
A Critical Subset Model Provides a Conceptual Basis for the High Antiviral Activity of Major HIV Drugs



Download the PDF
Sci Transl Med 13 July 2011
Lin Shen,1,2 S. Alireza Rabi,1 Ahmad R. Sedaghat,1* Liang Shan,1,2 Jun Lai,1
Sifei Xing,1,2 Robert F. Siliciano1,3
Control of HIV1 replication was first achieved with regimens that included a nonnucleoside reverse transcriptase inhibitor (NNRTI) or a protease inhibitor (PI); however, an explanation for the high antiviral activity of these drugs has been lacking. Indeed, conventional pharmacodynamic measures like IC50 (drug concentration causing 50% inhibition) do not differentiate NNRTIs and PIs from less active nucleoside reverse transcriptase inhibitors (NRTIs). Drug inhibitory potential depends on the slope of the doseresponse curve (m), which represents how inhibition increases as a function of increasing drug concentration and is related to the Hill coefficient, a measure of intramolecular cooperativity in ligand binding to a multivalent receptor. Although NNRTIs and PIs bind univalent targets, they unexpectedly exhibit cooperative doseresponse curves (m > 1). We show that this cooperative inhibition can be explained by a model in which infectivity requires participation of multiple copies of a drug target in an individual life cycle stage. A critical subset of these target molecules must be in the unbound state. Consistent with experimental observations, this model predicts m > 1 for NNRTIs and PIs and m = 1 in situations where a single drug target/virus mediates a step in the life cycle, as is the case with NRTIs and integrase strand transfer inhibitors. This model was tested experimentally by modulating the number of functional drug targets per virus, and doseresponse curves for modulated virus populations fit model predictions. This model explains the high antiviral activity of two drug classes important for successful HIV1 treatment and defines a characteristic of good targets for antiviral drugs in general, namely, intermolecular cooperativity.
Introduction
In 1997, regimens were developed that suppressed HIV1 viremia to below the detection limit in most treated patients. These regimens combined two nucleoside reverse transcriptase inhibitors (NRTIs) with an HIV1 protease inhibitor (PI) (13). Combinations of two NRTIs and a nonnucleoside reverse transcriptase inhibitor (NNRTI) also proved effective (4, 5). Collectively, these regimens, known as highly active antiretroviral therapy (HAART), transformed a previously fatal disease into a chronic condition that is well controlled in adherent patients. Despite HAART's success, a fundamental theory explaining its effectiveness is lacking.
Drug resistance, which results from both the high error rate of reverse transcriptase (RT) and the dynamic nature of HIV1 infection, is a major cause of treatment failure (610). The low probability of multiple simultaneous resistance mutations on the same genome contributes to the success of triple combination therapy (11). However, suppression of HIV1 replication is not simply the result of using three drugs; some triple NRTI combinations had suboptimal responses (5). Thus, in early treatment efforts, inclusion of a PI or a NNRTI appeared essential for control of viral replication. Although the use of drugs acting through different mechanisms also contributes to the effectiveness of combination therapy, PIs and NNRTIs appeared to have greater antiviral activity than most NRTIs. Therefore, treatment guidelines recommend inclusion of a PI or a NNRTI in most initial HAART regimens (5). Standard pharmacologic measures such as IC50 and inhibitory quotient do not distinguish PIs and NNRTIs from less active NRTIs (12). Thus, the fundamental pharmacodynamic principles underlying this successful treatment remain unclear. Instead, progress has depended on comparative clinical trials, which have recently established a role for newer drugs as well, such as integrase strand transfer inhibitors (InSTIs) and CCR5 antagonists (5).
