Population pharmacokinetics of polymyxin B: an individual participant data meta-analysis.

OBJECTIVES

Small-scale studies often lack sufficient statistical power to identify clinically relevant covariates affecting drug pharmacokinetics (PK) and dosing. To overcome these limitations, we pooled data from clinical trials and performed a population PK analysis using individual participant data meta-analysis (IPDMA) approach for polymyxin B. The aim was to uncover covariate-parameter relationships that may remain unidentifiable in isolated analyses of data from smaller studies.

METHODS

Data from five polymyxin B clinical studies, comprising both single- and multiple-dose regimens in healthy and critically ill subjects, were pooled. The dataset included dosing information, concentration-time profiles, and demographic and clinical characteristics. A stepwise modelling approach was used to develop a population PK model and identify significant covariate-parameter relationships. Monte Carlo simulations were conducted to evaluate the probability of target attainment (PTA) and the safety and efficacy of various dosing strategies.

RESULTS

Polymyxin B PK was best described using a two-compartment model, based on 1482 plasma concentrations from 315 subjects. A statistically significant association was observed between creatinine clearance (CrCL) and sex with drug clearance, with CrCL reducing interindividual variability (IIV) by 2.2% and sex contributing an additional 1.2% reduction. Although age and sex associated with the volume of distribution of the central compartment, with age explaining 14.3% of its IIV, inclusion of sex further reduced IIV by 2.3%. PTA analysis indicated that a 100 mg loading dose followed by 75 mg every 12 hours achieved PK/pharmacodynamic targets for nonpulmonary infections within the first 24 hours and maintained an optimal safety-efficacy balance at steady state.

CONCLUSIONS

PTA significantly declined for MICs >1 mg/L, highlighting the need to reassess current MIC breakpoints. Although IPDMA enhances data integration across studies, its capacity to identify additional covariates is limited by data heterogeneity and underrepresentation of specific patient subgroups.