Buy (150 RUB)
|Generate QR code
Open Access
Subscription or Fee Access
Personalized medicine: current trends and prospects
https://doi.org/10.21508/1027-4065-2022-67-4-14-21
Abstract
«Personalized» medicine is based on the belief that each person has unique molecular, physiological, environmental, and behavioral characteristics, and in case of disease, each patient should be treated taking into account these unique characteristics. This belief was to some extent confirmed by the use of the latest technologies, such as DNA sequencing, proteomics, imaging protocols and the use of wireless devices for health monitoring, which revealed large inter-individual differences. Literary sources (scientific articles) were searched, including those published in peer-reviewed journals indexed in PubMed, Wos, Scopus, and the Russian Science Citation Index. The review includes 49 articles on personalized medicine. It explores new technologies that make personalized medicine possible, new experiences, ways to test and apply individualized drugs, and potential treatments for people with fertility and infertility issues. It can be argued that the individualization of medical practice in certain cases is probably inevitable. Moreover, an individual approach to a patient becomes more efficient and cost-effective.
About the Authors
I. S. Dolgopolov
Tver State Medical University
Russian Federation
Russian Federation
Tver
M. Yu. Rykov
Tver State Medical University
Russian Federation
Russian Federation
Tver
References
1. Schork N.J. Personalized medicine: Time for one-person trials. Nature 2015; 520(7549): 609-611. DOI: 10.1038/520609a
2. Lillie E.O., Patay B., Diamant J., Issell B., Topol E.J., Schork N.J. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per Med 2011; 8(2): 161-173. DOI: 10.2217/pme.11.7
3. Duan N., Kravitz R.L., Schmid C.H. Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research. J Clin Epidemiol 2013; 66(8 Suppl): S21-28. DOI: 10.1016/j.jclinepi.2013.04.006
4. Scuffham P.A., Nikles J., Mitchell G.K., Yelland M.J., Vine N., Poulos C.J. et al. Using N-of-1 trials to improve patient management and save costs. J Gen Inter Med 2010; 25(9): 906-913. DOI: 10.1007/s11606-010-1352-7
5. Daza E.J. Causal analysis of self-tracked time series data using a counterfactual framework for N-of-1 trials. Meth Inform Med 2018; 57(1): e10-e21. DOI: 10.3414/ME16-02-0044
6. Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data 2018; 1(2): 85-99. DOI: 10.1089/big.2012.0002
7. Biankin A.V., Piantadosi S., Hollingsworth S.J. Patient-centric trials for therapeutic development in precision oncology. Nature 2015; 526(7573): 361-370. DOI: 10.1038/nature15819
8. Simon R., Roychowdhury S. Implementing personalized cancer genomics in clinical trials. Nat Rev Drug Discov 2013; 12(5): 358-369. DOI: 10.1038/nrd3979
9. Chen Y., Elenee Argentinis J.D., Weber G. IBM Watson: How cognitive computing can be applied to big data challenges in life sciences research. Clin Ther 2016; 38(4) :688-701. DOI: 10.1016/j.clinthera.2015.12.001
10. Klasnja P., Hekler E.B., Shiffman S., Boruvka A., Almirall D., Tewari A. et al. Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychol 2015; 34S: 1220-1228. DOI: 10.1037/hea0000305
11. Laber E.B., Lizotte D.J., Qian M., Pelham W.E., Murphy S.A. Dynamic treatment regimens: technical challenges and applications. Electron J Stat 2014; 8(1): 1225-1572. DOI: 10.1214/14-ejs920
12. Chakraborty B., Murphy S.A. Dynamic Treatment Regimes. Annu Rev Stat Appl 2014; 1: 447-464. DOI: 10.1146/annurev-statistics-022513-115553
13. Takahashi K., Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 2006; 126(4): 663-676. DOI: 10.1016/j.cell.2006.07.024
