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Preventing HIV Antiretroviral Resistance through Better Monitoring of Treatment Adherence

  1. David R. Bangsberg
  1. Epidemiology and Prevention Interventions Center, Division of Infectious Diseases, and Positive Health Program, San Francisco General Hospital, University of California, San Francisco
  1. Reprints or correspondence: Dr. David R. Bangsberg, Epidemiology and Prevention Interventions Center, University of California, San Francisco, PO Box 1372, San Francisco, CA 94143-1372 (db{at}epi-center.ucsf.edu).
 
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Abstract

Suboptimal adherence to antiretroviral therapy (ART) is the most common cause of viral rebound. Accurate and reliable measures of ART adherence will be needed in the transition from reactive response to proactive prevention of viral rebound in the era of chronic human immunodeficiency virus (HIV) disease management. Such tools could define individual “signature adherence patterns,” which could inform regimen choice and guide behavioral intervention. Upcoming advances in adherence monitoring present opportunities to better match HIV-disease treatment strategies with individual adherence behavior.

It has been >10 years since antiretroviral therapy (ART) transformed HIV/AIDS from a terminal to a chronic disease. Current disease-management goals are to bring individuals into medical care early by means of more-widespread testing for HIV infection, establish a trusting patient-provider relationship, provide HIV transmission and risk-reduction counseling, and monitor disease progression in anticipation of HIV ART.

The success of ART hinges on long-term adherence. To the extent possible, modifiable barriers to incomplete adherence should be addressed and ameliorated prior to treatment [1]. These include diagnosing and treating depression [2], treating drug and alcohol dependence [3–5], strengthening the patient-provider relationship [6, 7], and fostering social support through the incorporation of partners or other important family members into the care plan [8].

In most patients, initial viral suppression is achieved through current antiretroviral regimens. Eventual viral rebound is not uncommon, however, because adherence decreases over time [9–11]. Modest declines or even complete lapses in adherence rarely are detected in advance of viral rebound. Instead, patient-provider discussions of the causes and patterns of missed doses often occur after viral rebound is detected by routine laboratory monitoring. Viral rebound is followed by testing for drug-resistance susceptibility, to select a second-line regimen. This new regimen may be more complex than the initial regimen, thereby creating an additional barrier to adherence. The same pattern of gradual decline in adherence may necessitate an even more complex third-line regimen. Thus, failure to detect and address risky adherence patterns prior to the first viral rebound can lead to a continuous loop of less-effective, poorly tolerated therapies that require even higher levels of adherence to sustain viral suppression.

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The Case For Accurate, Real-Time Adherence Monitoring

Monitoring proximal therapeutic response to improve management of chronic disease. Continuous monitoring of proximal therapeutic response is an important component of the management of chronic disease and can improve treatment outcomes for several diseases, including congestive heart failure and diabetes [12, 13].

The success of these strategies requires the monitoring of proximal therapeutic response, to gauge the intensity of the disease management necessary to ensure treatment adherence and to prevent clinical disease progression. Although viral load is a good marker of response to ART, it is tightly linked to viral resistance. Reactive responses to viral rebound initiate irreversible loops of ever-more-limited treatment options. A more-effective management approach would be to focus on proactive adherence monitoring, with the goal of intervention before the virus is able to replicate and develop resistance mutations.

Informing adherence intervention for individual patients. Effective behavioral interventions exist for patients with suboptimal adherence [14, 15]. These strategies include adherence case management [16–19], couple-based counseling [8], pharmacist-based education [20], telephone support [21], reminder devices [22], home visits by a nurse [23], and directly observed therapy [24–27]. However, the use of these strategies in clinical practice settings poses several challenges. First, appropriate identification of at-risk patients is assumed. Research suggests that health care providers identify only a fraction of patients with suboptimal adherence [28–30].

Second, the impact of most adherence interventions decreases after the intervention ends. Ongoing monitoring past the end of the intervention increases the likelihood of identification of waning adherence before viral rebound. Third, knowledge of how individual patients adhere to therapy may inform the intensity of the intervention required for each patient. For example, directly observed therapy (i.e., in which the ingestion of doses is observed on a daily basis) is an intensive and costly adherence intervention that is effective for selected populations and settings, such as in prisons, methadone maintenance, and needle-exchange programs [25–27]. Directly observed therapy, however, may be intrusive [31] and may be cost-effective only with patients who have sufficiently low adherence and a high risk of viral rebound [32, 33]. Reliable measures to precisely identify patients for whom less-intensive interventions have failed may help to identify the best candidates for directly observed therapy. With regard to these 3 challenges, accurate and reliable adherence monitoring would communicate to health care providers the information necessary to guide the timing, duration, and intensity of adherence interventions for individual patients.

