Skip to main content
SpringerLink
Log in

A Proposed Framework for Digital Twins Driven Precision Medicine Platform: Values and Challenges

  • Chapter
  • First Online:
Book cover Digital Twins for Digital Transformation: Innovation in Industry

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 423))

Abstract

Precision medicine (PM) or personalized medicine analyzes huge data from the relationship between certain genes and several drugs. In this regard, Digital Twins (DT) will allow medical professionals to run controlled, repeatable trials to discover how outcomes differ for different interventions. The development of DT could critically advance the implementation necessary for the innovative, individualized management of any disease through artificial intelligence-based analysis of many disease parameters. This can provide more personalized and effective care by integrating data from multiple sources in a unified. This chapter presents a new framework of the DT in precision medicine. It provides the recent associated technologies with personalized medicine, including artificial intelligence, the internet of things, cloud computing, and cyber-physical system. An architecture for a DT-based intelligent wellness augmentation platform is discussed. Applications of DT in PM, challenges and future trends are addressed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Težak, Ž, Kondratovich, M.V., Mansfield, E.: US FDA and personalized medicine: in vitro diagnostic regulatory perspective. Per. Med. 7(5), 517–530 (2010). https://doi.org/10.2217/pme.10.53

    Article  Google Scholar 

  2. Sykiotis, G.P., Kalliolias, G.D., Papavassiliou, A.G.: Pharmacogenetic principles in the Hippocratic writings. J. Clin. Pharmacol. 45(11), 1218–1220 (2005). https://doi.org/10.1177/0091270005281091

    Article  Google Scholar 

  3. Sadée, W., Dai, Z.: Pharmacogenetics/genomics and personalized medicine. Hum. Mol. Genet. 14(SUPPL. 2), 207–214 (2005). https://doi.org/10.1093/hmg/ddi261

    Article  Google Scholar 

  4. Meyer, J.M., Ginsburg, G.S.: The path to personalized medicine. Curr. Opin. Chem. Biol. 6(4), 434–438 (2002). https://doi.org/10.1016/S1367-5931(02)00340-X

    Article  Google Scholar 

  5. Hammerstrom, A.E., Cauley, D.H., Atkinson, B.J., Sharma, P.: Cancer immunotherapy: Sipuleucel-T and beyond. Pharmacotherapy 31(8), 813–828 (2011). https://doi.org/10.1592/phco.31.8.813

    Article  Google Scholar 

  6. Mali, P., Esvelt, K.M., Church, G.M.: Cas9 as a versatile tool for engineering biology. Nat. Methods 10(10), 957–963 (2013). https://doi.org/10.1038/nmeth.2649

    Article  Google Scholar 

  7. Björnsson, B., et al.: Digital twins to personalize medicine. Genome Med. 12(1), 10–13 (2019). https://doi.org/10.1186/s13073-019-0701-3

    Article  Google Scholar 

  8. Wan, J.C.M., et al.: Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer 17(4), 223–238 (2017). https://doi.org/10.1038/nrc.2017.7

    Article  Google Scholar 

  9. Batty, M.: Digital twins. Environ. Plan. B Urban Anal. City Sci. 45(5), 817–820 (2018). https://doi.org/10.1177/2399808318796416

    Article  Google Scholar 

  10. Peleg, M., et al.: Ideating mobile health behavioral support for compliance to therapy for patients with chronic disease: a case study of atrial fibrillation management. J. Med. Syst. 42(11), 234 (2018). https://doi.org/10.1007/s10916-018-1077-4

  11. Mazumder, O., Roy, D., Bhattacharya, S., Sinha, A., Pal, A.: Synthetic PPG generation from haemodynamic model with baroreflex autoregulation: a Digital twin of cardiovascular system. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5024–5029 (2019). https://doi.org/10.1109/EMBC.2019.8856691

  12. Shengli, W.: Is human digital twin possible? Comput. Methods Programs Biomed. Updat. 1, 100014 (2021).https://doi.org/10.1016/j.cmpbup.2021.100014

  13. Bruynseels, K., de Sio, F.S., van den Hoven, J.: Digital Twins in health care: ethical implications of an emerging engineering paradigm. Front. Genet. 9(FEB), 1–11 (2018). https://doi.org/10.3389/fgene.2018.00031

  14. Ritto, T.G., Rochinha, F.A.: Digital twin, physics-based model, and machine learning applied to damage detection in structures. Mech. Syst. Signal Process., 155, 107614 (2021)

    Google Scholar 

  15. Tobón Vallejo, D., El Saddik, A.: Emotional states detection approaches based on physiological signals for healthcare applications: a review. In: El Saddik, A., Hossain, M., Kantarci, B. (eds.) Connected Health in Smart Cities. Springer, “No Title,” Tobón Vallejo D., El Saddik A. Emot. States Detect. Approaches Based Physiol. Signals Healthc. Appl. A Rev. El Saddik A., Hossain M., Kantarci B. Connect. Heal. Smart Cities. Springer, Cham., vol. 2020. https://doi.org/10.1007/978-3-030-27844-1_4

