Patients may choose to take some medications and not others – ‘differential adherence’. From 15–29% of individuals have different levels of adherence to individual components of a multidrug antiretroviral regimen [44,45]. Differential adherence is associated with an increased risk of virological failure and the development of antiretroviral resistance . In a large prospective clinical trial, participants who self-reported differential adherence more than once prior to first virological failure were twice as likely to have antiretroviral resistance at first failure . Factors associated with differential adherence include adverse drug events, three-times daily drug dosing, and lower baseline CD4 lymphocyte count . It also appears more likely to occur with NNRTIs and protease inhibitors, rather than NRTIs . Differential adherence appears to be relatively common and is clinically relevant.
The examples here are related to patterns of antiretroviral nonadherence. Differential drug exposure can also occur due to differences in absorption, distribution, metabolism, or elimination of individual regimen components. Drug–drug interactions and pharmacogenetic-antiretroviral associations can also predispose to differential drug exposure . These relationships are important but not the focus of this review.
Data suggest that the characteristics of the other regimen components affect the propensity for the development of drug resistance mutations. For example, in a randomized controlled trial comparing lopinavir/ritonavir with nelfinavir, both in combination with lamivudine and stavudine, 82% of individuals failing nelfinavir between weeks 24 and 48 developed lamivudine resistance compared with 41% of individuals failing lopinavir/ritonavir . These differences are likely based on both potency and the genetic barrier to antiretroviral resistance of the companion medications. Greater potency and higher genetic barrier to resistance prevent accumulation of drug resistance mutations to companion medications.
Several factors determine class-specific adherence–resistance relationships. First, antiretroviral regimen potency is important, as individuals with very low levels of viral replication are unlikely to develop resistance. Second, in the setting of viremia, circulating viral populations are determined by the interplay of the fold-change in resistance and fold-change in fitness caused by drug resistance mutations. Third, the genetic barrier to antiretroviral resistance determines the rate of development of resistance mutations at levels of drug exposure that favor resistant over wild-type virus. During multidrug therapy, differential drug exposure increases the likelihood of developing resistance. Long half-life drugs, in the presence of short half-life drugs, may be particularly susceptible to the development of resistance at low-adherence levels due to periods of differential drug exposure during intermittent dosing. Finally, antiretroviral medications with higher potency and higher genetic barrier to resistance decrease the incidence of resistance for companion antiretroviral medications.
The complexities of adherence–resistance relationships are related to characteristics of the virus, the medications, and to their interactions. Despite this complexity, adherence–resistance relationships have been consistent using diverse methods of adherence assessment (e.g. electronic prescription bottle caps, pill-count, self-report, or pharmacy refill data), study methodology (cross-sectional or prospective), and type of resistance testing (genotypic or phenotypic).
It is also important to understand the type of study when evaluating adherence–resistance relationships. Incident resistance describes new resistance mutations accumulating over time in individuals initiating antiretroviral therapy. Prevalent or cross-sectional resistance describes resistance present in individuals at the time they fail antiretroviral therapy. Both perspectives are useful in settings with limited availability of resistance testing, such as in many resource-poor settings, and in resource-rich settings in which loss to follow-up, transfers of care, and cyclical engagement in healthcare are common .
The World Health Organization supports a public health approach for the treatment of HIV infection , which necessitates that salvage therapy for a population be chosen in a way that provides effective treatment for most of the individuals . This requires knowledge not only of typical adherence levels and adherence patterns, but also an understanding of what types of resistance are predicted in individuals failing a particular therapy. Although some of this knowledge can be gained through experience, an understanding of the mechanisms behind adherence–resistance relationships may make it possible to predict expected resistance patterns for new medications and new classes of medications in the future. This understanding may also facilitate clinical trial design, including designs used to evaluate antiretroviral regimen sequencing and the use of specific combinations of medications, such as designing regimens with symmetrical half-lives. Below, brief examples are provided of the application of this information for these purposes.
Unknown adherence–resistance relationships can be hypothesized based on knowledge of drug potency, the fitness of resistant virus, and the genetic barrier to antiretroviral resistance (Table 1).
In the absence of preexisting resistance, poor adherence is the major risk factor for virological failure and the development of resistance. Table 2 shows the expected risks for resistance with typical initial drug combinations. Overall, resistance is most common for NNRTIs and deoxycytidine analogue NRTIs, followed by nonboosted protease inhibitors and nondeoxycytidine analogue NRTIs, and is least common for boosted protease inhibitors. Table 2 also presents potential associations between differential drug exposure, due to asymmetric medication half-lives or differential adherence, and the development of class-specific resistance.
