We describe fundamental knowledge of pharmacokinetics analysis for phase I trials, particularly focusing on basic parameters (such as bioavailability, volume of distribution, fraction unbound, clearance), estimation and analysis methods (such as compartmental and non-compartmental), points to consider (such as steady state and dose proportionality). The NCA is an abbreviation for Non Compartmental Analysis, and the meaning is pharmacokinetic analysis without pharmacokinetic model. There is something that we should consider in NCA such as AUC calculation method, handling method of not detectable concentrations, point selection for λz calculation, and selection of sampling time. Steady state occurs when the overall intake of a drug is equilibrium with its elimination. At steady state the mean plasma concentrations of the drug are similar by any dosing interval. In practice, it is generally considered that steady state is reached when a time of 5 times the half-life for a drug. For the dose proportionality, the measures of exposure, such as maximal blood concentration (Cmax), area under the curve from 0 to infinity (AUC), are proportional to the dose. The three methods, Analysis of variance of the PK response, normalized by dose, linear regression and power model, are used to assess dose proportionality.
This paper reviews the statistical aspects in pharmacokinetic analysis of clinical Phase 1 trials. Based on the understanding that most pharmacokinetic parameters follow a lognormal distribution, it is considered to be appropriate to summarize them by the geometric mean, geometric CV or geometric SD. Then we conducted simulation studies of a pharmacokinetic model to investigate whether pharmacokinetic parameters follow a lognormal distribution. Using numerical examples obtained by the simulation, we described in detail how to display the summary statistics of pharmacokinetic parameters. We also indicated that geometric mean is also useful to summarize the plasma concentration, and that the concetration below the lower limit of quantification shoud be carefully handled.
Longitudinal data are data collected repeatedly from each subject for a particular response variable over a certain time period. Specifically, in longitudinal data analysis, the researchers are interested in changes in the response levels over time and the differences in these changes among factor levels or covariates. Because of within-subject correlations, analysis methods considering the correlations or variance-covariance structures have been developed. One of the approaches is the use of mixed effects models that take into account between-subject heterogeneity by random effects. In population pharmacokinetics, the response variable corresponds to drug concentration and is analysed typically using nonlinear mixed effects models. In this article, longitudinal data analysis with a continuous response variable is introduced focusing on population pharmacokinetics. Longitudinal data analysis, linear mixed effects models, nonlinear mixed effects models, and population pharmacokinetics are discussed from a biostatistical point of view. This article is expected to be of interest to biostatisticians, pharmacologists, pharmacokineticists, and those in related fields.
The practical aspects of population pharmacokinetics / pharmacodynamics (PK/PD) are summarized. Recently, the progress and application of Model-Based Drug Development (MBDD) in pharmaceutical industries is remarkable and the acceptance of model-based results are getting wider in regulatories of US, EU and Japan. In MBDD, PKPD modeling based on nonlinear mixed effects model (NLMEM) plays an important role, and it provides the quantitative results which help the decision-making in projects. The understanding of its methodology is quite important for pharmacometricians and biostatisticians. In this article, objective, design and modeling methodology (scheme, diagnostics and validation) are briefly described. The research on methodology in NLMEM is still growing, and pharmacometricians should follow its rapid evolution.
In drug development, the low productivity causing escalated costs has been documented for over the past decade. To overcome this significant challenge, Model-Based Drug Development (MBDD) is considered as one of the opportunities. MBDD is the drug development based on the integrated pharmaco-statistical models of drug efficacy and safety with available data and their application to inform development strategy, trial design and decision-making in the drug development. MBDD covers overall drug development stage including both pre-clinical and clinical stage and improves knowledge management and decision-making in a quantitative manner. This article introduces basics of MBDD from the theoretical and the practical point of view with some examples and shows its application for the clinical drug development of the anti-diabetes drug.