Background:

  • Current 2D cell culture models lack physiological relevance for drug testing.
  • Liver metabolism significantly influences anticancer drug efficacy and toxicity.
  • Glioblastoma multiforme (GBM) drug response is complex and requires better in vitro models.

Purpose of the Study:

  • To develop and validate a three-dimensional micro-scale perfusion-based two-chamber (3D-microPTC) tissue model.
  • To assess the metabolism-dependent cytotoxicity of anticancer drugs on GBM cells using the 3D-microPTC system.
  • To compare the predictive power of the 3D-microPTC model against traditional 2D cell cultures.

Main Methods:

  • Fabrication of a 3D tissue engineering scaffold using biodegradable poly(lactic acid) (PLA).
  • Co-culture of liver cells with varying cytochrome P450 (CYP) subtypes and GBM cells in a tandem two-chamber system.
  • Testing cytotoxicity of temozolomide (TMZ) and ifosfamide (IFO) in the 3D-microPTC model and 2D cultures.

Main Results:

  • TMZ exhibited lower toxicity to GBM cells in the 3D model compared to 2D cultures, with higher GBM cell viability.
  • IFO's metabolism-dependent cytotoxicity was strongly influenced by CYP3A4 expression levels in liver cells.
  • The 3D-microPTC system demonstrated significant metabolism-dependent effects on drug-induced GBM cell death.

Conclusions:

  • The 3D-microPTC system offers a more physiologically relevant in vitro platform for evaluating drug metabolism and toxicity.
  • This model system can enhance the prediction of anticancer drug dosing and scheduling for personalized medicine.
  • The developed model holds promise for improving preclinical drug screening and reducing attrition rates in drug development.

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