Nature Reviews Microbiology | AOP, published online 29 September 2014; doi:10.1038/nrmicro3351
PERSPECTIVES OPINION
A mechanistic theory to explain the efficacy of antiretroviral therapy Sarah B. Laskey and Robert F. Siliciano
Abstract | In the early years of the AIDS epidemic, a diagnosis of HIV‑1 infection was equivalent to a death sentence. The development of combination antiretroviral therapy (cART) in the 1990s to combat HIV‑1 infection was one of the most impressive achievements of medical science. Today, patients who are treated early with cART can expect a near-normal lifespan. In this Opinion article, we propose a fundamental theory to explain the mechanistic basis of cART and why it works so well, including a model to assess and predict the efficacy of antiretroviral drugs alone or in combination. In the natural course of HIV‑1 infection, CD4+ T cells (which are crucial for adaptive immunity) are severely depleted, resulting in a condition known as acquired immune deficiency syndrome (AIDS)1. HIV‑1 primarily infects CD4+ T cells, as viral entry is medi‑ ated by the binding of the viral Env glycopro‑ tein to two cell surface proteins: CD4 and a co-receptor. The co-receptor for HIV-1 entry is usually either CC-chemokine receptor 5 (CCR5) or CXC-chemokine receptor 4 (CXCR4)2,3. Like all retroviruses, HIV‑1 viri‑ ons contain two copies of a single-stranded RNA (ssRNA) genome, which is reverse tran‑ scribed to double-stranded DNA by the viral reverse transcriptase and is integrated into the host genome by the viral integrase. The integrated viral genome (known as a provirus) functions as a cellular gene: in activated CD4+ T cells, the provirus is transcribed and translated to produce viral proteins, which assemble with the genomic viral RNA to form new virions. After the production and release of new virions from the cell surface, the HIV‑1 protease enables virion maturation by cleaving viral polyproteins into functional subunits to produce infectious particles. As shown in FIG. 1, the host chemokine recep‑ tors, viral Env, reverse transcriptase, integrase and protease are all targets for antiretroviral therapy (ART)4. ART prevents HIV‑1 infection of new target cells with remarkable efficacy but has
no effect on cells that contain a provirus. Although most infected cells die quickly, a few enter a metabolically inactive ‘resting memory’ state in which HIV‑1 gene expres‑ sion is reversibly silenced. These latently infected resting memory CD4+ T cells can persist for years or decades, even in the presence of ART5. Except in rare, poorly understood cases6,7, an interruption in ART leads to viral rebound, even in patients who have adhered to suppressive regimens for years8. Thus, the depletion of this latent reservoir is a high research priority, as it is considered to be the major barrier to a cure for HIV‑1 (REF. 9). As this viral reservoir per‑ sists indefinitely, lifelong ART is necessary to prevent disease progression. Although ART cannot target the latent reservoir, extensive clinical and experimental evidence (reviewed in REF. 10) suggests that an optimal ART regimen can inhibit most ongoing cycles of HIV‑1 replication and thus approaches the theoretical upper limit of its efficacy10. Despite the success of ART, there is no fundamental theory to explain how specific drug combinations can achieve com‑ plete inhibition of replication. Treatment guidelines are based on clinical trials of select drug combinations; consequently, most possible drug combinations have never been tested4,11,12. Furthermore, there is reason to believe that different combinations — or, in rare situations, monotherapy13,14 — could
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be similarly effective. Here, we explore what we know about quantifying the efficacy of monotherapy and combination ART (cART; also known as highly active antiretroviral therapy (HAART)). Conventional measures of drug efficacy, including half-maximal inhibitory concen‑ tration (IC50) and inhibitory quotient (IQ), are limited in their capacity to describe the efficacy of antiretroviral drugs. IC50 is the drug concentration that achieves half of the maximal efficacy of that drug, and IQ is the clinical concentration of a drug nor‑ malized to its IC50. In this Opinion article, we discuss the limitations of IC50 and IQ and propose a metric, known as instantaneous inhibitory potential (IIP), which captures drug effects across a range of concentrations. IIP is a function of both IC50 and slope, which is the pharmacodynamic parameter that describes how changes in drug efficacy depend on changes in drug concentration15. Although this model has limitations and cannot provide a complete framework for assessing the efficacy of antiretroviral drugs, it offers a starting point to enable a more accurate quantitative analysis than conven‑ tional metrics. A better understanding of the mechanisms underlying successful ART may eventually enable in silico prediction of the efficacy of new regimens and could increase the availability of ART to infected individu‑ als worldwide, two-thirds of whom currently lack access to optimal treatment16. History of ART In 1987, the FDA approved the first antiretroviral drug, zidovudine (3ʹ‑azido‑ 3ʹ‑deoxythymidine (AZT))17–20, which is a thymidine analogue that inhibits reverse transcription of wild-type HIV‑1. However, AZT monotherapy selects for drug resistance21 and the ineffectiveness of longterm monotherapy spurred the development of more drugs and cART17,22. During the 1990s, other nucleoside analogue reverse transcriptase inhibitors (NRTIs) were developed11,17,18, and although they were active individually and in combi‑ nation, treatment success was limited20,22. In addition to NRTIs, the first protease inhibitor was approved in 1995, and the first nonnucleoside reverse transcriptase inhibitor ADVANCE ONLINE PUBLICATION | 1
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PERSPECTIVES Protease inhibitors
Maturation Attachment
Budding Env Virus assembly
Co-receptor antagonists CD4
Gene expression
CCR5 or CXCR4
Fusion inhibitors
NRTIs, NNRTIs
Fusion
Reverse transcription
Figure 1 | Stages of the HIV‑1 life cycle that are targeted by antiretro viral drugs. The first step in the HIV‑1 replication cycle is attachment of the viral Env glycoprotein spike to the cell surface proteins CD4 and a co‑receptor (either CC-chemokine receptor 5 (CCR5) or CXC-chemokine receptor 4 (CXCR4)), which can be inhibited by HIV-1 co‑receptor antago‑ nists. Fusion of the viral and host cell membranes (which is blocked by fusion inhibitors) enables entry of the viral capsid into the cell, and the viral RNA genome is reverse transcribed to double-stranded DNA (dsDNA), which is integrated into the host genome. Reverse transcription is targeted by nucleo side analogue reverse transcriptase inhibitors (NRTIs) and non-nucleoside
(NNRTI) was approved in 1996. Monotherapy with these drugs initially produced a rapid, exponential decay in viraemia23,24, but it quickly became apparent that combination therapy was necessary to prevent resistance. The modern era of cART began in 1997, when three-drug combinations (which con‑ sisted of two NRTIs and either a protease inhibitor or an NNRTI) were shown to lower viraemia to clinically undetectable lev‑ els25–27. This standard treatment regimen has reduced HIV‑1 infection to a manageable, chronic disease11,28, and the range of available drugs has continued to expand with the addi‑ tion of a fusion inhibitor, CCR5 antagonists and integrase inhibitors (FIG. 1). Furthermore, co‑formulations have reduced the pill burden and improved patient adherence11. As early treatment correlates with improved outcome, current US treatment guidelines recommend cART for all infected individuals11. Ongoing replication despite cART? Although optimal cART can suppress viraemia to clin‑ ically undetectable levels, trace levels of free virus (~1 particle per ml) are still detectable by more sensitive methods in the plasma of
InSTIs, ALLINIs
Nucleus
Integration
reverse transcriptase inhibitors (NNRTIs), whereas the viral integrase is Nature Reviews | Microbiology inhibited by integrase strand transfer inhibitors (InSTIs) and allosteric inte‑ grase inhibitors (ALLINIs). Following successful integration, proviral tran‑ scription yields viral RNAs, which are translated into viral proteins that assemble with the unspliced viral RNA genome at the cell surface. Immature virions bud from the cell and mature via proteolytic processing of viral poly‑ proteins, which ultimately results in mature virions that are capable of infecting new cells. Protease inhibitors block the maturation step, which results in inhibition of reverse transcription and possibly other downstream steps in the life cycle, including integration.
