Quantitative justification for target concentration intervention – parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides

Authors

  • Ivan Matthews,

    1. Department of Pharmacology and Clinical Pharmacology, University of Auckland, Auckland, New Zealand,
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  • Carl Kirkpatrick,

    1. School of Pharmacy, University of Queensland, Brisbane, Australia
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  • Nicholas Holford

    Corresponding author
    1. Department of Pharmacology and Clinical Pharmacology, University of Auckland, Auckland, New Zealand,
      Nicholas Holford, Department of Pharmacology and Clinical Pharmacology, The University of Auckland, 85 Park Road, Grafton, Private Bag 92019, Auckland, New Zealand. E-mail: n.holford@auckland.ac.nz
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Nicholas Holford, Department of Pharmacology and Clinical Pharmacology, The University of Auckland, 85 Park Road, Grafton, Private Bag 92019, Auckland, New Zealand. E-mail: n.holford@auckland.ac.nz

Abstract

Aims  [1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration.

Methods  Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (>=20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance.

Results  A two compartment pharmacokinetic model best described the aminoglycoside data. The addition of weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h–1 70 kg–1; intercompartmental clearance (CLic), 1 l h–1 70 kg–1; volume of central compartment (V1), 19.5 l 70 kg–1; volume of peripheral compartment (V2) 11.2 l 70 kg–1.

Conclusions  Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80–125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.

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