Covariate pharmacokinetic model building in oncology and its potential clinical relevance
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abstract
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When modeling pharmacokinetic (PK) data, identifying covariates is
important in explaining interindividual variability, and thus
increasing the predictive value of the model. Nonlinear
mixed-effects modeling with stepwise covariate modeling is
frequently used to build structural covariate models, and the most
commonly used software-NONMEM-provides estimations for the
fixed-effect parameters (e.g., drug clearance), interindividual and
residual unidentified random effects. The aim of covariate modeling
is not only to find covariates that significantly influence the
population PK parameters, but also to provide dosing recommendations
for a certain drug under different conditions, e.g., organ
dysfunction, combination chemotherapy. A true covariate is usually
seen as one that carries unique information on a structural model
parameter. Covariate models have improved our understanding of the
pharmacology of many anticancer drugs, including busulfan or
melphalan that are part of high-dose pretransplant treatments, the
antifolate methotrexate whose elimination is strongly dependent on
GFR and comedication, the taxanes and tyrosine kinase inhibitors,
the latter being subject of cytochrome p450 3A4 (CYP3A4) associated
metabolism. The purpose of this review article is to provide a tool
to help understand population covariate analysis and their potential
implications for the clinic. Accordingly, several population
covariate models are listed, and their clinical relevance is
discussed. The target audience of this article are clinical
oncologists with a special interest in clinical and mathematical
pharmacology.
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citation
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Joerger M. Covariate pharmacokinetic model building in oncology and
its potential clinical relevance. AAPS J 2012; 14:119-32.
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type
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journal paper/review (English)
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date of publishing
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25-01-2012
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journal title
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AAPS J (14/1)
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ISSN electronic
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1550-7416
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pages
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119-32
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PubMed
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22274748
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DOI
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10.1208/s12248-012-9320-2
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