Integrase inhibitors are currently being studied as initial therapy for HIV-1 infection . The adherence–resistance relationship for integrase inhibitors is expected to be similar to deoxycytidine NRTIs. On the basis of the relatively short serum half-life of raltegravir, the potential for differential drug exposure based on pharmacokinetics should be similar to protease inhibitors. However, the situation may be more complex as recent evidence suggests that raltegravir is essentially an irreversible inhibitor of HIV-1 DNA integration . Differential adherence is unlikely, as raltegravir appears well tolerated. These characteristics suggest that resistance will be common in individuals failing integrase inhibitors and this has been seen in heavily pretreated patients . Limited data suggest that dual-class resistance at first failure may also be relatively common .
There are several important gaps in our current knowledge. Adherence–resistance relationships in the setting of transmitted or preexisting mutations may differ. Also, most studies have assessed class-specific relationships in the setting of antiretroviral regimens composed of a nucleoside backbone and one other component. How alternative combinations as initial or salvage therapy will interact is unclear. Recent studies have only begun to explore patterns of nonadherence, such as treatment gaps and differential adherence, which may be important in creating differential drug exposure leading to resistance. Adherence–resistance relationships for newer antiretroviral agents are not well characterized; future research should help to delineate these relationships. Finally, to date, studies reporting adherence–resistance relationships have used traditional resistance assays with sensitivities down to 10–20% of the circulating viral population. Failure with ‘susceptible’ virus as defined by standard assays may hide a more complex mixture of circulating and/or archived resistant viruses that could impact the effectiveness of future treatment regimens . More sensitive resistance assays are now available and will help to further delineate class-specific adherence–resistance relationships.
Existing research has laid the groundwork for a deeper understanding of the complex interplay between adherence and resistance. Information about newer medications and new classes of medications should prove useful in clinical practice and research settings. It may also suggest lines of investigation for the treatment of other pathogens for which drug potency, pathogen resistance, host or pathogen genetics, or differential adherence are important. The goal of antiretroviral therapy remains complete virological suppression. However, knowledge of class-specific adherence–resistance relationships will help clinicians and patients tailor therapy to match individual patterns of adherence in order to minimize the development of resistance at failure. This information should guide the selection of optimal drug combinations and regimen sequences to improve the durability of antiretroviral therapy.
W.J.B. has research contracts with Glaxo Smith-Kline, Boehringer-Ingelheim, Bristol Myers-Squibb, and Avexa and chairs a DSMB for Tibotec.
P.L.A. received research support from Bristol Myers-Squibb.
E.M.G. contributed to literature review, study design, and manuscript preparation; W.J.B. contributed to study design, manuscript preparation and editing; J.F.S. contributed to expert opinion on adherence, manuscript preparation and editing; P.L.A. contributed to expert opinion on pharmacokinetics, manuscript preparation and editing; D.R.B. contributed to expert opinion on adherence and resistance, manuscript preparation and editing.
Dr E.M. Gardner is supported by a career development award from the National Institutes of Health, National Institute of Allergy and Infectious Diseases (K01 AI067063). Dr D.R. Bangsberg is supported by NIMH 54907 and NIAAA 015287. Dr P.L. Anderson is supported by NIAID, R01 AI 64029.
1. Quinn TC. HIV epidemiology and the effects of antiviral therapy on long-term consequences. AIDS 2008; 22(Suppl 3):S7–S12.
2. Lohse N, Hansen AB, Pedersen G, Kronborg G, Gerstoft J, Sorensen HT, et al
. Survival of persons with and without HIV infection in Denmark, 1995–2005. Ann Intern Med 2007; 146:87–95.
3. Schackman BR, Gebo KA, Walensky RP, Losina E, Muccio T, Sax PE, et al
. The lifetime cost of current human immunodeficiency virus care in the United States. Med Care 2006; 44:990–997.
4. Preston BD, Poiesz BJ, Loeb LA. Fidelity of HIV-1 reverse transcriptase. Science 1988; 242:1168–1171.
5. Roberts JD, Bebenek K, Kunkel TA. The accuracy of reverse transcriptase from HIV-1. Science 1988; 242:1171–1173.
6. Coffin JM. HIV population dynamics in vivo: implications for genetic variation, pathogenesis, and therapy. Science 1995; 267:483–489.
