PROSPECTS, CHALLENGES AND NEW PARADIGMS OF PREDICTIVE MEDICINE
DOI:
https://doi.org/10.11603/mie.1996-1960.2024.3-4.15458Keywords:
predictive medicine, Big Data, predictive analytics, genomics, risk paradigmAbstract
Background. The role of predictive medicine in the formation of future healthcare is analyzed. It is emphasized that among various forecasting methodologies, such as genomics, proteomics and cytomics, the most fundamental method of predicting future diseases is based on genetics. Ethical problems associated with personal responsibility caused by epigenetic tests for predicting the risk of developing diseases are considered. The purpose of the study is to analyze the trend of development of predictive medicine taking into account possible risks and ethical considerations.
Materials and methods. Based on literature sources and systematic reviews, a theoretical analysis was conducted and information was summarized on the role of predictive medicine in the formation of future healthcare, as well as on the fundamental method of predicting future diseases - genetics.
The results of the study were systematized using databases of scientific periodicals: PubMed, Scopus, ScienceDirect, etc. Classical methods of searching and systematizing information were used at different stages of the study.
Results. Developing new treatments with predictive biomarkers to identify patients most or least likely to benefit complicates drug development, even though for many new cancer drugs it is the only scientifically sound approach that should increase the chances of success. Predictive medicine can lead to greater consistency in trial outcomes and has clear advantages in reducing the number of patients who end up receiving expensive drugs that put them at risk of side effects but do not provide benefits.
Conclusions. Predictive medicine has great potential for controlling public health costs. Developing treatments using predictive biomarkers requires major changes in the standard paradigms for planning and analyzing clinical trials. Some of the key assumptions on which current methods are based are no longer valid. The use of predictive medicine methods requires a review of a number of new clinical trial designs for the joint development of treatments and prognostic biomarkers, above all requiring careful prospective planning. It can become the basis for a new generation of predictive clinical trials that provide reliable individualized information.
References
Simon, R. (2010). Clinical trials for predictive medicine: new challenges and paradigms. Clin Trials., 7(5), 516-524. doi: 10.1177/1740774510366454. DOI: https://doi.org/10.1177/1740774510366454
Jen, M. Y., Shahrokhi, M., Varacallo, M. A. (2022). Predictive Medicine. In: StatPearls [Internet]. URL: https://www.ncbi.nlm.nih.gov/books/NBK441941/.
Ginsburg, G. S., Willard, H. F. (2009). Genomic and personalized medicine: foundations and applications. Transl Res., 154(6), 277-287. doi: 10.1016/j.trsl.2009.09.005. DOI: https://doi.org/10.1016/j.trsl.2009.09.005
Williams, D. (2004). The critical path to medical innovation. Med. Device Technol., 5, 8–10.
Morel, N. M., Holland, J. M., van der Greef, J. et al. (2004). Primer on medical genomics. Part XIV: Introduction to systems biology - a new approach to understanding disease and treatment. Mayo Clin Proc., 79(5), 651-658. doi: 10.4065/79.5.651. DOI: https://doi.org/10.4065/79.5.651
Kihlbom, U., Hansson, M. G., Schicktanz, S. (2021). Ethical, social and psychological impacts of genomic risk communication. Abingdon, Oxon; Routledge. DOI: https://doi.org/10.4324/9780429341038
Pharmacogenomics. Knowledge. Implementation. URL: https://web.archive.org/web/20130513122722/ http:/www.pharmgkb.org/ .
Dondorp, W. J., de Wert, G. M. (2013). The ‘thousand-dollar genome’: an ethical exploration. Eur J Hum Genet., 21, Suppl 1, S6-26. doi: 10.1038/ ejhg.2013.73. DOI: https://doi.org/10.1038/ejhg.2013.73
Wilson, J. M. G., Jungner, G. (1968). Principles and practice of screening for disease. Geneva: WHO. URL: http://www.who.int/bulletin/volumes/86/4/07- 050112BP.pdf.
Malachynska, M. Y. (2023). Neonatalʹnyy skryninh yak skladnyk derzhavnoyi polityky okhorony zdorovʺya: pryklady mizhnarodnoho dosvidu ta adaptatsiya v Ukrayini. [Neonatal screening as a component of state health policy: examples of international experience and adaptation in Ukraine].
Vcheni zapysky TNU imeni V. I. Vernadsʹkoho. Seriya: Publichne upravlinnya ta administruvannya. [Scientific notes of the V. I. Vernadsky TNU. Series: Public management and administration], Vol. 34, 3 (73), 50-57. doi:10.32782/TNU-2663-6468/2023.3/09. [In Ukrainian]. DOI: https://doi.org/10.32782/TNU-2663-6468/2023.3/09
Pro vprovadzhennya rozshyrenoho neonatalʹnoho skryninhu v Ukrayini. [On the introduction of expanded neonatal screening in Ukraine]. Order of the Ministry of Health dated March 29, 2023 No. 588. URL: https://zakon.rada.gov.ua/rada/show/v0588282-23#Text. [In Ukrainian].
Pro zabezpechennya rozshyrenoho neonatalʹnoho skryninhu v Ukrayini. [On ensuring expanded neonatal screening in Ukraine]. Order of the Ministry of Health of Ukraine dated 01.10.2021 No. 2142. URL: https://zakon.rada.gov.ua/laws/show/z1403-21#Text. [In Ukrainian].
Stovitz, S. D. (2020). The Inability to Calculate Predictive Values: An Old Problem that Has Not Gone Away. Med Sci Educ., 30(2), 685–688. doi: 10.1007/s40670-020-00954-9. DOI: https://doi.org/10.1007/s40670-020-00954-9
Peabody, F. W. (1922). The physician and the laboratory. Bost Med Surg J [Internet], 187(9), 324–327. doi:10.1056/NEJM192208311870905. DOI: https://doi.org/10.1056/NEJM192208311870905
Casscells, W., Schoenberger, A., Graboys, T. B. (1978). Interpretation by physicians of clinical laboratory results. N Engl J Med [Internet], 299(18), 999–1001. URL: http://www.ncbi.nlm.nih.gov/pubmed/692627. DOI: https://doi.org/10.1056/NEJM197811022991808
Zhang, Z. (2020). Predictive analytics in the era of big data: opportunities and challenges. Ann Transl Med., 8(4), 68. doi:10.21037/atm.2019.10.97. DOI: https://doi.org/10.21037/atm.2019.10.97
Collins, G. S., Reitsma, J. B., Altman, D. G., Moons, K. G. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 350, g7594. doi:10.1136/bmj.g7594. DOI: https://doi.org/10.1136/bmj.g7594
Adhikari, L., Ozrazgat-Baslanti, T., Ruppert, M. et al. (2019). I Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics. PLoS One, 14, e 0214904. doi:10.1371/journal.pone.0214904. DOI: https://doi.org/10.1371/journal.pone.0214904
Al-Quraishi, T., Al-Quraishi, N., AlNabulsi, H. et al. (2024). Big Data Predictive Analytics for Personalized Medicine: Perspectives and Challenges. Applied Data Science and Analysis, 32-38. doi:10.58496/ADSA/2024/004. DOI: https://doi.org/10.58496/ADSA/2024/004
Downloads
Published
How to Cite
Issue
Section
License
Journal Medical Informatics and Engineering allows the author(s) to hold the copyright without registration
The majority of Medical Informatics and Engineering Open Access journals publish open access articles under the terms of the Creative Commons Attribution (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited. The remaining journals offer a choice of licenses.
This journal is available through Creative Commons (CC) License CC-BY 4.0