patients on long-term cART29. This residual viraemia may simply represent the release of virus particles from the latent reservoir, which persists in patients on cART5,30–32, but it has also been suggested that ongoing cycles of replication could occur at a low level and contribute to this residual viraemia. Impor‑ tantly, owing to the error-prone nature of reverse transcriptase33, replication of HIV‑1 leads to the unavoidable evolution of its genome. Thus, the absence of genome evolu‑ tion in most studies of residual viraemia and infected cells suggests that cART does indeed completely suppress viral replication34–38. For isolated cases in which evolution has been documented, concurrent drug measurements are lacking, so it is not possible to exclude poor adherence to the treatment regimen as the potential cause of ongoing replication39,40. The emergence of drug resistance is generally not observed in patients who are adherent11, and the prevalence of resistance is declining in those populations that have access to optimal cART41,42. Several studies have evaluated the effects of adding an additional antiretroviral drug to a suppressive cART regimen43–48. If the
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infection of new cells contributes to residual viraemia, then such treatment intensifica‑ tion should further reduce residual viraemia. However, the failure of most intensification studies to show any reduction in residual viraemia argues against ongoing replica‑ tion43–46. By contrast, intensification has been shown to reduce markers of immune activation47 and inflammation48, and inten‑ sification with raltegravir (which is an inte‑ grase inhibitor) led to a transient increase in the level of two‑long terminal repeat (2-LTR) circles47,48. These episomal circles cannot replicate and are only formed during new infection events, which suggests that there is ongoing viral replication. Therefore, it is not clear how to reconcile the increase in 2‑LTR circles with the failure of intensification to reduce residual viraemia. Interestingly, the increase was mainly observed in patients on protease inhibitor‑based regimens47,48 and may therefore reflect the unique pharma‑ cokinetic and pharmacodynamic properties of protease inhibitors (see below). The most compelling evidence against ongoing replication as the sole mechanism of viral persistence comes from recent cases www.nature.com/reviews/micro
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PERSPECTIVES in which three patients who were thought to have been cured by bone marrow transplan‑ tation or by early treatment experienced a sudden rebound in viraemia months or years after the termination of cART49,50. The very delayed viral rebound can be explained by the activation of latently infected cells51. The lack of ongoing replication in patients who adhere to cART is consistent with the in vitro studies described below, which suggest that optimal three-drug regimens have sufficient inhibitory potential to completely suppress HIV‑1 replication12. A theory of ART efficacy The efficacy of ART depends on five factors: pharmacokinetics, which determines the concentration and the time period that each drug is in the body; pharmacodynamics, which determines the antiviral activity of a drug at a specific concentration; the additive, multiplicative, synergistic or antagonistic interactions of drug combinations; patient adherence, which is often affected by pill burden and side effects; and the genetic barrier to the evolution of drug resistance. The models that are discussed below are primarily focused on pharmacodynamics and the interactions between drugs in cART regimens.
The dose–response relationship. Dose– response curves, which show the therapeutic effect of a drug as a function of its concen‑ tration, are fundamental to understanding the efficacy of ART. For a classic sigmoidal dose–response curve, the independent vari‑ able (x axis) is the concentration of the drug, which is typically plotted on a logarithmic scale. Drug concentrations fluctuate in vivo according to well-characterized pharma‑ cokinetic properties, such as drug dose, dosing interval, absorption and clearance52. The dependent variable (y axis) of a classic dose–response curve is the response or effect of the drug at a specified concentration. For antiviral drugs, this is often defined as the fraction of infection events that are affected by the drug (fa) or the fraction that are unaffected by the drug (fu), which is equal to 1 – fa (FIG. 2a). The relationship between concentration and effect is described by fundamental pharmacodynamic equations53, all of which can be written in the form of the median effect model: m
( (
fa D fu = IC50
(1)
The ratio of fa to fu is equal to the drug con‑ centration (D) normalized to the IC50 and raised to the power m, where m is a slope
parameter that is analogous to the Hill coefficient53,54 (see below). In equation 1, m describes the steepness of the curve: the steeper the curve (and the higher m is), the stronger the effect of a drug as its concen tration increases (FIG. 2a). For a classic dose–response curve with a linear y axis, there is no apparent differ‑ ence between 99% inhibition and 99.9999% inhibition (FIG. 2a): at high concentrations, the difference between mediocre and highly effective drugs is obscured. As an alternative representation of the same dose–response relationship, we prefer the following form of the median effect equation53, which con‑ siders the logarithm of the response and thereby emphasizes such clinically relevant differences (FIG. 2b): log
( ff ( = m log ( ICD ( a
u
50
(2)
In this model, the dependent variable (y axis) is the log of the ratio of the number of infection events that are affected by the drug to the number of infection events that are unaffected by the drug. The independent variable (x axis) is the log of drug concentra‑ tion normalized to the IC50. The model pre‑ dicts a linear relationship between variables, and the slope m is analogous to the Hill coefficient54. A slope or Hill coefficient >1 indicates cooperative binding, typically of a ligand to a multivalent target. The Hill coef‑ ficient was initially developed to describe oxygen binding to its quadrivalent carrier protein haemoglobin: the binding of one oxygen molecule increases the likelihood that more oxygen molecules will bind, which is an example of positive cooperativity54. Surprisingly, recent studies show that NNRTIs and protease inhibitors have dose–response curves with particularly steep slopes12,15 (FIG. 2c). For these drugs, small increases in concentration cause large, multilog increases in viral inhibition. This is important as billions of virions are pro‑ duced daily in a typical untreated patient24. With so many infection events to block, the difference between inhibiting 99%, rather than 99.9999%, of new infection events is non-trivial. A drug that inhibits 99% of infection events misses 10,000 times as many new infection events as a drug that inhibits 99.9999% of infection events. Thus, a fourlog increase in inhibition can make the dif‑ ference between a regimen that is fully suppressive and one that is not12. Importance of the slope parameter. As the HIV‑1 enzymes that are targeted by antiretroviral drugs are univalent for their
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inhibitors, intramolecular cooperativity is impossible. Consequently, the slope param‑ eter for antiretroviral drugs was assumed to be equal to one and was initially ignored. The slope is not considered in conventional metrics of antiviral activity such as IC50 or IQ (REF. 55). However, recent studies have shown that the slope parameter varies sub‑ stantially among different antiretroviral drug classes and is a major determinant of the efficacy of ART12,15. In these studies, the slopes of dose–response curves for a range of different antiretroviral drugs were calculated by fitting empirical in vitro data to equation 2 by linear regression15, and the differences in the slope values were found to be highly significant12. A steep slope can produce strong inhibi‑ tion at clinical drug levels15; for example, some protease inhibitors have slopes >3, which means that a tenfold increase in drug concentration causes more than a 1,000‑fold increase in inhibition. This exponential relationship explains the very high antiviral activity that is achieved by these drugs. The protease inhibitors darunavir and lopinavir have each had success as monotherapy to maintain undetectable viraemia in patients whose viral loads were previously suppressed by combination regimens56,57. Cooperative dose–response curves The median effect model does not capture the full complexity of the dose–response relationship for some antiretroviral drugs. Equation 2 describes a linear relationship with a constant slope, m. However, empirical median effect plots for protease inhibitors and NNRTIs inflect upwards, so the slope increases at higher drug concentrations12 (FIG. 2c), which contributes to the strong inhibition that is achieved by these drugs. Two non-exclusive models explain these cooperative, upward-inflecting curves.
Critical subset model. This model explains how antiretroviral drugs that have only one binding site on the target enzyme can generate cooperative dose–response curves when the measured response is infectivity (rather than enzyme activity). Some steps in the HIV‑1 life cycle, such as reverse tran‑ scription and virion maturation, require multiple copies of the relevant viral enzymes. Although the binding of a drug to each enzyme molecule is independent, the pool of enzymes within a virion or infected cell functions as one multivalent drug target that is effectively inhibited when a critical proportion (known as the critical subset) of the target sites is bound by the drug. The ADVANCE ONLINE PUBLICATION | 3
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PERSPECTIVES a
b 10 Log [ ƒa/ƒu] (logs of inhibition)
ƒu (% of maximal infection)
100 80 60 40 m=1 m = 1.5 m=2 m=3 m=5
20 0 –2
c
2
5
0
–5
10
–2
–1 0 1 Drug concentration (log scale)
2
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–1 0 1 Drug concentration (log scale)
m=1 m = 1.5 m=2 m=3 m=5
NRTI NNRTI InSTI Protease inhibitor Fusion inhibitor CCR5a
1
0
–1
–2 –1
0 Drug concentration (log scale)
1
Figure 2 | Semi-log and median effect dose–response curves. a | Standard Naturesemi-log Reviewsdose–response | Microbiology curves for five hypothetical drugs (shown as different coloured curves). The fraction of infection events that are unaffected by the drug (fu) is plotted as a function of drug concentration (in log scale), normalized to IC50. The effect of variation in the slope (m) is obscured as all of the curves seem to approach complete inhibition (0% infection) at high drug concentrations. The blue-shaded region represents a typical clinical range of drug concentrations. b | Logarithmic median effect plot of the same dose–response curves shown in part a. At clinical concentrations (blue region), the differences in efficacy among the five drugs with different slopes are more evident on this plot than on the semi-log plot, and drugs with steeper slopes show stronger inhibition (by several logs). c | Empirical median effect plots for representative antiretroviral drugs from each class: emtricitabine (nucleoside analogue reverse transcriptase inhibitor (NRTI)), efa‑ virenz (non-nucleoside reverse transcriptase inhibitor (NNRTI)), raltegravir (integrase strand transfer inhibitor (InSTI)), darunavir (protease inhibitor), enfuvirtide (fusion inhibitor) and maraviroc (CC-chemokine receptor 5 antagonist (CCR5a)). The data show that protease inhibitors and NNRTIs have the steepest slopes, which means that small increases in their concentrations result in large increases in drug activity. When normalized to IC50, most drugs that have been tested follow these representative curves closely, but NRTIs show the most intra-class variation12. Part c adapted from REF. 12, Nature Publishing Group.