7. Mansky LM. The mutation rate of human immunodeficiency virus type 1 is influenced by the vpr gene. Virology 1996; 222:391–400.
8. Clavel F, Hance AJ. HIV drug resistance. N Engl J Med 2004; 350:1023–1035.
9. Kozal MJ, Hullsiek KH, Macarthur RD, Berg-Wolf M, Peng G, Xiang Y, et al
. The incidence of HIV drug resistance and its impact on progression of HIV disease among antiretroviral-naive participants started on three different antiretroviral therapy strategies. HIV Clin Trials 2007; 8:357–370.
10. Riddler SA, Haubrich R, DiRienzo AG, Peeples L, Powderly WG, Klingman KL, et al
. Class-sparing regimens for initial treatment of HIV-1 infection. N Engl J Med 2008; 358:2095–2106.
11. Louie M, Hogan C, Di Mascio M, Hurley A, Simon V, Rooney J, et al
. Determining the relative efficacy of highly active antiretroviral therapy. J Infect Dis 2003; 187:896–900.
12. Palmer S, Maldarelli F, Wiegand A, Bernstein B, Hanna GJ, Brun SC, et al
. Low-level viremia persists for at least 7 years in patients on suppressive antiretroviral therapy. Proc Natl Acad Sci USA 2008; 105:3879–3884.
13. Hermankova M, Ray SC, Ruff C, Powell-Davis M, Ingersoll R, D'Aquila RT, et al
. HIV-1 drug resistance profiles in children and adults with viral load of <50 copies/ml receiving combination therapy. JAMA 2001; 286:196–207.
14. Bangsberg DR, Hecht FM, Charlebois ED, Zolopa AR, Holodniy M, Sheiner L, et al
. Adherence to protease inhibitors, HIV-1 viral load, and development of drug resistance in an indigent population. AIDS 2000; 14:357–366.
15. Low-Beer S, Yip B, O'Shaughnessy MV, Hogg RS, Montaner JS. Adherence to triple therapy and viral load response. J Acquir Immune Defic Syndr 2000; 23:360–361.
16. Paterson DL, Swindells S, Mohr J, Brester M, Vergis EN, Squier C, et al
. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med 2000; 133:21–30.
17. Bangsberg DR. Less than 95% adherence to non-nucleoside reverse-transcriptase inhibitor therapy can lead to viral suppression. Clin Infect Dis 2006; 43:939–941.
18. Maggiolo F, Airoldi M, Kleinloog HD, Callegaro A, Ravasio V, Arici C, 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 2007; 8:282–292.
19. Gross R, Yip B, Wood E, Bangsberg D, Justice AC, Montaner JS, et al. Boosted PIs are more forgiving of suboptimal adherence than nonboosted PIs or NNRTIs (Poster #533)
. 13th Conference on Retroviruses and Opportunistic Infections
. Denver, CO, USA; 2006.
20. Hirsch MS, Gunthard HF, Schapiro JM, Brun-Vezinet F, Clotet B, Hammer SM, et al
. Antiretroviral drug resistance testing in adult HIV-1 infection: 2008 recommendations of an International AIDS Society-USA panel. Clin Infect Dis 2008; 47:266–285.
21. Deeks SG. Treatment of antiretroviral-drug-resistant HIV-1 infection. Lancet 2003; 362:2002–2011.
22. Bangsberg DR, Kroetz DL, Deeks SG. Adherence-resistance relationships to combination HIV antiretroviral therapy. Curr HIV/AIDS Rep 2007; 4:65–72.
23. Bangsberg DR, Acosta EP, Gupta R, Guzman D, Riley ED, Harrigan PR, et al
. Adherence-resistance relationships for protease and non-nucleoside reverse transcriptase inhibitors explained by virological fitness. AIDS 2006; 20:223–231.
24. Beerenwinkel N, Daumer M, Sing T, Rahnenfuhrer J, Lengauer T, Selbig J, et al
. Estimating HIV evolutionary pathways and the genetic barrier to drug resistance. J Infect Dis 2005; 191:1953–1960.
25. Braithwaite RS, Shechter S, Chang CC, Schaefer A, Roberts MS. Estimating the rate of accumulating drug resistance mutations in the HIV genome. Value Health 2007; 10:204–213.
26. Gallego O, de Mendoza C, Perez-Elias MJ, Guardiola JM, Pedreira J, Dalmau D, et al
. Drug resistance in patients experiencing early virological failure under a triple combination including indinavir. AIDS 2001; 15:1701–1706.
