Optimizing colistin dosing in acute-on-chronic liver failure patients with sepsis due to multidrug-resistant gram-negative pathogens

Acute-on-chronic liver failure (ACLF) patients with sepsis represent a special population which is highly susceptible to serious infections but remains understudied for antibiotic dose optimization. Colistin, a reserve antibiotic for multidrug-resistant Gram-negative infections, is frequently used for management of sepsis in this group of patients. There is an unmet medical need of describing dosing regimen developed using population pharmacokinetic approach in this group of patients. ACLF patients receiving intravenous colistin were enrolled. Population pharmacokinetic modelling was performed using nonlinear mixed-effects methods, with covariate analysis and bootstrap validation. Monte Carlo simulations were conducted to evaluate probability of target attainment (PTA) across dosing regimens and renal function strata. A total of 68 plasma concentrations from eligible patients admitted over two years were analysed. Colistin pharmacokinetics were adequately described by a one-compartment model, with considerable inter-individual variability. Creatinine clearance was found to be significant covariate for clearance. The probability of target attainment (PTA) for pertinent PK/PD index varied across different dosing regimens and renal function strata. Colistin pharmacokinetics in patients with ACLF is highly variable with creatinine clearance emerging as a significant covariate. We propose a creatinine-clearance–guided dosing regimen based on the findings of the study.

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Publication Details

Journal
Figshare
Published
2026-10-30
DOI
https://doi.org/10.6084/m9.figshare.34866664
Primary Topic
Antibiotics Pharmacokinetics and Efficacy
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article
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article

Optimizing colistin dosing in acute-on-chronic liver failure patients with sepsis due to multidrug-resistant gram-negative pathogens

Soumya Vij, Nipun Verma, Taneja Sunil, Tania Sidana et al.
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Figshare
Antibiotics Pharmacokinetics and Efficacy
article

Optimizing colistin dosing in acute-on-chronic liver failure patients with sepsis due to multidrug-resistant gram-negative pathogens

Soumya Vij, Nipun Verma, Taneja Sunil, Tania Sidana, Ritika Kondel Bhandari, Nusrat Shafiq, Samir Malhotra, Sunil Sharma, Ajay Duseja, Naveen Murali, Avaneesh K Pandey, Vineet Singla
article en

Abstract

Acute-on-chronic liver failure (ACLF) patients with sepsis represent a special population which is highly susceptible to serious infections but remains understudied for antibiotic dose optimization. Colistin, a reserve antibiotic for multidrug-resistant Gram-negative infections, is frequently used for management of sepsis in this group of patients. There is an unmet medical need of describing dosing regimen developed using population pharmacokinetic approach in this group of patients. ACLF patients receiving intravenous colistin were enrolled. Population pharmacokinetic modelling was performed using nonlinear mixed-effects methods, with covariate analysis and bootstrap validation. Monte Carlo simulations were conducted to evaluate probability of target attainment (PTA) across dosing regimens and renal function strata. A total of 68 plasma concentrations from eligible patients admitted over two years were analysed. Colistin pharmacokinetics were adequately described by a one-compartment model, with considerable inter-individual variability. Creatinine clearance was found to be significant covariate for clearance. The probability of target attainment (PTA) for pertinent PK/PD index varied across different dosing regimens and renal function strata. Colistin pharmacokinetics in patients with ACLF is highly variable with creatinine clearance emerging as a significant covariate. We propose a creatinine-clearance–guided dosing regimen based on the findings of the study.

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Antibiotics Pharmacokinetics and Efficacy
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Optimizing colistin dosing in acute-on-chronic liver failure patients with sepsis due to multidrug-resistant gram-negative pathogens — Soumya Vij, Nipun Verma, et al. · Figshare (2026) | TGRS Research Map | TGRS