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A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
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.
Abstract:
Lung cancer is the leading cause for cancer related death worldwide. One treatment option for patients with pulmonary adenocarcinoma is the targeted therapy gefitinib (Iressa®, ZD1839). As with other promising targeted therapies, desired in vivo effects of gefitinib came short to in vitro expectations, as preclinical testing is hampered by an inaccuracy of existing models and resistances develop frequently. We analysed the pharmacodynamics of gefitinib in a 3D tissue engineered human lung cancer model of HCC827 cells, as well as a resistant subpopulation of HCCres A2 cells. In 2D cell culture, a cell viability of 50 % was reached after treatment with 0.01-0.05 μM gefitinib. In the 3D model administering higher doses of gefitinib did not lead to a further reduction in viability and proliferation, or an increase in apoptosis. This shows that the pharmacodynamic effect of promising new therapies can often be overestimated in 2D versus 3D models. The results we obtained for gefitinib testing in the 3D models reflect astonishing well the efficacy of priorly published clinical data, suggesting that our 3D tumor models can display in vivo conditions more accurately, bridging the gap to living organisms. To mimic the clinical setting further, the lung cancer cells were cultivated under dynamic conditions in a bioreactor. The exploration of a resistant subpopulation confirmed an epithelial-mesenchymal transition. The described 3D lung cancer model can be used as a refined, preclinical testing platform for targeted therapies and with clinically relevant pharmacodynamic properties as shown exemplary for gefitinib.
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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