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A model of HIV drug resistance driven by heterogeneities in host immunity and adherence patterns.

Bershteyn A, Eckhoff PA - BMC Syst Biol (2013)

Bottom Line: Population transmission models of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use simplistic assumptions--typically constant, homogeneous rates--to represent the short-term risk and long-term effects of drug resistance.In the case of accidental PrEP use during infection, rapid transitions between adherence states and/or weak immunity fortifies the "memory" of previous PrEP exposure, increasing the risk of future drug resistance.This model framework provides a means for analyzing individual-level risks of drug resistance and implementing heterogeneities among hosts, thereby achieving a crucial prerequisite for improving population-level models of drug resistance.

View Article: PubMed Central - HTML - PubMed

Affiliation: Epidemiological Modeling Group, Intellectual Ventures Laboratory, Washington, USA. abershteyn@intven.com

ABSTRACT

Background: Population transmission models of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use simplistic assumptions--typically constant, homogeneous rates--to represent the short-term risk and long-term effects of drug resistance. In contrast, within-host models of drug resistance allow for more detailed dynamics of host immunity, latent reservoirs of virus, and drug PK/PD. Bridging these two levels of modeling detail requires an understanding of the "levers"--model parameters or combinations thereof--that change only one independent observable at a time. Using the example of accidental tenofovir-based pre-exposure prophyaxis (PrEP) use during HIV infection, we will explore methods of implementing host heterogeneities and their long-term effects on drug resistance.

Results: We combined and extended existing models of virus dynamics by incorporating pharmacokinetics, pharmacodynamics, and adherence behavior. We identified two "levers" associated with the host immune pressure against the virus, which can be used to independently modify the setpoint viral load and the shape of the acute phase viral load peak. We propose parameter relationships that can explain differences in acute and setpoint viral load among hosts, and demonstrate their influence on the rates of emergence and reversion of drug resistance. The importance of these dynamics is illustrated by modeling long-lived latent reservoirs of virus, through which past intervals of drug resistance can lead to failure of suppressive drug regimens. Finally, we analyze assumptions about temporal patterns of drug adherence and their impact on resistance dynamics, finding that with the same overall level of adherence, the dwell times in drug-adherent versus not-adherent states can alter the levels of drug-resistant virus incorporated into latent reservoirs.

Conclusions: We have shown how a diverse range of observable viral load trajectories can be produced from a basic model of virus dynamics using immunity-related "levers". Immune pressure, in turn, influences the dynamics of drug resistance, with increased immune activity delaying drug resistance and driving more rapid return to dominance of drug-susceptible virus after drug cessation. Both immune pressure and patterns of drug adherence influence the long-term risk of drug resistance. In the case of accidental PrEP use during infection, rapid transitions between adherence states and/or weak immunity fortifies the "memory" of previous PrEP exposure, increasing the risk of future drug resistance. This model framework provides a means for analyzing individual-level risks of drug resistance and implementing heterogeneities among hosts, thereby achieving a crucial prerequisite for improving population-level models of drug resistance.

