Estimate of the Frequency of Human Immunodeficiency Virus Type 1 Protease Inhibitor Resistance within Unselected Virus Populations In Vitro

Tucker, Simon P.; Stiebel, T.R.; Potts, Karen E.; Smidt, Mary L.; Bryant, Martin L.

doi:10.1128/aac.42.2.478

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…This is especially important given the dependence of on the frequency of resistance mutations: our model predicts that for infections where resistance rates are high ( ) may be negative (antagonistic), favoring the use of antagonistic drug pairs over mildly synergistic or even purely additive antibiotic combinations. Indeed, for the modified Jumbe et al model that we study, is nearly additive; and while the resistance frequency we use may be an overestimate (Jumbe et al determined this as the rate of all mutations conferring only a 3-fold increase in the MIC), these and higher mutation rates have been identified in human pathogens [35] , [36] . Together, the potential for strong competition and high mutation rates in infection suggest that the tradeoff and synergy ceiling behaviors observed in our model – as well as the ability of antagonistic drug pairs to minimize multi-drug resistance – may describe the properties of some clinical infections.…”

Section: Discussionmentioning

confidence: 95%

Optimal Drug Synergy in Antimicrobial Treatments

2010

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The rapid proliferation of antibiotic-resistant pathogens has spurred the use of drug combinations to maintain clinical efficacy and combat the evolution of resistance. Drug pairs can interact synergistically or antagonistically, yielding inhibitory effects larger or smaller than expected from the drugs' individual potencies. Clinical strategies often favor synergistic interactions because they maximize the rate at which the infection is cleared from an individual, but it is unclear how such interactions affect the evolution of multi-drug resistance. We used a mathematical model of in vivo infection dynamics to determine the optimal treatment strategy for preventing the evolution of multi-drug resistance. We found that synergy has two conflicting effects: it clears the infection faster and thereby decreases the time during which resistant mutants can arise, but increases the selective advantage of these mutants over wild-type cells. When competition for resources is weak, the former effect is dominant and greater synergy more effectively prevents multi-drug resistance. However, under conditions of strong resource competition, a tradeoff emerges in which greater synergy increases the rate of infection clearance, but also increases the risk of multi-drug resistance. This tradeoff breaks down at a critical level of drug interaction, above which greater synergy has no effect on infection clearance, but still increases the risk of multi-drug resistance. These results suggest that the optimal strategy for suppressing multi-drug resistance is not always to maximize synergy, and that in some cases drug antagonism, despite its weaker efficacy, may better suppress the evolution of multi-drug resistance.

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Section: Discussionmentioning

confidence: 95%

Optimal Drug Synergy in Antimicrobial Treatments

2010

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The Next Generation of Human Immunodeficiency Virus Protease Inhibitors: Targeting Viral Resistance

Furfine

2000

Handbook of Experimental Pharmacology

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Short Communication:HIV Type 1 Variants with Nevirapine Resistance Mutations Are Rarely Detected in Antiretroviral Drug-Naive African Women with Subtypes A, C, and D

Church

Hudelson

Guay

et al. 2007

AIDS Research and Human Retroviruses

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K103N is frequently detected in HIV-infected women after single dose (SD) nevirapine (NVP). K103N-containing variants were detected more frequently by the ViroSeq HIV-1 Genotyping System in women with subtype C (69.2%) than subtypes A (19.4%, p < 0.0001) or D (36.1%, p < 0.0001). K103N-containing variants were also detected more frequently and at higher levels in women with subtype C by the LigAmp assay. In this report, we analyzed samples collected prior to or within hours after SD NVP administration from antiretroviral drug-naive African women with subtypes A, C, and D. Only 1/254 samples had an NVP resistance mutation detected with the ViroSeq system, and only 4/236 samples had K103N detected at < 0.5% with the LigAmp assay [2/110 (1.8%) with subtype A, 1/46 (2.2%) with subtype C, and 1/80 (1.3%) with subtype D] (p = 0.92). We did not detect significant differences in the pre-NVP frequency of NVP resistance mutations or the pre-NVP levels of K103N-containing variants in women with subtypes A, C, and D that explain the dramatic subtype-based differences in emergence of HIV-1 variants with these mutations after SD NVP exposure.

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Estimate of the Frequency of Human Immunodeficiency Virus Type 1 Protease Inhibitor Resistance within Unselected Virus Populations In Vitro

Cited by 7 publications

References 21 publications

Optimal Drug Synergy in Antimicrobial Treatments

Optimal Drug Synergy in Antimicrobial Treatments

The Next Generation of Human Immunodeficiency Virus Protease Inhibitors: Targeting Viral Resistance

Short Communication:HIV Type 1 Variants with Nevirapine Resistance Mutations Are Rarely Detected in Antiretroviral Drug-Naive African Women with Subtypes A, C, and D

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