We recently showed that the superior antiviral activity of PIs and NNRTIs relative to most NRTIs can be partially explained by the doseresponse curve slope (m) (12). This parameter is included in all fundamental pharmacodynamic equations including the Hill equation (13), the sigmoidal Emax model (14), and the ChouTalalay medianeffect equation (15). The slope parameter is related to the Hill coefficient describing intramolecular cooperativity in the binding of ligands to a multivalent receptor (13, 16). Positive cooperativity (m > 1) reflects enhanced binding of additional ligands to a receptor after the first ligand binds. However, the enzymes targeted by NNRTIs and PIs are univalent with respect to these inhibitors. NNRTIs bind to a single pocket adjacent to the deoxynucleoside triphosphate (dNTP) binding site (17), and HIV1 protease is a homodimer with a single active site targeted by PIs, which are peptidomimetic substrate or transitionstate analogs (18, 19). Therefore, noncooperative doseresponse curves (m = 1) were expected. However, in singleround infectivity assays, doseresponse curves for NNRTIs and PIs have unexpectedly steep slopes (~1.7; ranging from 1.8 to 4.5, respectively), allowing these drugs to achieve extraordinarily high antiviral activity (12).
Because m > 1 for NNRTIs and PIs cannot be explained by intramolecular cooperativity, a mechanistic explanation requires reconsidering the concept of cooperativity in drug action. Here, we describe a critical subset model that explains steep slopes for these drugs in terms of intermolecular cooperativity, and we provide experimental validation for this model.
Results
Critical subset model
The simplest model describing doseresponse relationships is the medianeffect equation (15). It is derived from the law of mass action, which governs dynamic equilibria in chemical reactions. The medianeffect equation can be written as:(1)or(2)
In the context of HIV1 infection, fa and fu are the fractions of viruses affected and unaffected by the drug, [D] is the drug concentration, IC50 is the drug concentration causing 50% inhibition, and m is the slope parameter describing the steepness of the doseresponse curve.
Some antiHIV drugs have m > 1, even though their target enzymes are univalent. To explain this observation, we developed a critical subset model based on mass action. The model envisions independent binding of multiple drug molecules to a set of identical targets, viral proteins, within each virus or virusinfected cell. Within a given virion or infected cell, the targeted proteins behave as a set of linked receptor sites that can be occupied to variable extents (Fig. 1A), depending on [D] and the affinity of individual drugtarget interactions (Kd). Drug binding involves a series of equilibrium reactions mediating conversions between all possible partially occupied states (16). Assuming that replication requires some critical threshold number (c) of functional (unbound) enzyme molecules to complete the relevant reaction before irreversible decay processes intervene (Fig. 1B), the following expression describes fa as a function of [D], Kd, the unbound virus concentration [V], and the total number of enzyme molecules per virus nT (Eq. 3 and Table 1):
Consider, for example, HIV1 protease. Multiple copies of protease participate in the maturation of each virion. Although not physically linked, they are constrained spatially within the virion. Thus, the virion can be considered a multivalent receptor with nT binding sites, where nT is the total number of protease molecules per virus (Fig. 1A). Because simultaneous binding is unlikely, we have modeled drug binding as a series of equilibrium reactions (Fig. 1B). The derivation of Eq. 3 follows from a consideration of the concentration of each possible state along the path to saturation (16). Viruses with a critical subset of c or more unbound enzymes are infectious. To render a virus noninfectious, at least nT  c + 1 enzyme molecules must be occupied (Fig. 1B). Thus, in Eq. 3, the numerator is the sum of the concentrations of all viruses having ≥nT  c + 1 enzyme molecules bound by drug, and the denominator is the total virus concentration.
This model predicts the shapes of doseresponse curves. To illustrate these predictions, we plotted hypothetical doseresponse curves using medianeffect plots based on Eq. 1 (Fig. 2). The medianeffect plot linearizes the doseresponse curve for most drugs, with the slope of the resulting line equal to the Hill coefficient (Fig. 2, A and B). We obtained complex doseresponse curves with various m depending on nT and c (Fig. 2, C and D). When nT = c = 1, Eq. 3 simplifies to the medianeffect equation (Eq. 2) and predicts m = 1. However, for all cases with 1 < c < nT, the model predicts steeper doseresponse curves with m ≥ 1.5 at all drug concentrations.
