An intestinal matrix-based human lung cancer model reflects clinical data from gefitinib on dosage-dependent efficacy

Elena Johanna Weigl1, Thorsten Walles2, Sarah Nietzer3

  • 1Department for Functional Materials in Medicine and Dentistry, University Hospital Würzburg, Röntgenring 11, 97070 Würzburg, Germany; Department of Pediatric Surgery, Dr. von Hauner Children's Hospital, University Hospital, LMU Munich, Lindwurmstr. 4, 80337 München, Bayern, Germany.

PubMed

Insights

Three-dimensional (3D) lung cancer models more accurately predict gefitinib efficacy than 2D cultures. This 3D model improves preclinical testing of targeted therapies for pulmonary adenocarcinoma.

Area of Science:

  • Oncology
  • Biomedical Engineering
  • Pharmacology

Background:

  • Lung cancer remains a leading cause of cancer mortality globally.
  • Targeted therapies like gefitinib show promise for pulmonary adenocarcinoma but often underperform in vivo compared to in vitro.
  • Current preclinical models lack accuracy, leading to resistance development and overestimated treatment effects.

Purpose of the Study:

  • To analyze gefitinib pharmacodynamics in a 3D tissue-engineered human lung cancer model.
  • To compare gefitinib efficacy in 3D versus 2D cell culture models.
  • To evaluate a resistant lung cancer cell subpopulation and its potential for epithelial-mesenchymal transition.

Main Methods:

  • Developed a 3D tissue-engineered lung cancer model using HCC827 cells and a resistant subpopulation (HCCres A2).
  • Cultivated cells under dynamic conditions in a bioreactor to mimic clinical settings.
  • Administered gefitinib and assessed cell viability, proliferation, and apoptosis.

Main Results:

  • Gefitinib efficacy was overestimated in 2D cultures (50% viability at 0.01-0.05 μM).
  • Higher gefitinib doses in the 3D model did not significantly reduce viability, proliferation, or increase apoptosis.
  • The 3D model's results closely mirrored published clinical data for gefitinib efficacy.

Conclusions:

  • 3D lung cancer models provide a more accurate preclinical testing platform for targeted therapies.
  • These models bridge the gap between in vitro and in vivo studies, reflecting clinical relevance.
  • The developed 3D model can refine drug testing for pulmonary adenocarcinoma, accounting for resistance mechanisms like epithelial-mesenchymal transition.

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