Literature DB >> 23803529

Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs.

Elisabet I Nielsen1, Lena E Friberg.   

Abstract

Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation has evolved as an important tool for rational drug development and drug use, where developed models characterize both the typical trends in the data and quantify the variability in relationships between dose, concentration, and desired effects and side effects. In parallel, rapid emergence of antibiotic-resistant bacteria imposes new challenges on modern health care. Models that can characterize bacterial growth, bacterial killing by antibiotics and immune system, and selection of resistance can provide valuable information on the interactions between antibiotics, bacteria, and host. Simulations from developed models allow for outcome predictions of untested scenarios, improved study designs, and optimized dosing regimens. Today, much quantitative information on antibiotic PKPD is thrown away by summarizing data into variables with limited possibilities for extrapolation to different dosing regimens and study populations. In vitro studies allow for flexible study designs and valuable information on time courses of antibiotic drug action. Such experiments have formed the basis for development of a variety of PKPD models that primarily differ in how antibiotic drug exposure induces amplification of resistant bacteria. The models have shown promise for efficacy predictions in patients, but few PKPD models describe time courses of antibiotic drug effects in animals and patients. We promote more extensive use of modeling and simulation to speed up development of new antibiotics and promising antibiotic drug combinations. This review summarizes the value of PKPD modeling and provides an overview of the characteristics of available PKPD models of antibiotics based on in vitro, animal, and patient data.

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Year:  2013        PMID: 23803529     DOI: 10.1124/pr.111.005769

Source DB:  PubMed          Journal:  Pharmacol Rev        ISSN: 0031-6997            Impact factor:   25.468


  108 in total

Review 1.  The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections.

Authors:  T Tängdén; V Ramos Martín; T W Felton; E I Nielsen; S Marchand; R J Brüggemann; J B Bulitta; M Bassetti; U Theuretzbacher; B T Tsuji; D W Wareham; L E Friberg; J J De Waele; V H Tam; Jason A Roberts
Journal:  Intensive Care Med       Date:  2017-04-13       Impact factor: 17.440

2.  Stochastic process pharmacodynamics: dose timing in neonatal gentamicin therapy as an example.

Authors:  Tomas Radivoyevitch; Nopphon Siranart; Lynn Hlatky; Rainer Sachs
Journal:  AAPS J       Date:  2015-02-07       Impact factor: 4.009

3.  High-intensity meropenem combinations with polymyxin B: new strategies to overcome carbapenem resistance in Acinetobacter baumannii.

Authors:  Justin R Lenhard; Jürgen B Bulitta; Terry D Connell; Natalie King-Lyons; Cornelia B Landersdorfer; Soon-Ee Cheah; Visanu Thamlikitkul; Beom Soo Shin; Gauri Rao; Patricia N Holden; Thomas J Walsh; Alan Forrest; Roger L Nation; Jian Li; Brian T Tsuji
Journal:  J Antimicrob Chemother       Date:  2016-09-15       Impact factor: 5.790

4.  Two mechanisms of killing of Pseudomonas aeruginosa by tobramycin assessed at multiple inocula via mechanism-based modeling.

Authors:  Jürgen B Bulitta; Neang S Ly; Cornelia B Landersdorfer; Nicholin A Wanigaratne; Tony Velkov; Rajbharan Yadav; Antonio Oliver; Lisandra Martin; Beom Soo Shin; Alan Forrest; Brian T Tsuji
Journal:  Antimicrob Agents Chemother       Date:  2015-02-02       Impact factor: 5.191

5.  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

6.  Novel rate-area-shape modeling approach to quantify bacterial killing and regrowth for in vitro static time-kill studies.

Authors:  Soon-Ee Cheah; Jian Li; Roger L Nation; Jürgen B Bulitta
Journal:  Antimicrob Agents Chemother       Date:  2014-11-03       Impact factor: 5.191

7.  The Poisoned Well: Enhancing the Predictive Value of Antimicrobial Susceptibility Testing in the Era of Multidrug Resistance.

Authors:  Thea Brennan-Krohn; Kenneth P Smith; James E Kirby
Journal:  J Clin Microbiol       Date:  2017-05-03       Impact factor: 5.948

8.  Time-kill curves of daptomycin and Monte Carlo simulation for the treatment of bacteraemia caused by Enterococcus faecium.

Authors:  Bruna Kochhann Menezes; Izabel Almeida Alves; Keli Jaqueline Staudt; Betina Montanari Beltrame; Letícia Venz; Lessandra Michelin; Bibiana Verlindo Araujo; Leandro Tasso
Journal:  Braz J Microbiol       Date:  2019-12-16       Impact factor: 2.476

Review 9.  Dosage individualization in children: integration of pharmacometrics in clinical practice.

Authors:  Wei Zhao; Stéphanie Leroux; Evelyne Jacqz-Aigrain
Journal:  World J Pediatr       Date:  2014-08-15       Impact factor: 2.764

Review 10.  Pharmacokinetic and Pharmacodynamic Principles of Anti-infective Dosing.

Authors:  Nikolas J Onufrak; Alan Forrest; Daniel Gonzalez
Journal:  Clin Ther       Date:  2016-07-20       Impact factor: 3.393

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