Literature DB >> 27001806

Assessing Predictive Performance of Published Population Pharmacokinetic Models of Intravenous Tobramycin in Pediatric Patients.

Celeste Bloomfield1, Christine E Staatz1, Sean Unwin2, Stefanie Hennig3.   

Abstract

Several population pharmacokinetic models describe the dose-exposure relationship of tobramycin in pediatric patients. Before the implementation of these models in clinical practice for dosage adjustment, their predictive performance should be externally evaluated. This study tested the predictive performance of all published population pharmacokinetic models of tobramycin developed for pediatric patients with an independent patient cohort. A literature search was conducted to identify suitable models for testing. Demographic and pharmacokinetic data were collected retrospectively from the medical records of pediatric patients who had received intravenous tobramycin. Tobramycin exposure was predicted from each model. Predictive performance was assessed by visual comparison of predictions to observations, by calculation of bias and imprecision, and through the use of simulation-based diagnostics. Eight population pharmacokinetic models were identified. A total of 269 concentration-time points from 41 pediatric patients with cystic fibrosis were collected for external evaluation. Three models consistently performed best in all evaluations and had mean errors ranging from -0.4 to 1.8 mg/liter, relative mean errors ranging from 4.9 to 29.4%, and root mean square errors ranging from 47.8 to 66.9%. Simulation-based diagnostics supported these findings. Models that allowed a two-compartment disposition generally had better predictive performance than those that used a one-compartment disposition model. Several published models of the pharmacokinetics of tobramycin showed reasonable low levels of bias, although all models seemed to have some problems with imprecision. This suggests that knowledge of typical pharmacokinetic behavior and patient covariate values alone without feedback concentration measurements from individual patients is not sufficient to make precise predictions.
Copyright © 2016, American Society for Microbiology. All Rights Reserved.

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Year:  2016        PMID: 27001806      PMCID: PMC4879400          DOI: 10.1128/AAC.02654-15

Source DB:  PubMed          Journal:  Antimicrob Agents Chemother        ISSN: 0066-4804            Impact factor:   5.191


  38 in total

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Authors:  Johan W Mouton; Nico Jacobs; Harm Tiddens; Alphonsus M Horrevorts
Journal:  Diagn Microbiol Infect Dis       Date:  2005-06       Impact factor: 2.803

Review 2.  What do we learn from repeated population analyses?

Authors:  Stephen B Duffull; Daniel F B Wright
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

3.  Concordance between criteria for covariate model building.

Authors:  Stefanie Hennig; Mats O Karlsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2014-03-06       Impact factor: 2.745

4.  Prospective evaluation of the effect of an aminoglycoside dosing regimen on rates of observed nephrotoxicity and ototoxicity.

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Journal:  Antimicrob Agents Chemother       Date:  1999-07       Impact factor: 5.191

5.  A Bayesian feedback method of aminoglycoside dosing.

Authors:  M E Burton; D C Brater; P S Chen; R B Day; P J Huber; M R Vasko
Journal:  Clin Pharmacol Ther       Date:  1985-03       Impact factor: 6.875

Review 6.  Optimization of anti-pseudomonal antibiotics for cystic fibrosis pulmonary exacerbations: V. Aminoglycosides.

Authors:  David C Young; Jeffery T Zobell; Chris Stockmann; C Dustin Waters; Krow Ampofo; Catherine M T Sherwin; Michael G Spigarelli
Journal:  Pediatr Pulmonol       Date:  2013-09-02

7.  Association of aminoglycoside plasma levels with therapeutic outcome in gram-negative pneumonia.

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Journal:  Am J Med       Date:  1984-10       Impact factor: 4.965

Review 8.  Aminoglycoside pharmacokinetics: volume of distribution in specific adult patient subgroups.

Authors:  W E Dager
Journal:  Ann Pharmacother       Date:  1994 Jul-Aug       Impact factor: 3.154

Review 9.  Computerized advice on drug dosage to improve prescribing practice.

Authors:  Pierre Durieux; Ludovic Trinquart; Isabelle Colombet; Julie Niès; Rt Walton; Anand Rajeswaran; Myriam Rège Walther; Emma Harvey; Bernard Burnand
Journal:  Cochrane Database Syst Rev       Date:  2008-07-16

Review 10.  Fundamentals of population pharmacokinetic modelling: validation methods.

