In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.     

Bayesian estimation of a random effects heteroscedastic probit model

Summary Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.     

Improved estimates of discordance ratios in twin studies

In the study of monozygotic twins relative to disease and risk factors, particular interest focuses on the subset who are discordant for some suspected risk factor (for example, smoking), since such twins constitute a natural case-control pair. In such studies, questionnaires designed to identify the status of all twin pairs are sometimes error prone and can yield misleading estimates of the concordance-discordance ratios. Greater efforts to verify the characteristics of apparently discordant pairs than to verify those of apparently concordant pairs can result in the 'unequal ascertainment' fallacy. Using the results of a questionnaire with known error rates and the 'apparent' frequencies yielded, we present unbiased, maximum likelihood estimates of the 'true' proportions of concordant and discordant pairs. concordant and discordant pairs. We also present approximate covariances among these estimates.     

The predictive value of specific immunoglobulin E levels for the first diagnosis of cow''s milk allergy. A critical analysis of pediatric literature

Investigators have tried to identify a level of seric specific immunoglobulin E (IgE) that had a sufficient predictive value to diagnose a food allergy without having will resort to the food challenge. To search in literature, all the studies that have estimated the possibility to identify a level of seric specific cow milk IgE with a positive predictive value (PPV) of 95% for the first diagnosis of cow's milk allergy (CMA) in pediatric age. We have identified six studies, nearly all studies suffer from relevant methodological bias. Proposed cut-off are all different. The studied pediatric populations were highly selected. Also neglecting the methodological bias of the studies and the great difference of value between the proposed cut-off, it always remains to consider that the pre-test probability of having a CMA between the children enrolled in the six studies included in this review is particularly high. The likelihood ratio helps to transfer the results of a study on a diagnostic test just to our population, and it is more realistic rather than to entrust itself to the PPV or the negative predictive value, that are much influenced from the prevalence of the disease in the studied population.     

Evaluation of hypothesis testing for comparing two populations using NONMEM analysis

In a simulation study of inference on population pharmacokinetic parameters, two methods of performing tests of hypotheses comparing two populations using NONMEM were evaluated. These two methods are the test based upon 95% confidence intervals and the likelihood ratio test. Data were simulated according to a monoexponential model and, in that context, power curves for each test were generated for (i)the ratio of mean clearance and (ii)the ratio of the population standard deviations of clearance. To generate the power curves, a range of these parameters was employed; other pharmacokinetic parameters were selected to reflect the variability typically present in a Phase II clinical trial. For tests comparing the means, the confidence interval tests had approximately the same power as the likelihood ratio tests and were consistently more faithful to the nominal level of significance. For comparison of the standard deviations, and when the volume of information available was relatively small, however, the likelihood ratio test was more able to detect differences between the two groups. These results were then compared to results on parameter estimation in order to gain insight into the question of power. As an example, the nonnormality of estimates of the ratio of standard deviations plays an important role in explaining the low power for the confidence interval tests. We conclude that, except for the situation of modeling standard deviations with only sparse information, NONMEM produces tests of significance that are effective at detecting clinically significant differences between two populations.Partial support from the Upjohn Company, NIH-BRSG SO RR 07066, and the Burroughs Wellcome Foundation.     

Performance in population models for count data,part II: A new SAEM algorithm

Analysis of count data from clinical trials using mixed effect analysis has recently become widely used. However, algorithms available for the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain limitations, including bias in parameter estimates and the long analysis runtime. The stochastic approximation expectation maximization (SAEM) algorithm has proven to be a very efficient and powerful tool in the analysis of continuous data. The aim of this study was to implement and investigate the performance of a new SAEM algorithm for application to count data. A new SAEM algorithm was implemented in MATLAB for estimation of both, parameters and the Fisher information matrix. Stochastic Monte Carlo simulations followed by re-estimation were performed according to scenarios used in previous studies (part I) to investigate properties of alternative algorithms (Plan et al., 2008, Abstr 1372 []). A single scenario was used to explore six probability distribution models. For parameter estimation, the relative bias was less than 0.92% and 4.13% for fixed and random effects, for all models studied including ones accounting for over- or under-dispersion. Empirical and estimated relative standard errors were similar, with distance between them being <1.7% for all explored scenarios. The longest CPU time was 95 s for parameter estimation and 56 s for SE estimation. The SAEM algorithm was extended for analysis of count data. It provides accurate estimates of both, parameters and standard errors. The estimation is significantly faster compared to LAPLACE and GQ. The algorithm is implemented in Monolix 3.1, (beta-version available in July 2009).     

