Evaluation and application of population pharmacokinetic models for optimising linezolid treatment in non-adherence multidrug-resistant tuberculosis patients

Abstract

Background

Population pharmacokinetic (popPK) models can optimise linezolid dosage regimens in patients with multidrug-resistant tuberculosis (MDR-TB); however, unknown cross-centre precision and poor adherence remain problematic. This study aimed to assess the predictive ability of published models and use the most suitable model to optimise dosage regimens and manage compliance.

Methods

One hundred fifty-eight linezolid plasma concentrations from 27 patients with MDR-TB were used to assess the predictive performance of published models. Prediction-based metrics and simulation-based visual predictive checks were conducted to evaluate predictive ability. Individualised remedial dosing regimens for various delayed scenarios were optimised using the most suitable model and Monte Carlo simulations. The influence of covariates, scheduled dosing intervals, and patient compliance were assessed.

Results

Seven popPK models were identified. Body weight and creatinine clearance were the most frequently identified covariates influencing linezolid clearance. The model with the best performance had a median prediction error (PE%) of -1.62 %, median absolute PE of 29.50 %, and percentages of PE within 20 % (F₂₀, 36.97 %) and 30 % (F₃₀, 51.26 %). Monte Carlo simulations indicated that a twice-daily 300 mg linezolid dose may be more efficient than 600 mg once daily. For the ‘typical’ patient treated with 300 mg twice daily, half the dosage should be taken after a delay of ≥ 3 h.

Conclusions

Monte Carlo simulations based on popPK models can propose remedial regimens for delayed doses of linezolid in patients with MDR-TB. Model-based compliance management patterns are useful for balancing efficacy, adverse reactions, and resistance suppression.