LY293558, a Novel AMPA/GluR5 Antagonist, is Efficacious and Well-Tolerated in Acute Migraine

Glutamatergic hyperactivity is implicated migraine pathogenesis. Also, LY293558, an α-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.

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.

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.

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.