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.
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