Innovative prolonged-release oral alkalising formulation allowing sustained urine pH increase with twice daily administration: randomised trial in healthy adults.

A multi-particulate fixed-dose combination product, consisting of a combination of two alkalising salts formulated as prolonged-release granules, ADV7103, was developed to obtain a sustained and prolonged alkalising effect. The specific release of both types of granules was shown in vitro through their dissolution profiles, which indicated that potassium citrate was released within the first 2-3 h and potassium bicarbonate up to 10-12 h after administration. The long-lasting coverage of ADV7103 was confirmed through a randomised, placebo-controlled, double-blind, two-period study, measuring its effect on urine pH in healthy adults (n = 16) at doses of alkalising agent ranging between 0.98 and 2.88 meq/kg/day. A significant increase of urine pH with a positive dose-response in healthy adult subjects was shown. Urine pH above 7 was maintained during 24 h with a dosing equivalent to 1.44 meq/kg twice a day, while urine pH was below 6 most of the time with placebo. The effect observed was non-saturating within the range of doses evaluated and the formulation presented a good safety profile. ADV7103 provided an effective prolonged release of alkalising salts to cover a 12-h effect with adequate tolerability and could afford a twice a day (morning and evening) dosing in patients requiring long-term treatment.

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.

Bayesian hierarchical modeling of receptor occupancy in PET trials.

Receptor occupancy (RO) PET is a non-invasive way to determine drug on target. Given the complexity of procedures, long acquisition times, and high cost, ligand displacement imaging trials often have a limited size and produce sparse RO results over the time course of the blocking drug. To take the best advantage of the available data, we propose a Bayesian hierarchical model to analyze RO as a function of the displacing drug. The model has three components: the first estimates RO using brain regional time-radioactivity concentrations, the second shapes the pharmacokinetic profile of the blocking drug, and the last relates PK to RO. Compared to standard 2-steps RO estimation methods, our Bayesian approach quantifies the variability of the individual RO measures. The model has also useful prediction capabilities: to quantify brain RO for dosage regimens of the drug that were not tested in the experiment. This permits the optimal dose selection of neuroscience drugs at a limited cost. We illustrate the method in the prediction of RO after multiple dosing from a single-dose trial.

Joint detection of important biomarkers and optimal dose-response model using penalties.

We propose a method to jointly detect influential biomarkers and estimate how they change with dose. The assessment is made in dose-ranging trials collecting multiple outcomes for efficacy, safety, pharmacokinetics or pharmacodynamics. We regress all these outcomes versus a non-parametric transformation of the dose. The regression coefficients and the parameters from the dose-response model are simultaneously estimated using a penalized alternating least-squares method. We illustrate the technique with a phase I clinical trial and a metabonomic experiment in rats.

LY293558, a novel AMPA/GluR5 antagonist, is efficacious and well-tolerated in acute migraine.

Glutamatergic hyperactivity is implicated migraine pathogenesis. Also, LY293558, an alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)/kainate (KA) receptor antagonist, is effective in preclinical models of migraine. We therefore tested LY293558 in acute migraine. We conducted a randomized, triple-blind, parallel-group, double-dummy, multicentre trial of 1.2 mg/kg intravenous (IV) LY293558, 6 mg subcutaneous (SC) sumatriptan, or placebo in the treatment of acute migraine. The primary efficacy variable was the headache response rate, i.e. headache score improvement from moderate/severe at baseline to mild/none at 2 h. Of 45 enrolled patients, 44 patients (20M:24F; mean age +/- SD = 40 +/- 9 years) completed the study. Response rates were 69% for LY293558 (P = 0.017 vs. placebo), 86% for sumatriptan (P < 0.01 vs. placebo) and 25% for placebo. LY293558 and sumatriptan were superior to placebo (P < 0.01 for all comparisons) on all other measures of improvement in pain and migraine associated symptoms. Fifteen percent of patients who took LY293558 reported adverse events (AEs) (n = 2; one mild, one severe). Fifty-three percent of patients who took sumatriptan (n = 8; seven mild, one moderate) and 31% of those who received placebo reported AEs (n = 5; four mild, one severe). The efficacy and safety results of LY293558 in this small migraine proof of concept trial, together with supportive preclinical data, provide evidence for a potential role of nonvasoactive AMPA/KA antagonists in treating migraine. Larger trials are needed to further test the hypothesis.

Confidence interval criteria for assessment of dose proportionality.

PURPOSE: The aim of this work was a pragmatic, statistically sound and clinically relevant approach to dose-proportionality analyses that is compatible with common study designs. METHODS: Statistical estimation is used to derive a (1-alpha)% confidence interval (CI) for the ratio of dose-normalized, geometric mean values (Rdnm) of a pharmacokinetic variable (PK). An acceptance interval for Rdnm defining the clinically relevant, dose-proportional region is established a priori. Proportionality is declared if the CI for Rdnm is completely contained within the critical region. The approach is illustrated with mixed-effects models based on a power function of the form PK = beta0 x Dose(beta1); however, the logic holds for other functional forms. RESULTS: It was observed that the dose-proportional region delineated by a power model depends only on the dose ratio. Furthermore, a dose ratio (rho1) can be calculated such that the CI lies entirely within the pre-specified critical region. A larger ratio (rho2) may exist such that the CI lies completely outside that region. The approach supports inferences about the PK response that are not constrained to the exact dose levels studied. CONCLUSION: The proposed method enhances the information from a clinical dose-proportionality study and helps to standardize decision rules.

Construction of an optimal destructive sampling design for noncompartmental AUC estimation.

Based on toxicokinetic studies of a destructive sampling design, this work was aimed at selecting the number of time points, their locations, and the number of replicates per time point in order to obtain the most accurate and precise noncompartmental estimate of the area under the concentration-time curve (AUC). From a prior population pharmacokinetic model, the design is selected to minimize the scaled mean squared error of AUC. Designs are found for various sample sizes, number of time points, and a distribution of animals across time points from being very unbalanced to balanced. Their efficiencies are compared both theoretically and based on simulations. An algorithm has been implemented for this purpose using the symbolic resolution and numerical minimization capabilities of Mathematica and an example of its use is provided. This method provides efficient tools for constructing, validating, and comparing optimal sampling designs for destructive sampled toxicokinetic studies.