Article

A simulation-based confidence band for drug concentrations in blood: an application to a clinical phase I trial.

Clinic Sendagaya, 1-29-3 Sendagaya, Shibuya-ku, Tokyo 151-0051, Japan.
Statistics in Medicine (Impact Factor: 2.04). 11/2003; 22(19):3045-53. DOI: 10.1002/sim.1543
Source: PubMed

ABSTRACT Since drug concentrations in blood are usually related to the effectiveness and toxicity, a confidence band for the drug concentrations gives useful information for the treatment. This paper proposes a simulation-based approach for constructing confidence bands for drug concentrations in blood. The confidence band covers the whole profile of the drug concentrations with a specified probability. The proposed approach presupposes that actual concentrations of a subject fluctuate randomly around a curve specified by an appropriate pharmacokinetic model and the subject-specific pharmacokinetic parameters. The confidence band is constructed by taking into account both the random fluctuations of the actual concentrations and the statistical uncertainty of the parameter estimates. The proposed approach is applied to a simulation study and an actual phase I trial.

0 Bookmarks
 · 
44 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Phase I trials to study the pharmacokinetic properties of a new drug generally involve a restricted number of healthy volunteers. Because of the nature of the group involved in such studies, the appropriate distributional assumptions are not always obvious. These model assumptions include the actual distribution but also the ways in which the dispersion of responses is allowed to vary over time and the fact that small concentrations of a substance are not easily detectable and hence are left censored. We propose that a reasonably wide class of generalized nonlinear models allowing for left censoring be considered now that this is feasible with current computer power and sophisticated statistical packages. These modelling strategies are applied to a Phase I study of the drug flosequinan and its metabolite. This drug was developed for the treatment of heart failure. Because the metabolite also exhibits an active pharmacologic effect, study of both the parent drug and the metabolite is of interest.
    Biometrics 04/2000; 56(1):81-8. · 1.41 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: A fundamental assumption underlying pharmacokinetic compartment modelling is that each subject has a different individual curve. To some extent this runs counter to the statistical principle that similar individuals will have similar curves, thus making inferences to a wider population possible. In population pharmacokinetics, the compromise is to use random effects. We recommend that such models also be used in data rich situations instead of independently fitting individual curves. However, the additional information available in such studies shows that random effects are often not sufficient; generally, an autoregressive process is also required. This has the added advantage that it provides a means of tracking each individual, yielding predictions for the next observation. The compartment model curve being fitted may also be distorted in other ways. A widely held assumption is that most, if not all, pharmacokinetic concentration data follow a log-normal distribution. By examples, we show that this is not generally true, with the gamma distribution often being more suitable. When extreme individuals are present, a heavy-tailed distribution, such as the log Cauchy, can often provide more robust results. Finally, other assumptions that can distort the results include a direct dependence of the variance, or other dispersion parameter, on the mean and setting non-detectable values to some arbitrarily small value instead of treating them as censored. By pointing out these problems with standard methods of statistical modelling of pharmacokinetic data, we hope that commercial software will soon make more flexible and suitable models available.
    Statistics in Medicine 09/2001; 20(17-18):2775-83. · 2.04 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: 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.
    Journal of Pharmacokinetics and Biopharmaceutics 03/1998; 26(1):103-23.