Literature DB >> 12570013

Application of pharmacokinetic modelling to the routine therapeutic drug monitoring of anticancer drugs.

Annick Rousseau1, Pierre Marquet.   

Abstract

Over the last 10 years, proofs of the clinical interest of therapeutic drug monitoring (TDM) of certain anticancer drugs have been established. Numerous studies have shown that TDM is an efficient tool for controlling the toxicity of therapeutic drugs, and a few trials have even demonstrated that it can improve their efficacy. This article critically reviews TDM tools based on pharmacokinetic modelling of anticancer drugs. The administered dose of anticancer drugs is sometimes adjusted individually using either a priori or a posteriori methods. The most frequent clinical application of a priori formulae concerns carboplatin and allows the computation of the first dose based on biometrical and biological data such as weight, age, gender, creatinine clearance and glomerular filtration rate. A posteriori methods use drug plasma concentrations to adjust the subsequent dose(s). Thus, nomograms allowing dose adjustment on the basis of blood concentration are routinely used for 5-fluorouracil given as long continuous infusions. Multilinear regression models have been developed, for example for etoposide, doxorubicin. carboplatin, cyclophosphamide and irinotecan, to predict a single exposure variable [such as area under concentration-time curve (AUC)] from a small number of plasma concentrations obtained at predetermined times after a standard dose. These models can only be applied by using the same dose and schedule as the original study. Bayesian estimation offers more flexibility in blood sampling times and, owing to its precision and to the amount of information provided, is the method of choice for ensuring that a given patient benefits from the desired systemic exposure. Unlike the other a posteriori methods, Bayesian estimation is based on population pharmacokinetic studies and can take into account the effects of different individual factors on the pharmacokinetics of the drug. Bayesian estimators have been used to determine maximum tolerated systemic exposure thresholds (e.g. for topotecan or teniposide) as well as for the routine monitoring of drugs characterized by a very high interindividual pharmacokinetic variability such as methotrexate or carboplatin. The development of these methods has contributed to improving cancer chemotherapy in terms of patient outcome and survival and should be pursued.

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Year:  2002        PMID: 12570013     DOI: 10.1046/j.1472-8206.2002.00086.x

Source DB:  PubMed          Journal:  Fundam Clin Pharmacol        ISSN: 0767-3981            Impact factor:   2.748


  31 in total

1.  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 2.  Individualised cancer chemotherapy: strategies and performance of prospective studies on therapeutic drug monitoring with dose adaptation: a review.

Authors:  Milly E de Jonge; Alwin D R Huitema; Jan H M Schellens; Sjoerd Rodenhuis; Jos H Beijnen
Journal:  Clin Pharmacokinet       Date:  2005       Impact factor: 6.447

Review 3.  Sex-specific aspects of tumor therapy.

Authors:  Kerstin Borgmann; Ekkehard Dikomey; Cordula Petersen; Petra Feyer; Ulrike Hoeller
Journal:  Radiat Environ Biophys       Date:  2009-02-26       Impact factor: 1.925

Review 4.  Benchmarking therapeutic drug monitoring software: a review of available computer tools.

Authors:  Aline Fuchs; Chantal Csajka; Yann Thoma; Thierry Buclin; Nicolas Widmer
Journal:  Clin Pharmacokinet       Date:  2013-01       Impact factor: 6.447

5.  Therapeutic drug monitoring of imatinib: Bayesian and alternative methods to predict trough levels.

Authors:  Verena Gotta; Nicolas Widmer; Michael Montemurro; Serge Leyvraz; Amina Haouala; Laurent A Decosterd; Chantal Csajka; Thierry Buclin
Journal:  Clin Pharmacokinet       Date:  2012-03-01       Impact factor: 6.447

6.  A Critique of Pharmacokinetic Calculators for Drug Dosing Individualization.

Authors:  Slobodan M Janković
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2020-04       Impact factor: 2.441

7.  Genetic and metabolic determinants of methotrexate-induced mucositis in pediatric acute lymphoblastic leukemia.

Authors:  M A H den Hoed; E Lopez-Lopez; M L te Winkel; W Tissing; J D E de Rooij; A Gutierrez-Camino; A Garcia-Orad; E den Boer; R Pieters; S M F Pluijm; R de Jonge; M M van den Heuvel-Eibrink
Journal:  Pharmacogenomics J       Date:  2014-11-04       Impact factor: 3.550

8.  The impact of tacrolimus exposure on extrarenal adverse effects in adult renal transplant recipients.

Authors:  Olivia Campagne; Donald E Mager; Daniel Brazeau; Rocco C Venuto; Kathleen M Tornatore
Journal:  Br J Clin Pharmacol       Date:  2019-01-04       Impact factor: 4.335

9.  Randomized study of individualized pharmacokinetically-guided dosing of paclitaxel compared with body-surface area dosing in Chinese patients with advanced non-small cell lung cancer.

Authors:  Jie Zhang; Fei Zhou; Huiwei Qi; Huijuan Ni; Qiong Hu; Caicun Zhou; Yunying Li; Irina Baburina; Jodi Courtney; Salvatore J Salamone
Journal:  Br J Clin Pharmacol       Date:  2019-06-14       Impact factor: 4.335

Review 10.  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

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