resulting reduction in overall enzyme activ‑ ity prevents completion of the relevant step of the life cycle before some decay process blocks infection irreversibly58; for example, NNRTIs bind reversibly to reverse tran‑ scriptase but may irreversibly block infec‑ tion by delaying the completion of reverse transcription until the ssRNA viral genome template is degraded.
In this model, the slope of the median effect curve is a function of the number of target enzymes that are present and the critical number of functional enzyme mol‑ ecules that are necessary for productive infection. For drugs that target a step in the life cycle that is mediated by multiple copies of the relevant enzyme (such as pro‑ tease inhibitors and NNRTIs), this model
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predicts median effect curves that are steep and inflect upwards. By contrast, for drugs that target viral integration (which is carried out by a single integrase enzyme complex), this model predicts median effect curves with slopes ≈1, which is con‑ sistent with experimental slope measure‑ ments58. The same is true for NRTIs, which function by terminating individual reverse transcripts rather than by inhibiting enzyme activity. Entry inhibitors are more complex owing to the trimeric nature of the viral Env glycoprotein and the shapes of these dose–response curves have not been mechanistically explained. With this exception, the critical subset model provides a simple explanation for the differences in slope for different classes of antiretroviral drugs. Importantly, the model is applicable under the following assumptions: each target site binds the drug with an unchanging dissociation constant (Kd), even at extreme concentra‑ tions; and a certain proportion of available target sites must be unoccupied for suc‑ cessful completion of the relevant step in the virus life cycle. These assumptions are reasonable and are not contradicted by any available biochemical data. Multistep inhibition model of cooperativity. The binding of a drug to a target that is involved in multiple steps of the virus life cycle is another mechanism that can account for steep, upward-inflecting dose–response curves, such as those that are observed for the protease inhibitors. HIV‑1 protease is responsible for virion maturation, but protease inhibition actu‑ ally impairs several events in the life cycle (FIG. 1). Immature virions are incapable of entering target cells59, and the viral enzyme reverse transcriptase is formed by the cleavage of a polyprotein precursor. Con‑ sequently, protease inhibition manifests as inhibition of entry and reverse transcrip‑ tion. It is possible to deconstruct the steep, upward-inflecting dose–response curve of a protease inhibitor into linear curves show‑ ing the effect of the drug at individual steps of the viral life cycle60. The new class of allosteric HIV-1 inte‑ grase inhibitors (ALLINIs) also exhibit cooperative dose–response curves that have slopes >1 (REFS 61,62). By stabilizing catalyti‑ cally inactive integrase multimers, ALLINIs prevent multiple interactions with host factors that are important for integration61. ALLINI-induced integrase multimerization also inhibits other steps in the viral life cycle, including maturation62. www.nature.com/reviews/micro
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PERSPECTIVES Quantifying the activity of antiretroviral drugs. As emphasized by the data presented above, the slope is essential for accurate cal‑ culation of antiviral activity at drug concen‑ trations that are beyond the range of in vitro assays, including the clinical concentrations of some antiretroviral drugs12. As IC50 and IQ neglect the slope parameter, they are limited as metrics of antiviral activity. We propose that a superior metric of ART efficacy is IIP, which depends on D, IC50 and m (REF. 15): m
( ( ( (
IIP = log 1 +
D IC50
(3)
IIP is equivalent to the number of logs by which a single round of infection is reduced in the presence of a single drug or several drugs. According to a simple model of viral dynamics63,64, approximately 106 new infec‑ tion events occur per viral generation in a patient with a typical plasma HIV‑1 RNA level of 30,000 copies per ml (REF. 65). In this case, complete suppression of replication would require six logs of inhibition or an ART regimen with IIP = 6, which is equivalent to 99.9999% inhibition. A recent in vitro study12 showed that most individual antiretroviral drugs have maximal clinical IIP values below this six-log threshold, with the notable exception of certain protease inhibitors (such as darunavir) and NNRTIs (such as efavirenz). By contrast, most of the three-drug regimens that are recommended for initial ART have a combined IIP >6 (see below), which provides an explanation for the clinical observations that monotherapy and dual NRTI regimens do not control HIV‑1 replication, whereas many three-drug regi‑ mens that include an NNRTI or a protease inhibitor do control HIV-1 replication. Interestingly, a recommended threedrug regimen consisting of the integrase strand transfer inhibitor (InSTI) raltegravir and the NRTIs tenofovir disoproxil fuma‑ rate and emtricitabine was effective in clinical trials11 despite an in vitro combined IIP of less than six12. Patients starting ralte‑ gravir-based regimens experience a rapid drop in viraemia, which was initially inter‑ preted as an indication of greater antiviral activity; however, the dynamics of viral decay are probably a reflection of the stage in the viral life cycle that the drug targets66. As an InSTI, raltegravir functions later in the virus life cycle than other antiretroviral drugs (FIG. 1) and thus blocks virus produc‑ tion by some cells in which earlier events (such as entry and reverse transcription) have been completed at the initiation of therapy. Thus, even with equally effica‑ cious drugs, more rapid decay in viraemia
may be observed for drugs that target later events in the life cycle. The clinical efficacy of raltegravir and other recently approved InSTIs (such as elvitegravir and dolute‑ gravir) in combination therapy may be partially due to the favourable interactions of this drug class with all other classes (see below). These interactions result in a com‑ bined IIP that is greater than the relatively low IIP of raltegravir itself. There is no evi‑ dence that raltegravir or other InSTIs have a sufficient IIP to be used as monotherapy. The success of InSTIs in combination therapy could also be due to determinants of efficacy that are not accounted for in the IIP model, such as patient adherence to the treatment regimen. Raltegravir is extremely well tolerated67; thus, superior adherence to raltegravir-based regimens may contribute to their clinical success. For all regimens in which IIP is high enough to fully suppress viral replication (combined IIP >6; see below), adherence, rather than the absolute value of the IIP, is the main determinant of treatment success. Finally, it is possible that the in vivo activity of InSTIs is greater than that predicted by in vitro models, for reasons that are not yet clear. These arguments, which are based on in vitro studies of raltegravir and elvitegravir, probably also apply to dolutegravir, which is the most recently approved InSTI68. Treatment failure on ART Although an optimal cART regimen can suppress viraemia to undetectable levels, suboptimal treatment can lead to virologic failure, which is defined as persistent virae‑ mia above the threshold of clinical detection in a patient receiving cART. Virologic failure typically precedes immunological failure (which is characterized by a low CD4+ T cell count) and clinical failure (that is, sympto‑ matic disease progression)69, both of which are often, but not always, associated with the evolution of drug-resistant virus.
