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Integrated Population Pharmacokinetic/Viral Dynamic Modelling of Lopinavir/Ritonavir in HIV-1 Treatment-Naïve Patients

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Abstract

Background

Lopinavir (LPV)/ritonavir (RTV) co-formulation (LPV/RTV) is a widely used protease inhibitor (PI)-based regimen to treat HIV-infection. As with all PIs, the trough concentration (C trough) is a primary determinant of response, but the optimum exposure remains poorly defined. The primary objective was to develop an integrated LPV population pharmacokinetic model to investigate the influence of α-1-acid glycoprotein and link total and free LPV exposure to pharmacodynamic changes in HIV-1 RNA and assess viral dynamic and drug efficacy parameters.

Methods

Data from 35 treatment-naïve HIV-infected patients initiating therapy with LPV/RTV 400/100 mg orally twice daily across two studies were used for model development and simulations using ADAPT. Total LPV (LPVt) and RTV concentrations were measured by high-performance liquid chromatography with ultraviolet (UV) detection. Free LPV (LPVf) concentrations were measured using equilibrium dialysis and mass spectrometry.

Results

The LPVt typical value of clearance (\( {\text{CL}}_{{{\text{LPV}}_{\text{t}} }} /F \)) was 4.73 L/h and the distribution volume (\( V_{{{\text{LPV}}_{\text{t}} }} /F \)) was 55.7 L. The clearance (\( {\text{CL}}_{{{\text{LPV}}_{\text{f}} }} /F \)) and distribution volume (V f/F) for LPVf were 596 L/h and 6,370 L, respectively. The virion clearance rate was 0.0350 h−1. The simulated \( {\text{LPV}}_{{{\text{LPV}}_{\text{t}} }} \) C trough values at 90 % (EC90) and 95 % (EC95) of the maximum response were 316 and 726 ng/mL, respectively.

Conclusions

The pharmacokinetic–pharmacodynamic model provides a useful tool to quantitatively describe the relationship between LPV/RTV exposure and viral response. This comprehensive modelling and simulation approach could be used as a surrogate assessment of antiretroviral (ARV) activity where adequate early-phase dose-ranging studies are lacking in order to define target trough concentrations and possibly refine dosing recommendations.

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Acknowledgments

This work was supported by National Institutes of Health grants 1K23 A1073119 (IO), 5K12 RR017643 (IO), 1U01AI103408-01 (IO), P41-EB001978 (DZD), KL2TR000455 (ANS), UL1TR000454 (ANS), Emory University CFAR (NIH P30 A1050409), and the Atlanta Clinical and Translational Science Institute (NIH MO1 RR00039). We would like to acknowledge the individuals that participated in these studies. Without their dedication this work could not have been accomplished.

Conflict of Interest

None of the authors has any conflicts of interest that are relevant to the content of this article.

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Authors and Affiliations

  1. Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Kun Wang

  2. Biomedical Simulations Resource, University of Southern California, Los Angeles, CA, USA

    Kun Wang & David Z. D’Argenio

  3. Division of Clinical Pharmacology, University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA

    Kun Wang, Edward P. Acosta & Corenna Kerstner-Wood

  4. Division of Infectious Disease, Department of Medicine, Emory University School of Medicine, 49 Jesse Hill Jr. Drive, Atlanta, GA, 30303, USA

    Anandi N. Sheth, Cecile Delille, Jeffrey L. Lennox & Ighovwerha Ofotokun

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  1. Kun Wang
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Correspondence to Ighovwerha Ofotokun.

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Wang, K., D’Argenio, D.Z., Acosta, E.P. et al. Integrated Population Pharmacokinetic/Viral Dynamic Modelling of Lopinavir/Ritonavir in HIV-1 Treatment-Naïve Patients. Clin Pharmacokinet 53, 361–371 (2014). https://doi.org/10.1007/s40262-013-0122-1

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