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Effect of immune pressure on rate of development and reversion of drug resistance. (a) Concentration of digestive (Cd), plasma (Cb), intracellular (Cc), intracellular phosphorylated (Ccp), and intracellular diphosphorylated (Ccpp) tenofovir using the model of Dixit and Perelson [42] that includes drug partitioning across the cell membrane. (b) Example of viral load and CD4+ T cell response to TDF monotherapy with perfect adherence and cessation after 300 days. TDF-DP concentration, re-scaled from (a) in units of μg/mL along the righthand axis, is overlaid for reference. (c) Basic reproductive ratio of WT and drug-resistant virus at steady-state peak and trough TDF-DP concentrations, as a function CTL efficacy p varied with f to maintain a setpoint viral load of 51,000 copies/mL. (d) Influence of p, varied as in (c), on the time from HIV infection on TDF monotherapy until emergence of the K65R mutant at a viral load above 50 copies/mL. The time to resistance is 29 days at p = 0, and rises to ever-increasing durations as p reduces R0. (d) Influence of p on the time until reversion of resistance after monotherapy cessation. Reversion times range from 37 days at p = 0 to 15 days as R0 approaches 1.
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Figure 3: Effect of immune pressure on rate of development and reversion of drug resistance. (a) Concentration of digestive (Cd), plasma (Cb), intracellular (Cc), intracellular phosphorylated (Ccp), and intracellular diphosphorylated (Ccpp) tenofovir using the model of Dixit and Perelson [42] that includes drug partitioning across the cell membrane. (b) Example of viral load and CD4+ T cell response to TDF monotherapy with perfect adherence and cessation after 300 days. TDF-DP concentration, re-scaled from (a) in units of μg/mL along the righthand axis, is overlaid for reference. (c) Basic reproductive ratio of WT and drug-resistant virus at steady-state peak and trough TDF-DP concentrations, as a function CTL efficacy p varied with f to maintain a setpoint viral load of 51,000 copies/mL. (d) Influence of p, varied as in (c), on the time from HIV infection on TDF monotherapy until emergence of the K65R mutant at a viral load above 50 copies/mL. The time to resistance is 29 days at p = 0, and rises to ever-increasing durations as p reduces R0. (d) Influence of p on the time until reversion of resistance after monotherapy cessation. Reversion times range from 37 days at p = 0 to 15 days as R0 approaches 1.

Mentions: Using a pharmacokinetic model by Dixit and Perelson [42] to translate 300 mg daily dosing of TDF into intracellular diphosphate concentrations, our model confirmed that TDF monotherapy transiently suppresses viremia, but then selects for the K65R mutant. Figure 3a shows our reproduction of the Dixit and Perelson pharmacokinetic model, emphasizing the importance of modeling intracellular drug species, which exhibit a longer half-life and therefore higher concentrations than those found in plasma. Figure 3b shows an example of prolonged adherence to TDF monotherapy initiated during HIV infection, with p set to 1.5 × 10-5. Consistent with experimental data, resistance begins to emerge after 50 days, with the K65R mutant dominating the viral population at a lower viral load and a higher CD4 count than WT infection due to the mutant’s reduced replicative capacity. This reduced fitness drives reversion to a WT-dominated infection after cessation of TDF monotherapy.


A model of HIV drug resistance driven by heterogeneities in host immunity and adherence patterns.

Bershteyn A, Eckhoff PA - BMC Syst Biol (2013)

Effect of immune pressure on rate of development and reversion of drug resistance. (a) Concentration of digestive (Cd), plasma (Cb), intracellular (Cc), intracellular phosphorylated (Ccp), and intracellular diphosphorylated (Ccpp) tenofovir using the model of Dixit and Perelson [42] that includes drug partitioning across the cell membrane. (b) Example of viral load and CD4+ T cell response to TDF monotherapy with perfect adherence and cessation after 300 days. TDF-DP concentration, re-scaled from (a) in units of μg/mL along the righthand axis, is overlaid for reference. (c) Basic reproductive ratio of WT and drug-resistant virus at steady-state peak and trough TDF-DP concentrations, as a function CTL efficacy p varied with f to maintain a setpoint viral load of 51,000 copies/mL. (d) Influence of p, varied as in (c), on the time from HIV infection on TDF monotherapy until emergence of the K65R mutant at a viral load above 50 copies/mL. The time to resistance is 29 days at p = 0, and rises to ever-increasing durations as p reduces R0. (d) Influence of p on the time until reversion of resistance after monotherapy cessation. Reversion times range from 37 days at p = 0 to 15 days as R0 approaches 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3643872&req=5

