Three methods to optimize polymyxin B dosing using estimated AUC after first dose: Validation with the data generated by Monte-Carlo simulation

Abstract

Objectives: To achieve the AUC-guided dosing, we proposed three methods to estimate polymyxin B AUC across 24 h at steady state (AUCSS,24h) using limited concentrations after its first dose.

Method: Monte Carlo simulation based on a well-established population PK model was performed to generate the PK profiles of 1000 patients with normal or abnormal renal function. Polymyxin B AUC_SS,24h was estimated for each subject using three methods (two-point PK approach, three-point PK approach, and four-point PK approach) based on limited concentration data in its first dose and compared with the actual AUC at steady state calculated using the linear-trapezoidal formula. Sensitivity analysis was performed to examine the influence of each sampling time drifting on the estimated AUC_SS,24h.

Results: In patients with normal renal function, the mean bias of two-point PK approach, three-point PK approach, and four-point PK approach was -8.73%, 1.37%, and -0.48%, respectively. The corresponding value was -11.15%, 1.99%, and -0.28% in patients with renal impairment, respectively. The largest mean bias of two-point PK approach, three-point PK approach, and four-point PK approach was -12.63%, -6.47%, and -0.54% when the sampling time shifted. Three user-friendly and easy-to-use excel calculators were built based on these methods.

Conclusions: Two-point PK approach may be sufficient to guide polymyxin B dosing in patients with normal renal function. For patients with renal insufficiency, three-point PK approach or four-point PK may be a better choice. The Excel calculators designed based on the three methods can be potentially used to optimize the dosing regimen of polymyxin B in the clinic.

Keywords:

• polymyxin B
• dose optimization
• pharmacokinetics/pharmacodynamics
• population pharmacokinetics
• Monte Carlo simulation

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Funding

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