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Generation of Human 3D Lung Tissue Cultures 3D-LTCs for Disease Modeling
Published on: February 12, 2019
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Advanced models for respiratory disease and drug studies
Jesus Shrestha1, Keshav Raj Paudel2, Hojjatollah Nazari1
1School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.
Medicinal Research Reviews
|April 29, 2023
Summary
Respiratory disease drug development faces high failure rates. This review explores animal models and advanced cell cultures like organoids and lung-on-a-chip to improve preclinical screening for effective treatments.
Area of Science:
- Pulmonary Medicine
- Pharmacology
- Toxicology
Background:
- Respiratory diseases pose a significant global health burden, exacerbated by pandemics, pollution, and wildfires.
- High drug failure rates in clinical trials necessitate improved preclinical screening methods.
- Current drug development for respiratory conditions is costly and inefficient.
Purpose of the Study:
- To review existing animal models for respiratory disease research and drug evaluation.
- To highlight advancements in cell culture technologies for preclinical screening.
- To discuss challenges and future directions in respiratory drug development models.
Main Methods:
- Review of literature on animal models for pulmonary diseases.
- Analysis of emerging cell culture technologies (organoids, lung-on-a-chip).
- Discussion of model utility in recapitulating human disease physiology and drug responses.
Main Results:
- Animal models offer physiological complexity and disease phenotypes relevant to respiratory diseases.
- Organoids and lung-on-a-chip models show promise in mimicking human lung function and drug responses.
- These advanced models can potentially improve the accuracy of preclinical drug screening.
Conclusions:
- There is a critical need for more efficient and accurate preclinical models in respiratory drug development.
- Advanced cell culture technologies offer promising alternatives or complements to traditional animal models.
- Integrating various models may enhance the prediction of clinical efficacy and reduce drug development costs.
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