Informing choice of antiretroviral regimen. Medication side effects are one of the most consistent predictors of incomplete adherence [34, 35]. Some patients tolerate side effects without missing doses, whereas others miss doses or discontinue ART entirely. Some medication side effects (e.g., nausea and central nervous system symptoms) are most severe during the first 2 weeks of treatment. Other side effects, such as lipodystrophy or neuropathy, reach their peak after years of treatment. More-accurate adherence monitoring may help clinicians to better gauge the impact of these side effects on adherence over time, in order to guide decisions on whether regimens need to be modified to improve adherence and prevent viral rebound.

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Promising Approaches To Improving Adherence Monitoring

Several strategies may move us closer to sufficiently accurate adherence monitoring (table 1). The most commonly used adherence-monitoring strategy is interviewing patients about recent missed doses [36]. Although there are many different approaches to asking patients about the frequency of missed doses [37–40], most are imprecise and have relatively poor specificity for detecting levels of adherence that put patients at risk for viral rebound. Furthermore, most adherence-monitoring interviews address behavior just prior to the clinical encounter. This period is too short to provide a full profile of adherence between clinical encounters. The clinical encounter also may increase underreporting of missed doses, because of a social desirability bias. In short, the routine clinical interview is important to establishing a dialogue about adherence but is unlikely to be frequent or accurate enough to reliably predict and prevent viral rebound.

Table 1.
Table 1.

Strategies for measurement of adherence to HIV treatment.

Web-based strategies may improve the precision and frequency of patient self-reporting (e.g., West Portal Software; http://www.westportal.com/) [41]. Internet-based options can provide low-cost, continuous adherence monitoring by increasing the frequency of adherence assessment for those patients who have Internet access. For example, pictures of actual medication may improve the accuracy of adherence reporting for specific antiretroviral medications [42]. Internet-based strategies may increase the accuracy and frequency of adherence assessment [43] and facilitate the disclosure of socially sensitive information [44]. Patients can be queried about specific patterns of adherence, such as treatment interruptions, that may increase the risk of treatment failure [45]. Detection of adherence patterns through regular Web-based assessment then could trigger more-detailed behavioral assessment and intervention.

The primary limitation to adherence monitoring using patient report (clinician interview or Web-based interview) is overcoming patients' difficulties in remembering and reporting routine behavior, as well as sustaining adherence to the adherence-assessment strategy over decades of treatment. It is reasonable to assume that barriers to adherence to a medication regimen also may impair adherence to any Internet-based adherence program. Finally, many patients at highest risk for poor adherence also lack Internet access.

Pharmacy-dispensing information is another potential approach to routine adherence monitoring. Pharmacy refill information can be used to calculate the drug-possession ratio, which is the percentage of the number of doses prescribed relative to the number of scheduled dosing intervals between refills [46, 47]. For example, a patient who refills a prescription 3 days late for a twice-daily medication dispensed over 30 days has a drug-possession ratio of 90%. The drug-possession ratio represents the maximum possible adherence for a patient over a defined refill period and is associated with viral suppression, drug resistance, and death among HIV-positive individuals [48– 51]. Use of the drug-possession ratio to detect risky adherence patterns could be a relatively low-cost approach to prompt more-intensive assessment, guide behavioral intervention, and inform possible regimen modification. It also is the only approach currently available that can be realistically used in resource-limited settings [52].

The limitations of this approach include the use of multiple pharmacies by the same person, which complicates data collection, and the inability to calculate patient drug-possession ratios for medications regularly delivered by mail. Moreover, the prediction of viral rebound relies on ongoing, real-time data [53]; whether monthly monitoring is sufficiently precise to predict and prevent viral rebound in individual patients is unclear.

Many of the limitations of patient-reported adherence and pharmacy refill adherence were overcome with the novel strategy of unannounced home-based pill counts conducted by telephone. Although clinic-based pill counts generally perform poorly, unannounced pill counts conducted in the home have been closely associated with viral suppression, the development of drug resistance, and progression to AIDS [54–56]. However, home-based pill counts are not feasible for clinical practice settings. Kalichman et al. [57] recently adapted this approach to guiding patients, through a telephone interview, in conducting their own pill counts on unannounced days. Their approach closely replicated unannounced home-based pill counts and may be a promising approach for testing in clinical practice settings.

