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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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Model schematic. (a) The model first assumes an adherence pattern, which is translated into a series of taken or missed doses over time. Doses, if taken, are assumed to be taken at a specified interval, e.g., daily for TDF. (b) A pharmacokinetic model of TDF translates the series of doses into a time-varying concentration of TDF-DP in the active intracellular compartment. By including the longer half-life of intracellular TDF-DP compared to TFV, this model captures the “pharmacologically forgiving” properties of TDF. (c) The relationship between the TDF-DP concentration and the replication of WT or mutant virus is assumed to be a Hill function following the median-effect model. WT is better able to replicate in the absence of drug, whereas the drug-resistant mutants are able to replicate at higher drug concentrations. K65R has higher fitness than M184V at all drug concentrations, and thus is expected to predominate over M184V in all simulations. (d) The basic virus dynamics model, governed by Equations 2–5, is depicted graphically. The additional factors of forward- and back-mutation due to error in reverse transcription, not depicted here, are shown in Equations 6–7. (e) The expanded virus dynamics model, which includes a latently infected cell compartment w, is depicted here and in Equations 11–12.
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Figure 1: Model schematic. (a) The model first assumes an adherence pattern, which is translated into a series of taken or missed doses over time. Doses, if taken, are assumed to be taken at a specified interval, e.g., daily for TDF. (b) A pharmacokinetic model of TDF translates the series of doses into a time-varying concentration of TDF-DP in the active intracellular compartment. By including the longer half-life of intracellular TDF-DP compared to TFV, this model captures the “pharmacologically forgiving” properties of TDF. (c) The relationship between the TDF-DP concentration and the replication of WT or mutant virus is assumed to be a Hill function following the median-effect model. WT is better able to replicate in the absence of drug, whereas the drug-resistant mutants are able to replicate at higher drug concentrations. K65R has higher fitness than M184V at all drug concentrations, and thus is expected to predominate over M184V in all simulations. (d) The basic virus dynamics model, governed by Equations 2–5, is depicted graphically. The additional factors of forward- and back-mutation due to error in reverse transcription, not depicted here, are shown in Equations 6–7. (e) The expanded virus dynamics model, which includes a latently infected cell compartment w, is depicted here and in Equations 11–12.

Mentions: Figure 1 illustrates the components of the model and the way in which they are linked. First, an adherence pattern is created based on a hypothesis about the timescales of adherent and non-adherent time intervals. For example, adherence could be assumed to be random in time, periodic with a given duration of adherence and non-adherence, or stochastic based on a Markov model described in detail below.


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

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

Model schematic. (a) The model first assumes an adherence pattern, which is translated into a series of taken or missed doses over time. Doses, if taken, are assumed to be taken at a specified interval, e.g., daily for TDF. (b) A pharmacokinetic model of TDF translates the series of doses into a time-varying concentration of TDF-DP in the active intracellular compartment. By including the longer half-life of intracellular TDF-DP compared to TFV, this model captures the “pharmacologically forgiving” properties of TDF. (c) The relationship between the TDF-DP concentration and the replication of WT or mutant virus is assumed to be a Hill function following the median-effect model. WT is better able to replicate in the absence of drug, whereas the drug-resistant mutants are able to replicate at higher drug concentrations. K65R has higher fitness than M184V at all drug concentrations, and thus is expected to predominate over M184V in all simulations. (d) The basic virus dynamics model, governed by Equations 2–5, is depicted graphically. The additional factors of forward- and back-mutation due to error in reverse transcription, not depicted here, are shown in Equations 6–7. (e) The expanded virus dynamics model, which includes a latently infected cell compartment w, is depicted here and in Equations 11–12.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3643872&req=5

Figure 1: Model schematic. (a) The model first assumes an adherence pattern, which is translated into a series of taken or missed doses over time. Doses, if taken, are assumed to be taken at a specified interval, e.g., daily for TDF. (b) A pharmacokinetic model of TDF translates the series of doses into a time-varying concentration of TDF-DP in the active intracellular compartment. By including the longer half-life of intracellular TDF-DP compared to TFV, this model captures the “pharmacologically forgiving” properties of TDF. (c) The relationship between the TDF-DP concentration and the replication of WT or mutant virus is assumed to be a Hill function following the median-effect model. WT is better able to replicate in the absence of drug, whereas the drug-resistant mutants are able to replicate at higher drug concentrations. K65R has higher fitness than M184V at all drug concentrations, and thus is expected to predominate over M184V in all simulations. (d) The basic virus dynamics model, governed by Equations 2–5, is depicted graphically. The additional factors of forward- and back-mutation due to error in reverse transcription, not depicted here, are shown in Equations 6–7. (e) The expanded virus dynamics model, which includes a latently infected cell compartment w, is depicted here and in Equations 11–12.
Mentions: Figure 1 illustrates the components of the model and the way in which they are linked. First, an adherence pattern is created based on a hypothesis about the timescales of adherent and non-adherent time intervals. For example, adherence could be assumed to be random in time, periodic with a given duration of adherence and non-adherence, or stochastic based on a Markov model described in detail below.

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