The model predicts complex doseresponse curves with nonlinear medianeffect plots. That is, m is different for different segments of the curve. As shown below, the doseresponse curves of some antiretroviral drugs are similarly complex. According to the model, slope values are determined by nT and c. As [D] increases above IC50, m approaches c. As [D] decreases below IC50, m approaches nT  c + 1 (Supplementary Note 1). Within the range of log (fa/fu) = 2 to 4 (1 to 99.99% inhibition), the curves inflect upward when c is close to nT and downward when c is small relative to nT (Fig. 2, C and D). For example, when nT = 5, the model predicts a family of curves with slopes ranging from 1.30 to 1.88 at the IC50 (Fig. 2C). Slope at the IC50 is highest when c = 3, and lowest when c = 1 or 5. However, the slope at high [D] approaches c, and thus, the maximum slope is 5. The same pattern with steeper slopes is observed for nT = 30 (Fig. 2D). Whenever simultaneous inhibition of multiple copies of a target molecule within a virion or infected cell is required to block replication, doseresponse curves with m > 1 are predicted by this model. This unique form of intermolecular cooperativity does not depend on changes in the binding properties (Kd) of individual target sites.
The model also predicts the relationship between IC50 and Kd. When nT is odd and c = (nT + 1)/2, IC50 = Kd. When c > (nT + 1)/2, IC50 < Kd and vice versa (Fig. 2, C and D). Similarly, for a series of curves with constant c, IC50 decreases as nT decreases (Fig. 2, E and F). This is expected because a lower IC50 is achieved with the same Kd when the fractional occupancy of target enzymes required for inhibition is low.
Effect of heterogeneity on doseresponse curves
A second factor affecting slope is heterogeneity in nT. In other biological systems with allornone outcomes, heterogeneity in the number of drug targets flattens doseresponse curves (20). For individual virions, infection is all or none, and thus, heterogeneity arguments apply. When heterogeneity is limited, doseresponse curves are steeper. Intuitively, this may be understood by considering that a homogeneous virus population will be inhibited over a narrow range of drug concentrations, whereas a more heterogeneous population will contain viruses that are easier or harder to inhibit, and therefore, inhibition will be observed over a wider concentration range.
If nT follows an integervalued normal distribution with an SD of σ, then doseresponse curves follow Eq. 4, which gives fa for a population of viruses with variability in nT.(4)where(5)and fa(ni) is calculated using Eq. 3. The derivation of Eq. 4 follows from a consideration of fa for viruses with a given total number of enzyme molecules per virus (ni) and the probability of finding viruses with ni enzyme molecules within a population of viruses with nT ± σ total enzyme molecules per virus (fig. S1A). Because viruses with ni < c are noninfectious, the numerator must be divided by the probability of infectious virus for a given nT, σ, and c(fig. S1A). nmax is the maximum total number of enzyme molecules per virus, which is set at >nT + 3σ. Equation 4 predicts a decrease in m with increasing σ at drug concentrations below the IC50 (fig. S1B), because heterogeneity leads to a subpopulation of viruses with low nT that are inhibited more easily at low [D].
Testing the critical subset model with phenotypic mixing experimentstheoretical considerations
The critical subset model makes testable predictions about how variation in nT and c affect doseresponse curves. For example, the model predicts that at constant c, IC50 decreases as nT decreases (Fig. 2, E and F). To test this model, we used a phenotypic mixing strategy (21, 22) to reduce the total number of functional target molecules per virion (nF) (Fig. 3A). To reduce nF, we mixed defined fractions (fm) of mutant proviral constructs containing inactivating point mutations in the target enzymes with wildtype constructs and transfected them into human embryonic kidney (HEK) 293T cells to generate virions with reduced nF. Note that nF = nT when no mutant enzyme is present. We then examined the relative infectivity (RI) of these virus populations compared to wildtype virus and obtained estimates of nT and c (Fig. 3, B and C). Finally, we determined whether the doseresponse curves for virus populations with lower nF behaved as predicted by the model (Fig. 3, D and E). Note that larger decreases in IC50 are predicted when c is small relative to nT.
