Literature DB >> 14531726

Forecasting of blood tacrolimus concentrations based on the Bayesian method in adult patients receiving living-donor liver transplantation.

Masahide Fukudo1, Ikuko Yano, Sachio Fukatsu, Hideyuki Saito, Shinji Uemoto, Tetsuya Kiuchi, Koichi Tanaka, Ken-ichi Inui.   

Abstract

OBJECTIVE: To evaluate Bayesian prediction of blood tacrolimus concentrations in adult patients receiving living-donor liver transplantation (LDLT) using previously obtained population pharmacokinetic parameters. PATIENTS AND METHODS: Data were retrospectively collected from 47 adult patients receiving LDLT who were not included in the estimation of population pharmacokinetic parameters. Blood tacrolimus concentrations were predicted without or with the empirical Bayesian method using sparse samples obtained in the previous week. Predictive performance of the concentrations was evaluated by the mean prediction error (ME), mean absolute prediction error (MAE) and root mean square error (RMSE) as well as the percentage of successful predictions (percentage of absolute prediction error less than 3 microg/L, %PRED3).
RESULTS: Concentrations predicted by the population mean pharmacokinetic parameter values coincided well with observed concentrations during the period of tacrolimus infusion immediately after the operation. For concentrations during subsequent oral therapy with tacrolimus, predictability by the population mean pharmacokinetic parameter values alone was not satisfactory. Bayesian forecasting using one or two blood concentrations obtained in the previous week significantly decreased (p<0.05) MAE and RMSE compared with predictions based on the population mean pharmacokinetic parameters on postoperative days 21 and 28, but not on day 14. During postoperative days 15-21, %PRED3 was increased to 68.6% or 71.2% with the Bayesian method using one or two blood concentrations, respectively, from 44.9% with the population mean pharmacokinetic parameter values.
CONCLUSION: The present study demonstrated the applicability of the Bayesian method with use of one or two samples for prediction of blood tacrolimus concentrations in adult patients receiving LDLT.

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Year:  2003        PMID: 14531726     DOI: 10.2165/00003088-200342130-00006

Source DB:  PubMed          Journal:  Clin Pharmacokinet        ISSN: 0312-5963            Impact factor:   6.447


  12 in total

1.  Tacrolimus pharmacokinetics in the early post-liver transplantation period and clinical applicability via Bayesian prediction.

Authors:  Itziar Oteo; John C Lukas; Nerea Leal; Elena Suarez; Andres Valdivieso; Mikel Gastaca; Jorge Ortiz de Urbina; Rosario Calvo
Journal:  Eur J Clin Pharmacol       Date:  2012-06-03       Impact factor: 2.953

2.  Pharmacokinetic study of tacrolimus in cystic fibrosis and non-cystic fibrosis lung transplant patients and design of Bayesian estimators using limited sampling strategies.

Authors:  Franck Saint-Marcoux; Christiane Knoop; Jean Debord; Philippe Thiry; Annick Rousseau; Marc Estenne; Pierre Marquet
Journal:  Clin Pharmacokinet       Date:  2005       Impact factor: 6.447

3.  Significance of trough monitoring for tacrolimus blood concentration and calcineurin activity in adult patients undergoing primary living-donor liver transplantation.

Authors:  Ikuko Yano; Satohiro Masuda; Hiroto Egawa; Mitsuhiro Sugimoto; Masahide Fukudo; Yuko Yoshida; Sachiyo Hashi; Atsushi Yoshizawa; Yasuhiro Ogura; Kohei Ogawa; Akira Mori; Toshimi Kaido; Shinji Uemoto; Ken-Ichi Inui
Journal:  Eur J Clin Pharmacol       Date:  2011-10-04       Impact factor: 2.953

4.  Determination of the most influential sources of variability in tacrolimus trough blood concentrations in adult liver transplant recipients: a bottom-up approach.

Authors:  Cécile Gérard; Jeanick Stocco; Anne Hulin; Benoit Blanchet; Céline Verstuyft; François Durand; Filomena Conti; Christophe Duvoux; Michel Tod
Journal:  AAPS J       Date:  2014-02-14       Impact factor: 4.009

Review 5.  Population Pharmacokinetic Modelling and Bayesian Estimation of Tacrolimus Exposure: Is this Clinically Useful for Dosage Prediction Yet?

Authors:  Emily Brooks; Susan E Tett; Nicole M Isbel; Christine E Staatz
Journal:  Clin Pharmacokinet       Date:  2016-11       Impact factor: 6.447

6.  Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach.

Authors:  Tom M Nanga; Thao T P Doan; Pierre Marquet; Flora T Musuamba
Journal:  Br J Clin Pharmacol       Date:  2019-12-17       Impact factor: 4.335

Review 7.  Population Pharmacokinetics of Tacrolimus in Transplant Recipients: What Did We Learn About Sources of Interindividual Variabilities?

Authors:  Olivia Campagne; Donald E Mager; Kathleen M Tornatore
Journal:  J Clin Pharmacol       Date:  2018-10-29       Impact factor: 3.126

8.  In vitro immune cell monitoring as a guide for long-term immunosuppression in adult liver transplant recipients.

Authors:  Eunkyoung Jwa; Shin Hwang; Yong-Jae Kwon; Nayoung Kim; Gi-Won Song; Dong-Hwan Jung; Chul-Soo Ahn; Eunyoung Tak; Deok-Bog Moon; Ki-Hun Kim; Tae-Yong Ha; Gil-Chun Park; Sung-Gyu Lee
Journal:  Korean J Hepatobiliary Pancreat Surg       Date:  2015-11-30

9.  Predicting tacrolimus concentrations in children receiving a heart transplant using a population pharmacokinetic model.

Authors:  Joseph E Rower; Chris Stockmann; Matthew W Linakis; Shaun S Kumar; Xiaoxi Liu; E Kent Korgenski; Catherine M T Sherwin; Kimberly M Molina
Journal:  BMJ Paediatr Open       Date:  2017-11-22

10.  A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.

Authors:  Stijn Van Looy; Thierry Verplancke; Dominique Benoit; Eric Hoste; Georges Van Maele; Filip De Turck; Johan Decruyenaere
Journal:  Crit Care       Date:  2007       Impact factor: 9.097

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