14. Beltrao-Braga P.C., Pignatari G.C., Russo F.B., Fernandes I.R., Muotri A.R. In-a-dish: induced pluripotent stem cells as a novel model for human diseases. Cytometry A 2013; 83(1): 11-17. DOI: 10.1002/cyto.a.22231
15. Sayed N., Liu C., Wu J.C. Translation of human-induced pluripotent stem cells: from clinical trial in a dish to precision medicine. J Am Coll Cardiol 2016; 67(18) :2161-2176. DOI: 10.1016/j.jacc.2016.01.083
16. Wu J., Izpisua Belmonte J.C. Stem Cells: A renaissance in human biology research. Cell 2016; 165(7): 1572-1585. DOI: 10.1016/j.cell.2016.05.043
17. Uppada V., Gokara M., Rasineni G.K. Diagnosis and therapy with CRISPR advanced CRISPR based tools for point of care diagnostics and early therapies. Gene 2018; 656: 22-29. DOI: 10.1016/j.gene.2018.02.066
18. Ho B.X., Pek N.M.Q., Soh B.S. Disease modeling using 3D organoids derived from human induced pluripotent stem cells. Int J Mol Sci 2018; 19(4): 936. DOI: 10.3390/ijms19040936
19. Aboulkheyr E.H., Montazeri L., Aref A.R., Vosough M., Baharvand H. Personalized cancer medicine: an organoid approach. Trends Biotechnol 2018; 36(4): 358-371. DOI: 10.1016/j.tibtech.2017.12.005
20. Crystal A.S., Shaw A.T., Sequist L.V., Friboulet L., Niederst M.J., Lockerman E.L. et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science. 2014; 346(6216): 1480-1486. DOI: 10.1126/science.1254721
21. Jonas O., Landry H.M., Fuller J.E., Santini J.T. Jr, Baselga J., Tepper R.I. et al. An implantable microdevice to perform high-throughput in vivo drug sensitivity testing in tumors. Sci Transl Med 2015; 7(284): 284ra57. DOI: 10.1126/scitranslmed.3010564
22. Klinghoffer R.A., Bahrami S.B., Hatton B.A., Frazier J.P., Moreno-Gonzalez A., Strand A.D. et al. A technology platform to assess multiple cancer agents simultaneously within a patient’s tumor. Sci Transl Med 2015; 7(284): 284ra58. DOI: 10.1126/scitranslmed.aaa7489
23. Robinton D.A., Daley G.Q. The promise of induced pluripotent stem cells in research and therapy. Nature 2012; 481(7381): 295-305. DOI: 10.1038/nature10761
24. Appelboom G., Camacho E., Abraham M.E., Bruce S.S., Dumont E.L., Zacharia B.E. et al. Smart wearable body sensors for patient self-assessment and monitoring. Arch Public Health 2014; 72(1): 28. DOI: 10.1186/2049-3258-72-28
25. Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data 2013; 1(2): 85-99. DOI: 10.1089/big.2012.0002
26. Schork N.J., Nazor K. Integrated genomic medicine: a paradigm for rare diseases and beyond. Adv Genet 2017; 97: 81-113. DOI: 10.1016/bs.adgen.2017.06.001
27. Worthey E.A., Mayer A.N., Syverson G.D., Helbling D., Bonacci B.B., Decker B., Serpe J.M. et al. Making a definitive diagnosis: successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease. Genet Med 2011; 13(3): 255-262. DOI: 10.1097/GIM.0b013e3182088158
28. Bainbridge M.N., Wiszniewski W., Murdock D.R., Friedman J., Gonzaga-Jauregui C., Newsham I. et al. Whole-genome sequencing for optimized patient management. Sci Transl Med 2011; 3(87): 87re3. DOI: 10.1126/scitranslmed.3002243
29. O’Rawe J.A., Fang H., Rynearson S., Robison R., Kiruluta E.S., Higgins G. et al. Integrating precision medicine in the study and clinical treatment of a severely mentally ill person. Peer J 2013; 1: e177. DOI: 10.7717/peerj.177
30. Chen Y.Z., Friedman J.R., Chen D.H., Chan G.C., Bloss C.S., Hisama F.M. et al. Gain-of-function ADCY5 mutations in familial dyskinesia with facial myokymia. Ann Neurol 2014; 75(4): 542-549. DOI: 10.1002/ana.24119
31. Wartman L.D. A case of me: clinical cancer sequencing and the future of precision medicine. Cold Spring Harb Mol Case Stud 2015; 1(1): a000349. DOI: 10.1101/mcs.a000349
32. Chen R., Mias G.I., Li-Pook-Than J., Jiang L., Lam H.Y., Chen R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012; 148(6): 1293-1307. DOI: 10.1016/j.cell.2012.02.009
