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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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Influence of CTL efficacypon the dynamics of early infection with changing or constant setpoint viral load. (a) Copies/mL of WT virus (black) and drug resistant mutant K65R (red), and number/μL of CTLs (green), uninfected CD4+ cells/mL (blue) and infected CD4+ cells/mL (magenta), for ten p values spaced between 0 and 10-4. Lighter shading corresponds to smaller values of p, and arrows point in the direction of increasing p to show its influence on the trajectories. (b) Calculated estimate of the basic reproductive ratio for WT virus and K65R mutant as a function of p. (c) Simulated WT viral load after 500 days, sweeping over p while holding all other parameters constant, reveals that the final viral load drops toward zero as p reduces the R0 of WT toward 1. (d) Increasing p and correspondingly decreasing f according to Equation 9 maintains the steady-state viral load at 51,000 copies/mL, but raises the acute phase peak and leads to a longer “shoulder” before the viral load reaches setpoint. (Same color scheme as (a), but with a timescale of only 60 days to show the details of acute phase dynamics.) The effects on the height and timing of the peak are shown in (e) and (f), respectively, over the same range of p as in (b) and (c).
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Figure 2: Influence of CTL efficacypon the dynamics of early infection with changing or constant setpoint viral load. (a) Copies/mL of WT virus (black) and drug resistant mutant K65R (red), and number/μL of CTLs (green), uninfected CD4+ cells/mL (blue) and infected CD4+ cells/mL (magenta), for ten p values spaced between 0 and 10-4. Lighter shading corresponds to smaller values of p, and arrows point in the direction of increasing p to show its influence on the trajectories. (b) Calculated estimate of the basic reproductive ratio for WT virus and K65R mutant as a function of p. (c) Simulated WT viral load after 500 days, sweeping over p while holding all other parameters constant, reveals that the final viral load drops toward zero as p reduces the R0 of WT toward 1. (d) Increasing p and correspondingly decreasing f according to Equation 9 maintains the steady-state viral load at 51,000 copies/mL, but raises the acute phase peak and leads to a longer “shoulder” before the viral load reaches setpoint. (Same color scheme as (a), but with a timescale of only 60 days to show the details of acute phase dynamics.) The effects on the height and timing of the peak are shown in (e) and (f), respectively, over the same range of p as in (b) and (c).

Mentions: Increasing the immune pressure parameter p reduced setpoint viral load with little influence on the acute phase (Figure 2a), which occurs during the ramp-up in CTL frequency and therefore is subject to lower levels of immune pressure compared to steady-state levels. As predicted by Equation 10 and illustrated in Figure 2b, increasing p reduces the basic reproductive ratio (R0) of both wild-type and mutant virus. Because mutant virus incurs a higher fitness cost in the absence of drug, the level of p required to suppress the mutant virus population (R0 < 1) is lower for the mutant than for the wild-type virus. The steady-state viral load declines with increasing p, providing a “lever” to modulate setpoint viral load over the range of physiologically relevant values.


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

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

Influence of CTL efficacypon the dynamics of early infection with changing or constant setpoint viral load. (a) Copies/mL of WT virus (black) and drug resistant mutant K65R (red), and number/μL of CTLs (green), uninfected CD4+ cells/mL (blue) and infected CD4+ cells/mL (magenta), for ten p values spaced between 0 and 10-4. Lighter shading corresponds to smaller values of p, and arrows point in the direction of increasing p to show its influence on the trajectories. (b) Calculated estimate of the basic reproductive ratio for WT virus and K65R mutant as a function of p. (c) Simulated WT viral load after 500 days, sweeping over p while holding all other parameters constant, reveals that the final viral load drops toward zero as p reduces the R0 of WT toward 1. (d) Increasing p and correspondingly decreasing f according to Equation 9 maintains the steady-state viral load at 51,000 copies/mL, but raises the acute phase peak and leads to a longer “shoulder” before the viral load reaches setpoint. (Same color scheme as (a), but with a timescale of only 60 days to show the details of acute phase dynamics.) The effects on the height and timing of the peak are shown in (e) and (f), respectively, over the same range of p as in (b) and (c).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Influence of CTL efficacypon the dynamics of early infection with changing or constant setpoint viral load. (a) Copies/mL of WT virus (black) and drug resistant mutant K65R (red), and number/μL of CTLs (green), uninfected CD4+ cells/mL (blue) and infected CD4+ cells/mL (magenta), for ten p values spaced between 0 and 10-4. Lighter shading corresponds to smaller values of p, and arrows point in the direction of increasing p to show its influence on the trajectories. (b) Calculated estimate of the basic reproductive ratio for WT virus and K65R mutant as a function of p. (c) Simulated WT viral load after 500 days, sweeping over p while holding all other parameters constant, reveals that the final viral load drops toward zero as p reduces the R0 of WT toward 1. (d) Increasing p and correspondingly decreasing f according to Equation 9 maintains the steady-state viral load at 51,000 copies/mL, but raises the acute phase peak and leads to a longer “shoulder” before the viral load reaches setpoint. (Same color scheme as (a), but with a timescale of only 60 days to show the details of acute phase dynamics.) The effects on the height and timing of the peak are shown in (e) and (f), respectively, over the same range of p as in (b) and (c).
Mentions: Increasing the immune pressure parameter p reduced setpoint viral load with little influence on the acute phase (Figure 2a), which occurs during the ramp-up in CTL frequency and therefore is subject to lower levels of immune pressure compared to steady-state levels. As predicted by Equation 10 and illustrated in Figure 2b, increasing p reduces the basic reproductive ratio (R0) of both wild-type and mutant virus. Because mutant virus incurs a higher fitness cost in the absence of drug, the level of p required to suppress the mutant virus population (R0 < 1) is lower for the mutant than for the wild-type virus. The steady-state viral load declines with increasing p, providing a “lever” to modulate setpoint viral load over the range of physiologically relevant values.

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