27. Walsh JC, Pozniak AL, Nelson MR, Mandalia S, Gazzard BG. Virologic rebound on HAART in the context of low treatment adherence is associated with a low prevalence of antiretroviral drug resistance. J Acquir Immune Defic Syndr 2002; 30:278–287.
28. Harrigan PR, Hogg RS, Dong WW, Yip B, Wynhoven B, Woodward J, et al
. Predictors of HIV drug-resistance mutations in a large antiretroviral-naive cohort initiating triple antiretroviral therapy. J Infect Dis 2005; 191:339–347.
29. Sethi AK, Celentano DD, Gange SJ, Moore RD, Gallant JE. Association between adherence to antiretroviral therapy and human immunodeficiency virus drug resistance. Clin Infect Dis 2003; 37:1112–1118.
30. Bangsberg DR, Charlebois ED, Grant RM, Holodniy M, Deeks SG, Perry S, et al
. High levels of adherence do not prevent accumulation of HIV drug resistance mutations. AIDS 2003; 17:1925–1932.
31. Bangsberg DR, Porco TC, Kagay C, Charlebois ED, Deeks SG, Guzman D, et al
. Modeling the HIV protease inhibitor adherence-resistance curve by use of empirically derived estimates. J Infect Dis 2004; 190:162–165.
32. King MS, Brun SC, Kempf DJ. Relationship between adherence and the development of resistance in antiretroviral-naïve, HIV-1-infected patients receiving lopinavir/ritonavir or nelfinavir. J Infect Dis 2005; 191:2046–2052.
33. Eron J Jr, Yeni P, Gathe J Jr, Estrada V, DeJesus E, Staszewski S, et al
. The KLEAN study of fosamprenavir-ritonavir versus lopinavir-ritonavir, each in combination with abacavir-lamivudine, for initial treatment of HIV infection over 48 weeks: a randomised noninferiority trial. Lancet 2006; 368:476–482.
34. Gardner EM, Peng G, Telzak E, Sharma S, Huppler Hullsiek K, Burman W, et al. Analysis of the relationship between antiretroviral medication adherence and class-specific resistance in a large prospective clinical trial (Poster #777)
. 15th Conference on Retroviruses and Opportunistic Infections
. Boston, MA, USA; 2008.
35. MacArthur RD, Novak RM, Peng G, Chen L, Xiang Y, Hullsiek KH, et al
. A comparison of three highly active antiretroviral treatment strategies consisting of non-nucleoside reverse transcriptase inhibitors, protease inhibitors, or both in the presence of nucleoside reverse transcriptase inhibitors as initial therapy (CPCRA 058 FIRST Study): a long-term randomised trial. Lancet 2006; 368:2125–2135.
36. Tam LW, Chui CK, Brumme CJ, Bangsberg DR, Montaner JS, Hogg RS, et al
. The relationship between resistance and adherence in drug-naive individuals initiating HAART is specific to individual drug classes. J Acquir Immune Defic Syndr 2008; 49:266–271.
37. Jacobsen H, Hanggi M, Ott M, Duncan IB, Owen S, Andreoni M, et al
. In vivo resistance to a human immunodeficiency virus type 1 proteinase inhibitor: mutations, kinetics, and frequencies. J Infect Dis 1996; 173:1379–1387.
38. Palella FJ Jr, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, et al
. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med 1998; 338:853–860.
39. Saravolatz LD, Winslow DL, Collins G, Hodges JS, Pettinelli C, Stein DS, et al
. Zidovudine alone or in combination with didanosine or zalcitabine in HIV-infected patients with the acquired immunodeficiency syndrome or fewer than 200 CD4 cells per cubic millimeter. N Engl J Med 1996; 335:1099–1106.
40. Parienti JJ, Massari V, Descamps D, Vabret A, Bouvet E, Larouze B, et al
. Predictors of virologic failure and resistance in HIV-infected patients treated with nevirapine- or efavirenz-based antiretroviral therapy. Clin Infect Dis 2004; 38:1311–1316.
41. Oyugi JH, Byakika-Tusiime J, Ragland K, Laeyendecker O, Mugerwa R, Kityo C, et al
. Treatment interruptions predict resistance in HIV-positive individuals purchasing fixed-dose combination antiretroviral therapy in Kampala, Uganda. AIDS 2007; 21:965–971.
