Frontiers in pharmacology · 2026
Physiologically-based pharmacokinetic model to predict loading dose polymyxin B exposure in critically ill patients with sepsis.
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Cao Yixuan, Galvidis Inna, Alimov Akmal, Standing Joseph F, Burkin Maksim, Surovoy Yury
Аннотация
OBJECTIVES: To build a physiologically based pharmacokinetic (PBPK) model to predict polymyxin B (PMB) exposure in critically ill patients with sepsis.
PATIENTS AND METHODS: A new PBPK model was built using published data on PMB pharmacokinetics (PK) in healthy volunteers and patients with end-stage renal disease (ESRD). The model was then tested to predict individual PK curves on clinical samples from critically ill patients with sepsis (N = 15) accounting for common pathophysiological alterations observed in this cohort (changes of unbound drug fraction, protein levels, fluids shifts, renal function). The developed model was then used to predict PMB plasma and lung concentrations for probability of target attainment (PTA) analysis.
RESULTS: The PBPK model demonstrated good prediction of mean population PK parameters, including area under the concentration time curve (AUC0-last), maximal concentration (Cmax) and clearance (CL), with predicted-to-observed ratios ranging from 0.98-1.2. Mean absolute prediction error (MAPE) and root mean squared error (RMSE) for individual predictions reached 26.7% and 7.7%, although the correlation was moderate (r = 0.47). Based on the predicted plasma exposures only the loading dose of 2.5 mg/kg and above achieved the PTA >90% with MIC of 1 mg/L.
CONCLUSION: The first PBPK model of PMB in critically ill patients allows good prediction of population-level PK parameters and supports a loading dose of at least 2.5 mg/kg. It also provides a mechanistic framework for future research as PMB distribution, metabolism and excretion mechanisms become better characterised.
PATIENTS AND METHODS: A new PBPK model was built using published data on PMB pharmacokinetics (PK) in healthy volunteers and patients with end-stage renal disease (ESRD). The model was then tested to predict individual PK curves on clinical samples from critically ill patients with sepsis (N = 15) accounting for common pathophysiological alterations observed in this cohort (changes of unbound drug fraction, protein levels, fluids shifts, renal function). The developed model was then used to predict PMB plasma and lung concentrations for probability of target attainment (PTA) analysis.
RESULTS: The PBPK model demonstrated good prediction of mean population PK parameters, including area under the concentration time curve (AUC0-last), maximal concentration (Cmax) and clearance (CL), with predicted-to-observed ratios ranging from 0.98-1.2. Mean absolute prediction error (MAPE) and root mean squared error (RMSE) for individual predictions reached 26.7% and 7.7%, although the correlation was moderate (r = 0.47). Based on the predicted plasma exposures only the loading dose of 2.5 mg/kg and above achieved the PTA >90% with MIC of 1 mg/L.
CONCLUSION: The first PBPK model of PMB in critically ill patients allows good prediction of population-level PK parameters and supports a loading dose of at least 2.5 mg/kg. It also provides a mechanistic framework for future research as PMB distribution, metabolism and excretion mechanisms become better characterised.