Study of the measurement of defense style using Bond's Defense Style Questionnaire

Two hundred and seventy healthy university students were surveyed in December 1995 using Bond's Defense Style Questionnaire (DSQ) to measure the subjects' defense mechanisms. At the same time, a survey using Byrne's R-S Scale (Repression–Sensitization Scale) of the MMPI (Minnesota multiphasic personality inventory) and five psychiatric symptom indexes (anxiety, sense of inadequacy, sensitivity, depression and impulsive anger) selected from the CMI (Cornell Medical Index-Health Questionnaire) was conducted. Three factors were extracted from the DSQ through factor analysis: immature defenses, neurotic defenses, and mature defenses. The results of analysis of variance revealed the following: (i) for anxiety and anxiety related symptoms, both immature defenses and neurotic defenses indicated principal effect; (ii) for impulsive anger and depression, immature defenses presented principal effect; and (iii) for sensitivity and impulsive anger, interaction between a mature defense style and neurotic defense style was noted. The relationship between defense styles and psychiatric symptoms in healthy people is studied in this paper.     

超声多普勒管壁搏动和血流信号的分离研究

本研究提出利用经验模式分解(EMD)算法分解混叠有管壁成分的超声多普勒血流信号来实现管壁搏动和血流信号的分离。该方法首先将混叠有管壁搏动的超声多普勒血流信号分解为少量有限的分量,即内模函数(IMFs),然后根据管壁搏动信号与血流信号的功率比变化曲线,用比值法自动确定并去除低频管壁博动成分。在仿真实验中用提出的方法处理模拟的多普勒信号,对于靠近管腔内壁的血流信号其在频域功率谱上的相对误差为50%,在时域幅度的相对误差为45%,与高通滤波器方法的相对误差95%相比,准确性得到提高。基于个人计算机用C语言编程实现提出的算法,对实际采集的人体颈动脉多普勒信号可实现实时分离处理。结果表明:基于经验模式分解的滤波方法能有效客观地滤除管壁搏动信号,更准确地保留低频血流信号成分。     

Heritabilities,by the multiple abstract variance analysis (MAVA) model and objective test measures,of personality traits U.I.23, capacity to mobilize,U.I.24, anxiety,U.I.26, narcistic ego and U.I.28, asthenia,by maximum-likelihood methods

Heritability coefficients are offered for four personality source traits, measured by the O-A (objective-analytic) 2-h performance battery. Five family constellations covering a total sample of 1221 boys 12–18 years old yielded nine concrete variances which the MAVA (multiple abstract variance analysis) model resolves into seven abstract variances: ² _wg , within family genetic; ² _wt.s , within family threptic; ² _wt.t , within family threptic for twins; ² _bg , between family genetic; _bgbt , correlation of genetic and threptic deviations across families, etc. Maximum likelihood was the method here used for the MAVA analysis. The best fit with maximum parsimony was to assume no genothreptic ( _wgwt , _bgbt ) correlations, but extension to the parsimony of assuming either no genetic or no threptic components gave no fit. The heritabilities found were compared with those from an earlier research and from a different (OSES) method applied to the present data. The agreement is quite good in assigning a moderate heritability value tocapacity to mobilize vs. regression, U.I.23 (H about 0.30), and toanxiety, U.I.24 (H about 0.50); only moderately consistent in assigning a moderateH value toasthenia, U.I.28 (H about 0.30); and poorly consistent in assigning a lowH value tonarcistic ego, U.I.26. It is pointed out (a) that the lowH for U.I.28 fits the theory of the origin of this trait well and (b) that, in view of estimates of the function fluctuation of U.I.23 and 24, a most probable conclusion is that a capacity to mobilize is quitesubstantially innate and a general proneness to anxiety islargely innate.