Authors:  Catherine M T Sherwin; Tony K L Kiang; Michael G Spigarelli; Mary H H Ensom
Journal:  Clin Pharmacokinet       Date:  2012-09-01       Impact factor: 6.447

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  12 in total

Review 1.  Pharmacokinetic and Pharmacodynamic Optimization of Antibiotic Therapy in Cystic Fibrosis Patients: Current Evidences, Gaps in Knowledge and Future Directions.

Authors:  Charlotte Roy; Manon Launay; Sophie Magréault; Isabelle Sermet-Gaudelus; Vincent Jullien
Journal:  Clin Pharmacokinet       Date:  2021-01-24       Impact factor: 6.447

2.  Evaluation of Tobramycin Exposure Predictions in Three Bayesian Forecasting Programmes Compared with Current Clinical Practice in Children and Adults with Cystic Fibrosis.

Authors:  Marc Burgard; Indy Sandaradura; Sebastiaan J van Hal; Sonya Stacey; Stefanie Hennig
Journal:  Clin Pharmacokinet       Date:  2018-08       Impact factor: 6.447

3.  Can Population Pharmacokinetics of Antibiotics be Extrapolated? Implications of External Evaluations.

Authors:  Yu Cheng; Chen-Yu Wang; Zi-Ran Li; Yan Pan; Mao-Bai Liu; Zheng Jiao
Journal:  Clin Pharmacokinet       Date:  2021-01       Impact factor: 6.447

4.  A Pharmacokinetic Analysis of Tobramycin in Patients Less than Five Years of Age with Cystic Fibrosis: Assessment of Target Attainment with Extended-Interval Dosing through Simulation.

Authors:  Kevin J Downes; Austyn Grim; Laura Shanley; Ronald C Rubenstein; Athena F Zuppa; Marc R Gastonguay
Journal:  Antimicrob Agents Chemother       Date:  2022-04-28       Impact factor: 5.191

5.  Predicting Antibiotic Effect of Vancomycin Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation: Dense Sampling versus Sparse Sampling.

Authors:  Yong Kyun Kim; Jae Ha Lee; Hang-Jea Jang; Dae Young Zang; Dong-Hwan Lee
Journal:  Antibiotics (Basel)       Date:  2022-05-31

6.  Bayesian Estimation of Tobramycin Exposure in Patients with Cystic Fibrosis.

Authors:  Michael A Barras; David Serisier; Stefanie Hennig; Katrina Jess; Ross L G Norris
Journal:  Antimicrob Agents Chemother       Date:  2016-10-21       Impact factor: 5.191

7.  Monitoring of Tobramycin Exposure: What is the Best Estimation Method and Sampling Time for Clinical Practice?

Authors:  Yanhua Gao; Stefanie Hennig; Michael Barras
Journal:  Clin Pharmacokinet       Date:  2019-03       Impact factor: 6.447

Review 8.  A systematic review of population pharmacokinetic analyses of digoxin in the paediatric population.

Authors:  Mariam H Abdel Jalil; Noura Abdullah; Mervat M Alsous; Mohammad Saleh; Khawla Abu-Hammour
Journal:  Br J Clin Pharmacol       Date:  2020-04-01       Impact factor: 4.335

9.  Evaluating tacrolimus pharmacokinetic models in adult renal transplant recipients with different CYP3A5 genotypes.

Authors:  Can Hu; Wen-Jun Yin; Dai-Yang Li; Jun-Jie Ding; Ling-Yun Zhou; Jiang-Lin Wang; Rong-Rong Ma; Kun Liu; Ge Zhou; Xiao-Cong Zuo
Journal:  Eur J Clin Pharmacol       Date:  2018-07-17       Impact factor: 2.953

10.  Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy.

Authors:  Anil R Maharaj; Huali Wu; Christoph P Hornik; Antonio Arrieta; Laura James; Varsha Bhatt-Mehta; John Bradley; William J Muller; Amira Al-Uzri; Kevin J Downes; Michael Cohen-Wolkowiez
Journal:  J Pharmacokinet Pharmacodyn       Date:  2020-04-22       Impact factor: 2.410

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