All Half-Lives Are Wrong, But Some Half-Lives Are Useful

Pharmacokinetic parameters estimation using adaptive Bayesian P-splines models

In preclinical and clinical experiments, pharmacokinetic (PK) studies are designed to analyse the evolution of drug concentration in plasma over time i.e. the PK profile. Some PK parameters are estimated in order to summarize the complete drug's kinetic profile: area under the curve (AUC), maximal concentration (C(max)), time at which the maximal concentration occurs (t(max)) and half-life time (t(1/2)).Several methods have been proposed to estimate these PK parameters. A first method relies on interpolating between observed concentrations. The interpolation method is often chosen linear. This method is simple and fast. Another method relies on compartmental modelling. In this case, nonlinear methods are used to estimate parameters of a chosen compartmental model. This method provides generally good results. However, if the data are sparse and noisy, two difficulties can arise with this method. The first one is related to the choice of the suitable compartmental model given the small number of data available in preclinical experiment for instance. Second, nonlinear methods can fail to converge. Much work has been done recently to circumvent these problems (J. Pharmacokinet. Pharmacodyn. 2007; 34:229-249, Stat. Comput., to appear, Biometrical J., to appear, ESAIM P&S 2004; 8:115-131).In this paper, we propose a Bayesian nonparametric model based on P-splines. This method provides good PK parameters estimation, whatever be the number of available observations and the level of noise in the data. Simulations show that the proposed method provides better PK parameters estimations than the interpolation method, both in terms of bias and precision. The Bayesian nonparametric method provides also better AUC and t(1/2) estimations than a correctly specified compartmental model, whereas this last method performs better in t(max) and C(max) estimations.We extend the basic model to a hierarchical one that treats the case where we have concentrations from different subjects. We are then able to get individual PK parameter estimations. Finally, with Bayesian methods, we can get easily some uncertainty measures by obtaining credibility sets for each PK parameter.

Oral antidiabetic drugs: effect of food on absorption of pioglitazone and metformin from a fixed-dose combination tablet

An open-label, randomized, crossover study involving 28 healthy subjects was conducted to compare the peak (Cmax) and total (AUC(lqc), AUC(infinity)) exposures to pioglitazone and metformin after single-dose administration of a fixed-dose combination tablet containing 15 mg of pioglitazone plus 850 mg metformin when given under fasted versus fed states, with a washout period of 7 days between treatments. Two different fixed-dose combination formulations (bilayer and pioglitazone-micronized fixed-dose combination tablets) were tested. The pioglitazone-micronized fixed-dose combination formulation was selected for clinical development and regulatory approval; the present study describes food effect results with this formulation. For pioglitazone, least squares mean ratios (fed/fasted) and the 90% confidence intervals of these ratios were 1.05 (0.93-1.18) for Cmax, 1.13 (1.02-1.25) for AUC(lqc), and 1.11 (1.01-1.22) for AUC(infinity). For metformin, these values were 0.72 (0.65-0.79) for Cmax, 0.87 (0.81-0.94) for AUC(lqc), and 0.87 (0.81-0.94) for AUC(infinity). Dosing with food resulted in median prolongation of tmax values by 1.5 hours for metformin and 2.0 hours for pioglitazone. Because bioequivalency criteria were met (fed/fasted 90% confidence interval between 0.80 and 1.25) for both pioglitazone and metformin AUC, fixed-dose combination tablets can be taken with or without food, but to minimize gastrointestinal adverse effects of metformin, the fixed-dose combination tablets are recommended to be taken with food.