Nlmixr2 Versus NONMEM: An Evaluation of Maximum A Posteriori Bayesian Estimates Following External Evaluation of Gentamicin and Tobramycin Population Pharmacokinetic Models.

Publisher: Wiley Country of Publication: United States NLM ID: 101572899 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2160-7648 (Electronic) Linking ISSN: 2160763X NLM ISO Abbreviation: Clin Pharmacol Drug Dev Subsets: MEDLINE

Publication: 2013- : Hoboken, NJ : Wiley
Original Publication: Thousand Oaks, Calif. : Sage Publications, c2012-

Tobramycin*/pharmacokinetics ; Tobramycin*/administration & dosage ; Tobramycin*/blood ; Bayes Theorem* ; Gentamicins*/pharmacokinetics ; Gentamicins*/administration & dosage ; Anti-Bacterial Agents*/pharmacokinetics ; Anti-Bacterial Agents*/administration & dosage ; Models, Biological*; Humans ; Adult ; Female ; Male ; Middle Aged ; Retrospective Studies ; Critical Illness ; Aged

The objective of this project is to compare the results of the same study carried out on NONMEM and nlmixr2. This analysis consists of evaluating previously published population pharmacokinetic models of gentamicin and tobramycin in our population of interest with sparse concentrations. A literature review was performed to determine the gentamicin and tobramycin models in critically ill adult patients. In parallel, gentamicin and tobramycin dosing data, information on the treatment, the patient, and the bacteria were collected retrospectively in 2 Quebec establishments. The external evaluations were previously performed using NONMEM Version 7.5. Model equations were rewritten with R, and external evaluations were performed using nlmixr2. Predictive performance was assessed based on the estimation of bias and imprecision of the prediction error for maximum a posteriori (MAP) Bayesian PK parameter and observed concentrations. Comparison between nlmixr2 and NONMEM was performed on 4 gentamicin and 3 tobramycin population pharmacokinetic models. Compared to NONMEM, for gentamicin and tobramycin clearance and central volume of distribution, nlmixr2 produced individual pharmacokinetic parameters with bias values ranging from -32.5% to 5.67% and imprecision values ranging from 6.33% to 32.5%. Despite these differences, population bias and imprecision for sparse concentrations were low and ranged from 0% to 5.3% and 0.2% to 6.5%, respectively. The external evaluations performed with both software packages resulted in the same interpretation in terms of population predictive performance for all 7 models. Nlmxir2 showed comparable predictive performance with NONMEM with sparse concentrations that are, at most, sampled twice within a single dose administration (peak and trough).
(© 2024 The Authors. Clinical Pharmacology in Drug Development published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.)

Mould DR, Upton RN. Basic concepts in population modeling, simulation, and model‐based drug development. CPT Pharmacometrics Syst Pharmacol. 2012;1(9):e6.
Mould DR, Upton RN. Basic concepts in population modeling, simulation, and model‐based drug development‐part 2: introduction to pharmacokinetic modeling methods. CPT Pharmacometrics Syst Pharmacol. 2013;2(4):e38.
Fidler M, Wilkins JJ, Hooijmaijers R, et al. Nonlinear mixed‐effects model development and simulation using nlmixr and related R open‐source packages. CPT Pharmacometrics Syst Pharmacol. 2019;8(9):621‐633.
Wang W, Hallow KM, James DA. A tutorial on RxODE: simulating differential equation pharmacometric models in R. CPT Pharmacometrics Syst Pharmacol. 2016;5(1):3‐10.
Schoemaker R, Fidler M, Laveille C, et al. Performance of the SAEM and FOCEI algorithms in the open‐source, nonlinear mixed effect modeling tool nlmixr. CPT Pharmacometrics Syst Pharmacol. 2019;8(12):923‐930.
Mak WY, Ooi QX, Cruz CV, Looi I, Yuen KH, Standing JF. Assessment of the nlmixr R package for population pharmacokinetic modeling: a metformin case study. Br J Clin Pharmacol. 2023;89(1):330‐339.
Duong A, Simard C, Williamson D, Marsot A. Tobramycin a priori dosing regimens based on PopPK model simulations in critically ill patients: are they transferable? Ther Drug Monit. 2023;45(5):616‐622.
Duong A, Simard C, Williamson D, Marsot A. Model re‐estimation: an alternative for poor predictive performance during external evaluations? Example of gentamicin in critically ill patients. Pharmaceutics. 2022;14(7):1426.
Rea RS, Capitano B, Bies R, Bigos KL, Smith R, Lee H. Suboptimal aminoglycoside dosing in critically ill patients. Ther Drug Monit. 2008;30(6):674.
Bos JC, Prins JM, Mistício MC, et al. Population pharmacokinetics with Monte Carlo simulations of gentamicin in a population of severely ill adult patients from Sub‐Saharan Africa. Antimicrob Agents Chemother. 2019;63(4):e02328‐18.
Hodiamont CJ, Juffermans NP, Bouman CSC, de Jong MD, Mathôt RAA, van Hest RM. Determinants of gentamicin concentrations in critically ill patients: a population pharmacokinetic analysis. Int J Antimicrob Agents. 2017;49(2):204‐211.
Hodiamont CJ, Janssen JM, de Jong MD, Mathôt RA, Juffermans NP, van Hest RM. Therapeutic drug monitoring of gentamicin peak concentrations in critically ill patients. Ther Drug Monit. 2017;39(5):522.
Aarons L, Vozeh S, Wenk M, Weiss P, Follath F. Population pharmacokinetics of tobramycin. Br J Clin Pharmacol. 1989;28(3):305‐314.
Conil JM, Georges B, Ruiz S, et al. Tobramycin disposition in ICU patients receiving a once daily regimen: population approach and dosage simulations. Br J Clin Pharmacol. 2011;71(1):61‐71.
Hennig S, Standing JF, Staatz CE, Thomson AH. Population pharmacokinetics of tobramycin in patients with and without cystic fibrosis. Clin Pharmacokinet. 2013;52(4):289‐301.
Wu G, Baraldo M, Furlanut M. Calculating percentage prediction error: a user's note. Pharmacol Res. 1995;32(4):241‐248.
Hara M, Masui K, Eleveld DJ, Struys MMRF, Uchida O. Predictive performance of eleven pharmacokinetic models for propofol infusion in children for long‐duration anaesthesia. Br J Anaesth. 2017;118(3):415‐423.
de Velde F, Mouton JW, de Winter BCM, van Gelder T, Koch BCP. Clinical applications of population pharmacokinetic models of antibiotics: challenges and perspectives. Pharmacol Res. 2018;134:280‐288.
Duong A, Simard C, Wang YL, Williamson D, Marsot A. Aminoglycosides in the intensive care unit: what is new in population PK modeling? Antibiotics. 2021;10(5):507.

Keywords: antibiotics; antimicrobial stewardship; pharmacometrics; population pharmacokinetic; therapeutic drug monitoring

VZ8RRZ51VK (Tobramycin)
0 (Gentamicins)
0 (Anti-Bacterial Agents)

Date Created: 20240311 Date Completed: 20240703 Latest Revision: 20240703

20240704

10.1002/cpdd.1395

38465725