The introduction of mutant enzyme subunits reduces infectivity, because some viruses will no longer contain a critical number of functional enzymes (Fig. 3A). The enzyme molecules targeted by RT and protease inhibitors are heterodimers and homodimers, respectively. If formation of mixed dimers from wildtype and mutant monomers follows random mixing, then the virions produced should contain reduced nF following a binomial distribution. The RI of mixed virions compared to wildtype virions is a function of nT, c, and Pf, where Pf is the probability of obtaining a functional enzyme multimer for a given fm (Eq. 6).(6)
P(nF) is the probability of getting nF copies of functional enzyme molecules with a given nT, c, and Pf. The calculation of Pf from fm is based on the subunit stoichiometry of the target enzyme and is different for RT and protease, as described in Materials and Methods. Based on Eq. 6, the RI of phenotypically mixed virions is the sum of the probabilities of obtaining viruses with nF ≥ c. Plotting RI as a function of fm, we obtain RI curves with various patterns depending on nT and c (Fig. 3, B and C). When most copies of the enzyme are required to complete the relevant step in the life cycle, decreases in nF have a major effecta small increase in fm results in loss of infectivity. In contrast, when c is small relative to nT and thus available enzyme exceeds that which is necessary, infectivity is not affected until fm is large (Fig. 3, B and C). As demonstrated below, the former situation applies to RT, whereas the latter applies to protease. Note that the analysis of RI is based on the same assumptions that underlie the critical subset model, namely, that a critical threshold number (c) of functional copies of a viral enzyme out of nT copies are required for infectivity (Figs. 1B and 3A).
To test the critical subset model, phenotypically mixed viruses can be assayed for susceptibility to drug inhibition. Doseresponse curves for inhibition of viruses with various fm can be predicted by Eq. 4, where the calculation of fa(ni) follows Eq. 7 (Fig. 3, D and E). The derivation of Eq. 7 follows from a consideration of fa for each nF (Eq. 3) and the probability of viruses with nF functional enzyme molecules within a population of viruses with ni total enzyme molecules and an average probability Pf of functional ones (Eq. 6 and Fig. 3A).(7)
According to this model, the change in IC50 depends on the fractional occupancy of enzyme molecules required for inhibiting replication. Two contrasting situations with the same value of c but different values of nT are illustrated in Fig. 3, D and E. When c is close to nT (Fig. 3D, nT = 5), the fractional occupancy required for inhibition is low, and the model predicts that IC50 should only decrease slightly with increasing fm. This is because the number of enzyme molecules is already close to the limiting value of nT = c. However, when c is small relative to nT (Fig. 3E, nT = 30), the fractional occupancy required for inhibition is high, and the model predicts that IC50 can decrease over a wider range with increasing fm until the limiting condition is reached.
Experimental validation of the critical subset modelgeneration of phenotypically mixed virions
Before testing the critical subset model, we validated several assumptions. One is that wildtype and mutant enzymes can be expressed in cells and incorporated into virions with similar efficiency. We chose wellcharacterized point mutations in the active sites of RT (D185N), protease (D25N), and integrase (D64E) that abolish enzyme activity without effects on multimer formation or virus assembly (18, 23, 24). Transfection efficiency was similar for the wildtype plasmid and mutant plasmids by flow cytometry (fig. S2A). Wildtype and mutant RT and integrase were incorporated into the virions with similar efficiency (fig. S2, B and C). We also tested whether formation of mixed dimers from wildtype and mutant monomers follows random mixing and hence the binomial distribution. To obtain direct biochemical evidence for mixed dimer formation in cotransfected cells, we performed coimmunoprecipitations (fig. S3). We introduced either a hemagglutinin (HA) or a Flag tag into the N terminus of the wildtype and mutant enzymes. Viral proteins immunoprecipitated by either antiHA or antiFlag antibodies from cell lysates were immunoblotted with antiFlag or antiHA antibodies, respectively. Our results suggest that for both RT and protease, dimers with all of the four possible combinations (wildtypewildtype, wildtypemutant, mutantwildtype, and mutantmutant) can be formed with about equal probability (fig. S3, A and B).