Resistance with and without mutations. Drug resistance, owing to suboptimal treat‑ ment, is a major barrier to the effective treatment of HIV‑1 infection70. The most common forms of resistance are point muta‑ tions in target viral proteins, which often inhibit drug binding70,71. Interestingly, AZT resistance can arise by a different mechanism: in some cases, resistance mutations create a binding pocket for ATP, which participates in reverse transcriptase‑mediated excision of the incorporated chain-terminating nucleoside analogue72. The dose–response curves for the inhibition of drug-resistant virus have
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higher IC50 and/or lower slope values than curves for wild-type virus73. Both of these differences adversely affect the efficacy of the drug (see equation 2), but unfortunately, current clinical measures consider only changes in the IC50. In cells that are simultaneously infected by multiple virions, drug efficacy can be lower despite the absence of resistance mutations. A recent in vitro study showed that, at a high multiplicity of infection (MOI), wild-type virus can exhibit a drug-resistant phenotype on a simple probabilistic basis: a cell that is exposed to many virions only remains unin‑ fected if all infection events are inhibited74. The magnitude of this high MOI-based drug resistance may vary substantially with antiretroviral class. Whereas high resistance to reverse transcriptase inhibitors by wildtype virus has been repeatedly shown in high-MOI in vitro systems, protease inhibi‑ tors show a similar ability to suppress both high-MOI and low-MOI infection in vitro75. The clinical importance of these findings is not yet clear, as most infected cells in vivo contain only one viral genome76,77. Role of pharmacokinetics in ART efficacy. Pharmacokinetic properties are an impor‑ tant determinant of antiretroviral efficacy and may be used to calculate IIP throughout the dosing interval or following missed doses12,78. At clinical concentrations, protease inhibitors have the highest intrinsic activity of any antiretroviral drug class as they have steep median effect slopes, but they are also rapidly cleared in vivo. Thus, to increase the half-life of protease inhibitors in vivo, they are typically co‑administered with ritonavir, which is a protease inhibitor that is also a potent inhibitor of the host metabolic enzymes that are responsible for the elimination of all protease inhibitors79,80. Many protease inhibitor‑based regimens fail despite the absence of resistance muta‑ tions in the viral protease gene81,82, and this failure can be partially explained by pharmacokinetics78. Owing to the steep slope of protease inhibitor dose–response curves, small changes in drug concentra‑ tion cause large changes in viral inhibition. Thus, protease inhibitors are very effective drugs at maximal concentrations in vivo, but their effectiveness rapidly declines as the drug is cleared. Drug-resistant virus typi‑ cally evolves in patients with suboptimal adherence to treatment: missed doses cause a decline in systemic drug levels to sub optimal concentrations, which enable lowlevel replication of the virus and select for drug-resistant variants. However, protease ADVANCE ONLINE PUBLICATION | 5
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PERSPECTIVES Box 1 | Quantifying interactions between antiretroviral drugs The Loewe additivity84 and Bliss independence85 theories are applied to predict the efficacy of drug combinations using the individual efficacies of each drug. Loewe additivity assumes that the drugs have a shared mechanism of action (see the figure, part a, left-hand panel) and compete for the same binding site. By contrast, Bliss independence assumes that the drugs function independently (see the figure, part a, right-hand panel) and have distinct targets. The two models are described by different mathematical expressions for the combined efficacy84,85. The combined efficacy that is predicted by Bliss independence is generally greater than that predicted by Loewe additivity. Interestingly, the efficacies of many empirical drug combinations deviate from the predictions of both theories12, which suggests that there is a spectrum of interactions, ranging from additive (for Loewe additivity) to multiplicative (for Bliss independence). Importantly, those drugs that have independent mechanisms of action tend to have greater efficacy when combined. In part b of the figure, the left-hand plot shows empirical dose–response relationships for two hypothetical drugs individually and in combination. The right-hand plot compares the dose–response relationship of the drug combination curve (black) with that which would be observed if the two drugs interact according to Loewe additivity (orange) or Bliss independence (green). In this example, the measured drug combination curve (black) does not fit either theory well, but lies between them.
a
Loewe additivity
Bliss independence
Drug 2 Drug 1
Drug 2
Drug 1
Drug targets
Drug target
b
7
7
Drug 1 Drug 2 Drugs 1 and 2
5 4 3 2
5 4 3 2 1
1 0
Drugs 1 and 2 Loewe additivity Bliss independence
6
[ ƒa/ƒu] (logs of inhibition)
[ ƒa/ƒu] (logs of inhibition)
6
-2
-1
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1
Drug concentration (log scale)
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-2
-1
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Drug concentration (log scale) Nature Reviews | Microbiology
inhibitors are only transiently present at intermediate levels that select for resist‑ ance, as these drugs are rapidly cleared. As a result, resistance mutations in the protease gene are rare, even in patients who fail protease inhibitor‑based regimens78. Failure of protease inhibitor‑based treatment despite the absence of resistance mutations in the protease gene can also be explained by the multistep mechanism that is discussed above60. Protease inhibitors are most effective at inhibiting the entry step: interactions between Env and uncleaved Gag
inhibit entry until Gag is cleaved by pro‑ tease83. However, some Env mutations seem to confer resistance to protease inhibitors by enabling entry even when Gag is not fully cleaved60. These Env mutations are not detected in standard resistance testing but may contribute to clinical resistance to protease inhibitors. Combining antiretroviral drugs To prevent the emergence of resistance, antiretroviral drugs are usually administered in combination (that is, in cART)11 and it is
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therefore crucial to understand the interac‑ tions between individual drugs and their combined therapeutic effects. Two main pharmacodynamic theories are used to esti‑ mate the activity of a drug combination on the basis of the efficacies of each individual drug (BOX 1). One theory (known as Loewe additivity84) assumes that drug efficacies are additive, whereas the other theory (known as Bliss independence85) assumes that they are multiplicative. In the Loewe additivity theory, individual drugs are assumed to share a mechanism of action84; for example, drugs of the NNRTI class bind allosterically to a single site on reverse transcriptase. When these drugs are combined, they follow Loewe additivity as they have the same mechanism of action and compete for the same target site (FIG. 3). At any given time, a single reverse transcriptase molecule can be inhibited by one drug or the other, but not by both drugs. By contrast, the Bliss independence theory assumes that individual drugs have independent mechanisms of action and inhibit distinct viral processes85 (BOX 1). This theory fits empirical in vitro data for most combinations of an integrase inhibitor with a reverse transcriptase inhibitor (FIG. 3), as these drugs inhibit distinct steps in the viral life cycle. Compared with Loewe additivity, Bliss independence generally predicts greater inhi‑ bition of viral replication with cooperative, upward-inflecting median effect curves12. The terms synergy and antagonism are frequently used to describe the effect of combination therapy, but they have meaning only in relation to a model that can predict what the combined effect should be. How‑ ever, as described above, the two most widely used models (that is, Loewe additivity and Bliss independence) give different predic‑ tions for the combined efficacy of drugs with known individual efficacies. In fact, most pairs of antiretroviral drugs do not fit either model well12. Thus, we suggest that the term synergy be reserved for combined effects that exceed those predicted by Bliss inde‑ pendence and that the term antagonism be reserved for combined effects that are lower than those predicted by Loewe additivity. Empirical analysis of pairwise combinations of antiretroviral drugs (FIG. 3) has shown that many combinations are synergistic, whereas antagonism does not seem to occur, which suggests that the Loewe additivity model sets a lower threshold for the efficacy of cART12. Furthermore, many drug pairs show intermediate efficacy, which is higher than that predicted by Loewe additivity but lower than that predicted by Bliss independence12 www.nature.com/reviews/micro
© 2014 Macmillan Publishers Limited. All rights reserved
PERSPECTIVES
Perspectives Drug resistance has been one of the great‑ est obstacles for the successful treatment of HIV‑1 infection. Resistance mutations have been characterized for every antiret‑ roviral drug71 and an estimated 10–17% of new infections in high-income countries are caused by a virus that is resistant to at least one drug16. However, with the contin‑ ued development of novel drugs and more efficacious treatment regimens, the preva‑ lence of resistance has begun to decline41,42. Single-pill, once‑a‑day formulations have improved patient adherence, which coun‑ teracts the evolution of resistance86. The availability of multiple well-tolerated, fully suppressive regimens means that there are treatment options even for patients who are infected with drug-resistant virus or who have adverse reactions to specific antiretroviral drugs11.