42. Parienti JJ, Das-Douglas M, Massari V, Guzman D, Deeks SG, Verdon R, et al
. Not all missed doses are the same: sustained NNRTI treatment interruptions predict HIV rebound at low-to-moderate adherence levels. PLoS ONE 2008; 3:e2783.
43. Anderson PL, Kakuda TN, Lichtenstein KA. The cellular pharmacology of nucleoside- and nucleotide-analogue reverse-transcriptase inhibitors and its relationship to clinical toxicities. Clin Infect Dis 2004; 38:743–753.
44. Gardner EM, Burman WJ, Maravi ME, Davidson AJ. Selective drug taking during combination antiretroviral therapy in an unselected clinic population. J Acquir Immune Defic Syndr 2005; 40:294–300.
45. Gardner EM, Sharma S, Peng G, Hullsiek KH, Burman WJ, Macarthur RD, et al
. Differential adherence to combination antiretroviral therapy is associated with virological failure with resistance. AIDS 2008; 22:75–82.
46. Cohen K, van Cutsem G, Boulle A, McIlleron H, Goemaere E, Smith PJ, et al
. Effect of rifampicin-based antitubercular therapy on nevirapine plasma concentrations in South African adults with HIV-associated tuberculosis. J Antimicrob Chemother 2008; 61:389–393.
47. Walmsley S, Bernstein B, King M, Arribas J, Beall G, Ruane P, et al
. Lopinavir-ritonavir versus nelfinavir for the initial treatment of HIV infection. N Engl J Med 2002; 346:2039–2046.
48. Rajabiun S, Mallinson RK, McCoy K, Coleman S, Drainoni ML, Rebholz C, et al
. ‘Getting me back on track’: the role of outreach interventions in engaging and retaining people living with HIV/AIDS in medical care. AIDS Patient Care STDS 2007; 21(Suppl 1):S20–S29.
49. Guidelines Development Group (chaired by Professor Scott Hammer of Columbia University, New York City, USA). Antiretroviral therapy for HIV infection in adults and adolescents in resource-limited settings: towards universal access. Recommendations for a public health approach
. Geneva: World Health Organization; 2006.
50. Elliott JH, Lynen L, Calmy A, De Luca A, Shafer RW, Zolfo M, et al
. Rational use of antiretroviral therapy in low-income and middle-income countries: optimizing regimen sequencing and switching. AIDS 2008; 22:2053–2067.
51. Fransen S, Gupta S, Frantzell A, Petropoulos C and Huang W. HIV-1 mutations at positions 143, 148, 155 of integrase define different genetic barriers to raltegravir resistance in vivo
. (Oral Abstract #69). 16th Conference on Retroviruses and Opportunistic Infections
. Montreal, QC, Canada; 2009.
52. Fatkenheuer G, Nelson M, Lazzarin A, Konourina I, Hoepelman AI, Lampiris H, et al
. Subgroup analyses of maraviroc in previously treated R5 HIV-1 infection. N Engl J Med 2008; 359:1442–1455.
53. MacArthur RD, Novak RM. Reviews of antiinfective agents: maraviroc – the first of a new class of antiretroviral agents. Clin Infect Dis 2008; 47:236–241.
54. Markowitz M, Nguyen BY, Gotuzzo E, Mendo F, Ratanasuwan W, Kovacs C, et al
. Rapid and durable antiretroviral effect of the HIV-1 integrase inhibitor raltegravir as part of combination therapy in treatment-naive patients with HIV-1 infection: results of a 48-week controlled study. J Acquir Immune Defic Syndr 2007; 46:125–133.
55. Miller M, Danovich R, Fransen S, Gupta S, Huang W, Ke Y, et al. Analysis of resistance to the HIV-1 integrase inhibitor raltegravir: results from the benchmrk 1 and 2 (abstract H-898)
. 48th Interscience Conference on Antimicrobial Agents and Chemotherapy
. Washington DC; 2008.
56. Cooper DA, Steigbigel RT, Gatell JM, Rockstroh JK, Katlama C, Yeni P, et al
. Subgroup and resistance analyses of raltegravir for resistant HIV-1 infection. N Engl J Med 2008; 359:355–365.
57. Simen BB, Simons JF, Hullsiek KH, Novak RM, Macarthur RD, Baxter JD, et al
. Low-abundance drug-resistant viral variants in chronically HIV-infected, antiretroviral treatment-naive patients significantly impact treatment outcomes. J Infect Dis 2009; 199:693–701.