Modeling HIV dynamics and antiviral response with consideration of time-varying drug exposures, adherence and phenotypic sensitivity

Huang, Yangxin; Rosenkranz, Susan L.; Wu, Hulin

doi:10.1016/s0025-5564(03)00058-0

Cited by 107 publications

(99 citation statements)

References 32 publications

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“…[20][21][22] If efficacy is not high enough (i.e., below its critical value), then theory predicts that HIV rather than declining monotonically during therapy, will decline initially but ultimately stabilize at a therapy-induced set point. We show that the same concept applies to HCV treatment, and estimate ⑀ c , the critical drug efficacy needed for ultimate clearance of HCV (i.e., sustained virological response).…”

Section: Discussionmentioning

confidence: 99%

Triphasic decline of hepatitis C virus RNA during antiviral therapy

2007

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When patients chronically infected with hepatitis C virus (HCV) are placed on antiviral therapy with pegylated interferon (IFN)-␣ or IFN-␣ plus ribavirin (RBV), HCV RNA generally declines in a biphasic manner. However, a triphasic decline has been reported in a subset of patients. A triphasic decline consists of a first phase (1-2 days) with rapid virus load decline, followed by a "shoulder phase" (4-28 days) in which virus load decays slowly or remains constant, and a third phase of renewed viral decay. We show that by including the proliferation of both uninfected and infected cells, a viral kinetic model can account for a triphasic HCV RNA decay. The model predicts that a triphasic decline occurs only in patients in which a majority of hepatocytes are infected before therapy. The shoulder phase does not represent the intrinsic death rate of infected cells, but rather the third phase slope is close to the intrinsic death rate of infected cells when overall drug efficacy is close to 1. The typical HCV RNA decay during therapy with IFN-␣ alone or in combination with RBV is characterized by an initial rapid viral decline (first phase) followed by a slower decay (second phase). [9][10][11][12] In some patients, a triphasic decline has been observed in which there is a rapid initial decline in viral load, followed by a "shoulder phase" lasting 4-28 days in which HCV RNA decays slowly or remains constant, and a third phase of renewed viral decay. 12-15 For example, Herrmann et al. 12 observed triphasic declines in 8 of 10 patients treated with pegylated IFN-␣ plus RBV, in 9 of 17 patients treated with pegylated IFN-␣ alone, and in 4 of 7 patients treated with IFN-␣ plus RBV. Why some patients respond to therapy with a biphasic decline whereas others respond in a triphasic manner has not been addressed previously and is the subject of our report. Conclusion:In patients treated with IFN-␣ plus RBV, compared with patients treated with IFN-␣ alone, RBV tends to increase the last-phase slope (i.e., the second phase slope in biphasic viral decays and the third phase slope in triphasic viral decays). 10,12 Modeling HCV RNA kinetics during therapy suggested that the increase in last-phase slope could be due to a delayed immunomodulatory effect of RBV, which increases the death rate of HCV productively infected cells after the shoulder phase. 12 In particular, in the model of Herrmann et al., 12 the slope of the "shoulder phase" in patients with triphasic viral decay represents the pretreatment death rate of infected cells and the third-phase slope represents the treatment-enhanced death rate of infected cells due to the immunomodulatory effect of RBV. Thus, to obtain a flat second phase in this model requires the assumption that the pretreatment death rate of infected cells is close to 0 and then increases due to the effect of RBV. However, we contend that a flat second phase can be obtained without assuming Abbreviation: HCV, hepatitis C virus; IFN, interferon; RBV, ribavirin.

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Section: Discussionmentioning

confidence: 99%

Triphasic decline of hepatitis C virus RNA during antiviral therapy

2007

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“…These models assumed that the treatment effect was constant over time. However, due to the fluctuations in drug concentration over time resulting from absorption, distribution and elimination in the animal, it appears more appropriate to assume that the treatment effect is time-varying (Huang et al, 2003;Rong et al, 2007). Lower resistance selection intensities are known to be associated with intramuscular injections as compared to oral drug administration in animals (Wiuff et al, 2003;Bibbal et al, 2007).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Impact of antimicrobial usage on the transmission dynamics of antimicrobial resistant bacteria among pigs

Abatih

Alban

Ersbøll

et al. 2009

Journal of Theoretical Biology

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There is increasing evidence showing that antimicrobial consumption provides a powerful selective force that promotes the emergence of resistance in pathogenic, commensal as well as zoonotic bacteria in animals. The main aim of this study was to develop a modeling framework that can be used to assess the impact of antimicrobial usage in pigs on the emergence and transmission of resistant bacteria within a finisher pig farm. The transmission dynamics of drug-sensitive and drug-resistant bacteria among pigs in the herd were characterized by studying the local and global stability properties of steady state solutions of the system. Numerical simulations demonstrating the influence of factors such as initial prevalence of infection, presence of pre-existing antimicrobial resistant mutants, and frequency of treatment on Preprint submitted to 22 September 2008 A c c e p t e d m a n u s c r i p tpredicted prevalence were performed. Sensitivity analysis revealed that two parameters had a huge influence on the predicted proportion of pigs carrying resistant bacteria: a) the transmission coefficient between uninfected pigs and those infected with drug-resistant bacteria during treatment (β 2 ) and after after treatment stops (β 3 ), and b) the spontaneous clear-out rate of drug-resistant bacteria during treatment (γ 2 ) and immediately after treatment stops (γ 3 ). Control measures should therefore be geared towards reducing the magnitudes of β 2 and β 3 or increasing those of γ 2 and γ 3 .

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“…By doing so we obtain an extended model that uses the drug dosage as the control input, which is more practical than previous models for biomedical applications. It is notable that different HIV infection models have been employed to research the relationship between the virus dynamics and the concentration of HIV drugs [18], [29]- [31]. In particular an extended HIV infection model has been presented in [18] based on the HIV infection model with immune response modeling of [6], [28].…”

Section: B Pharmacological Dynamics For Ftc-tdfmentioning

confidence: 99%