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(2
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an al og
Synergy Intermediate effect
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(1
)
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Bliss independence
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Loewe additivity
dG TP
Not determined
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in
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Fusion inhibitor (1)
Fu sio
Protease inhibitor (5)
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InSTI (2)
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NNRTI (3)
to
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dCTP analogue (2)
Greater combined inhibition
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dC TP
dT TP
protease inhibitor with a drug from several other classes typically results in intermediate efficacy, which reflects the fact that inhibi‑ tion of HIV‑1 protease indirectly affects several steps in the HIV‑1 life cycle. The integrase inhibitors are an exception to this trend as they seem to interact with protease inhibitors in a synergistic manner (FIG. 3). It is possible to extrapolate from the data that describe the efficacy of drug pairs to estimate the inhibitory capacity of regimens that combine three drugs12. Many three-drug regimens have an extremely high combined IIP (>10 logs), which is consistent with the ability of modern cART to exert long-term control of HIV‑1 replication. Importantly, although the predictions of the combined IIP metric correlate well with clinical outcome12, the model has not yet been validated as a successful basis for designing new drug regimens. Only one of 31 evaluated regimens with a combined IIP of
PERSPECTIVES OPINION
A mechanistic theory to explain the efficacy of antiretroviral therapy Sarah B. Laskey and Robert F. Siliciano
Abstract | In the early years of the AIDS epidemic, a diagnosis of HIV‑1 infection was equivalent to a death sentence. The development of combination antiretroviral therapy (cART) in the 1990s to combat HIV‑1 infection was one of the most impressive achievements of medical science. Today, patients who are treated early with cART can expect a near-normal lifespan. In this Opinion article, we propose a fundamental theory to explain the mechanistic basis of cART and why it works so well, including a model to assess and predict the efficacy of antiretroviral drugs alone or in combination. In the natural course of HIV‑1 infection, CD4+ T cells (which are crucial for adaptive immunity) are severely depleted, resulting in a condition known as acquired immune deficiency syndrome (AIDS)1. HIV‑1 primarily infects CD4+ T cells, as viral entry is medi‑ ated by the binding of the viral Env glycopro‑ tein to two cell surface proteins: CD4 and a co-receptor. The co-receptor for HIV-1 entry is usually either CC-chemokine receptor 5 (CCR5) or CXC-chemokine receptor 4 (CXCR4)2,3. Like all retroviruses, HIV‑1 viri‑ ons contain two copies of a single-stranded RNA (ssRNA) genome, which is reverse tran‑ scribed to double-stranded DNA by the viral reverse transcriptase and is integrated into the host genome by the viral integrase. The integrated viral genome (known as a provirus) functions as a cellular gene: in activated CD4+ T cells, the provirus is transcribed and translated to produce viral proteins, which assemble with the genomic viral RNA to form new virions. After the production and release of new virions from the cell surface, the HIV‑1 protease enables virion maturation by cleaving viral polyproteins into functional subunits to produce infectious particles. As shown in FIG. 1, the host chemokine recep‑ tors, viral Env, reverse transcriptase, integrase and protease are all targets for antiretroviral therapy (ART)4. ART prevents HIV‑1 infection of new target cells with remarkable efficacy but has
no effect on cells that contain a provirus. Although most infected cells die quickly, a few enter a metabolically inactive ‘resting memory’ state in which HIV‑1 gene expres‑ sion is reversibly silenced. These latently infected resting memory CD4+ T cells can persist for years or decades, even in the presence of ART5. Except in rare, poorly understood cases6,7, an interruption in ART leads to viral rebound, even in patients who have adhered to suppressive regimens for years8. Thus, the depletion of this latent reservoir is a high research priority, as it is considered to be the major barrier to a cure for HIV‑1 (REF. 9). As this viral reservoir per‑ sists indefinitely, lifelong ART is necessary to prevent disease progression. Although ART cannot target the latent reservoir, extensive clinical and experimental evidence (reviewed in REF. 10) suggests that an optimal ART regimen can inhibit most ongoing cycles of HIV‑1 replication and thus approaches the theoretical upper limit of its efficacy10. Despite the success of ART, there is no fundamental theory to explain how specific drug combinations can achieve com‑ plete inhibition of replication. Treatment guidelines are based on clinical trials of select drug combinations; consequently, most possible drug combinations have never been tested4,11,12. Furthermore, there is reason to believe that different combinations — or, in rare situations, monotherapy13,14 — could
NATURE REVIEWS | MICROBIOLOGY
be similarly effective. Here, we explore what we know about quantifying the efficacy of monotherapy and combination ART (cART; also known as highly active antiretroviral therapy (HAART)). Conventional measures of drug efficacy, including half-maximal inhibitory concen‑ tration (IC50) and inhibitory quotient (IQ), are limited in their capacity to describe the efficacy of antiretroviral drugs. IC50 is the drug concentration that achieves half of the maximal efficacy of that drug, and IQ is the clinical concentration of a drug nor‑ malized to its IC50. In this Opinion article, we discuss the limitations of IC50 and IQ and propose a metric, known as instantaneous inhibitory potential (IIP), which captures drug effects across a range of concentrations. IIP is a function of both IC50 and slope, which is the pharmacodynamic parameter that describes how changes in drug efficacy depend on changes in drug concentration15. Although this model has limitations and cannot provide a complete framework for assessing the efficacy of antiretroviral drugs, it offers a starting point to enable a more accurate quantitative analysis than conven‑ tional metrics. A better understanding of the mechanisms underlying successful ART may eventually enable in silico prediction of the efficacy of new regimens and could increase the availability of ART to infected individu‑ als worldwide, two-thirds of whom currently lack access to optimal treatment16. History of ART In 1987, the FDA approved the first antiretroviral drug, zidovudine (3ʹ‑azido‑ 3ʹ‑deoxythymidine (AZT))17–20, which is a thymidine analogue that inhibits reverse transcription of wild-type HIV‑1. However, AZT monotherapy selects for drug resistance21 and the ineffectiveness of longterm monotherapy spurred the development of more drugs and cART17,22. During the 1990s, other nucleoside analogue reverse transcriptase inhibitors (NRTIs) were developed11,17,18, and although they were active individually and in combi‑ nation, treatment success was limited20,22. In addition to NRTIs, the first protease inhibitor was approved in 1995, and the first nonnucleoside reverse transcriptase inhibitor ADVANCE ONLINE PUBLICATION | 1
© 2014 Macmillan Publishers Limited. All rights reserved
PERSPECTIVES Protease inhibitors
Maturation Attachment
Budding Env Virus assembly
Co-receptor antagonists CD4
Gene expression
CCR5 or CXCR4
Fusion inhibitors
NRTIs, NNRTIs
Fusion
Reverse transcription
Figure 1 | Stages of the HIV‑1 life cycle that are targeted by antiretro viral drugs. The first step in the HIV‑1 replication cycle is attachment of the viral Env glycoprotein spike to the cell surface proteins CD4 and a co‑receptor (either CC-chemokine receptor 5 (CCR5) or CXC-chemokine receptor 4 (CXCR4)), which can be inhibited by HIV-1 co‑receptor antago‑ nists. Fusion of the viral and host cell membranes (which is blocked by fusion inhibitors) enables entry of the viral capsid into the cell, and the viral RNA genome is reverse transcribed to double-stranded DNA (dsDNA), which is integrated into the host genome. Reverse transcription is targeted by nucleo side analogue reverse transcriptase inhibitors (NRTIs) and non-nucleoside
(NNRTI) was approved in 1996. Monotherapy with these drugs initially produced a rapid, exponential decay in viraemia23,24, but it quickly became apparent that combination therapy was necessary to prevent resistance. The modern era of cART began in 1997, when three-drug combinations (which con‑ sisted of two NRTIs and either a protease inhibitor or an NNRTI) were shown to lower viraemia to clinically undetectable lev‑ els25–27. This standard treatment regimen has reduced HIV‑1 infection to a manageable, chronic disease11,28, and the range of available drugs has continued to expand with the addi‑ tion of a fusion inhibitor, CCR5 antagonists and integrase inhibitors (FIG. 1). Furthermore, co‑formulations have reduced the pill burden and improved patient adherence11. As early treatment correlates with improved outcome, current US treatment guidelines recommend cART for all infected individuals11. Ongoing replication despite cART? Although optimal cART can suppress viraemia to clin‑ ically undetectable levels, trace levels of free virus (~1 particle per ml) are still detectable by more sensitive methods in the plasma of
InSTIs, ALLINIs
Nucleus
Integration
reverse transcriptase inhibitors (NNRTIs), whereas the viral integrase is Nature Reviews | Microbiology inhibited by integrase strand transfer inhibitors (InSTIs) and allosteric inte‑ grase inhibitors (ALLINIs). Following successful integration, proviral tran‑ scription yields viral RNAs, which are translated into viral proteins that assemble with the unspliced viral RNA genome at the cell surface. Immature virions bud from the cell and mature via proteolytic processing of viral poly‑ proteins, which ultimately results in mature virions that are capable of infecting new cells. Protease inhibitors block the maturation step, which results in inhibition of reverse transcription and possibly other downstream steps in the life cycle, including integration.
