Article | Published:

Antiretroviral dynamics determines HIV evolution and predicts therapy outcome

Nature Medicine volume 18, pages 13781385 (2012) | Download Citation

Abstract

Despite the high inhibition of viral replication achieved by current anti-HIV drugs, many patients fail treatment, often with emergence of drug-resistant virus. Clinical observations show that the relationship between adherence and likelihood of resistance differs dramatically among drug classes. We developed a mathematical model that explains these observations and predicts treatment outcomes. Our model incorporates drug properties, fitness differences between susceptible and resistant strains, mutations and adherence. We show that antiviral activity falls quickly for drugs with sharp dose-response curves and short half-lives, such as boosted protease inhibitors, limiting the time during which resistance can be selected for. We find that poor adherence to such drugs causes treatment failure via growth of susceptible virus, explaining puzzling clinical observations. Furthermore, our model predicts that certain single-pill combination therapies can prevent resistance regardless of patient adherence. Our approach represents a first step for simulating clinical trials of untested anti-HIV regimens and may help in the selection of new drug regimens for investigation.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N. Engl. J. Med. 338, 853–860 (1998).

  2. 2.

    , , , & Patient compliance and drug failure in protease inhibitor monotherapy. J. Am. Med. Assoc. 276, 1955–1956 (1996).

  3. 3.

    , , , & Antiretroviral medication adherence and the development of class-specific antiretroviral resistance. AIDS 23, 1035–1046 (2009).

  4. 4.

    et al. Effect of adherence to HAART on virologic outcome and on the selection of resistance-conferring mutations in NNRTI- or PI-treated patients. HIV Clin. Trials 8, 282–292 (2007).

  5. 5.

    et al. Predictors of HIV drug-resistance mutations in a large antiretroviral- naive cohort initiating triple antiretroviral therapy. J. Infect. Dis. 191, 339–347 (2005).

  6. 6.

    US Department of Health and Human Services Panel on Antiretroviral Guidelines for Adults & Adolescents. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents. <> (2011).

  7. 7.

    et al. The MONET trial: darunavir/ritonavir with or without nucleoside analogues, for patients with HIV RNA below 50 copies/ml. AIDS 24, 223–230 (2010).

  8. 8.

    et al. Efficacy of a nucleoside-sparing regimen of darunavir/ritonavir plus raltegravir in treatment-naive HIV-1-infected patients (ACTG a5262). AIDS 25, 2113–2122 (2011).

  9. 9.

    et al. Drug susceptibility in HIV infection after viral rebound in patients receiving indinavir-containing regimens. J. Am. Med. Assoc. 283, 229–234 (2000).

  10. 10.

    , , & No evidence for evolution of genotypic resistance after three years of treatment with darunavir/ritonavir, with or without nucleoside analogues. AIDS Res. Hum. Retroviruses published online, doi:10.1089/aid.2011.0256 (20 April 2012).

  11. 11.

    Resistance to HIV protease inhibitors. Haemophilia 4, 610–615 (1998).

  12. 12.

    et al. Incidence of resistance in a double-blind study comparing lopinavir/ritonavir plus stavudine and lamivudine to nelfinavir plus stavudine and lamivudine. J. Infect. Dis. 189, 51–60 (2004).

  13. 13.

    , & The latent HIV-1 reservoir in patients undergoing HAART: an archive of pre-HAART drug resistance. J. Antimicrob. Chemother. 55, 410–412 (2005).

  14. 14.

    & Virus Dynamics: Mathematical Principles of Immunology and Virology (Oxford University Press, USA, 2000).

  15. 15.

    Derivation and properties of Michaelis-Menten type and Hill type equations for reference ligands. J. Theor. Biol. 59, 253–276 (1976).

  16. 16.

    et al. Dose-response curve slope sets class-specific limits on inhibitory potential of anti-HIV drugs. Nat. Med. 14, 762–766 (2008).

  17. 17.

    , , & Dose-response curve slope is a missing dimension in the analysis of HIV-1 drug resistance. Proc. Natl. Acad. Sci. USA 108, 7613–7618 (2011).

  18. 18.

    The mutant selection window and antimicrobial resistance. J. Antimicrob. Chemother. 52, 11–17 (2003).

  19. 19.

    & Mutant selection window hypothesis updated. Clin. Infect. Dis. 44, 681–688 (2007).

  20. 20.

    & Adherence and drug resistance: predictions for therapy outcome. Proc. Bio. Sci. 267, 835–843 (2000).

  21. 21.

    et al. Modeling long-term HIV dynamics and antiretroviral response: effects of drug potency, pharmacokinetics, adherence, and drug resistance. J. Acquir. Immune Defic. Syndr. 39, 272–283 (2005).

  22. 22.

    Adherence to antiretroviral HIV drugs: how many doses can you miss before resistance emerges? Proc. Biol. Sci. 273, 617–624 (2006).

  23. 23.

    , & Emergence of HIV-1 drug resistance during antiretroviral treatment. Bull. Math. Biol. 69, 2027–2060 (2007).