  16. Barnes, C., Mercer, G.: Exploring Disability, 2nd edn (2011)

    Google Scholar 

  17. Lareyre, F., Adam, C., Carrier, M., Raffort, J.: Using digital twins for precision medicine in vascular surgery. Ann. Vasc. Surg. 67, e577–e578 (2020). https://doi.org/10.1016/j.avsg.2020.04.042

    Article  Google Scholar 

  18. Villalonga, A., et al.: A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins. Annu. Rev. Control 51, 357–373 (2021). https://doi.org/10.1016/j.arcontrol.2021.04.008

    Article  Google Scholar 

  19. Canedo, A.: Industrial IoT lifecycle via digital twins. In: 2016 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS) , p. 1 (2016)

    Google Scholar 

  20. Edwards, T.L., Breeyear, J., Piekos, J.A., Velez Edwards, D.R.: Equity in health: consideration of race and ethnicity in precision medicine. Trends Genet. 36(11), 807–809 (2020). https://doi.org/10.1016/j.tig.2020.07.001

  21. Mishra, V., et al.: Health inequalities during COVID-19 and their effects on morbidity and mortality. J. Healthc. Leadersh. 13, 19–26 (2021). https://doi.org/10.2147/JHL.S270175

    Article  Google Scholar 

  22. Nastic, S., Sehic, S., Le, D.H., Truong, H.L., Dustdar, S.: Provisioning software-defined IoT cloud systems. In: Proc. - 2014 Int. Conf. Futur. Internet Things Cloud, FiCloud 2014, pp. 288–295 (2014). https://doi.org/10.1109/FiCloud.2014.52

  23. Fortino, G., Guerrieri, A., Russo, W., Savaglio, C.: Integration of agent-based and Cloud Computing for the smart objects-oriented IoT. In: Proc. 2014 IEEE 18th Int. Conf. Comput. Support. Coop. Work Des. CSCWD 2014, pp. 493–498 (2014). https://doi.org/10.1109/CSCWD.2014.6846894

  24. Hu, L., et al.: Modeling of cloud-based digital twins for smart manufacturing with MT connect. Procedia Manuf. 26, 1193–1203 (2018). https://doi.org/10.1016/j.promfg.2018.07.155

    Article  Google Scholar 

  25. Schork, N.J.: Artificial intelligence and personalized medicine. Cancer Treat. Res. 178, 265–283 (2019). https://doi.org/10.1007/978-3-030-16391-4_11

    Article  Google Scholar 

  26. Shaban-Nejad, A., Michalowski, M., Peek, N., Brownstein, J.S., Buckeridge, D.L.: Seven pillars of precision digital health and medicine. Artif. Intell. Med. 103, 101793 (2020).https://doi.org/10.1016/j.artmed.2020.101793

  27. Leslie, D., Mazumder, A., Peppin, A., Wolters, M.K., Hagerty, A.: Does ‘AI’ stand for augmenting inequality in the era of covid-19 healthcare? BMJ 372 (2021). https://doi.org/10.1136/bmj.n304

  28. Tao, F., Qi, Q., Wang, L., Nee, A.Y.C.: Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering 5(4), 653–661 (2019). https://doi.org/10.1016/j.eng.2019.01.014

    Article  Google Scholar 

  29. Nebeker, C., Torous, J., Bartlett Ellis, R.J.: Building the case for actionable ethics in digital health research supported by artificial intelligence. BMC Med. 17(1), 1–7 (2019). https://doi.org/10.1186/s12916-019-1377-7

  30. Qureshi, B.: Towards a digital ecosystem for predictive healthcare analytics. In: MEDES 2014—Proceedings of the 6th International Conference on Management of Emergent Digital Ecosystems, vol. 0, pp. 34–41 (2014).https://doi.org/10.1145/2668260.2668286

  31. Chen, Y., Yang, O., Sampat, C., Bhalode, P., Ramachandran, R., Ierapetritou, M.: Digital twins in pharmaceutical and biopharmaceutical manufacturing. Processes 8(1088), 1–33 (2020)

    Google Scholar 

  32. Bhalode, P., Ierapetritou, M.: Discrete element modeling for continuous powder feeding operation: calibration and system analysis. Int. J. Pharm. 585, 1–4 (2020). https://doi.org/10.1016/j.ijpharm.2020.119427

  33. Bhalode, P., Ierapetritou, M.: Discrete element modeling for continuous powder feeding operation: calibration and system analysis. Int. J. Pharm. 585, 119427 (2020).https://doi.org/10.1016/j.ijpharm.2020.119427

  34. Defraeye, T., Bahrami, F., Ding, L., Malini, R.I., Terrier, A., Rossi, R.M.: Predicting transdermal fentanyl delivery using mechanistic simulations for tailored therapy. Front. Pharmacol. 11(September), 1–23 (2020). https://doi.org/10.3389/fphar.2020.585393

