ArticleThe EPMA journal2019
Inappropriate modeling of chronic and complex disorders: How to reconsider the approach in the context of predictive, preventive and personalized medicine, and translational medicine.
Article in The EPMA journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
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- Bridging the prevention gap: funding distribution and methodological shifts in prevention-focused biomedical research under EU framework programmes.Journal of translational medicine · 2025Article
- Modeling dry eye with an air-liquid interface in corneal epithelium-on-a-chip.Scientific reports · 2024Article
- Functional genomics in stem cell models: considerations and applications.Frontiers in cell and developmental biology · 2023Review
- Alginate-based 3D cell culture technique to evaluate the half-maximal inhibitory concentration: an in vitro model of anticancer drug study for anaplastic thyroid carcinoma.Thyroid research · 2021Article
- Flavonoids against non-physiologic inflammation attributed to cancer initiation, development, and progression-3PM pathways.The EPMA journal · 2021Review
- Glioma glycolipid metabolism: MSI2-SNORD12B-FIP1L1-ZBTB4 feedback loop as a potential treatment target.Clinical and translational medicine · 2021Article
- Cell-free nucleic acid patterns in disease prediction and monitoring-hype or hope?The EPMA journal · 2020Review
- Pirfenidone: A novel hypothetical treatment for COVID-19.Medical hypotheses · 2020Article
- Prostate cancer management: long-term beliefs, epidemic developments in the early twenty-first century and 3PM dimensional solutions.The EPMA journal · 2020Review
- Identification of pathology-specific regulators of mThe EPMA journal · 2020Article
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- Commentary: Antifibrotics in COVID-19 Lung Disease: Let Us Stay Focused.Frontiers in medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preclinical investigations such as animal modeling make the basis of clinical investigations and subsequently patient care. Predictive, preventive, and personalized medicine (PPPM) not only highlights a patient-tailored approach by choosing the right medication, the right dose at the right time point but it as well essentially requires early identification, by the means of complex and state-of-the-art technologies of unmanifested pathological processes in an individual, in order to deliver targeted prevention early enough to reverse manifestation of a pathology. Such an approach can be achieved by taking into account clinical, pathological, environmental, and psychosocial characteristics of the patients or an individual who has a suboptimal health condition. Inappropriate modeling of chronic and complex disorders, in this context, may diminish the predictive potential and slow down the development of PPPM and consequently modern healthcare. Therefore, it is the common goal of PPPM and translational medicine to find the solution for the problem we present in our review. Both, translational medicine and PPPM in parallel, essentially need accurate surrogates for misleading animal models. This study was therefore undertaken to provide shreds of evidence against the validity of animal models. Limitations of current animal models and drug development strategies based on animal modeling have been systematically discussed. Finally, a variety of potential surrogates have been suggested to change the unfavorable situation in medical research and consequently in healthcare.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.