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Application of a Loading Dose of Colistin Methanesulfonate in Critically Ill Patients: Population Pharmacokinetics, Protein Binding, and Prediction of Bacterial Kill
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Infectious Diseases.
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2012 (English)In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 56, no 8, 4241-4249 p.Article in journal (Refereed) Published
Abstract [en]

A previous pharmacokinetic study on dosing of colistin methanesulfonate (CMS) at 240 mg (3 million units [MU]) every 8 h indicated that colistin has a long half-life, resulting in insufficient concentrations for the first 12 to 48 h after initiation of treatment. A loading dose would therefore be beneficial. The aim of this study was to evaluate CMS and colistin pharmacokinetics following a 480-mg (6-MU) loading dose in critically ill patients and to explore the bacterial kill following the use of different dosing regimens obtained by predictions from a pharmacokinetic-pharmacodynamic model developed from an in vitro study on Pseudomonas aeruginosa. The unbound fractions of colistin A and colistin B were determined using equilibrium dialysis and considered in the predictions. Ten critically ill patients (6 males; mean age, 54 years; mean creatinine clearance, 82 ml/min) with infections caused by multidrug-resistant Gram-negative bacteria were enrolled in the study. The pharmacokinetic data collected after the first and eighth doses were analyzed simultaneously with the data from the previous study (total, 28 patients) in the NONMEM program. For CMS, a two-compartment model best described the pharmacokinetics, and the half-lives of the two phases were estimated to be 0.026 and 2.2 h, respectively. For colistin, a one-compartment model was sufficient and the estimated half-life was 18.5 h. The unbound fractions of colistin in the patients were 26 to 41% at clinical concentrations. Colistin A, but not colistin B, had a concentration-dependent binding. The predictions suggested that the time to 3-log-unit bacterial kill for a 480-mg loading dose was reduced to half of that for the dose of 240 mg.

Place, publisher, year, edition, pages
2012. Vol. 56, no 8, 4241-4249 p.
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-184479DOI: 10.1128/AAC.06426-11ISI: 000306826300027OAI: oai:DiVA.org:uu-184479DiVA: diva2:565739
Available from: 2012-11-08 Created: 2012-11-07 Last updated: 2013-02-11Bibliographically approved
In thesis
1. Pharmacokinetic and Pharmacodynamic Modeling of Antibiotics and Bacterial Drug Resistance
Open this publication in new window or tab >>Pharmacokinetic and Pharmacodynamic Modeling of Antibiotics and Bacterial Drug Resistance
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Exposure to antibiotics is an important factor influencing the development of bacterial resistance.  In an era where very few new antibiotics are being developed, a strategy for the development of optimal dosing regimen and combination treatment that reduces the rate of resistance development and overcome existing resistance is of utmost importance. In addition, the optimal dosing in subpopulations is often not fully elucidated. The aim of this thesis was to develop pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PKPD) models that characterize the interaction of antibiotics with bacterial growth, killing and resistance over time, and can be applied to guide optimization of dosing regimens that enhance the efficacy of mono- and combination antibiotic therapy.

A mechanism-based PKPD model that incorporates the growth, killing kinetics and adaptive resistance development in Escherichia coli against gentamicin was developed based on  in vitro time-kill curve data. After some adaptations, the model was successfully applied for similar data on colistin and meropenem alone, and in combination, on one wild type and one meropenem-resistant strain of Pseudomonas aeruginosa.

The developed population PK model for colistin and its prodrug colistin methanesulfonate (CMS) in combination with the PKPD model showed the benefits for applying a loading dose for this drug. Simulations predicted the variability in bacteria kill to be larger between dosing occasions than between patients. A flat-fixed loading dose followed by an 8 or 12 hourly maintenance dose with infusion duration of up to 2 hours was shown to result in satisfactory bacterial kill under these conditions.

Pharmacometric models that characterize the time-course of drug concentrations, bacterial growth, antibacterial killing and resistance development were successfully developed. Predictions illustrated how PKPD models based on in vitro data can be utilized to guide development of antibiotic dosing, with examples advocating regimens that (i) promote bacterial killing and reduce risk for toxicity in preterm and term newborn infants receiving gentamicin, (ii) achieve a fast initial bacterial killing and reduced resistance development of colistin in critically ill patients by application of a loading dose, and (iii) overcome existing meropenem resistance by combining colistin and meropenem

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2013. 73 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 170
Pharmacometrics, pharmacokinetics, pharmacodynamics, modeling, antibiotics, resistance, combination, Gram-negative bacteria, gentamicin, colistin, meropenem, newborn infants, critically ill patients
National Category
Pharmaceutical Sciences Infectious Medicine
Research subject
Infectious Diseases; Pharmaceutical Science
urn:nbn:se:uu:diva-188306 (URN)978-91-554-8568-9 (ISBN)
Public defence
2013-02-08, B41, Uppsala Biomedical Center, Husargatan 3, Uppsala, 09:15 (English)
Available from: 2013-01-18 Created: 2012-12-14 Last updated: 2013-02-11Bibliographically approved

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Mohamed, Ami Fazlin SyedKarvanen, MattiCars, OttoFriberg, Lena E.
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