Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Predicting the unpredictable: Transmission of drug-resistant HIV

Nature Medicine volume 7, pages 1016–1020 (2001)Cite this article

An Erratum to this article was published on 01 January 2003

Abstract

We use a mathematical model to understand (from 1996 to 2001) and to predict (from 2001 to 2005) the evolution of the epidemic of drug-resistant HIV in San Francisco. We predict the evolutionary trajectories for 1,000 different drug-resistant strains with each strain having a different fitness relative to a drug-sensitive strain. We calculate that the current prevalence of resistance is high, and predict it will continue to rise. In contrast, we calculate that transmission of resistance is currently low, and predict it will remain low. We show that the epidemic of resistance is being generated mainly by the conversion of drug-sensitive cases to drug-resistant cases, and not by the transmission of resistant strains. We also show that transmission of resistant strains has not increased the overall number of new HIV infections. Our results indicate that transmission of resistant strains is, and will remain, a relatively minor public health problem.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Predictions calculated using model described previously16 and time-dependent uncertainty analyses29,30.
Figure 2: The rate of acquired resistance is a key factor in determining the prevalence of ARV resistance.
Figure 3: Temporal predictions for transmitted resistance
Figure 4

Similar content being viewed by others

References

  1. Espinal, M.A. et al. Global trends in resistance to antituberculosis drugs. N. Engl. J. Med. 344, 1294–1303 (2001).

    Article  Google Scholar 

  2. Blower, S.M., Small, P.M. & Hopewell, P.C. Control strategies for tuberculosis epidemics: new models for old problems. Science 273, 497–500 (1996).

    Article  Google Scholar 

  3. Blower, S.M. & Gerberding J.L.G., Understanding, predicting and controlling the emergence of drug-resistant tuberculosis: A theoretical framework. J. Mol. Med. 76, 624–636 (1998).

    Article  Google Scholar 

  4. Blower, S.M., Koelle K. & Lietman, T. Antibiotic resistance—to treat....(or not to treat)? Nature Med. 5, 358–359 (1999).

    Article  Google Scholar 

  5. Blower, S.M., Porco, T.C. & Darby, G. Predicting and preventing the emergence of antiviral drug resistance in HSV-2. Nature Med. 4, 664–665 (1998).

    Article  Google Scholar 

  6. Catania, J.A. et al. Health care policy: U.S. priorities-HIV prevention. Science 290, 717 (2000).

    Article  Google Scholar 

  7. Annual Surveillance Report 2000. San Francisco Department of Public Health HIV Seroepidemiology and Surveillance Section AIDS surveillance Unit. San Francisco Department of Health, San Francisco, California. Also available at http://www.dph.sf.ca.us

  8. Ledergerber, B. et al. AIDS-related opportunistic illnesses occurring after initiation of potent antiretroviral therapy: The Swiss HIV cohort study. JAMA 282, 2220–2226 (1999).

    Article  Google Scholar 

  9. Hogg, R.S. et al. Improved survival among HIV-infected individuals following initiation of antiretroviral therapy. JAMA 279, 50–54 (1998).

    Article  Google Scholar 

  10. Detels, R. et al. Effectiveness of potent antiretroviral therapy on time to AIDS and death in men with known HIV infection duration. Multicenter AIDS Cohort Study Investigators. JAMA 280, 1497–503 (1998).

    Article  Google Scholar 

  11. Palella, F.J. Jr et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. N. Engl. J. Med. 338, 853–860 (1998).

    Article  Google Scholar 

  12. Hecht F.M. et al. Sexual transmission of an HIV-1 variant resistant to multiple reverse-transcriptase and protease inhibitors. N. Engl. J. Med. 339, 307–311 (1998).

    Article  Google Scholar 

  13. Little, S.J. et al. Reduced antiretroviral drug susceptibility among patients with primary HIV infection. JAMA 282, 1142–1149 (1999).

    Article  Google Scholar 

  14. Solomon H. et al. Prevalence of HIV-1 resistant to antiretroviral drugs in 81 individuals newly infected by sexual contact or injecting drug use. Investigators of the Quebec Primary Infection Study. AIDS 14, F17–23 (2000).

    Article  Google Scholar 

  15. Yerly, S. et al. Transmission of antiretroviral-drug-resistant HIV-1 variants. Lancet 354, 729–733 (1999).

    Article  Google Scholar 

  16. Blower, S.M., Gershengorn, H.B. & Grant, R.M. A tale of two futures: HIV and antiretroviral therapy in San Francisco. Science 287, 650–654 (2000).

    Article  Google Scholar 

  17. CDC. Increases in unsafe sex and rectal gonorrhea among men who have sex with men—San Francisco, California, 1994–1997. Morb. Mortal. Wkly Rep. Surveill. Summ. 48, 45–48 (1998).

  18. CDC. Resurgent bacterial sexually transmitted disease among men who have sex with men—King County, Washington, 1997–1999. MMWR 48, 773–777 (1999).

  19. CDC. Outbreak of syphilis among men who have sex with men—Southern California, 2000. MMWR 50, 117–120 (2001).

  20. Huang, W. et al. Patterns of plasma human immunodeficiency virus type 1 RNA response to antiretroviral therapy. J. Infect. Dis. 183, 1455–1465 (2001).

    Article  Google Scholar 

  21. Hansel, A., Bucher, H.C, Nuesch, R. & Battegay, M.J. Reasons for discontinuation of first highly active antiretroviral therapy in a cohort of protease inhibitor-naive HIV-infected patients. AIDS 26, 191–193 (2001).