Figure 3: Effect of immune pressure on rate of development and reversion of drug resistance. (a) Concentration of digestive (Cd), plasma (Cb), intracellular (Cc), intracellular phosphorylated (Ccp), and intracellular diphosphorylated (Ccpp) tenofovir using the model of Dixit and Perelson [42] that includes drug partitioning across the cell membrane. (b) Example of viral load and CD4+ T cell response to TDF monotherapy with perfect adherence and cessation after 300 days. TDF-DP concentration, re-scaled from (a) in units of μg/mL along the righthand axis, is overlaid for reference. (c) Basic reproductive ratio of WT and drug-resistant virus at steady-state peak and trough TDF-DP concentrations, as a function CTL efficacy p varied with f to maintain a setpoint viral load of 51,000 copies/mL. (d) Influence of p, varied as in (c), on the time from HIV infection on TDF monotherapy until emergence of the K65R mutant at a viral load above 50 copies/mL. The time to resistance is 29 days at p = 0, and rises to ever-increasing durations as p reduces R0. (d) Influence of p on the time until reversion of resistance after monotherapy cessation. Reversion times range from 37 days at p = 0 to 15 days as R0 approaches 1.
Mentions: Using a pharmacokinetic model by Dixit and Perelson [42] to translate 300 mg daily dosing of TDF into intracellular diphosphate concentrations, our model confirmed that TDF monotherapy transiently suppresses viremia, but then selects for the K65R mutant. Figure 3a shows our reproduction of the Dixit and Perelson pharmacokinetic model, emphasizing the importance of modeling intracellular drug species, which exhibit a longer half-life and therefore higher concentrations than those found in plasma. Figure 3b shows an example of prolonged adherence to TDF monotherapy initiated during HIV infection, with p set to 1.5 × 10-5. Consistent with experimental data, resistance begins to emerge after 50 days, with the K65R mutant dominating the viral population at a lower viral load and a higher CD4 count than WT infection due to the mutant’s reduced replicative capacity. This reduced fitness drives reversion to a WT-dominated infection after cessation of TDF monotherapy.

Bottom Line: Population transmission models of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use simplistic assumptions--typically constant, homogeneous rates--to represent the short-term risk and long-term effects of drug resistance.In the case of accidental PrEP use during infection, rapid transitions between adherence states and/or weak immunity fortifies the "memory" of previous PrEP exposure, increasing the risk of future drug resistance.This model framework provides a means for analyzing individual-level risks of drug resistance and implementing heterogeneities among hosts, thereby achieving a crucial prerequisite for improving population-level models of drug resistance.

View Article: PubMed Central - HTML - PubMed

Affiliation: Epidemiological Modeling Group, Intellectual Ventures Laboratory, Washington, USA. abershteyn@intven.com

ABSTRACT

Background: Population transmission models of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use simplistic assumptions--typically constant, homogeneous rates--to represent the short-term risk and long-term effects of drug resistance. In contrast, within-host models of drug resistance allow for more detailed dynamics of host immunity, latent reservoirs of virus, and drug PK/PD. Bridging these two levels of modeling detail requires an understanding of the "levers"--model parameters or combinations thereof--that change only one independent observable at a time. Using the example of accidental tenofovir-based pre-exposure prophyaxis (PrEP) use during HIV infection, we will explore methods of implementing host heterogeneities and their long-term effects on drug resistance.

Results: We combined and extended existing models of virus dynamics by incorporating pharmacokinetics, pharmacodynamics, and adherence behavior. We identified two "levers" associated with the host immune pressure against the virus, which can be used to independently modify the setpoint viral load and the shape of the acute phase viral load peak. We propose parameter relationships that can explain differences in acute and setpoint viral load among hosts, and demonstrate their influence on the rates of emergence and reversion of drug resistance. The importance of these dynamics is illustrated by modeling long-lived latent reservoirs of virus, through which past intervals of drug resistance can lead to failure of suppressive drug regimens. Finally, we analyze assumptions about temporal patterns of drug adherence and their impact on resistance dynamics, finding that with the same overall level of adherence, the dwell times in drug-adherent versus not-adherent states can alter the levels of drug-resistant virus incorporated into latent reservoirs.

Conclusions: We have shown how a diverse range of observable viral load trajectories can be produced from a basic model of virus dynamics using immunity-related "levers". Immune pressure, in turn, influences the dynamics of drug resistance, with increased immune activity delaying drug resistance and driving more rapid return to dominance of drug-susceptible virus after drug cessation. Both immune pressure and patterns of drug adherence influence the long-term risk of drug resistance. In the case of accidental PrEP use during infection, rapid transitions between adherence states and/or weak immunity fortifies the "memory" of previous PrEP exposure, increasing the risk of future drug resistance. This model framework provides a means for analyzing individual-level risks of drug resistance and implementing heterogeneities among hosts, thereby achieving a crucial prerequisite for improving population-level models of drug resistance.

Show MeSH
Related in: MedlinePlus