Analysis of doseresponse curves for NNRTIs and PIs against phenotypically mixed viruses
According to the model, drugs targeting steps involving multiple copies of the drug target should show steeper doseresponse curves and markedly better inhibition at drug concentrations above the IC50. NNRTIs can interact with all RT molecules in the preintegration complex, and PIs can interact with all protease molecules in the maturing virion. These drug classes exhibit slopes >1.7 and high inhibitory potential (12). For NNRTIs and PIs, the model predicts that IC50 should decrease as nF is reduced and that the higher the fractional occupancy required for inhibiting replication, the greater the change in IC50 (Fig. 3, D and E).
We tested these predictions using the algorithm shown in Fig. 4A. We first determined the apparent nT and c by measuring in the absence of drug the RI of virions with mixtures of catalytically active and inactive enzymes. The RI of phenotypically mixed virions can be calculated with Eq. 6 for any given nT and c (Fig. 3A). We fitted experimental RI data to theoretical RI curves for RT and protease and calculated r2 values for all combinations of nT between 1 and 100 and c between 1 and nT (Fig. 4, B and C). The r2 values shown in Fig. 4, B and C, indicate poor fitting for nT > 50. The experimental RI curves fit the theoretical curves best with either nT = 2, c = 2 or nT = 4, and c = 3 for RT (Fig. 4, B and D), and nT = 9 to 15 and c = 2 for protease (Fig. 4, C and E). With these nT and c values, the model predicts m = 1.4 to 2.0 for NNRTIs and m > 1.8 for PIs, consistent with experimental observations.
To refine values for nT and c and find the approximate Kd, we used estimates from RI curves as a starting point to find σ and Kd values that allowed optimal fitting of doseresponse curves for NNRTIs and PIs against wildtype viruses (Fig. 4A, Eqs. 4 to 7, and Supplementary Note 2). For NNRTIs, nT = 4, σ = 1, c = 3 gave the best average fit (Fig. 5A), with Kd = 0.018, 0.33, and 0.018 μM for efavirenz (EFV), nevirapine (NVP), and etravirine (ETR), respectively. The inclusion of σ for RT improved the fitting of the RI curve (Fig. 4D and table S1). The values developed using RI data in the absence of drugs allowed good fitting of experimental doseresponse curves, including the upward inflection that is apparent in the experimental data and that is predicted by the model for these nT and c values (Fig. 5A).
Discussion
Steep doseresponse curves generally reflect positive cooperativity. In the classical Hill model, a slope >1 reflects cooperative binding of multiple ligands to a multivalent receptor. However, RT and protease have only one binding site for NNRTIs and PIs, respectively, and extensive kinetics studies with purified RT and protease have not provided evidence for cooperativity. Rather, the high slopes of NNRTIs and PIs in infectivity assays reflect unique aspects of the inhibition of viral replication that are not apparent in the behavior of the isolated enzymes.
Here, we present a critical subset model that provides a molecular explanation for these steep doseresponse curves. We hypothesized that within a given infected cell or virus particle, the viral proteins targeted by a drug behave as a set of linked receptor sites whose fractional occupancy determines whether the relevant step in the virus life cycle is completed before irreversible decay processes intervene. When multiple copies of a target are involved in the inhibited reaction, there exists a form of intermolecular cooperativity that does not depend on alterations in the binding properties of individual sites. This model predicts slopes >1 for NNRTIs and PIs because multiple copies of RT and protease are involved in the processes inhibited by these drugs, and a critical subset of these molecules is required for viral replication. The model successfully predicted shifts in the doseresponse curves against viruses with decreased nF. For NRTIs and InSTIs, only one copy of RT or integrase is relevant to the inhibited reaction. In this situation, m = ~1 is predicted, as is observed experimentally.
