Literature DB >> 27978987

Translational PK/PD of anti-infective therapeutics.

Chetan Rathi1, Richard E Lee2, Bernd Meibohm3.   

Abstract

Translational PK/PD modeling has emerged as a critical technique for quantitative analysis of the relationship between dose, exposure and response of antibiotics. By combining model components for pharmacokinetics, bacterial growth kinetics and concentration-dependent drug effects, these models are able to quantitatively capture and simulate the complex interplay between antibiotic, bacterium and host organism. Fine-tuning of these basic model structures allows to further account for complicating factors such as resistance development, combination therapy, or host responses. With this tool set at hand, mechanism-based PK/PD modeling and simulation allows to develop optimal dosing regimens for novel and established antibiotics for maximum efficacy and minimal resistance development.
Copyright © 2016 Elsevier Ltd. All rights reserved.

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Year:  2016        PMID: 27978987      PMCID: PMC5172400          DOI: 10.1016/j.ddtec.2016.08.004

Source DB:  PubMed          Journal:  Drug Discov Today Technol        ISSN: 1740-6749


  44 in total

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Journal:  Trends Pharmacol Sci       Date:  2008-03-18       Impact factor: 14.819

2.  Modelling time-kill studies to discern the pharmacodynamics of meropenem.

Authors:  Vincent H Tam; Amy N Schilling; Michael Nikolaou
Journal:  J Antimicrob Chemother       Date:  2005-03-16       Impact factor: 5.790

Review 3.  Determination of antibiotic dosage adjustments in patients with renal impairment: elements for success.

Authors:  Nimish Patel; Marc H Scheetz; George L Drusano; Thomas P Lodise
Journal:  J Antimicrob Chemother       Date:  2010-08-24       Impact factor: 5.790

4.  Quantitative impact of neutrophils on bacterial clearance in a murine pneumonia model.

Authors:  Beining Guo; Kamilia Abdelraouf; Kimberly R Ledesma; Kai-Tai Chang; Michael Nikolaou; Vincent H Tam
Journal:  Antimicrob Agents Chemother       Date:  2011-08-01       Impact factor: 5.191

Review 5.  The effect of critical illness on drug distribution.

Authors:  Daniel Gonzalez; Daniela J Conrado; Ursula Theuretzbacher; Hartmut Derendorf
Journal:  Curr Pharm Biotechnol       Date:  2011-12       Impact factor: 2.837

Review 6.  Penetration of anti-infective agents into pulmonary epithelial lining fluid: focus on antifungal, antitubercular and miscellaneous anti-infective agents.

Authors:  Keith A Rodvold; Liz Yoo; Jomy M George
Journal:  Clin Pharmacokinet       Date:  2011-11-01       Impact factor: 6.447

7.  Use of preclinical data for selection of a phase II/III dose for evernimicin and identification of a preclinical MIC breakpoint.

Authors:  G L Drusano; S L Preston; C Hardalo; R Hare; C Banfield; D Andes; O Vesga; W A Craig
Journal:  Antimicrob Agents Chemother       Date:  2001-01       Impact factor: 5.191

8.  Impact of burden on granulocyte clearance of bacteria in a mouse thigh infection model.

Authors:  G L Drusano; Christine Fregeau; Weiguo Liu; D L Brown; Arnold Louie
Journal:  Antimicrob Agents Chemother       Date:  2010-06-01       Impact factor: 5.191

Review 9.  Aminoglycosides: nephrotoxicity.

Authors:  M P Mingeot-Leclercq; P M Tulkens
Journal:  Antimicrob Agents Chemother       Date:  1999-05       Impact factor: 5.191

10.  A multistate tuberculosis pharmacometric model: a framework for studying anti-tubercular drug effects in vitro.

Authors:  Oskar Clewe; Linda Aulin; Yanmin Hu; Anthony R M Coates; Ulrika S H Simonsson
Journal:  J Antimicrob Chemother       Date:  2015-12-24       Impact factor: 5.790

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  4 in total

1.  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

2.  Model-Based Exposure-Response Assessment for Spectinamide 1810 in a Mouse Model of Tuberculosis.

Authors:  Santosh Wagh; Chetan Rathi; Pradeep B Lukka; Keyur Parmar; Zaid Temrikar; Jiuyu Liu; Michael S Scherman; Richard E Lee; Gregory T Robertson; Anne J Lenaerts; Bernd Meibohm
Journal:  Antimicrob Agents Chemother       Date:  2021-08-23       Impact factor: 5.191

Review 3.  Antimicrobial Peptides: A New Hope in Biomedical and Pharmaceutical Fields.

Authors:  Antonio Moretta; Carmen Scieuzo; Anna Maria Petrone; Rosanna Salvia; Michele Dario Manniello; Antonio Franco; Donatella Lucchetti; Antonio Vassallo; Heiko Vogel; Alessandro Sgambato; Patrizia Falabella
Journal:  Front Cell Infect Microbiol       Date:  2021-06-14       Impact factor: 5.293

Review 4.  Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance.

Authors:  Kun Mi; Kaixiang Zhou; Lei Sun; Yixuan Hou; Wenjin Ma; Xiangyue Xu; Meixia Huo; Zhenli Liu; Lingli Huang
Journal:  Pharmaceutics       Date:  2022-01-21       Impact factor: 6.321

  4 in total

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