  24. 24.

    , & Paradoxes of adherence and drug resistance to HIV antiretroviral therapy. J. Antimicrob. Chemother. 53, 696–699 (2004).

  25. 25.

    & Protease inhibitor monotherapy. Curr. Opin. Infect. Dis. 24, 7–11 (2011).

  26. 26.

    , & Adherence-resistance relationships to combination HIV antiretroviral therapy. Curr. HIV/AIDS Rep. 4, 65–72 (2007).

  27. 27.

    The toxicity of poisons applied jointly. Ann. Appl. Biol. 26, 585–615 (1939).

  28. 28.

    et al. A quantitative basis for antiretroviral therapy for HIV-1 infection. Nat. Med. 18, 446–451 (2012).

  29. 29.

    et al. A critical subset model provides a conceptual basis for the high antiviral activity of major HIV drugs. Sci. Transl. Med. 3, 91ra63 (2011).

  30. 30.

    et al. Adherence-resistance relationships for protease and non-nucleoside reverse transcriptase inhibitors explained by virological fitness. AIDS 20, 223–231 (2006).

  31. 31.

    et al. A novel substrate-based HIV-1 protease inhibitor drug resistance mechanism. PLoS Med. 4, e36 (2007).

  32. 32.

    et al. Gag determinants of fitness and drug susceptibility in protease inhibitor–resistant human immunodeficiency virus type 1. J. Virol. 83, 9094–9101 (2009).

  33. 33.

    et al. Gag mutations strongly contribute to HIV-1 resistance to protease inhibitors in highly drug-experienced patients besides compensating for fitness loss. PLoS Pathog. 5, e1000345 (2009).

  34. 34.

    et al. Full length HIV-1 gag determines protease inhibitor susceptibility within in vitro assays. AIDS 24, 1651–1655 (2010).

  35. 35.

    et al. Differential adherence to combination antiretroviral therapy is associated with virological failure with resistance. AIDS 22, 75–82 (2008).

  36. 36.

    , , , & Drug interactions modulate the potential for evolution of resistance. Proc. Natl. Acad. Sci. USA 105, 14918–14923 (2008).

  37. 37.

    et al. Phase I/II evaluation of nevirapine alone and in combination with zidovudine for infection with human immunodeficiency virus. J. Acquir. Immune Defic. Syndr. Hum. Retrovirol. 8, 141–151 (1995).

  38. 38.

    et al. Impact of reverse transcriptase resistance on the efficacy of TMC125 (etravirine) with two nucleoside reverse transcriptase inhibitors in protease inhibitor-naive, nonnucleoside reverse transcriptase inhibitor-experienced patients: study TMC125–C227. HIV Med. 9, 883–896 (2008).

  39. 39.

    & Pre-existence and emergence of drug resistance in HIV-1 infection. Proc. Biol. Sci. 264, 631–637 (1997).

  40. 40.

    et al. Pre-existing minority drug-resistant HIV-1 variants, adherence, and risk of antiretroviral treatment failure. J. Infect. Dis. 201, 662–671 (2010).

  41. 41.

    et al. Association between detection of HIV-1 DNA resistance mutations by a sensitive assay at initiation of antiretroviral therapy and virologic failure. Clin. Infect. Dis. 50, 1397–1404 (2010).

  42. 42.

    et al. Low-abundance drug-resistant viral variants in chronically HIV-infected, antiretroviral treatment–naive patients significantly impact treatment outcomes. J. Infect. Dis. 199, 693–701 (2009).

  43. 43.

    et al. Virological monitoring and resistance to first-line highly active antiretroviral therapy in adults infected with HIV-1 treated under who guidelines: a systematic review and meta-analysis. Lancet Infect. Dis. 9, 409–417 (2009).

  44. 44.

    et al. Viremia, resuppression, and time to resistance in human immunodeficiency virus (HIV) subtype c during first-line antiretroviral therapy in South Africa. Clin. Infect. Dis. 49, 1928–1935 (2009).

  45. 45.

    Not all missed doses are the same: sustained NNRTI treatment interruptions predict HIV rebound at low-to-moderate adherence levels. PLoS ONE 3, e2783 (2008).

  46. 46.

    Average adherence to boosted protease inhibitor therapy, rather than the pattern of missed doses, as a predictor of HIV RNA replication. Clin. Infect. Dis. 50, 1192–1197 (2010).

  47. 47.

    et al. Repeated measures analyses of dose timing of antiretroviral medication and its relationship to HIV virologic outcomes. Stat. Med. 26, 991–1007 (2007).

  48. 48.

    et al. Characterizing patterns of drug-taking behavior with a multiple drug regimen in an AIDS clinical trial. AIDS 12, 2295–2303 (1998).

  49. 49.

    et al. Cell-to-cell spread of HIV permits ongoing replication despite antiretroviral therapy. Nature 477, 95–98 (2011).

  50. 50.