33. Smarr L. Quantifying your body: a how-to guide from a systems biology perspective. Biotechnol J 2012; 7(8): 980-991. DOI: 10.1002/biot.201100495
34. David L.A., Materna A.C., Friedman J., Campos-Baptista M.I., Blackburn M.C., Perrotta A. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol 2014; 15(7): R89. DOI: 10.1186/gb-2014-15-7-r89
35. Forsdyke D.R. Summertime dosage-dependent hypersensitivity to an angiotensin II receptor blocker. BMC Res Notes 2015; 8:227. DOI: 10.7287/peerj.preprints.144v2. DOI: 10.1186/s13104-015-1215-8
36. Trammell S.A., Schmidt M.S., Weidemann B.J., Redpath P., Jaksch F., Dellinger R.W. et al. Nicotinamide riboside is uniquely and orally bioavailable in mice and humans. Nat Commun 2016; 7: 12948. DOI: 10.1038/ncomms12948
37. Schork N.J. Genetic parts to a preventive medicine whole. Genome Med 2013; 5(6): 54. DOI: 10.1186/gm458
38. Patel C.J., Sivadas A., Tabassum R., Preeprem T., Zhao J., Arafat D. et al. Whole genome sequencing in support of wellness and health maintenance. Genome Med 2013; 5(6): 58. DOI: 10.1186/gm462
39. Sverdlov O., van Dam J., Hannesdottir K., Thornton-Wells T. Digital Therapeutics: An Integral Component of Digital Innovation in Drug Development. Clin Pharmacol Ther 2018;104(1): 72-80. DOI: 10.1002/cpt.1036
40. Kaner E.F., Beyer F.R., Garnett C., Crane D., Brown J., Muirhead C. et al. Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community-dwelling populations. Cochrane Database Syst Rev 2017; 9(9): CD011479. DOI: 10.1002/14651858.CD011479.pub2
41. Iacoviello B.M., Steinerman J.R., Klein D.B., Silver T.L., Berger A.G., Luo S.X. et al. Clickotine, A Personalized Smartphone App for Smoking Cessation: Initial Evaluation. JMIR Mhealth Uhealth 2017; 5(4): e56. DOI: 10.2196/mhealth.7226
42. Jungheim E.S., Carson K.R. Leveraging real-world data to move toward more personalized fertility treatment. Fertil Steril 2018; 109(4): 608-609. DOI: 10.1016/j.fertnstert.2018.01.036
43. van Dijk M.R., Koster M.P.H., Willemsen S.P., Huijgen N.A., Laven J.S.E., Steegers-Theunissen R.P.M. Healthy preconception nutrition and lifestyle using personalized mobile health coaching is associated with enhanced pregnancy chance. Reprod Biomed Online 2017; 35(4): 453-460. DOI: 10.1016/j.rbmo.2017.06.014
44. Yurttas Beim P., Parfitt D.E., Tan L., Sugarman E.A., Hu-Seliger T., Clementi C. et al. At the dawn of personalized reproductive medicine: opportunities and challenges with incorporating multigene panel testing into fertility care. J Assist Reprod Genet 2017; 34(12): 1573-1576. DOI: 10.1007/s10815-017-1068-2
45. DeAngelis A.M., Roy-O’Reilly M., Rodriguez A. Genetic alterations affecting cholesterol metabolism and human fertility. Biol Reprod 2014; 91(5): 117. DOI: 10.1095/biolreprod.114.119883
46. Jungheim E.S., Meyer M.F., Broughton D.E. Best practices for controlled ovarian stimulation in in vitro fertilization. Semin Reprod Med 2015; 33(2): 77-82. DOI: 10.1055/s-0035-1546424
47. Tao T., Del Valle A. Human oocyte and ovarian tissue cryopreservation and its application. J Assist Reprod Genet 2008; 25(7): 287-296. DOI: 10.1007/s10815-008-9236-z
48. Geel T.M., Ruiters M.H.J., Cool R.H., Halby L., Voshart D.C., Andrade Ruiz L. et al. The past and presence of gene targeting: from chemicals and DNA via proteins to RNA. Philos Trans R Soc Lond B Biol Sci 2018; 373(1748): 20170077. DOI: 10.1098/rstb.2017.0077
49. Nagamatsu G., Hayashi K. Stem cells, in vitro gametogenesis and male fertility. Reproduction 2017; 154(6): F79-F91. DOI: 10.1530/REP-17-0510
Review
For citations:
Dolgopolov I.S., Rykov M.Yu. Personalized medicine: current trends and prospects. Rossiyskiy Vestnik Perinatologii i Pediatrii (Russian Bulletin of Perinatology and Pediatrics). 2022;67(4):14-21. (In Russ.) https://doi.org/10.21508/1027-4065-2022-67-4-14-21
Views:
1236
Email this article 