patients on long-term cART29. This residual viraemia may simply represent the release of virus particles from the latent reservoir, which persists in patients on cART5,30–32, but it has also been suggested that ongoing cycles of replication could occur at a low level and contribute to this residual viraemia. Impor‑ tantly, owing to the error-prone nature of reverse transcriptase33, replication of HIV‑1 leads to the unavoidable evolution of its genome. Thus, the absence of genome evolu‑ tion in most studies of residual viraemia and infected cells suggests that cART does indeed completely suppress viral replication34–38. For isolated cases in which evolution has been documented, concurrent drug measurements are lacking, so it is not possible to exclude poor adherence to the treatment regimen as the potential cause of ongoing replication39,40. The emergence of drug resistance is generally not observed in patients who are adherent11, and the prevalence of resistance is declining in those populations that have access to optimal cART41,42. Several studies have evaluated the effects of adding an additional antiretroviral drug to a suppressive cART regimen43–48. If the
2 | ADVANCE ONLINE PUBLICATION
infection of new cells contributes to residual viraemia, then such treatment intensifica‑ tion should further reduce residual viraemia. However, the failure of most intensification studies to show any reduction in residual viraemia argues against ongoing replica‑ tion43–46. By contrast, intensification has been shown to reduce markers of immune activation47 and inflammation48, and inten‑ sification with raltegravir (which is an inte‑ grase inhibitor) led to a transient increase in the level of two‑long terminal repeat (2-LTR) circles47,48. These episomal circles cannot replicate and are only formed during new infection events, which suggests that there is ongoing viral replication. Therefore, it is not clear how to reconcile the increase in 2‑LTR circles with the failure of intensification to reduce residual viraemia. Interestingly, the increase was mainly observed in patients on protease inhibitor‑based regimens47,48 and may therefore reflect the unique pharma‑ cokinetic and pharmacodynamic properties of protease inhibitors (see below). The most compelling evidence against ongoing replication as the sole mechanism of viral persistence comes from recent cases www.nature.com/reviews/micro
© 2014 Macmillan Publishers Limited. All rights reserved
PERSPECTIVES in which three patients who were thought to have been cured by bone marrow transplan‑ tation or by early treatment experienced a sudden rebound in viraemia months or years after the termination of cART49,50. The very delayed viral rebound can be explained by the activation of latently infected cells51. The lack of ongoing replication in patients who adhere to cART is consistent with the in vitro studies described below, which suggest that optimal three-drug regimens have sufficient inhibitory potential to completely suppress HIV‑1 replication12. A theory of ART efficacy The efficacy of ART depends on five factors: pharmacokinetics, which determines the concentration and the time period that each drug is in the body; pharmacodynamics, which determines the antiviral activity of a drug at a specific concentration; the additive, multiplicative, synergistic or antagonistic interactions of drug combinations; patient adherence, which is often affected by pill burden and side effects; and the genetic barrier to the evolution of drug resistance. The models that are discussed below are primarily focused on pharmacodynamics and the interactions between drugs in cART regimens.
The dose–response relationship. Dose– response curves, which show the therapeutic effect of a drug as a function of its concen‑ tration, are fundamental to understanding the efficacy of ART. For a classic sigmoidal dose–response curve, the independent vari‑ able (x axis) is the concentration of the drug, which is typically plotted on a logarithmic scale. Drug concentrations fluctuate in vivo according to well-characterized pharma‑ cokinetic properties, such as drug dose, dosing interval, absorption and clearance52. The dependent variable (y axis) of a classic dose–response curve is the response or effect of the drug at a specified concentration. For antiviral drugs, this is often defined as the fraction of infection events that are affected by the drug (fa) or the fraction that are unaffected by the drug (fu), which is equal to 1 – fa (FIG. 2a). The relationship between concentration and effect is described by fundamental pharmacodynamic equations53, all of which can be written in the form of the median effect model: m
( (
fa D fu = IC50
(1)
The ratio of fa to fu is equal to the drug con‑ centration (D) normalized to the IC50 and raised to the power m, where m is a slope
parameter that is analogous to the Hill coefficient53,54 (see below). In equation 1, m describes the steepness of the curve: the steeper the curve (and the higher m is), the stronger the effect of a drug as its concen tration increases (FIG. 2a). For a classic dose–response curve with a linear y axis, there is no apparent differ‑ ence between 99% inhibition and 99.9999% inhibition (FIG. 2a): at high concentrations, the difference between mediocre and highly effective drugs is obscured. As an alternative representation of the same dose–response relationship, we prefer the following form of the median effect equation53, which con‑ siders the logarithm of the response and thereby emphasizes such clinically relevant differences (FIG. 2b): log
( ff ( = m log ( ICD ( a
u
50
(2)
In this model, the dependent variable (y axis) is the log of the ratio of the number of infection events that are affected by the drug to the number of infection events that are unaffected by the drug. The independent variable (x axis) is the log of drug concentra‑ tion normalized to the IC50. The model pre‑ dicts a linear relationship between variables, and the slope m is analogous to the Hill coefficient54. A slope or Hill coefficient >1 indicates cooperative binding, typically of a ligand to a multivalent target. The Hill coef‑ ficient was initially developed to describe oxygen binding to its quadrivalent carrier protein haemoglobin: the binding of one oxygen molecule increases the likelihood that more oxygen molecules will bind, which is an example of positive cooperativity54. Surprisingly, recent studies show that NNRTIs and protease inhibitors have dose–response curves with particularly steep slopes12,15 (FIG. 2c). For these drugs, small increases in concentration cause large, multilog increases in viral inhibition. This is important as billions of virions are pro‑ duced daily in a typical untreated patient24. With so many infection events to block, the difference between inhibiting 99%, rather than 99.9999%, of new infection events is non-trivial. A drug that inhibits 99% of infection events misses 10,000 times as many new infection events as a drug that inhibits 99.9999% of infection events. Thus, a fourlog increase in inhibition can make the dif‑ ference between a regimen that is fully suppressive and one that is not12. Importance of the slope parameter. As the HIV‑1 enzymes that are targeted by antiretroviral drugs are univalent for their
NATURE REVIEWS | MICROBIOLOGY
inhibitors, intramolecular cooperativity is impossible. Consequently, the slope param‑ eter for antiretroviral drugs was assumed to be equal to one and was initially ignored. The slope is not considered in conventional metrics of antiviral activity such as IC50 or IQ (REF. 55). However, recent studies have shown that the slope parameter varies sub‑ stantially among different antiretroviral drug classes and is a major determinant of the efficacy of ART12,15. In these studies, the slopes of dose–response curves for a range of different antiretroviral drugs were calculated by fitting empirical in vitro data to equation 2 by linear regression15, and the differences in the slope values were found to be highly significant12. A steep slope can produce strong inhibi‑ tion at clinical drug levels15; for example, some protease inhibitors have slopes >3, which means that a tenfold increase in drug concentration causes more than a 1,000‑fold increase in inhibition. This exponential relationship explains the very high antiviral activity that is achieved by these drugs. The protease inhibitors darunavir and lopinavir have each had success as monotherapy to maintain undetectable viraemia in patients whose viral loads were previously suppressed by combination regimens56,57. Cooperative dose–response curves The median effect model does not capture the full complexity of the dose–response relationship for some antiretroviral drugs. Equation 2 describes a linear relationship with a constant slope, m. However, empirical median effect plots for protease inhibitors and NNRTIs inflect upwards, so the slope increases at higher drug concentrations12 (FIG. 2c), which contributes to the strong inhibition that is achieved by these drugs. Two non-exclusive models explain these cooperative, upward-inflecting curves.
Critical subset model. This model explains how antiretroviral drugs that have only one binding site on the target enzyme can generate cooperative dose–response curves when the measured response is infectivity (rather than enzyme activity). Some steps in the HIV‑1 life cycle, such as reverse tran‑ scription and virion maturation, require multiple copies of the relevant viral enzymes. Although the binding of a drug to each enzyme molecule is independent, the pool of enzymes within a virion or infected cell functions as one multivalent drug target that is effectively inhibited when a critical proportion (known as the critical subset) of the target sites is bound by the drug. The ADVANCE ONLINE PUBLICATION | 3
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PERSPECTIVES a
b 10 Log [ ƒa/ƒu] (logs of inhibition)
ƒu (% of maximal infection)
100 80 60 40 m=1 m = 1.5 m=2 m=3 m=5
20 0 –2
c
2
5
0
–5
10
–2
–1 0 1 Drug concentration (log scale)
2
4
3
2 Log [ ƒa/ƒu]
–1 0 1 Drug concentration (log scale)
m=1 m = 1.5 m=2 m=3 m=5
NRTI NNRTI InSTI Protease inhibitor Fusion inhibitor CCR5a
1
0
–1
–2 –1
0 Drug concentration (log scale)
1
Figure 2 | Semi-log and median effect dose–response curves. a | Standard Naturesemi-log Reviewsdose–response | Microbiology curves for five hypothetical drugs (shown as different coloured curves). The fraction of infection events that are unaffected by the drug (fu) is plotted as a function of drug concentration (in log scale), normalized to IC50. The effect of variation in the slope (m) is obscured as all of the curves seem to approach complete inhibition (0% infection) at high drug concentrations. The blue-shaded region represents a typical clinical range of drug concentrations. b | Logarithmic median effect plot of the same dose–response curves shown in part a. At clinical concentrations (blue region), the differences in efficacy among the five drugs with different slopes are more evident on this plot than on the semi-log plot, and drugs with steeper slopes show stronger inhibition (by several logs). c | Empirical median effect plots for representative antiretroviral drugs from each class: emtricitabine (nucleoside analogue reverse transcriptase inhibitor (NRTI)), efa‑ virenz (non-nucleoside reverse transcriptase inhibitor (NNRTI)), raltegravir (integrase strand transfer inhibitor (InSTI)), darunavir (protease inhibitor), enfuvirtide (fusion inhibitor) and maraviroc (CC-chemokine receptor 5 antagonist (CCR5a)). The data show that protease inhibitors and NNRTIs have the steepest slopes, which means that small increases in their concentrations result in large increases in drug activity. When normalized to IC50, most drugs that have been tested follow these representative curves closely, but NRTIs show the most intra-class variation12. Part c adapted from REF. 12, Nature Publishing Group.
