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Updated: Nov 13, 2025
Preparation, Characteristics, Toxicity, and Efficacy Evaluation of the Nasal Self-Assembled Nanoemulsion Tumor Vaccine In Vitro and In Vivo
Published on: September 28, 2022
Preclinical models and technologies to advance nanovaccine development
Carina Peres1, Ana I Matos1, Liane I F Moura2
1Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisbon, Portugal; Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisbon, Portugal.
Cancer nano-based vaccines show promise, but tumor heterogeneity and immunosuppression hinder clinical success. Selecting appropriate preclinical models is crucial for translating promising cancer vaccine research to patient treatments.
Area of Science:
- Oncology
- Immunology
- Nanotechnology
Background:
- Targeted immunotherapies are revolutionizing cancer treatment.
- Cancer vaccines face challenges like tumor heterogeneity, low immunogenicity, and immunosuppression.
- Nanotechnology offers potential for improved cancer vaccine delivery.
Purpose of the Study:
- To review challenges in translating cancer nano-based vaccines to the clinic.
- To discuss the importance of preclinical models for vaccine development.
- To identify requirements for ex vivo and in vivo models to ensure clinical translation.
Main Methods:
- Literature review of cancer nano-based vaccine research.
- Analysis of factors hindering clinical translation.
- Discussion of preclinical model selection criteria.
Main Results:
- Preclinical models show promise for cancer vaccines, but clinical translation remains limited.
- Tumor characteristics and immunosuppressive mechanisms are key barriers.
- Effective preclinical models are essential for predicting clinical outcomes.
Conclusions:
- Translating cancer nano-based vaccines requires overcoming significant hurdles.
- Careful selection and validation of preclinical models are fundamental for success.
- Further research into robust preclinical models is needed to bridge the gap between lab and clinic.
