Optimal sampling times for Bayesian estimation of the pharmacokinetic parameters of nortriptyline during therapeutic drug monitoring

Monitoring of Tobramycin Exposure: What is the Best Estimation Method and Sampling Time for Clinical Practice?

OBJECTIVES

The objective of this article is to investigate the influence of blood sampling times on tobramycin exposure estimation and clinical decisions and to determine the best sampling times for two estimation methods used for therapeutic drug monitoring.

METHODS

Adult patients with cystic fibrosis, treated with once-daily intravenous tobramycin, were intensively sampled over one 24-h dosing interval to determine true exposure (AUC_0-24). The AUC_0-24s were then estimated using both log-linear regression and Bayesian forecasting methods for 21 different sampling time combinations. These were compared to true exposure using relative prediction errors. The differences in subsequent dose recommendations were calculated.

RESULTS

Twelve patients, with a median (range) age of 25 years (18-36) and weight of 66.5 kg (50.6-76.4) contributed 96 tobramycin concentrations. Five hundred and eighty-eight estimated AUC_0-24s were compared to 12 measured true AUC_0-24 values. Median relative prediction errors ranged from - 34.7 to 45.5% for the log-linear regression method and from - 14.46 to 11.23% for the Bayesian forecasting method across the 21 sampling combinations. The most unbiased exposure estimation was provided from concentrations sampled at 100/640 min after the start of the infusion using log-linear regression and at 70/160 min using Bayesian forecasting. Subsequent dosing recommendations varied greatly depending on the estimation method and the sampling times used.

CONCLUSION

Sampling times markedly influence bias in AUC_0-24 estimation, leading to greatly varied dose adjustments. The impact of blood sampling times on dosing decisions is reduced when using Bayesian forecasting.

Inter occasion variability in individual optimal design

Inter occasion variability (IOV) is of importance to consider in the development of a design where individual pharmacokinetic or pharmacodynamic parameters are of interest. IOV may adversely affect the precision of maximum a posteriori (MAP) estimated individual parameters, yet the influence of inclusion of IOV in optimal design for estimation of individual parameters has not been investigated. In this work two methods of including IOV in the maximum a posteriori Fisher information matrix (FIM_MAP) are evaluated: (i) MAP_occ—the IOV is included as a fixed effect deviation per occasion and individual, and (ii) POP_occ—the IOV is included as an occasion random effect. Sparse sampling schedules were designed for two test models and compared to a scenario where IOV is ignored, either by omitting known IOV (Omit) or by mimicking a situation where unknown IOV has inflated the IIV (Inflate). Accounting for IOV in the FIM_MAP markedly affected the designs compared to ignoring IOV and, as evaluated by stochastic simulation and estimation, resulted in superior precision in the individual parameters. In addition MAP_occ and POP_occ accurately predicted precision and shrinkage. For the investigated designs, the MAP_occ method was on average slightly superior to POP_occ and was less computationally intensive.

Application of the optimal design approach to improve a pretransplant drug dose finding design for ciclosporin

A time and sampling intensive pretransplant test dose design was to be reduced, but at the same time optimized so that there was no loss in the precision of predicting the individual pharmacokinetic (PK) estimates of posttransplant dosing. The following variables were optimized simultaneously: sampling times, ciclosporin dose, time of second dose, infusion duration, and administration order, using a published ciclosporin population PK model as prior information. The original design was reduced from 22 samples to 6 samples/patient and both doses (intravenous oral) were administered within 8 hours. Compared with the prior information given by the published ciclosporin population PK model, the expected standard deviations (SDs) of the individual parameters for clearance and bioavailability could be reduced by, on average, 40% under the optimized sparse designs. The gain of performing the original rich design compared with the optimal reduced design, considering the standard errors of the parameter estimates, was found to be minimal. This application demonstrates, in a practical clinical scenario, how optimal design techniques may be used to improve diagnostic procedures given available software and methods.

Enhancement of nortriptyline penetration through human epidermis: influence of chemical enhancers and iontophoresis

Different known percutaneous chemical enhancers and iontophoresis have been tested in-vitro to study their ability to increase transdermal absorption of nortriptyline hydrochloride (20 mg mL−1). The chemicals 1-dodecanol, Span 20, Azone, (R)-(+)-limonene or isopropyl myristate were used as an overnight pretreatment at 5% (w/w) in ethanol. Furthermore, isopropyl myristate (20%, w/w) and propylene glycol (15%, w/w) were tested in the same vehicle. Iontophoresis was applied directly to the nortriptyline hydrochloride donor solution for three different concentrations (20, 2 and 0.5 mgmL−1). The chemical enhancers slightly increased the nortriptyline transdermal flux but iontophoresis was more efficient. In this case, nortriptyline transdermal flux was concentration dependent, having a higher flux when the concentration was lowered. Therefore, iontophoresis was the most suitable technique to increase transdermal absorption of nortriptyline and it could be an alternative method to provide therapeutic concentrations of this drug in smoking cessation treatment.

Sampling schedule design towards optimal drug monitoring for individualizing therapy

We study the individualization of therapy by simultaneously taking into account the design of sampling schedule and optimal therapeutic drug monitoring. The sampling schedule design in this work is to determine the number of samples, the sampling times, the switching time from the loading to the maintenance period, and the drug dosages. A closed-loop control policy is employed to determine the sampling schedule, and an advanced stochastic global optimization algorithm, which integrates the stochastic approximation and simulated annealing techniques, is implemented to search the optimal sampling schedule. A simulated one-compartment model of intravenous theophylline therapy is used to illustrate our method. This method can be readily extended to multiple compartment systems and allow incorporating other criteria of drug control. While currently the method is mainly of theoretical interest, it offers a starting point for practical applications and thus is hopefully of great value for the clinically individualizing therapy in the future.