resulting reduction in overall enzyme activ‑ ity prevents completion of the relevant step of the life cycle before some decay process blocks infection irreversibly58; for example, NNRTIs bind reversibly to reverse tran‑ scriptase but may irreversibly block infec‑ tion by delaying the completion of reverse transcription until the ssRNA viral genome template is degraded.
In this model, the slope of the median effect curve is a function of the number of target enzymes that are present and the critical number of functional enzyme mol‑ ecules that are necessary for productive infection. For drugs that target a step in the life cycle that is mediated by multiple copies of the relevant enzyme (such as pro‑ tease inhibitors and NNRTIs), this model
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predicts median effect curves that are steep and inflect upwards. By contrast, for drugs that target viral integration (which is carried out by a single integrase enzyme complex), this model predicts median effect curves with slopes ≈1, which is con‑ sistent with experimental slope measure‑ ments58. The same is true for NRTIs, which function by terminating individual reverse transcripts rather than by inhibiting enzyme activity. Entry inhibitors are more complex owing to the trimeric nature of the viral Env glycoprotein and the shapes of these dose–response curves have not been mechanistically explained. With this exception, the critical subset model provides a simple explanation for the differences in slope for different classes of antiretroviral drugs. Importantly, the model is applicable under the following assumptions: each target site binds the drug with an unchanging dissociation constant (Kd), even at extreme concentra‑ tions; and a certain proportion of available target sites must be unoccupied for suc‑ cessful completion of the relevant step in the virus life cycle. These assumptions are reasonable and are not contradicted by any available biochemical data. Multistep inhibition model of cooperativity. The binding of a drug to a target that is involved in multiple steps of the virus life cycle is another mechanism that can account for steep, upward-inflecting dose–response curves, such as those that are observed for the protease inhibitors. HIV‑1 protease is responsible for virion maturation, but protease inhibition actu‑ ally impairs several events in the life cycle (FIG. 1). Immature virions are incapable of entering target cells59, and the viral enzyme reverse transcriptase is formed by the cleavage of a polyprotein precursor. Con‑ sequently, protease inhibition manifests as inhibition of entry and reverse transcrip‑ tion. It is possible to deconstruct the steep, upward-inflecting dose–response curve of a protease inhibitor into linear curves show‑ ing the effect of the drug at individual steps of the viral life cycle60. The new class of allosteric HIV-1 inte‑ grase inhibitors (ALLINIs) also exhibit cooperative dose–response curves that have slopes >1 (REFS 61,62). By stabilizing catalyti‑ cally inactive integrase multimers, ALLINIs prevent multiple interactions with host factors that are important for integration61. ALLINI-induced integrase multimerization also inhibits other steps in the viral life cycle, including maturation62. www.nature.com/reviews/micro
© 2014 Macmillan Publishers Limited. All rights reserved
PERSPECTIVES Quantifying the activity of antiretroviral drugs. As emphasized by the data presented above, the slope is essential for accurate cal‑ culation of antiviral activity at drug concen‑ trations that are beyond the range of in vitro assays, including the clinical concentrations of some antiretroviral drugs12. As IC50 and IQ neglect the slope parameter, they are limited as metrics of antiviral activity. We propose that a superior metric of ART efficacy is IIP, which depends on D, IC50 and m (REF. 15): m
( ( ( (
IIP = log 1 +
D IC50
(3)
IIP is equivalent to the number of logs by which a single round of infection is reduced in the presence of a single drug or several drugs. According to a simple model of viral dynamics63,64, approximately 106 new infec‑ tion events occur per viral generation in a patient with a typical plasma HIV‑1 RNA level of 30,000 copies per ml (REF. 65). In this case, complete suppression of replication would require six logs of inhibition or an ART regimen with IIP = 6, which is equivalent to 99.9999% inhibition. A recent in vitro study12 showed that most individual antiretroviral drugs have maximal clinical IIP values below this six-log threshold, with the notable exception of certain protease inhibitors (such as darunavir) and NNRTIs (such as efavirenz). By contrast, most of the three-drug regimens that are recommended for initial ART have a combined IIP >6 (see below), which provides an explanation for the clinical observations that monotherapy and dual NRTI regimens do not control HIV‑1 replication, whereas many three-drug regi‑ mens that include an NNRTI or a protease inhibitor do control HIV-1 replication. Interestingly, a recommended threedrug regimen consisting of the integrase strand transfer inhibitor (InSTI) raltegravir and the NRTIs tenofovir disoproxil fuma‑ rate and emtricitabine was effective in clinical trials11 despite an in vitro combined IIP of less than six12. Patients starting ralte‑ gravir-based regimens experience a rapid drop in viraemia, which was initially inter‑ preted as an indication of greater antiviral activity; however, the dynamics of viral decay are probably a reflection of the stage in the viral life cycle that the drug targets66. As an InSTI, raltegravir functions later in the virus life cycle than other antiretroviral drugs (FIG. 1) and thus blocks virus produc‑ tion by some cells in which earlier events (such as entry and reverse transcription) have been completed at the initiation of therapy. Thus, even with equally effica‑ cious drugs, more rapid decay in viraemia
may be observed for drugs that target later events in the life cycle. The clinical efficacy of raltegravir and other recently approved InSTIs (such as elvitegravir and dolute‑ gravir) in combination therapy may be partially due to the favourable interactions of this drug class with all other classes (see below). These interactions result in a com‑ bined IIP that is greater than the relatively low IIP of raltegravir itself. There is no evi‑ dence that raltegravir or other InSTIs have a sufficient IIP to be used as monotherapy. The success of InSTIs in combination therapy could also be due to determinants of efficacy that are not accounted for in the IIP model, such as patient adherence to the treatment regimen. Raltegravir is extremely well tolerated67; thus, superior adherence to raltegravir-based regimens may contribute to their clinical success. For all regimens in which IIP is high enough to fully suppress viral replication (combined IIP >6; see below), adherence, rather than the absolute value of the IIP, is the main determinant of treatment success. Finally, it is possible that the in vivo activity of InSTIs is greater than that predicted by in vitro models, for reasons that are not yet clear. These arguments, which are based on in vitro studies of raltegravir and elvitegravir, probably also apply to dolutegravir, which is the most recently approved InSTI68. Treatment failure on ART Although an optimal cART regimen can suppress viraemia to undetectable levels, suboptimal treatment can lead to virologic failure, which is defined as persistent virae‑ mia above the threshold of clinical detection in a patient receiving cART. Virologic failure typically precedes immunological failure (which is characterized by a low CD4+ T cell count) and clinical failure (that is, sympto‑ matic disease progression)69, both of which are often, but not always, associated with the evolution of drug-resistant virus.