The last approach to real-time adherence monitoring is the use of containers with electronic monitoring. Of these types of adherence measures, the medication event monitoring system (MEMS; Ardex) is the most widely used. MEMS uses a microswitch, a battery, and flash memory to detect and record the date and time when a medication bottle is opened. These time/date stamps create an electronic record of pill bottle-opening behavior that is closely associated with viral suppression and drug resistance [54, 58, 59].

Different pill bottles are needed for each medication, however, which can be cumbersome and has limited portability. As a result, patients often take out more than 1 dose at a time, or “pocket doses.” This leads to an underestimation of actual adherence. At other times, patients may open the pill bottle without taking the medication, called “curiosity events” [60, 61]. MEMS also precludes the use of medication pill-box organizers, which have been associated with modest (4%) but highly cost-effective increases in adherence in observational studies [62, 63].

A newer type of electronic monitoring involves pill-box organizers that store each medication in a separate, adjacent tray in a device the size of a video cassette. As with MEMS, dosing behavior is recorded by storing the date and time that the lid of the tray is opened. Adherence behavior is stored in flash memory and then downloaded on a daily basis through a telephone modem. Computer algorithms can then be used to detect high-risk adherence patterns. The system can immediately alert the health care provider or chronic disease case manager that an intervention is necessary, when the patient's adherence pattern increases the risk of viral rebound. Other unique benefits of electronic pill boxes include storing multiple medications in 1 monitoring device, using alarms or other mechanisms to remind patients when to take their pills, and collecting additional real-time clinical information from patients, such as information on medication side effects. On the other hand, portability may be a challenge for some patients, and tray capacity may limit the patient's ability to use the device for some medications. Patients familiar with pill boxes that store daily doses of all pills in the same bin may find it difficult to transition to pill boxes that place each medication in a separate bin. “Curiosity events” and “pocketing” of doses also may occur. The use of such devices has been associated with improvements in adherence for some diseases [64], but their utility for the management of chronic HIV disease is unknown. Integration with pharmacy dispensing would be important to ensuring that medications are placed in the correct bins between refills.

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Regimen-Specific Adherence Requirements

Although early data suggested that 95%–100% adherence was necessary to sustain viral suppression and prevent drug resistance [59], more-recent findings suggest that each antiretroviral drug has different relationships between adherence, viral suppression, and drug resistance [50, 65–68]. Furthermore, recent patterns of adherence, rather than average adherence over time, may be important in determining the risk of viral rebound.

The 2 most commonly used regimens for first-line therapy include either a nonnucleoside reverse-transcriptase inhibitor (NNRTI) or a ritonavir-boosted protease inhibitor (PI). Data from small studies indicate that both regimens appear to produce reliable viral suppression at moderate adherence levels [50, 65–68]. These findings do not precisely determine what level of adherence places a patient at risk for viral rebound. Furthermore, the timing of viral rebound after a partial decline in adherence is unclear and may vary with the length of time that treatment has been received. Specifically, reliable viral suppression during treatment initiation and when virus burden is high may require levels of adherence that are higher than those needed during chronic treatment, when viral suppression has been established [69].

The levels and patterns of adherence that lead to drug resistance are quite different for NNRTI-and PI-based regimens. Because of the relatively preserved virologic fitness of NNRTI-resistant virus, very little drug pressure is required to create a selective advantage for NNRTI resistance. Levels of adherence as low as 2% are sufficient to select for NNRTI-resistant virus [70]. In contrast, very low levels of adherence to PIs are insufficient to select for PI-resistant virus. In fact, 85% adherence is required to create a selective advantage for unboosted PI- resistant virus. The level of adherence that selects for boosted PI-resistant virus is unknown, but chaotic adherence, possibly combined with extended monotherapy, is likely to be required [71].

The level of adherence required to select for and maintain nucleoside-analogue resistance is not known. Since these drugs are less potent than other drug classes and since these drugs select for mutations that reduce replication capacity, the level of adherence necessary to select for and maintain a drug-resistance mutation is likely to be higher than that observed with NNRTIs (i.e., >10% adherence) and may be comparable to that observed with unboosted PIs [66].

