Modeling Glioblastoma for Translation: Strengths and Pitfalls of Preclinical Studies
Concetta D'Antonio1, Giovanna L Liguori1
1Institute of Genetics and Biophysics (IGB) "Adriano Buzzati-Traverso", National Research Council (CNR) of Italy, 80131 Naples, Italy.
Abstract:
Glioblastoma (GB) is an extremely aggressive tumor for which effective therapy is still in its infancy. Although several candidate therapeutics have been identified in functional preclinical assays, clinical trials have not supported their effectiveness in GB patients. The poor clinical efficacy of the treatments can be attributed to the insufficient mimicry of GB in patients by the preclinical models used. In this review article, we provide a comprehensive overview of the available GB preclinical models, which are classified according to their origin (animal or human), species, type and modeling strategy (two- or three-dimensional cell culture, in vivo grafting or in silico modeling). Moreover, the article compares developing cutting-edge technologies, including GB-derived organoids, bioprinting, microfluidic devices, and their multimodal integration in GB-on-chip systems, which aim to replicate the GB microenvironment with high precision. In silico and in vivo approaches are also reviewed, including zebrafish transplantation models. The costs, benefits, applications and clinical relevance of each model system and/or modeling strategy are discussed in detail and compared. We highlight that the most appropriate, or combination of, GB preclinical models must be selected (or even customized) based on the specific aims and constraints of each study. Finally, to improve the reliability and translational relevance of GB research, we propose a practical roadmap that addresses critical challenges in preclinical assay development, ranging from short-term adjustments to long-term strategic planning.
Insights
Selecting appropriate glioblastoma (GB) preclinical models is crucial for effective therapy development. This review compares various models, highlighting advanced technologies and proposing a roadmap to improve translational relevance for GB research.
Area of Science:
- Oncology
- Biomedical Engineering
- Translational Research
Background:
- Glioblastoma (GB) is a highly aggressive brain tumor with limited therapeutic options.
- Current preclinical models often fail to accurately mimic GB in patients, hindering clinical translation.
- There is a critical need for more predictive and reliable preclinical models for GB drug development.
Purpose of the Study:
- To provide a comprehensive review of existing and emerging glioblastoma preclinical models.
- To compare the strengths, weaknesses, and clinical relevance of various modeling strategies.
- To propose a roadmap for improving preclinical assay development and translational success in GB research.
Main Methods:
- Classification of GB models based on origin, species, type, and strategy (2D/3D cell culture, in vivo, in silico).
- Review and comparison of cutting-edge technologies like organoids, bioprinting, microfluidic devices, and GB-on-chip systems.
- Analysis of in silico and in vivo approaches, including zebrafish transplantation models.
Main Results:
- GB models vary significantly in their ability to replicate the human tumor microenvironment and predict clinical outcomes.
- Emerging technologies offer enhanced precision in mimicking GB complexity, potentially improving preclinical assay reliability.
- The selection of the most appropriate GB model(s) should be tailored to specific research objectives and constraints.
Conclusions:
- No single GB model is universally superior; a combination or customized approach is often necessary.
- Advancements in organoids, GB-on-chip systems, and other novel models show promise for increasing translational relevance.
- A strategic roadmap is proposed to address challenges and enhance the reliability of preclinical glioblastoma research.