Resistance with and without mutations. Drug resistance, owing to suboptimal treat‑ ment, is a major barrier to the effective treatment of HIV‑1 infection70. The most common forms of resistance are point muta‑ tions in target viral proteins, which often inhibit drug binding70,71. Interestingly, AZT resistance can arise by a different mechanism: in some cases, resistance mutations create a binding pocket for ATP, which participates in reverse transcriptase‑mediated excision of the incorporated chain-terminating nucleoside analogue72. The dose–response curves for the inhibition of drug-resistant virus have
NATURE REVIEWS | MICROBIOLOGY
higher IC50 and/or lower slope values than curves for wild-type virus73. Both of these differences adversely affect the efficacy of the drug (see equation 2), but unfortunately, current clinical measures consider only changes in the IC50. In cells that are simultaneously infected by multiple virions, drug efficacy can be lower despite the absence of resistance mutations. A recent in vitro study showed that, at a high multiplicity of infection (MOI), wild-type virus can exhibit a drug-resistant phenotype on a simple probabilistic basis: a cell that is exposed to many virions only remains unin‑ fected if all infection events are inhibited74. The magnitude of this high MOI-based drug resistance may vary substantially with antiretroviral class. Whereas high resistance to reverse transcriptase inhibitors by wildtype virus has been repeatedly shown in high-MOI in vitro systems, protease inhibi‑ tors show a similar ability to suppress both high-MOI and low-MOI infection in vitro75. The clinical importance of these findings is not yet clear, as most infected cells in vivo contain only one viral genome76,77. Role of pharmacokinetics in ART efficacy. Pharmacokinetic properties are an impor‑ tant determinant of antiretroviral efficacy and may be used to calculate IIP throughout the dosing interval or following missed doses12,78. At clinical concentrations, protease inhibitors have the highest intrinsic activity of any antiretroviral drug class as they have steep median effect slopes, but they are also rapidly cleared in vivo. Thus, to increase the half-life of protease inhibitors in vivo, they are typically co‑administered with ritonavir, which is a protease inhibitor that is also a potent inhibitor of the host metabolic enzymes that are responsible for the elimination of all protease inhibitors79,80. Many protease inhibitor‑based regimens fail despite the absence of resistance muta‑ tions in the viral protease gene81,82, and this failure can be partially explained by pharmacokinetics78. Owing to the steep slope of protease inhibitor dose–response curves, small changes in drug concentra‑ tion cause large changes in viral inhibition. Thus, protease inhibitors are very effective drugs at maximal concentrations in vivo, but their effectiveness rapidly declines as the drug is cleared. Drug-resistant virus typi‑ cally evolves in patients with suboptimal adherence to treatment: missed doses cause a decline in systemic drug levels to sub optimal concentrations, which enable lowlevel replication of the virus and select for drug-resistant variants. However, protease ADVANCE ONLINE PUBLICATION | 5
© 2014 Macmillan Publishers Limited. All rights reserved
PERSPECTIVES Box 1 | Quantifying interactions between antiretroviral drugs The Loewe additivity84 and Bliss independence85 theories are applied to predict the efficacy of drug combinations using the individual efficacies of each drug. Loewe additivity assumes that the drugs have a shared mechanism of action (see the figure, part a, left-hand panel) and compete for the same binding site. By contrast, Bliss independence assumes that the drugs function independently (see the figure, part a, right-hand panel) and have distinct targets. The two models are described by different mathematical expressions for the combined efficacy84,85. The combined efficacy that is predicted by Bliss independence is generally greater than that predicted by Loewe additivity. Interestingly, the efficacies of many empirical drug combinations deviate from the predictions of both theories12, which suggests that there is a spectrum of interactions, ranging from additive (for Loewe additivity) to multiplicative (for Bliss independence). Importantly, those drugs that have independent mechanisms of action tend to have greater efficacy when combined. In part b of the figure, the left-hand plot shows empirical dose–response relationships for two hypothetical drugs individually and in combination. The right-hand plot compares the dose–response relationship of the drug combination curve (black) with that which would be observed if the two drugs interact according to Loewe additivity (orange) or Bliss independence (green). In this example, the measured drug combination curve (black) does not fit either theory well, but lies between them.
a
Loewe additivity
Bliss independence
Drug 2 Drug 1
Drug 2
Drug 1
Drug targets
Drug target
b
7
7
Drug 1 Drug 2 Drugs 1 and 2
5 4 3 2
5 4 3 2 1
1 0
Drugs 1 and 2 Loewe additivity Bliss independence
6
[ ƒa/ƒu] (logs of inhibition)
[ ƒa/ƒu] (logs of inhibition)
6
-2
-1
0
1
Drug concentration (log scale)
0
-2
-1
0
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Drug concentration (log scale) Nature Reviews | Microbiology
inhibitors are only transiently present at intermediate levels that select for resist‑ ance, as these drugs are rapidly cleared. As a result, resistance mutations in the protease gene are rare, even in patients who fail protease inhibitor‑based regimens78. Failure of protease inhibitor‑based treatment despite the absence of resistance mutations in the protease gene can also be explained by the multistep mechanism that is discussed above60. Protease inhibitors are most effective at inhibiting the entry step: interactions between Env and uncleaved Gag
inhibit entry until Gag is cleaved by pro‑ tease83. However, some Env mutations seem to confer resistance to protease inhibitors by enabling entry even when Gag is not fully cleaved60. These Env mutations are not detected in standard resistance testing but may contribute to clinical resistance to protease inhibitors. Combining antiretroviral drugs To prevent the emergence of resistance, antiretroviral drugs are usually administered in combination (that is, in cART)11 and it is
6 | ADVANCE ONLINE PUBLICATION
therefore crucial to understand the interac‑ tions between individual drugs and their combined therapeutic effects. Two main pharmacodynamic theories are used to esti‑ mate the activity of a drug combination on the basis of the efficacies of each individual drug (BOX 1). One theory (known as Loewe additivity84) assumes that drug efficacies are additive, whereas the other theory (known as Bliss independence85) assumes that they are multiplicative. In the Loewe additivity theory, individual drugs are assumed to share a mechanism of action84; for example, drugs of the NNRTI class bind allosterically to a single site on reverse transcriptase. When these drugs are combined, they follow Loewe additivity as they have the same mechanism of action and compete for the same target site (FIG. 3). At any given time, a single reverse transcriptase molecule can be inhibited by one drug or the other, but not by both drugs. By contrast, the Bliss independence theory assumes that individual drugs have independent mechanisms of action and inhibit distinct viral processes85 (BOX 1). This theory fits empirical in vitro data for most combinations of an integrase inhibitor with a reverse transcriptase inhibitor (FIG. 3), as these drugs inhibit distinct steps in the viral life cycle. Compared with Loewe additivity, Bliss independence generally predicts greater inhi‑ bition of viral replication with cooperative, upward-inflecting median effect curves12. The terms synergy and antagonism are frequently used to describe the effect of combination therapy, but they have meaning only in relation to a model that can predict what the combined effect should be. How‑ ever, as described above, the two most widely used models (that is, Loewe additivity and Bliss independence) give different predic‑ tions for the combined efficacy of drugs with known individual efficacies. In fact, most pairs of antiretroviral drugs do not fit either model well12. Thus, we suggest that the term synergy be reserved for combined effects that exceed those predicted by Bliss inde‑ pendence and that the term antagonism be reserved for combined effects that are lower than those predicted by Loewe additivity. Empirical analysis of pairwise combinations of antiretroviral drugs (FIG. 3) has shown that many combinations are synergistic, whereas antagonism does not seem to occur, which suggests that the Loewe additivity model sets a lower threshold for the efficacy of cART12. Furthermore, many drug pairs show intermediate efficacy, which is higher than that predicted by Loewe additivity but lower than that predicted by Bliss independence12 www.nature.com/reviews/micro
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PERSPECTIVES
Perspectives Drug resistance has been one of the great‑ est obstacles for the successful treatment of HIV‑1 infection. Resistance mutations have been characterized for every antiret‑ roviral drug71 and an estimated 10–17% of new infections in high-income countries are caused by a virus that is resistant to at least one drug16. However, with the contin‑ ued development of novel drugs and more efficacious treatment regimens, the preva‑ lence of resistance has begun to decline41,42. Single-pill, once‑a‑day formulations have improved patient adherence, which coun‑ teracts the evolution of resistance86. The availability of multiple well-tolerated, fully suppressive regimens means that there are treatment options even for patients who are infected with drug-resistant virus or who have adverse reactions to specific antiretroviral drugs11.
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protease inhibitor with a drug from several other classes typically results in intermediate efficacy, which reflects the fact that inhibi‑ tion of HIV‑1 protease indirectly affects several steps in the HIV‑1 life cycle. The integrase inhibitors are an exception to this trend as they seem to interact with protease inhibitors in a synergistic manner (FIG. 3). It is possible to extrapolate from the data that describe the efficacy of drug pairs to estimate the inhibitory capacity of regimens that combine three drugs12. Many three-drug regimens have an extremely high combined IIP (>10 logs), which is consistent with the ability of modern cART to exert long-term control of HIV‑1 replication. Importantly, although the predictions of the combined IIP metric correlate well with clinical outcome12, the model has not yet been validated as a successful basis for designing new drug regimens. Only one of 31 evaluated regimens with a combined IIP of
