Advertisement

Efficacy Evaluation of Antiretroviral Drug Combinations for HIV-1 Treatment by Using the Fuzzy PROMETHEE

  • Murat Sayan
  • Dilber Uzun OzsahinEmail author
  • Tamer Sanlidag
  • Nazife Sultanoglu
  • Figen Sarigul Yildirim
  • Berna Uzun
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1095)

Abstract

The Human Immunodeficiency Virus (HIV) causes disease by damaging the immune system. If treatment is not initiated, the immune system collapses and this leads to Acquired Immune Deficiency Syndrome (AIDS). The drugs used in the treatment of HIV infection slow or stop the damage caused by the virus to the immune system. In this study, we analyzed the treatment options of HIV since there are many antiretroviral drug combinations available for the treatment and each combination has different properties. The variety of different combinations can cause confusion for physicians in practice. Based on this aim, we proposed the fuzzy PROMETHEE technique, a multi-criteria decision making technique based on mutual comparison of the options. The most common antiretroviral drug combinations used in the HIV treatment were evaluated and compared corresponding to their parameters by the PROMETHEE technique. According to our results, integrase-based inhibitor drug combinations were predominantly preferred. BIC + TAF/FTC (bictegravir + tenofoviralafenamide/emtricitabine) outranked the other antiretroviral drug combinations with a net flow of 0.0437, followed by DTG + ABC/3TC (dolutegravir + abacavir/lamivudine) then DTG + TAF/FTC (dolutegravir + tenofoviralafenamide/emtricitabine). The results obtained with the application of decision-making theories on these option treatment methods will provide significant information for relevant patients, HIV treatment specialists and drug-makers.

Keywords

HIV treatment options Multi criteria decision making Preference ranking organization method for enrichment evaluations (PROMETHEE) Fuzzy PROMETHEE 

References

  1. 1.
    Walker, B., McMichael, A.: The T-cell response to HIV. Cold Spring Harb. Perspect. Med. 2(11) (2012)CrossRefGoogle Scholar
  2. 2.
    CDC: Opportunistic Infections-Living with HIV-HIV Basics-HIV/AIDS-CDC. https://www.cdc.gov/hiv/basics/livingwithhiv/opportunisticinfections.html. Accessed 15 Apr 2019
  3. 3.
    U.S. Department of Veterans Affairs: CD4 count (or T-cell test) - HIV/AIDS. https://www.hiv.va.gov/HIV/patient/diagnosis/labs-CD4-count.asp. Accessed 15 Apr 2019
  4. 4.
    HIV/AIDS-CDC: HIV Transmission-HIV Basics. https://www.cdc.gov/hiv/basics/transmission.html. Accessed 16 Apr 2019
  5. 5.
    WHO: WHO-Data and statistics. https://www.who.int/hiv/data/en/. Accessed 16 Apr 2019
  6. 6.
    AIDSinfo: Antiretroviral Therapy (ART)-Definition-AIDSinfo. https://aidsinfo.nih.gov/understanding-hiv-aids/glossary/883/antiretroviral-therapy. Accessed 16 Apr 2019
  7. 7.
    U.S. Food and Drug Administration: HIV/AIDS - Antiretroviral drugs used in the treatment of HIV infection. https://www.fda.gov/ForPatients/Illness/HIVAIDS/ucm118915.htm. Accessed 16 Apr 2019
  8. 8.
    AIDSinfo: Drug Class-Definition-AIDSinfo. https://aidsinfo.nih.gov/understanding-hiv-aids/glossary/1561/drug-class. Accessed 16 Apr 2019
  9. 9.
    European AIDS Clinical Society: European AIDS Clinical Society Guidelines Version 9.0 (2017)Google Scholar
  10. 10.
    Michael, S., et al.: Antiretroviral drugs for treatment and prevention of HIV infection in adults. JAMA 320(4), 379–396 (2018)CrossRefGoogle Scholar
  11. 11.
    Oguntibeju, O.: Quality of life of people living with HIV and AIDS and antiretroviral therapy. HIV AIDS (Auckl) 4, 117–124 (2012)Google Scholar
  12. 12.
    AIDSinfo: Guidelines for the Use of Antiretroviral Agents in Adults and Adolescents Living with HIV (2018)Google Scholar
  13. 13.
    British HIV Association: Clinical Guidelines. https://www.bhiva.org/Clinical-Guidelines. Accessed 16 Apr 2019
  14. 14.
    Brans, J.P., Vincke, P.H.: A preference ranking organisation method: the PROMETHEE method for multiple criteria decision-making. Manag. Sci. 31(6), 647–656 (1985)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefGoogle Scholar
  16. 16.
    Geldermann, J., Spengler, T., Rentz, O.: Fuzzy outranking for environmental assessment case study: iron and steel making industry. Fuzzy Sets Syst. 115(1), 45–65 (2000)CrossRefGoogle Scholar
  17. 17.
    Ozsahin, I., Uzun Ozsahin, D., Uzun, B.: Evaluation of solid-state detectors in medical imaging with fuzzy PROMETHEE. J. Instrum. 14 (2019)CrossRefGoogle Scholar
  18. 18.
    Uzun Ozsahin, D., Ozsahin, I.: A fuzzy PROMETHEE approach for breast cancer treatment techniques. Int. J. Med. Res. Health Sci. 7(5), 29–32 (2018)Google Scholar
  19. 19.
    Uzun Ozsahin, D., et al.: Evaluating X-Ray based medical imaging devices with fuzzy preference ranking organization method for enrichment evaluations. Int. J. Adv. Comput. Sci. Appl. 9(3) (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Medicine, Clinical Laboratory, PCR UnitKocaeli UniversityKocaeliTurkey
  2. 2.Research Center of Experimental Health SciencesNear East UniversityNicosiaCyprus
  3. 3.Department of Biomedical EngineeringNear East UniversityNicosiaCyprus
  4. 4.Department of Medical MicrobiologyManisa Celal Bayar UniversityManisaTurkey
  5. 5.Faculty of Medicine, Department of Medical Microbiology and Clinical MicrobiologyNear East UniversityNicosiaCyprus
  6. 6.Clinical of Infectious DiseasesHealth Science University, Antalya Educational and Research HospitalAntalyaTurkey
  7. 7.Department of MathematicsNear East UniversityNicosiaCyprus
  8. 8.Gordon Center for Medical Imaging, Radiology Department, Massachusetts General Hospital and Harvard Medical SchoolBostonUSA

Personalised recommendations