Literature DB >> 26850719

Dynamic interaction of colistin and meropenem on a WT and a resistant strain of Pseudomonas aeruginosa as quantified in a PK/PD model.

Ami F Mohamed1, Anders N Kristoffersson2, Matti Karvanen3, Elisabet I Nielsen4, Otto Cars3, Lena E Friberg4.   

Abstract

OBJECTIVES: Combination therapy can be a strategy to ensure effective bacterial killing when treating Pseudomonas aeruginosa, a Gram-negative bacterium with high potential for developing resistance. The aim of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model that describes the in vitro bacterial time-kill curves of colistin and meropenem alone and in combination for one WT and one meropenem-resistant strain of P. aeruginosa.
METHODS: In vitro time-kill curve experiments were conducted with a P. aeruginosa WT (ATCC 27853) (MICs: meropenem 1 mg/L; colistin 1 mg/L) and a meropenem-resistant type (ARU552) (MICs: meropenem 16 mg/L; colistin 1.5 mg/L). PK/PD models characterizing resistance were fitted to the observed bacterial counts in NONMEM. The final model was applied to predict the bacterial killing of ARU552 for different combination dosages of colistin and meropenem.
RESULTS: A model with compartments for growing and resting bacteria, where the bacterial killing by colistin reduced with continued exposure and a small fraction (0.15%) of the start inoculum was resistant to meropenem, characterized the bactericidal effect and resistance development of the two antibiotics. For a typical patient, a loading dose of colistin combined with a high dose of meropenem (2000 mg q8h) was predicted to result in a pronounced kill of the meropenem-resistant strain over 24 h.
CONCLUSIONS: The developed PK/PD model successfully described the time course of bacterial counts following exposures to colistin and meropenem, alone and in combination, for both strains, and identified a dynamic drug interaction. The study illustrates the application of a PK/PD model and supports high-dose combination therapy of colistin and meropenem to overcome meropenem resistance.
© The Author 2016. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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Year:  2016        PMID: 26850719     DOI: 10.1093/jac/dkv488

Source DB:  PubMed          Journal:  J Antimicrob Chemother        ISSN: 0305-7453            Impact factor:   5.790


  15 in total

1.  Dose Optimization of Colistin Combinations against Carbapenem-Resistant Acinetobacter baumannii from Patients with Hospital-Acquired Pneumonia in China by Using an In Vitro Pharmacokinetic/Pharmacodynamic Model.

Authors:  Xingchen Bian; Xiaofen Liu; Yuancheng Chen; Daijie Chen; Jian Li; Jing Zhang
Journal:  Antimicrob Agents Chemother       Date:  2019-03-27       Impact factor: 5.191

2.  Colistin Is Extensively Lost during Standard In Vitro Experimental Conditions.

Authors:  Matti Karvanen; Christer Malmberg; Pernilla Lagerbäck; Lena E Friberg; Otto Cars
Journal:  Antimicrob Agents Chemother       Date:  2017-10-24       Impact factor: 5.191

3.  Simulation-Based Evaluation of PK/PD Indices for Meropenem Across Patient Groups and Experimental Designs.

Authors:  Anders N Kristoffersson; Pascale David-Pierson; Neil J Parrott; Olaf Kuhlmann; Thierry Lave; Lena E Friberg; Elisabet I Nielsen
Journal:  Pharm Res       Date:  2016-01-19       Impact factor: 4.200

Review 4.  Clinical Pharmacokinetics and Pharmacodynamics of Colistin.

Authors:  Nicolas Grégoire; Vincent Aranzana-Climent; Sophie Magréault; Sandrine Marchand; William Couet
Journal:  Clin Pharmacokinet       Date:  2017-12       Impact factor: 6.447

5.  Translational Pharmacometric Evaluation of Typical Antibiotic Broad-Spectrum Combination Therapies Against Staphylococcus Aureus Exploiting In Vitro Information.

Authors:  S G Wicha; W Huisinga; C Kloft
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2017-07-13

6.  Assessing Pharmacodynamic Interactions in Mice Using the Multistate Tuberculosis Pharmacometric and General Pharmacodynamic Interaction Models.

Authors:  Chunli Chen; Sebastian G Wicha; Gerjo J de Knegt; Fatima Ortega; Laura Alameda; Veronica Sousa; Jurriaan E M de Steenwinkel; Ulrika S H Simonsson
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2017-10-10

7.  Pharmacokinetic-pharmacodynamic modelling to investigate in vitro synergy between colistin and fusidic acid against MDR Acinetobacter baumannii.

Authors:  Lynette M Phee; Frank Kloprogge; Rebecca Morris; John Barrett; David W Wareham; Joseph F Standing
Journal:  J Antimicrob Chemother       Date:  2019-04-01       Impact factor: 5.790

Review 8.  Generating Robust and Informative Nonclinical In Vitro and In Vivo Bacterial Infection Model Efficacy Data To Support Translation to Humans.

Authors:  Jürgen B Bulitta; William W Hope; Ann E Eakin; Tina Guina; Vincent H Tam; Arnold Louie; George L Drusano; Jennifer L Hoover
Journal:  Antimicrob Agents Chemother       Date:  2019-04-25       Impact factor: 5.191

9.  Differences in Colistin Administration and Bacterial and Treatment Outcomes in Critically Ill Patients.

Authors:  Sunmi Jung; Eun Kyoung Chung; Min Sun Jun; Eun Sun Son; Sandy Jeong Rhie
Journal:  Sci Rep       Date:  2019-06-19       Impact factor: 4.379

10.  A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection.

Authors:  Muhammad W Sadiq; Elisabet I Nielsen; Dalia Khachman; Jean-Marie Conil; Bernard Georges; Georges Houin; Celine M Laffont; Mats O Karlsson; Lena E Friberg
Journal:  J Pharmacokinet Pharmacodyn       Date:  2016-08-30       Impact factor: 2.745

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