Year: 2026
Type: review
Abstract: 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.
Authors: Saikumar Matcha, Gauri G. Rao, Patrick O. Hanafin, Xiaofen Liu, Zhenwei Yu, Jing Zhang, Hongqiang Qiu, Yu Cheng, Alexandre P. Zavascki, Ana Maria Sandri, Andrea Kwa, Marc H. Scheetz, Christine J. Kubin, Jayesh Shah, Benjamin P.Z. Cherng, Michael T. Yin, Jiping Wang, Lu Wang, David P. Calfee, Maureen Bolon, Anthony W. Purcell, Roger L. Nation, Jason M. Pogue, Keith S. Kaye, Jian Li
Institutions: University of Southern California, University of North Carolina at Chapel Hill, Fudan University, Huashan Hospital, National Health Commission of the People's Republic of China, Sir Run Run Shaw Hospital, Fujian Medical University, Union Hospital, Universidade Federal do Rio Grande do Sul, Hospital Moinhos de Vento, Universidade Federal de Ciências da Saúde de Porto Alegre, Hospital São Lucas da PUCRS, Pontifícia Universidade Católica do Rio Grande do Sul, National University of Singapore, Singapore General Hospital, SingHealth Duke-NUS Academic Medical Centre, Duke-NUS Medical School, Midwestern University, NewYork–Presbyterian Hospital, Columbia University Irving Medical Center, Columbia University, Monash University, Weill Cornell Medicine, Northwestern University, Northwestern Memorial Hospital, Northwestern Medicine, University of Michigan, Rutgers, The State University of New Jersey
Language: English
Subfield: Pharmacology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,780
Funders: National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, Science and Technology Commission of Shanghai Municipality, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Hospital de Clínicas de Porto Alegre, Medical Research Council, National Health and Medical Research Council, National Institute of Allergy and Infectious Diseases, Natural Science Foundation of Zhejiang Province
OpenAlex ID: W7162993385