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Evaluating Translational Methods for Personalized Medicine-A Scoping Review.
Vibeke Fosse1, Emanuela Oldoni2, Chiara Gerardi3
1Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway.
Journal of Personalized Medicine
|July 27, 2022
Summary
Current preclinical models for personalized medicine, particularly in oncology and brain disorders, require significant development and validation. Addressing gaps in model relevance, quality, and translation is crucial for advancing predictive translational research.
Area of Science:
- Biomedical research
- Translational medicine
- Disease modeling
Background:
- Personalized medicine relies on multi-omics data, presenting challenges for disease modeling.
- Stratified medicine approaches require robust preclinical methodologies for evaluating treatment efficacy.
Purpose of the Study:
- To evaluate the relevance, validity, and predictive value of current preclinical methodologies in personalized medicine.
- To identify gaps and limitations in preclinical research for oncology and brain disorders.
Main Methods:
- A scoping review following Joanna Briggs Institute guidelines.
- Searched PubMed, EMBASE, and other databases for preclinical models in personalized medicine.
- Quantitative and qualitative synthesis of 63 oncology and 94 brain disorder studies.
Main Results:
- Existing preclinical models need further development and validation for personalized medicine.
- Gaps identified in experimental model relevance, quality assessment, reporting, and regulation.
- A significant gap exists between preclinical research and clinical application.
Conclusions:
- Sophisticated biological systems are needed to assess integrated response mechanisms in personalized medicine.
- Fundamental deficits in preclinical research must be addressed for effective implementation of predictive translational models.
- Further validation and development of preclinical models are essential for realizing the potential of personalized medicine.
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