Standard Error of Empirical Bayes Estimate in NONMEM® VI.

The pharmacokinetics/pharmacodynamics analysis software NONMEM® output provides model parameter estimates and associated standard errors. However, the standard error of empirical Bayes estimates of inter-subject variability is not available. A simple and direct method for estimating standard error of the empirical Bayes estimates of inter-subject variability using the NONMEM® VI internal matrix POSTV is developed and applied to several pharmacokinetic models using intensively or sparsely sampled data for demonstration and to evaluate performance. The computed standard error is in general similar to the results from other post-processing methods and the degree of difference, if any, depends on the employed estimation options

Accelerating Monte Carlo power studies through parametric power estimation

Estimating the power for a non-linear mixed-effects model-based analysis is challenging due to the lack of a closed form analytic expression. Often, computationally intensive Monte Carlo studies need to be employed to evaluate the power of a planned experiment. This is especially time consuming if full power versus sample size curves are to be obtained. A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte Carlo simulation and estimations. The estimated parameter linearly scales with study size allowing a quick generation of the full power versus study size curve. A comparison of the PPE with the classical, purely Monte Carlo-based power estimation (MCPE) algorithm for five diverse pharmacometric models showed an excellent agreement between both algorithms, with a low bias of less than 1.2 % and higher precision for the PPE. The power extrapolated from a specific study size was in a very good agreement with power curves obtained with the MCPE algorithm. PPE represents a promising approach to accelerate the power calculation for non-linear mixed effect models

Adsorption and surface dissociation of HNCO on Pt(110) surfaces: LEED, AES, ELS and TDS studies

Bronchoalveolar lavage (BAL) is a pulmonary sampling technique for characterization of drug concentrations in epithelial lining fluid and alveolar cells. Two hypothetical drugs with different pulmonary distribution rates (fast and slow) were considered. An optimized BAL sampling design was generated assuming no previous information regarding the pulmonary distribution (rate and extent) and with a maximum of two samples per subject. Simulations were performed to evaluate the impact of the number of samples per subject (1 or 2) and the sample size on the relative bias and relative root mean square error of the parameter estimates (rate and extent of pulmonary distribution). The optimized BAL sampling design depends on a characterized plasma concentration time profile, a population plasma pharmacokinetic model, the limit of quantification (LOQ) of the BAL method and involves only two BAL sample time points, one early and one late. The early sample should be taken as early as possible, where concentrations in the BAL fluid a parts per thousand yen LOQ. The second sample should be taken at a time point in the declining part of the plasma curve, where the plasma concentration is equivalent to the plasma concentration in the early sample. Using a previously described general pulmonary distribution model linked to a plasma population pharmacokinetic model, simulated data using the final BAL sampling design enabled characterization of both the rate and extent of pulmonary distribution. The optimized BAL sampling design enables characterization of both the rate and extent of the pulmonary distribution for both fast and slowly equilibrating drugs

What opportunities do the New EU international investment agreements offer for developing countries?

Purpose: Cancer chemotherapy, although based on body surface area, often causes unpredictable myelosuppression, especially severe neutropenia. The aim of this study was to evaluate qualitatively and quantitatively the influence of patient-specific characteristics on the neutrophil concentration-time course, to identify patient subgroups, and to compare covariates on system-related pharmacodynamic variable between drugs. Experimental Design: Drug and neutrophil concentration, demographic, and clinical chemistry data of several trials with docetaxel (637 patients), paclitaxel (45 patients), etoposide (71 patients), or topotecan (191 patients) were included in the covariate analysis of a physiology-based pharmacokinetic-pharmacodynamic neutropenia model. Comparisons of covariate relations across drugs were made. Results: A population model incorporating four to five relevant patient factors for each drug to explain variability in the degree and duration of neutropenia has been developed. Sex, previous anticancer therapy, performance status, height, binding partners, or liver enzymes influenced system-related variables and alpha(1)-acid glycoprotein, albumin, bilirubin, concomitant cytotoxic agents, or administration route changed drug-specific variables. Overall, female and pretreated patients had a lower baseline neutrophil concentration. Across-drug comparison revealed that several covariates (e.g., age) had minor (clinically irrelevant) influences but consistently shifted the pharmacodynamic variable in the same direction. Conclusions: These mechanistic models, including patient characteristics that influence drug-specific parameters, form the rationale basis for more tailored dosing of individual patients or subgroups to minimize the risk of infection and thus might contribute to a more successful therapy. In addition, nonsignificant or clinically irrelevant relations on system-related parameters suggest that these covariates could be negligible in clinical trails and daily use