    Google Scholar 

  22. Gallant, J.E. Strategies for long-term success in the treatment of HIV infection. JAMA 283, 1329–1334 (2000).

    Article  Google Scholar 

  23. Descamps, D. et al. Mechanisms of virologic failure in previously untreated HIV-infected patients from a trial of induction-maintenance therapy. Trilege (Agence Nationale de Recherches sur le SIDA 072) Study Team). JAMA 283, 205–211 (2000).

    Article  Google Scholar 

  24. Pialoux, G. et al. A randomized trial of three maintenance regimens given after three months of induction therapy with zidovudine, lamivudine, and indinavir in previously untreated HIV-1–infected patients. Trilege (Agence Nationale de Recherches sur le SIDA 072) Study Team. N. Engl. J. Med. 339, 1269–1276 (1998).

    Article  Google Scholar 

  25. Havlir, D.V. et al. Drug susceptibility in HIV infection after viral rebound in patients receiving indinavir-containing regimens. JAMA 283, 229–234 (2000).

    Article  Google Scholar 

  26. Ghani, A.C., Henley W.E., Donnelly, C.A., Mayer S. & Anderson R.M. Comparison of the effectiveness of non-nucleoside reverse transcriptase inhibitor-containing and protease inhibitor-containing regimens using observational databases. AIDS 15, 1133–1142 (2001).

    Article  Google Scholar 

  27. Paredes, R. et al. Predictors of virological success and ensuing failure in HIV-positive patients starting highly active antiretroviral therapy in Europe: Results from the EuroSIDA study. Arch. Intern. Med. 160, 1123–1132 (2000).

    Article  Google Scholar 

  28. Conway, B. et al. Development of drug resistance in patients receiving combinations of zidovudine, didanosine and nevirapine. AIDS 6, 1269–1274 (2001).

    Article  Google Scholar 

  29. Blower, S.M. & Dowlatabadi, H. Sensitivity and uncertainty analysis of complex models of disease transmission: An HIV model, as an example. Internat. Stat. Rev. 2, 229–243 (1994).

    Article  Google Scholar 

  30. Blower, S.M. et al. The intrinsic transmission dynamics of tuberculosis epidemics. Nature Med. 1, 815–821 (1995).

    Article  Google Scholar 

  31. Quinn, T.C. et al. Viral load and heterosexual transmission of HIV-1. N. Engl. J. Med. 342, 921–972 (2000).

    Article  Google Scholar 

  32. Little, S.J. Is transmitted drug resistance in HIV on the rise? Brit. Med. J. 322, 1074–1075 (2001).

    Article  Google Scholar 

  33. Stoddart, C.A. et al. Impaired replication of protease inhibitor-resistant HIV-1 in human thymus. Nature Med. 7, 712–718 (2001).

    Article  Google Scholar 

  34. Gazzard B. & Moyle, G. 1998 Revision to the British HIV Association guidelines for antiretroviral treament of HIV seropositive individuals. BHIVA Guidelines Writing Committee. Lancet 352, 314–316 (1998).

    Article  Google Scholar 

  35. Tchetgen, E., Kaplan, E.H. & Friedland, G.H. Public health consequences of screening patients for adherence to highly active antiretroviral therapy. J. Acquir. Immune Defic. Syndr. 26, 118–129 (2001).

    Article  Google Scholar 

  36. Blower, S.M., Koelle, K., Kirschner, D. & Mills, J. Live attenuated HIV vaccines: Predicting the trade-off between efficacy and safety. Proc. Natl. Acad. Sci. USA 98, 3618–3623 (2001).

    Article  Google Scholar 

  37. Blower S.M. Calculating the consequences: HAART and risky sex. AIDS 15, 1309–1310 (2001).

    Article  Google Scholar 

  38. Iman, R.L. & Helton, J.C. An investigation of uncertainty and sensitivity analysis techniques for computer models. Risk Anal. 8, 71–90 (1988).

    Article  Google Scholar 

  39. Kleijnen, J.P.C. & Helton, J.C. Statistical analyses of scatterplots to identify important factors in large-scale simulations, 1: Review and comparison of techniques. Reliability Engineering and System Safety 65, 147–185 (1999).

    Article  Google Scholar 

Download references

Acknowledgements

We thank J. Catania, B. Goldberg, F. Hecht, R. Grant, M. Markowitz, M. McCune P. Volberding and D., J. and N. Freimer for helpful discussions. This study was supported by the NIH/NIAID (Grant No. AI41935 to S.M.B.), the UCSF-GIVI Center for AIDS Research (p30MH59037 to J.O.K.) and the NIH (Grant No. U01A41531 (J. Levy PI)) and University of California-wide AIDS Research program (CC99-SF-001).

Author information

Authors and Affiliations

  1. Department of Biomathematics and UCLA AIDS Institute, UCLA School of Medicine, Los Angeles, California, USA

    S.M. Blower & A.N. Aschenbach

  2. Harvard Medical School, Boston, Massachusetts, USA

    H.B. Gershengorn

  3. University of California, San Francisco and San Francisco General Hospital, San Francisco, California, USA

    J.O. Kahn

Authors
  1. S.M. Blower
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A.N. Aschenbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H.B. Gershengorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J.O. Kahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S.M. Blower.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Blower, S., Aschenbach, A., Gershengorn, H. et al. Predicting the unpredictable: Transmission of drug-resistant HIV. Nat Med 7, 1016–1020 (2001). https://doi.org/10.1038/nm0901-1016

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nm0901-1016

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing