Biomedical subjects

A Yafune

Publications and source records attributed to A Yafune.

5 recordsLinked to original sources

PubMed2001

Modification of pharmacokinetic sampling schedules in clinical phase I trials: a use of Kullback-Leibler information.

In clinical phase I trials, pharmacokinetic sampling schedules often have to be modified while the trials proceed. The modifications are based on the observations from a limited number of subjects or only one subject in extreme cases. This paper describes a Kullback-Leibler information approach for modifying pharmacokinetic sampling schedules in clinical phase I trials. The proposed approach presupposes that the pharmacokinetic profile for each subject is specified by an appropriate pharmacokinetic model and the subject-specific parameters. The best modification plan is selected from arbitrarily chosen candidates based on the Kullback-Leibler information. Applications are given to a simulation study and an actual phase I trial of an anti-cancer drug.

Clinical Trials, Phase I as Topic↗

PubMed2001

Population pharmacokinetic analysis of ephedrine in Kampo prescriptions: a study in healthy volunteers and clinical use of the pharmacokinetic results.

We conducted a population pharmacokinetic analysis of ephedrine, the main effective constituent of the two Kampo prescriptions, Sho-seiryu-to (Xiao-Qing-Long-Tang, Formula divinitatis caeruleae minor) and Kakkon-to (Ge-Gen-Tang, Formula puerariae), which are routinely used in the treatment of allergic rhinitis. Major pharmacokinetic differences were found in AUC(0-infinity), Cmax and the averages of the distribution volume between the two prescriptions. Using the pharmacokinetic results, we also conducted a simulation-based study comparing the pharmacokinetic profiles of ephedrine for the usual dosage regimen, a constant dose D three times a day before meals, and two alternative dosage regimens which are often used when patients' symptoms are not improved in the morning using the usual dosage regimen. The results suggested that one of the two alternative dosage regimens, two times D before breakfast and D in the afternoon, was the most effective from the pharmacokinetic viewpoint. This result was in accordance with our clinical experience.

Adult↗

PubMed1999

Bootstrap approach for constructing confidence intervals for population pharmacokinetic parameters. I: A use of bootstrap standard error.

In population pharmacokinetic studies, one of the main objectives is to estimate population pharmacokinetic parameters specifying the population distributions of pharmacokinetic parameters. Confidence intervals for population pharmacokinetic parameters are generally estimated by assuming the asymptotic normality, which is a large-sample property, that is, a property which holds for the cases where sample sizes are large enough. In actual clinical trials, however, sample sizes are limited and not so large in general. Likelihood functions in population pharmacokinetic modelling include a multiple integral and are quite complicated. We hence suspect that the sample sizes of actual trials are often not large enough for assuming the asymptotic normality and that the asymptotic confidence intervals underestimate the uncertainties of the estimates of population pharmacokinetic parameters. As an alternative to the asymptotic normality approach, we can employ a bootstrap approach. This paper proposes a bootstrap standard error approach for constructing confidence intervals for population pharmacokinetic parameters. Comparisons between the asymptotic and bootstrap confidence intervals are made through applications to a simulated data set and an actual phase I trial.

Adult↗

PubMed1999

Bootstrap approach for constructing confidence intervals for population pharmacokinetic parameters. II: A bootstrap modification of standard two-stage (STS) method for phase I trial.

For population pharmacokinetics in phase I trials, the standard two-stage (STS) method is quite appealing, especially to non-statisticians, because the method is theoretically and computationally simple. The method, however, does not take into account the uncertainty in estimating individual-specific parameters and gives biased estimates for population variances of pharmacokinetic parameters. This is one of the main reasons why the STS method is not generally recommended. This paper proposes a simple bootstrap modification of the STS method for estimating confidence intervals of population means and standard deviations of pharmacokinetic parameters in phase I trials. The proposed approach adopts a bootstrap bias correction in estimating population variances of pharmacokinetic parameters. Applications are given to a simulated data set and an actual phase I trial to show how the proposed approach works in practice.

Clinical Trials, Phase I as Topic↗

PubMed1998

A use of Monte Carlo integration for population pharmacokinetics with multivariate population distribution.

This paper describes a use of Monte Carlo integration for population pharmacokinetics with multivariate population distribution. In the proposed approach, a multivariate lognormal distribution is assumed for a population distribution of pharmacokinetic (PK) parameters. The maximum likelihood method is employed to estimate the population means, variances, and correlation coefficients of the multivariate lognormal distribution. Instead of a first-order Taylor series approximation to a nonlinear PK model, the proposed approach employs a Monte Carlo integration for the multiple integral in maximizing the log likelihood function. Observations below the lower limit of detection, which are usually included in Phase 1 PK data, are also incorporated into the analysis. Applications are given to a simulated data set and an actual Phase 1 trial to show how the proposed approach works in practice.

Algorithms↗