According to the model, slope is determined by nT and c. There are ~5000 copies of Gag per virion, and the ratio of GagPol to Gag is 1:10 to 1:20 (28, 29). Thus, if the GagPol polypeptides are converted with 100% efficiency into functional dimeric RT and protease enzymes, there would be a maximum of 125 to 250 copies of these enzymes per virion. The nT values for RT and protease obtained here are smaller, perhaps reflecting inefficiencies in the assembly of functional enzymes and additional complexities in the relevant reactions. The nT and c parameters in this model are apparent numbers that allow description of the relevant reactions in terms of a simple model that does not capture the full complexity of the processes of reverse transcription and virion maturation. In addition, nT and c refer only to enzyme molecules located in the microenvironment where the reaction occurs. For these reasons, nT and c may differ from the actual physical number of RT or protease molecules present.
Because nT and c for a given drug target should be constant, the model does not explain slope differences within the PI class. The model particularly cannot explain doseresponse curves for PIs with m > 2, because c = 2 for protease predicts a maximum slope of 2. We hypothesize that drugtodrug variability in m for PIs may reflect the complexity of viral maturation, which involves multiple molecular states of the enzyme acting on multiple distinct substrates (30). The protease domains of adjacent GagPol precursor proteins must first dimerize to form an active protease enzyme, which then carries out intramolecular cleavages to liberate itself from the GagPol precursor protein. Ultimately, free protease dimers complete the remaining cleavages of the Gag and GagPol precursor proteins (31). The embedded protease is less sensitive to PIs than the mature, free enzyme (30, 31). The effective substrate concentration may also differ for intramolecular and intermolecular reactions. Different PIs may preferentially inhibit distinct molecular forms of protease for which nT and c are different, resulting in slope differences. In addition, virions generated in the presence of PIs may be blocked at subsequent steps such as reverse transcription (32). Thus, unlike other antiretroviral drugs, PIs affect multiple steps in the life cycle, possibly with different nT and c values. These complexities may explain the upward inflection of PI doseresponse curves at high [D].
The concepts developed here can be applied to other drug classes and other viruses. However, additional factors may complicate the analysis. For drugs affecting HIV1 entry, the targets are trimeric, and interactions within and between trimers must be considered. One particularly unique aspect of HIV1 RT, protease, and integrase inhibitors is that they target enzymes that are present within the virion or that are assembled from precursor polyproteins present within the virion. In this situation, constraints inherent in virion assembly limit heterogeneity (σ) in nT. As shown here, heterogeneity flattens doseresponse curves. Thus, in the case of viruses for which the drug targets are expressed and act within infected cells, heterogeneity in the number of enzymes per cell may obscure the cooperativity that is expected when multiple copies of a drug target act together to complete a step in the life cycle.
A variety of elegant models describing steep doseresponse curves for ion channels and other multivalent receptors have been developed (16, 3335), but models to explain cooperative doseresponse curves for antiviral drugs have not been described. It is possible that other models will be developed to explain the complex doseeffect relationships for antiretroviral drugs. For example, saturable drug binding to alternative intracellular sites could result in an upward inflection within a small concentration range above the concentration that saturates the alternative sites. However, a model such as the one described here would still be needed to explain slopes >1 in other regions of the doseresponse curve. Although the overall slopes for NRTIs are close to 1, there are subtle inflections in the doseresponse curves for some NRTIs (Fig. 6B and fig. S4). These may in part reflect binding to alternative intracellular sites or the reactions needed to produce the active triphosphate forms of the NRTIs. Nevertheless, the fact that slope varies in a classspecific way despite large intraclass differences in structure suggests that the slope parameter is a direct reflection of the mechanism of inhibition, as in the model described here.
The critical subset model represents an oversimplification of a very complex problem. Nevertheless, it explains the steep doseresponse curves of NNRTIs and PIs without assuming that the binding of drug to one site affects the Kd of binding to other sites. A steep slope means that small increases in drug concentration above the IC50 can produce large increases in antiviral activity, permitting extremely high levels of inhibition in the clinical concentration range. Thus, the present study provides a conceptual framework for understanding the high antiviral activity of NNRTIs and PIs, drugs that form the basis of successful HIV1 treatment, and provides important insights to the characteristics of good targets for antiviral drugs.
 