Market size, competition, and the product mix of exporters

We build a theoretical model of multi-product firms that highlights how market size and ge- ography (the market sizes of and bilateral economic distances to trading partners) affect both a firm's exported product range and its exported product mix across market destinations (the dis- tribution of sales across products for a given product range). We show how tougher competition in an export market induces a firm to skew its export sales towards its best performing products. We find very strong confirmation of this competitive effect for French exporters across export market destinations. Trade models based on exogenous markups cannot explain this strong sig- nificant link between destination market characteristics and the within-firm skewness of export sales (after controlling for bilateral trade costs). Theoretically, this within firm change in prod- uct mix driven by the trading environment has important repercussions on firm productivity and how it responds to changes in that trading environment

Pourquoi les politiques publiques sont-elles si peu suivies d’effets ?:Quelques interrogations

L’insertion des femmes sur le marché du travail a connu à la fois des avancées et des reculs. Si davantage de femmes accèdent à l’éducation supérieure et aux emplois qualifiés, d’autres sont touchées par la précarité et connaissent une dégradation de leurs conditions de travail et de vie. Face à ce constat ambivalent, on peut questionner la mise en œuvre et l’efficacité des politiques qui visent à promouvoir l’égalité entre les femmes et les hommes. Cet article a pour objectif de soulever quelques débats. Le plus souvent, les politiques publiques au sens large (y compris la protection sociale) sont définies en termes de compensation et de correction des inégalités et des discriminations. Mais elles ne concernent pas les causes effectives de l’extension du sous-emploi des femmes, qui relèvent du fonctionnement même du marché du travail. C’est donc la définition des politiques publiques qu’il faut interroger, en dépassant une vision binaire qui oppose d’une part un champ économique extérieur, d’autre part un champ social, juridique et culturel qui, seul, pourrait être l’objet d’inflexions. En réalité, le champ économique est aussi le produit des politiques publiques : la libre-concurrence et la prééminence du marché sont le résultat d’une action volontaire des États. Il faut donc réintégrer les politiques économiques dans le champ de la réflexion sur les moyens de combattre les discriminations à l’encontre des femmes.The integration of women into the labour market has gone through both upswings and downturns. In view of this ambivalent result, we can question the efficiency of public policies set up to overcome gender inequality and fight gender discrimination. Does a real will exist, and if so why is it so inefficient or so poorly implemented? What forms do individual and collective resistance take? Most of the time, public policies are defined in terms of compensation and correction. But they don’t deal with the actual causes of women’s underemployment resulting from labour market adjustments. It is therefore the definition of the public policies that we need to examine, going beyond a binary view that opposes economic issues, on the one hand, to social, juridical and cultural concerns on the other

Two-stage model-based clinical trial design to optimize phase I development of novel anticancer agents

Background The phase I program of anticancer agents usually consists of multiple dose escalation studies to select a safe dose for various administration schedules. We hypothesized that pharmacokinetic and pharmacodynamic (PK–PD) modeling of an initial phase I study (stage 1) can be used for selection of an optimal starting dose for subsequent studies (stage 2) and that a post-hoc PK–PD analysis enhances the selection of a recommended dose for phase II evaluation. The aim of this analysis was to demonstrate that this two-stage model-based design, which does not interfere in the conduct of trials, is safe, efficient and effective. Methods PK and PD data of dose escalation studies were simulated for nine compounds and for five administration regimens (stage 1) for drugs with neutropenia as dose-limiting toxicity. PK–PD models were developed for each simulated study and were used to determine a starting dose for additional phase I studies (stage 2). The model-based design was compared to a conventional study design regarding safety (number of dose-limiting toxicities (DLTs)), efficiency (number of patients treated with a dose below the recommended dose) and effectiveness (precision of dose selection). Retrospective data of the investigational anticancer drug indisulam were used to show the applicability of the model-based design. Results The model-based design was as safe as the conventional design (median number of DLTs = 3) and resulted in a reduction of the number of patients who were treated with a dose below the recommended dose (−27%, power 89%). A post-hoc model-based determination of the recommended dose for future phase II studies was more precise than the conventional selection of the recommended dose (root mean squared error 8.3% versus 30%). Conclusions A two-stage model-based phase I design is safe for anticancer agents with dose-limiting myelosuppression and may enhance the efficiency of dose escalation studies by reducing the number of patients treated with a dose below the recommended dose and by increasing the precision of dose selection for phase II evaluation