Population pharmacokinetic model and limited sampling strategies for personalized dosing of levofloxacin in tuberculosis patients [Clinical Therapeutics]

Levofloxacin is an anti-tuberculosis drug with substantial interindividual pharmacokinetic variability; therapeutic drug monitoring (TDM) could therefore be helpful to improve treatment results. TDM would be more feasible with limited sampling strategies (LSSs), a method to estimate area under the concentration curve for the 24 h dosing interval (AUC_0-24) by using a limited number of samples. This study aimed to develop a population pharmacokinetic (popPK) model of levofloxacin in tuberculosis patients, along with LSSs using a Bayesian and multiple linear regression approach.

The popPK model and Bayesian LSS were developed using data of 30 patients and externally validated with 20 patients. The LSS based on multiple linear regression was internally validated using jackknife analysis. Only clinically suitable LSSs (maximum timespan 8 h, minimum interval 1 h, 1 to 3 samples) were tested. Performance criteria were root mean squared error (RMSE) <15%, mean prediction error (MPE) <5%, and r² > 0.95.

A one compartment model with lag time best described the data while only slightly underestimating the AUC_0-24 (mean –7.9%, SE 1.7%). The Bayesian LSS using 0 and 5 h post-dose samples (RMSE 8.8%, MPE 0.42%, r² 0.957) adequately estimated the AUC_0-24 with a mean underestimation of –4.4% (SE 2.7%). The multiple linear regression LSS using 0 and 4 h post-dose samples (RMSE 7.0%, MPE 5.5%, r² 0.977) was internally validated with a mean underestimation of -0.46% (SE 2.0%).

In this study, we successfully developed a popPK model and two LSSs that could be implemented in clinical practice to assist TDM of levofloxacin.

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