Bayesian estimation and prediction of clearance in high-dose methotrexate infusions

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Abstract
Much attention has been paid to the problem of estimating the pharmacokinetic parameters of individual patients in order to optimize dosage choices. Individual kinetics determined by test-dose bolus injection are a basis for predicting drug concentrations after high-dose methotrexate infusion and for computing appropriate dosages. Simplifications may be attempted, even allowing the test-dose to be omitted by using Bayesian estimation rather than likelihood estimation. To individualize pharmacokinetic parameters, Bayesian estimation combines information about population characteristics and those of individuals based on few measured plasma levels during high-dose infusion. Application of this procedure to methotrexate reveals interesting predictive performances and ability to handle variation due to intraindividual time variability without using test doses. The methodology promises to be more efficient in computing dosages in order to avoid toxic levels and will be less expensive in routine clinical use.

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