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Optimization of Teicoplanin Dosing Regimen in Adult Patients Using an Externally Evaluated Population Pharmacokinetic
Xiaojing Li1, Qiang Sun, Genzhu Wang
1Department of Pharmacy, Beijing Electric Power Hospital of State Grid Co. of China, Capital Medical University Electric Teaching Hospital, Beijing, China .
Background:
This study aimed to identify population pharmacokinetic models suitable for optimizing individualized teicoplanin dosing regimens in adult Chinese patients.
Methods:
PubMed and Web of Science were searched for teicoplanin population pharmacokinetic models developed for the general adult population. Patient data used for the external evaluation, including demographics, teicoplanin-related information (administration and blood concentration), and laboratory test values, were collected from the Beijing Electric Power Hospital. External evaluation was performed using the Nonlinear Mixed-Effects Modeling software. Models with excellent predictive abilities were identified, and Monte Carlo simulations were conducted.
Results:
A total of 66 teicoplanin concentrations used for external evaluation were obtained from 62 patients. The model built by Ogami et al performed excellently, with a bias of -7.56% and inaccuracy of 26.28%. The model consisted of the following parameters: clearance (L/h) = (0.379 + 0.211 × creatinine clearance/100) × (total body weight/70) 0.75 ; volume (V) 1 (L) = 38.2 × (fat-free mass/70); Q (L/h) = 2.42 × (total body weight/70) 0.75 ; V2 (L) = 106 × (fat-free mass/70). The model was subsequently used in Monte Carlo simulations (n = 1000). For general infections (minimum plasma concentration [C min ] = 10-15 mg/L), the loading dose (LD) and maintenance dose (MD) of teicoplanin should be at least 400 mg to achieve the target concentration. For endocarditis or severe infections, where a target concentration (C min = 15-30 mg/L) is required, LD should be at least 800 mg. Alternatively, the LD and MD of teicoplanin should be at least 600 mg to achieve desired therapeutic levels.
Conclusions:
By combining external evaluations using Nonlinear Mixed-Effects Modeling with Monte Carlo simulations, the model developed by Ogami et al was identified as the most suitable for guiding clinical dosing under different pathophysiological conditions.
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