Literature DB >> 12081632

Bayesian population model of methotrexate to guide dosage adjustments for folate rescue in patients with breast cancer.

S Monjanel-Mouterde1, C Lejeune, J Ciccolini, N Merite, D Hadjaj, P Bonnier, P Piana, A Durand.   

Abstract

BACKGROUND: Methotrexate (MTX) infusions may induce severe side-effects, and alkaline hydration along with folinic acid rescue is a common way to reduce such toxic risks. The purpose of this study was to develop an adaptive rescue strategy based upon the early detection of patients with impaired MTX elimination. METHODS AND
RESULTS: In this study, we propose a simple population-based Bayesian approach for predicting MTX plasma concentration from a limited number of samples, so as to adapt both duration and dosage of the rescue agent to be used next. Ten kinetic profiles obtained after 10 courses of MTX (1.5 g/m2) in seven patients with inflammatory breast cancer were used to establish the population pharmacokinetic parameters (Cl, 8.16 L/h; t1/2, 12.7 h). This population was next involved in the Bayesian estimation of MTX individual pharmacokinetic parameters from only two blood samples (T24 and T36 h), thus allowing one to forecast the elimination of this drug by predicting MTX levels at 48 h. According to the MTX concentrations predicted during the elimination phase, folinic acid rescue was then prolonged in patients likely to be overexposed.
CONCLUSION: The Bayesian estimation presented in this study was an easy and convenient method to efficiently detect patients with impaired MTX elimination in routine clinical practice. This information enabled the introduction of strategies for minimizing the risk of severe drug toxicity.

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Year:  2002        PMID: 12081632     DOI: 10.1046/j.1365-2710.2002.00402.x

Source DB:  PubMed          Journal:  J Clin Pharm Ther        ISSN: 0269-4727            Impact factor:   2.512


  7 in total

1.  Evaluating performance of a decision support system to improve methotrexate pharmacotherapy in children and young adults with cancer.

Authors:  Erin Dombrowsky; Bhuvana Jayaraman; Mahesh Narayan; Jeffrey S Barrett
Journal:  Ther Drug Monit       Date:  2011-02       Impact factor: 3.681

2.  Population pharmacokinetics of high-dose methotrexate in children with acute lymphoblastic leukaemia.

Authors:  Dolores Aumente; Dolores Santos Buelga; John C Lukas; Pedro Gomez; Antonio Torres; Maria José García
Journal:  Clin Pharmacokinet       Date:  2006       Impact factor: 6.447

Review 3.  Population pharmacokinetics and pharmacodynamics for treatment optimization in clinical oncology.

Authors:  Anthe S Zandvliet; Jan H M Schellens; Jos H Beijnen; Alwin D R Huitema
Journal:  Clin Pharmacokinet       Date:  2008       Impact factor: 6.447

4.  Association of genetic polymorphism in the folate metabolic pathway with methotrexate pharmacokinetics and toxicity in childhood acute lymphoblastic leukaemia and malignant lymphoma.

Authors:  Barbara Faganel Kotnik; Iztok Grabnar; Petra Bohanec Grabar; Vita Dolžan; Janez Jazbec
Journal:  Eur J Clin Pharmacol       Date:  2011-04-21       Impact factor: 2.953

Review 5.  Computational oncology--mathematical modelling of drug regimens for precision medicine.

Authors:  Dominique Barbolosi; Joseph Ciccolini; Bruno Lacarelle; Fabrice Barlési; Nicolas André
Journal:  Nat Rev Clin Oncol       Date:  2015-11-24       Impact factor: 66.675

6.  Evaluation of body-surface-area adjusted dosing of high-dose methotrexate by population pharmacokinetics in a large cohort of cancer patients.

Authors:  Usman Arshad; Max Taubert; Tamina Seeger-Nukpezah; Sami Ullah; Kirsten C Spindeldreier; Ulrich Jaehde; Michael Hallek; Uwe Fuhr; Jörg Janne Vehreschild; Carolin Jakob
Journal:  BMC Cancer       Date:  2021-06-20       Impact factor: 4.430

7.  Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy.

Authors:  Jeffrey S Barrett; John T Mondick; Mahesh Narayan; Kalpana Vijayakumar; Sundararajan Vijayakumar
Journal:  BMC Med Inform Decis Mak       Date:  2008-01-28       Impact factor: 2.796

  7 in total

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