    & Drug concentration heterogeneity facilitates the evolution of drug resistance. Proc. Natl. Acad. Sci. USA 95, 11514–11519 (1998).

  51. 51.

    , , & Compartmentalization and clonal amplification of HIV-1 variants in the cerebrospinal fluid during primary infection. J. Virol. 84, 2395–2407 (2010).

  52. 52.

    et al. Compartmentalization of the gut viral reservoir in HIV-1 infected patients. Retrovirology 4, 87 (2007).

  53. 53.

    et al. Efavirenz concentrations in CSF exceed IC50 for wild-type HIV. J. Antimicrob. Chemother. 66, 354–357 (2011).

  54. 54.

    , & Evolutionary dynamics of HIV at multiple spatial and temporal scales. J. Mol. Med. 90, 543–561 (2012).

  55. 55.

    et al. Antiretroviral drug resistance testing in adult HIV-1 infection: recommendations of an International AIDS Society–USA panel. J. Am. Med. Assoc. 283, 2417–2426 (2000).

  56. 56.

    et al. Differential impact of adherence on long-term treatment response among naive HIV-infected individuals. AIDS 22, 2371–2380 (2008).

  57. 57.

    et al. Role of low-frequency HIV-1 variants in failure of nevirapine-containing antiviral therapy in women previously exposed to single-dose nevirapine. Proc. Natl. Acad. Sci. USA 108, 9202–9207 (2011).

  58. 58.

    et al. Antigenic diversity thresholds and the development of AIDS. Science 254, 963–969 (1991).

  59. 59.

    et al. Consistent viral evolutionary changes associated with the progression of human immunodeficiency virus type 1 infection. J. Virol. 73, 10489–10502 (1999).

  60. 60.

    & Recombination rate and selection strength in HIV intra-patient evolution. PLoS Comput. Biol. 6, e1000660 (2010).

  61. 61.

    & Restricting the selection of antibiotic-resistant mutant bacteria: measurement and potential use of the mutant selection window. J. Infect. Dis. 185, 561–565 (2002).

  62. 62.

    et al. Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathog. 7, e1002158 (2011).

  63. 63.

    , , , & Decay dynamics of HIV-1 depend on the inhibited stages of the viral life cycle. Proc. Natl. Acad. Sci. USA 105, 4832–4837 (2008).

  64. 64.

    et al. Estimation of the initial viral growth rate and basic reproductive number during acute HIV-1 infection. J. Virol. 84, 6096–6102 (2010).

  65. 65.

    , & The frequency of resistant mutant virus before antiviral therapy. AIDS 12, 461–465 (1998).

Download references

Acknowledgements

We thank T. Antal, I. Božić, F. Fu, M. Sampah and L. Shen for discussion during the conception of this work, and we thank J. Gallant, J.-B. Michel and P. Pennings for their comments on the manuscript. We thank D. Bangsberg of Massachusetts General Hospital for supplying adherence data from the REACH study (supported by US National Institutes of Health grant R01 MH054907). Simulations were run on the Odyssey cluster supported by the Research Computing Group of Harvard University. We are grateful for support from the US National Institutes of Health (R01 AI081600 (R.F.S., S.A.R.), R01 GM078986 (M.A.N., A.L.H.)), the Bill & Melinda Gates Foundation (M.A.N., A.L.H.), a Cancer Research Institute Fellowship (S.A.R.), a US National Science Foundation Graduate Research Fellowship (D.I.S.R.), the Howard Hughes Medical Institute (R.F.S., S.A.R.), a Canadian Natural Sciences and Engineering Research Council Post-Graduate Scholarship (A.L.H.), the John Templeton Foundation (M.A.N.) and J. Epstein (M.A.N.).

Author information

Author notes

    • Daniel I S Rosenbloom
    • , Alison L Hill
    •  & S Alireza Rabi

    These authors contributed equally to this work.

Affiliations

  1. Program for Evolutionary Dynamics, Department of Mathematics, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA.

    • Daniel I S Rosenbloom
    • , Alison L Hill
    •  & Martin A Nowak
  2. Biophysics Program and Harvard-MIT Division of Health Sciences and Technology, Harvard University, Cambridge, Massachusetts, USA.

    • Alison L Hill
  3. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • S Alireza Rabi
    •  & Robert F Siliciano
  4. Howard Hughes Medical Institute, Baltimore, Maryland, USA.

    • Robert F Siliciano

Authors

  1. Search for Daniel I S Rosenbloom in:

  2. Search for Alison L Hill in:

  3. Search for S Alireza Rabi in:

  4. Search for Robert F Siliciano in:

  5. Search for Martin A Nowak in:

Contributions

D.I.S.R., A.L.H. and S.A.R. designed the models and conducted the simulations. D.I.S.R., A.L.H., S.A.R., R.F.S. and M.A.N. conceived of the study and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Robert F Siliciano or Martin A Nowak.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–13, Supplementary Tables 1–7 and Supplementary Methods

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nm.2892

Further reading