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Population Pharmacokinetic Analysis of Drug-Drug Interactions Between Perampanel and Carbamazepine Using Enzyme Induction Model in Epileptic Patients.
Therapeutic Drug Monitoring 2023 January 17
BACKGROUND: Perampanel (PER) is an oral antiepileptic drug and its concomitant use with carbamazepine (CBZ) leads to decreased PER concentrations. However, the magnitude of its influence may vary, depending on the dynamics of the enzyme induction properties of CBZ. This study aimed to develop a population pharmacokinetic (PPK) model considering the dynamics of enzyme induction and evaluate the effect of CBZ on PER pharmacokinetics.
METHODS: We retrospectively collected data on patient background, laboratory tests, and prescribed drugs from electronic medical records. We developed 2 PPK models incorporating the effect of CBZ-mediated enzyme induction to describe time-concentration profiles of PER using the following different approaches: (1) treating the concomitant use of CBZ as a categorical covariate (empirical PPK model) and (2) incorporating the time-course of changes in the amount of enzyme by CBZ-mediated induction (semimechanistic PPK model). The bias and precision of the predictions were investigated by calculating the mean error, mean absolute error, and root mean squared error.
RESULTS: A total of 133 PER concentrations from 64 patients were available for PPK modelling. PPK analyses showed that the co-administration of CBZ increased the clearance of PER. Goodness-of-fit plots indicated a favorable description of the observed data and low bias. The mean error, mean absolute error, and root mean square error values based on the semimechanistic model were smaller than those obtained using the empirical PPK model for predicting PER concentrations in patients with CBZ.
CONCLUSIONS: We developed 2 PPK models to describe PER pharmacokinetics based on different approaches, using electronic medical record data. Our PPK models support the use of PER in clinical practice.
METHODS: We retrospectively collected data on patient background, laboratory tests, and prescribed drugs from electronic medical records. We developed 2 PPK models incorporating the effect of CBZ-mediated enzyme induction to describe time-concentration profiles of PER using the following different approaches: (1) treating the concomitant use of CBZ as a categorical covariate (empirical PPK model) and (2) incorporating the time-course of changes in the amount of enzyme by CBZ-mediated induction (semimechanistic PPK model). The bias and precision of the predictions were investigated by calculating the mean error, mean absolute error, and root mean squared error.
RESULTS: A total of 133 PER concentrations from 64 patients were available for PPK modelling. PPK analyses showed that the co-administration of CBZ increased the clearance of PER. Goodness-of-fit plots indicated a favorable description of the observed data and low bias. The mean error, mean absolute error, and root mean square error values based on the semimechanistic model were smaller than those obtained using the empirical PPK model for predicting PER concentrations in patients with CBZ.
CONCLUSIONS: We developed 2 PPK models to describe PER pharmacokinetics based on different approaches, using electronic medical record data. Our PPK models support the use of PER in clinical practice.
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