Clinical Pharmacokinetics

, Volume 56, Issue 11, pp 1369–1379 | Cite as

A Bayesian Approach for Population Pharmacokinetic Modeling of Pegylated Interferon α-2a in Hepatitis C Patients

Original Research Article
First Online:

Abstract

Background

Pegylated interferon α-2a (PEG-IFN-α-2a) is an antiviral drug used for the treatment of chronic hepatitis C virus (HCV) infection. This study describes the population pharmacokinetics of PEG-IFN-α-2a in hepatitis C patients using a Bayesian approach. A possible association between patient characteristics and pharmacokinetic parameters is also explored.

Methods

A Bayesian population pharmacokinetic modeling approach, using WinBUGS version 1.4.3, was applied to a cohort of patients (n = 292) with chronic HCV infection. Data were obtained from two phase III studies sponsored by Hoffmann-La Roche. Demographic and clinical information were evaluated as possible predictors of pharmacokinetic parameters during model development.

Results

A one-compartment model with an additive error best fitted the data, and a total of 2271 PEG-IFN-α-2a measurements from 292 subjects were analyzed using the proposed population pharmacokinetic model. Sex was identified as a predictor of PEG-IFN-α-2a clearance, and hemoglobin baseline level was identified as a predictor of PEG-IFN-α-2a volume of distribution.

Conclusion

A population pharmacokinetic model of PEG-IFN-α-2a in patients with chronic HCV infection was presented in this study. The proposed model can be used to optimize PEG-IFN-α-2a dosing in patients with chronic HCV infection. Optimal PEG-IFN-α-2a selection is important to maximize response and/or to avoid potential side effects such as thrombocytopenia and neutropenia.

Clinical Trials Registration Numbers

NV15942 and NV15801.

Keywords

Pharmacokinetic Model Deviance Information Criterion Eltrombopag Bayesian Credible Interval High Basal Metabolic Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Compliance with Ethical Standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Funding

No funding has been received for the conduct of this study and/or preparation of this article.

Conflicts of interest

Mohammad I. Saleh declares no conflicts of interest.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.School of PharmacyThe University of JordanAmmanJordan

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