Adherence patterns, in addition to adherence levels, are important to determining when patients place themselves at risk for drug resistance. The best-characterized regimen-specific pattern of adherence that puts patients at risk for drug resistance is treatment discontinuation of an NNRTI regimen. Because NNRTIs have longer half-lives, treatment discontinuation can lead to NNRTI monotherapy after nucleoside reverse-transcriptase inhibitor levels decay. In support of this hypothesis, Parienti et al. [45] found that ≥2 patient-reported treatment interruptions for >48 h were independently associated with time to rebound of drug resistance. More recently, Oyugi et al. [72] determined that treatment interruptions for >48 h, measured by electronic monitoring of medication pill bottles, was associated with resistance to nevirapine fixed-dose combination therapy. The precise window of interruption that puts patients at risk for developing NNRTI resistance is unknown; treatment interruptions in the study by Oyugi et al. averaged 11.5 days. Indirect evidence suggests that lamivudine, emtricitabine, and enfuvirtide may behave like NNRTIs—that is, low levels of drug exposure are capable of rapidly producing high-level drug resistance [69]. In contrast to NNRTIs and PIs, the relationships between adherence, viral suppression, and drug resistance for many medications that have been in use for some time (e.g., zidovudine, abacavir, and tenofovir) as well as for newer agents (e.g. darunavir, maraviroc, and etravirine) are mostly unknown.

Real-time adherence monitoring also could identify the impact of differential adherence on virologic outcomes. Although adherence to one medication often predicts adherence to another medication [73], differential adherence is not uncommon [74] and may be related to differences in dosing schedule and/ or differences in actual or perceived side effects. Given the observation that viral suppression is possible with boosted-PI monotherapy [75–77], how differential adherence will impact virologic outcomes is unclear. The impact of differential adherence will be likely to vary by medication and by the degree and duration of differential adherence.

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Constructing Regimens Tailored To “Signature Adherence Patterns”

The development of precise adherence monitoring, combined with the full characterization of the outcomes associated with adherence, viral suppression, and drug resistance, creates the potential to tailor regimens to individual patterns of adherence. Although adherence may decline over time, the best predictor of future adherence is past adherence. Objective adherence monitoring creates the potential for defining individual signature adherence patterns. Some patients may consistently take 70% of their medications. Others may have higher levels of adherence with intermittent treatment interruptions, and others may have differential adherence to an individual medication. Signature adherence patterns could be determined early during therapy, prior to viral rebound and drug resistance. Regimen choice could be reevaluated when the patient's regimen is not optimal for their adherence pattern. There is much discussion about tailoring medication choice to individual genomic predictors of treatment response and treatment toxicity, including the possibility of using pharmacogenomic data in the US Food and Drug Administration approval process [78]. Full characterization of the behavioral, as well as the biological, determinants of treatment response should be equally important. Accurate, real-time adherence monitoring could define the scenario for each regimen that would require either a behavioral intervention or a change in regimen, to prevent viral rebound and drug resistance. Such a strategy would both inform the use of and extend the effectiveness of current and future regimens.

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Summary

The first decade of effective HIV ART was defined by the use of ARTs that transformed HIV infection from a terminal to a chronic disease. Treatment choices were expanded by the introduction of new medications within existing classes, as well as the introduction of new classes. More-potent regimens that achieve viral suppression in most individuals were introduced. Despite these important advances, relatively little progress has been made in the development of feasible, reliable, and effective approaches to monitoring adherence. Moreover, much remains unknown about the relationships between adherence, viral suppression, and drug resistance for some of the most commonly used medications, as well as for the new medications and drug classes likely to receive widespread use in the near future. Effective adherence monitoring, coupled with improved characterization of biological outcomes of specific adherence patterns, will allow us to change from reactive response to proactive prevention of viral rebound. Continuously monitoring individual adherence patterns will enable health care providers to target interventions and guide regimen selection. This transition to individualized management of chronic disease, based on how each patient uses lifelong ART, should be a priority for the second decade of HIV disease treatment.

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Acknowledgments

I thank Steve Deeks and Michele Ybarra for their review of and suggestions on the manuscript.

Supplement sponsorship. This article was published as part of a supplement entitled “Significant Challenges Facing HIV Practitioners,” sponsored by Bristol-Myers Squibb.

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Footnotes

  • Financial support: National Institutes of Health (grants MH54907 and AA015287); University of California, San Francisco, Center for AIDS Research (grant P30 MH59037); and an independent educational grant from Bristol-Myers Squibb. Supplement sponsorship is detailed in the Acknowledgments.

  • Potential conflicts of interest: D.R.B. receives research support from Abbott Laboratories, Gilead Sciences, and Bristol-Myers Squibb.

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  1. J Infect Dis. (2008) 197 (Supplement 3): S272-S278. doi: 10.1086/533415
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