Background and Objectives: Therapeutic drug monitoring is applied to a range of drugs. To predict an appropriate dosing regimen, models based on Bayesian techniques have been used. However, this approach requires a well trained professional and sophisticated software. The objectives of this study were first to develop kinetic nomograms as a useful tool to achieve individual drug blood concentrations within the therapeutic window, using few samples and in a short period of time; and second to evaluate the performance of these nomograms in dosage adjustment and compare them with the Bayesian procedure by use of simulation.
Methods: Kinetic nomograms involve collection of concentration-time profiles following repeated administrations of a fixed identification protocol and targeting of a steady-state concentration. The profiles divide the concentration-time space into several areas, each of them corresponding to a given adjusted drug dose. Kinetic nomograms are grounded on the statistical description of the interindividual variability provided by population pharmacokinetic approaches. To use them, the assayed drug concentration in a blood sample is first located in the kinetic nomogram and then the dose corresponding to the area containing this location is read. Evaluation of performance and comparison with the traditional Bayesian procedure were done by a simulation study using the immunosuppressant drug sirolimus (rapamycin). All calculations were performed by use of Matlab software.
Results: The simulation study confirmed the need for individual dosage adjustment; 71.6% of individuals underwent modification of the identification protocol of 1 mg twice daily in order to reach steady-state trough concentrations of 8 ng/mL. When the regimens were adjusted by kinetic nomograms and the Bayesian procedure, the steady-state trough concentrations of sirolimus showed low variability (coefficients of variation [CVs] of 23.4% and 24.0%, respectively) as compared with those obtained by standard recommended protocols of 4mg once daily (CV 68.6%). The doses adjusted by kinetic nomograms and the Bayesian procedure were linearly linked and highly correlated (r = 0.96), and both provided simultaneous control of minimum and maximum drug concentrations (63.9% and 68.7% of cases between 6 and 20 ng/mL, respectively).
Conclusion: Kinetic nomograms allow rapid and reliable dosage adjustment after the start of drug therapy. They are interesting alternatives to the cumbersome Bayesian procedure, and they provide dosage adjustment even for drugs that exhibit large intraindividual variability. In the clinical context, kinetic nomograms render individual dosage adjustment a simplified bedside application, and they could assist population studies aiming at dose individualization.
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