    Article  Google Scholar 

  35. Spindler, J., Kec, T., Ley, T.: Lead-time and risk reduction assessment of a sterile drug product manufacturing line using simulation. Comput. Chem. Eng. 152, 107401 (2021).https://doi.org/10.1016/j.compchemeng.2021.107401

  36. Naegel, A., et al.: In-silico model of skin penetration based on experimentally determined input parameters. Part II: mathematical modelling of in-vitro diffusion experiments. Identification of critical input parameters. Eur. J. Pharm. Biopharm. 68(2), 368–379 (2008). https://doi.org/10.1016/j.ejpb.2007.05.018

    Article  MathSciNet  Google Scholar 

  37. Shaban-Nejad, A., Michalowski, M.: From precision medicine to precision health: a full angle from diagnosis to treatment and prevention. In: Shaban-Nejad, A., Michalowski, M. (eds.) Precision Health and Medicine: A Digital Revolution in Healthcare, pp. 1–7. Springer International Publishing, Cham (2020)

    Google Scholar 

  38. Peirlinck, M., et al.: Precision medicine in human heart modeling: perspectives, challenges, and opportunities. Biomech. Model. Mechanobiol. 20(3), 803–831 (2021). https://doi.org/10.1007/s10237-021-01421-z

    Article  Google Scholar 

  39. Chen, M., Yang, J., Zhou, J., Hao, Y., Zhang, J., Youn, C.-H.: 5G-smart diabetes: toward personalized diabetes diagnosis with healthcare big data clouds. IEEE Commun. Mag. 56(4), 16–23 (2018). https://doi.org/10.1109/MCOM.2018.1700788

    Article  Google Scholar 

  40. Fang, C., Zhang, P., Qi, X.: Digital and intelligent liver surgery in the new era: prospects and dilemmas. EBioMedicine 41, 693–701 (2019). https://doi.org/10.1016/j.ebiom.2019.02.017

    Article  Google Scholar 

  41. Pelletier, D., Hafler, D.A.: Fingolimod for Multiple Sclerosis, pp. 339–347 (2012)

    Google Scholar 

  42. Voigt, I., Inojosa, H., Dillenseger, A., Haase, R., Akgün, K., Ziemssen, T.: Digital twins for multiple sclerosis. Front. Immunol. 12(May), 1–17 (2021). https://doi.org/10.3389/fimmu.2021.669811

    Article  Google Scholar 

  43. Palanica, A., Docktor, M.J., Lee, A., Fossat, Y.: Using mobile virtual reality to enhance medical comprehension and satisfaction in patients and their families. Perspect. Med. Educ. 8(2), 123–127 (2019). https://doi.org/10.1007/s40037-019-0504-7

    Article  Google Scholar 

  44. Makris, E., Hu, L., Jones, G.B., Wright, J.M.: Moving the dial on heart failure patient adherence rates. Patient Prefer. Adherence 14, 2407–2418 (2020). https://doi.org/10.2147/PPA.S283277

    Article  Google Scholar 

  45. Subramanian, K.: Digital twin for drug discovery and development—the virtual liver. J. Indian Inst. Sci. 100(4), 653–662 (2020). https://doi.org/10.1007/s41745-020-00185-2

    Article  Google Scholar 

  46. Vamathevan, J., et al.: Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18(6), 463–477 (2019). https://doi.org/10.1038/s41573-019-0024-5

    Article  Google Scholar 

  47. Berg, E.L.: Systems biology in drug discovery and development. Drug Discov. Today 19(2), 113–125 (2014). https://doi.org/10.1016/j.drudis.2013.10.003

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Organic and medicinal chemistry department, Faculty of Pharmacy, University of Sadat City, Sadat City, Menoufiya, Egypt

    Yaseen A. M. M. Elshaier

  2. Scientific Research Group in Egypt (SRGE), Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  3. Scientific Research Group in Egypt (SRGE), Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwsih

  4. College of Business Administration, Kuwait University, Sabah Al Salem University City, Kuwait City, Kuwait

    Hameed AlQaheri

Authors
  1. Yaseen A. M. M. Elshaier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aboul Ella Hassanien
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashraf Darwsih
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hameed AlQaheri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yaseen A. M. M. Elshaier .

Editor information

Editors and Affiliations

  1. Scientific Research Group in Egypt (SRGE), Faculty of Computer and AI, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Scientific Research Group in Egypt (SRGE), Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  3. VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Elshaier, Y.A.M.M., Hassanien, A.E., Darwsih, A., AlQaheri, H. (2022). A Proposed Framework for Digital Twins Driven Precision Medicine Platform: Values and Challenges. In: Hassanien, A.E., Darwish, A., Snasel, V. (eds) Digital Twins for Digital Transformation: Innovation in Industry. Studies in Systems, Decision and Control, vol 423. Springer, Cham. https://doi.org/10.1007/978-3-030-96802-1_4

Download citation

Publish with us

Policies and ethics

Search

Navigation