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A Population Pharmacokinetic/Pharmacodynamic Model of Methotrexate and Mucositis Scores in Osteosarcoma
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
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2011 (English)In: Therapeutic Drug Monitoring, ISSN 0163-4356, E-ISSN 1536-3694, Vol. 33, no 6, 711-718 p.Article in journal (Refereed) Published
Abstract [en]

Methotrexate, when used in high doses (12 g/m(2)) in the treatment of osteosarcoma, shows wide between-subject variability (BSV) in its pharmacokinetics. High-dose methotrexate is associated with severe toxicity; therefore, therapeutic drug monitoring (TDM) is carried out to guide rescue therapy and monitor for nephrotoxicity. Mucositis is a commonly encountered dose-limiting toxicity that often leads to delays in subsequent courses of chemotherapy. This, in turn, results in a reduction in the dosing intensity, which is essential in the treatment of osteosarcoma. The aims of this study were to develop a population pharmacokinetic (PK) model from TDM using physiologically relevant covariates and to investigate the correlation between mucositis scores and methotrexate pharmacokinetics. In total, 46 osteosarcoma patients (30 men and 16 women; age, 4-51 years) were recruited, and blood samples were collected for routine TDM once every 24 hours. Mucositis scores, graded according to the National Cancer Institute Common Toxicity Criteria, were recorded for 28 of the patients (18 men and 10 women; age, 8-51 years) predose and postdose. A population PK model was developed in NONMEM VI. A 2-compartment PK model was chosen, and clearance (CL) was divided into filtration and secretion/metabolism components. All parameters were scaled with body weight, and, in addition, total CL was scaled with age-and sex-adjusted serum creatinine. Between-subject variability was modeled for all parameters, and betweenoccasion variability was included in CL. For a typical 70 kg man of 18 years or older, the parameter estimates for the final model were CL(filt) = 2.69 L/h/70 kg, CL(sec) = 10.9 L/h/70 kg, V(1) = 74.3 L/70 kg, Q = 0.110 L/h/70 kg, and V(2) = 4.10 L/70 kg. Sequential pharmacodynamic modeling consisted of mucositis scores as 5-point ordered categorical data. A significant linear relationship between individual area under the curve (AUC) and mucositis score probability was found, and the probability of having mucositis score >= 1 increased with increasing AUC and was almost 50% at the average cumulative AUC after 2 consecutive methotrexate doses.

Place, publisher, year, edition, pages
2011. Vol. 33, no 6, 711-718 p.
Keyword [en]
methotrexate, osteosarcoma, mucositis, NONMEM, pharmacokinetics/pharmacodynamics, therapeutic drug monitoring
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-165678DOI: 10.1097/FTD.0b013e31823615e1ISI: 000297318200008OAI: oai:DiVA.org:uu-165678DiVA: diva2:474965
Available from: 2012-01-10 Created: 2012-01-09 Last updated: 2017-12-08Bibliographically approved
In thesis
1. Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling
Open this publication in new window or tab >>Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

To obtain a better understanding of the pharmacokinetic and/or pharmacodynamic characteristics of an investigated treatment, clinical data is often analysed with nonlinear mixed effects modelling. The developed models can be used to design future clinical trials or to guide individualised drug treatment. Missing data is a frequently encountered problem in analyses of clinical data, and to not venture the predictability of the developed model, it is of great importance that the method chosen to handle the missing data is adequate for its purpose. The overall aim of this thesis was to develop methods for handling missing data in the context of nonlinear mixed effects models and to compare strategies for handling missing data in order to provide guidance for efficient handling and consequences of inappropriate handling of missing data.

In accordance with missing data theory, all missing data can be divided into three categories; missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). When data are MCAR, the underlying missing data mechanism does not depend on any observed or unobserved data; when data are MAR, the underlying missing data mechanism depends on observed data but not on unobserved data; when data are MNAR, the underlying missing data mechanism depends on the unobserved data itself.

Strategies and methods for handling missing observation data and missing covariate data were evaluated. These evaluations showed that the most frequently used estimation algorithm in nonlinear mixed effects modelling (first-order conditional estimation), resulted in biased parameter estimates independent on missing data mechanism. However, expectation maximization (EM) algorithms (e.g. importance sampling) resulted in unbiased and precise parameter estimates as long as data were MCAR or MAR. When the observation data are MNAR, a proper method for handling the missing data has to be applied to obtain unbiased and precise parameter estimates, independent on estimation algorithm.

The evaluation of different methods for handling missing covariate data showed that a correctly implemented multiple imputations method and full maximum likelihood modelling methods resulted in unbiased and precise parameter estimates when covariate data were MCAR or MAR. When the covariate data were MNAR, the only method resulting in unbiased and precise parameter estimates was a full maximum likelihood modelling method where an extra parameter was estimated, correcting for the unknown missing data mechanism's dependence on the missing data.

This thesis presents new insight to the dynamics of missing data in nonlinear mixed effects modelling. Strategies for handling different types of missing data have been developed and compared in order to provide guidance for efficient handling and consequences of inappropriate handling of missing data.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 75 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 189
Keyword
Pharmacometrics, population models, censored observations, missing covariates, missing dependent variable, missing data mechanism, missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR), estimation algorithms
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-224098 (URN)978-91-554-8970-0 (ISBN)
Public defence
2014-08-29, B41, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2014-05-27 Created: 2014-05-03 Last updated: 2014-06-30Bibliographically approved

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Johansson, Åsa M.Karlsson, Mats O.

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