OMNE: a new robust membership-set estimator for the parameters of nonlinear models

A new method for estimating parameters and their uncertainty is presented. Data are assumed to be corrupted by a noise whose statistical properties are unknown but for which bounds are available at each sampling time. The method estimates the set of all parameter vectors consistent with this hypothesis. Its results are compared with those of the weighted least squares, extended least squares, and biweight robust regression approaches on two data sets, one of which includes 33% outliers. On the basis of these preliminary results, the new method appears to have attractive properties of reliability and robustness.     

Analysis of pharmacokinetic data using parametric models—1: Regression models

This is the first in a series of tutorial articles discussing the analysis of pharmacokinetic data using parametric models. In this article, the purposes of modelling are discussed; regression models for individuals and populations are defined; and structural and variance models are discussed as the two required submodels of the overall regression model. Topics of future articles are: point estimates of parameters; interval estimates of parameters; model criticism and choosing among contending models; population kinetic models and estimation; and elements of optimal design.Work supported in part by NIH Grant GM 26676, and GM 26691.     

Analysis of pharmacokinetic data using parametric models. II. Point estimates of an individual's parameters

This is the second in a series of tutorial articles discussing the analysis of pharmacokinetic data using parametric models. In this article the basic issue is how to estimate the parameters of such models. Primary emphasis is placed on point estimates of the parameters of the structural (pharmacokinetic) model. All the estimation methods discussed are least squares (LS) methods: ordinary least squares, weighted least squares, iteratively reweighted least squares, and extended least squares. The choice of LS method depends on the variance model. Some discussion is also provided of computer methods used to find the LS estimates, identifiability, and robust LS-based estimation methods.Work supported in part by NIH grants GM26676 and GM 26691.     

Extended least squares nonlinear regression: A possible solution to the “choice of weights” problem in analysis of individual pharmacokinetic data

It is often difficult to specify weights for weighted least squares nonlinear regression analysis of pharmacokinetic data. Improper choice of weights may lead to inaccurate and/or imprecise estimates of pharmacokinetic parameters. Extended least squares nonlinear regression provides a possible solution to this problem by allowing the incorporation of a general parametric variance model. Weighted least squares and extended least squares analyses of data from a simulated pharmacokinetic experiment were compared. Weighted least squares analysis of the simulated data, using commonly used weighting schemes, yielded estimates of pharmacokinetic parameters that were significantly biased, whereas extended least squares estimates were unbiased. Extended least squares estimates were often significantly more precise than were weighted least squares estimates. It is suggested that extended least squares regression should be further investigated for individual pharmacokinetic data analysis.This work was supported in part by USUHS Grant RO-7516 and NIH Grants GM26676 and GM26691.     

Analysis of pharmacokinetic data using parametric models. III. Hypothesis tests and confidence intervals

This is the third in a series of tutorial articles discussing the analysis of pharmacokinetic data using parametric models. In this article the concern is how to test hypotheses about, and assign confidence intervals to, the values of the parameters of such models. The basic approach to both tasks involves determining the goodness of fit of the model to the data for alternative values of the parameters and using the change in goodness of fit to assess the plausibility of the alternative values. The goodness of fit is measured by the value of a (least-squares-type) objective function. An approximation to the dependence of the latter on the parameter values yields an estimate of the familiar asymptotic covariance matrix of the estimates. The latter can also be used to test hypotheses about, and assign confidence intervals to, functions of parameters.Work supported in part by grants GM26676 and GM26691.     

Estimation of spatial autoregressive models with randomly missing data in the dependent variable

Summary We suggest and compare different methods for estimating spatial autoregressive models with randomly missing data in the dependent variable. Aside from the traditional expectation‐maximization (EM) algorithm, a nonlinear least squares method is suggested and a generalized method of moments estimation is developed for the model. A two‐stage least squares estimation with imputation is proposed as well. We analytically compare these estimation methods and find that generalized nonlinear least squares, best generalized two‐stage least squares with imputation and best method of moments estimators have identical asymptotic variances. These methods are less efficient than maximum likelihood estimation implemented with the EM algorithm. When unknown heteroscedasticity exists, however, EM estimation produces inconsistent estimates. Under this situation, these methods outperform EM. We provide finite sample evidence through Monte Carlo experiments.     

A new technique for spectrophotometric determination of pseudoephedrine and guaifenesin in syrup and synthetic mixture

The accuracy in predicting different chemometric methods was compared when applied on ordinary UV spectra and first order derivative spectra. Principal component regression (PCR) and partial least squares with one dependent variable (PLS1) and two dependent variables (PLS2) were applied on spectral data of pharmaceutical formula containing pseudoephedrine (PDP) and guaifenesin (GFN). The ability to derivative in resolved overlapping spectra chloropheniramine maleate was evaluated when multivariate methods are adopted for analysis of two component mixtures without using any chemical pretreatment. The chemometrics models were tested on an external validation dataset and finally applied to the analysis of pharmaceuticals. Significant advantages were found in analysis of the real samples when the calibration models from derivative spectra were used. It should also be mentioned that the proposed method is a simple and rapid way requiring no preliminary separation steps and can be used easily for the analysis of these compounds, especially in quality control laboratories.     

The influence of weight with assay error on gentamicin pharmacokinetics using the Bayesian and nonlinear least square regression analysis in appendicitis patients

The purpose of this study was to determine the influence of weight with gentamicin assay error on the Bayesian and nonlinear least squares regression analysis in 12 Korean appendicitis patients. Gentamicin was administered intravenously over 0.5 h every 8 h. Three specimens were collected 48 h after the first dose from all patients at the following times, just before the regularly scheduled infusion, at 0.5 h and 2 h after the end of the 0.5 h infusion. Serum gentamicin levels were analysed by fluorescence polarization immunoassay technique with TDxFLx. The standard deviation (SD) of the assay over its working range had been determined at the serum gentamicin concentrations of 0, 2, 4, 8, 12 and 16 microg/ml in quadruplicate. The polynominal equation of gentamicin assay error was found to be SD (microg/ml) = 0.0246-(0.0495C) + (0.00203C(2)). There were differences in the influence of weight with gentamicin assay error on pharmacokinetic parameters of gentamicin using the nonlinear least squares regression analysis but there were no differences on the Bayesian analysis. This polynominal equation can be used to improve the precision of fitting of pharmacokinetic models to optimize the process of model simulation both for population and for individualized pharmacokinetic models. The result would be improved dosage regimens and the better, safer care of patients receiving gentamicin.     

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).     

A study of critical functionality-related characteristics of HPMC for sustained-release tablets

Abstract

The drug release profile from hydrophilic matrix tablets can be crucially affected by the variability of physicochemical properties of the controlled release agent. This study investigates and seeks to understand the functionality-related characteristics (FRCs) of hydroxypropyl methylcellulose (HPMC) type 2208, K4M grade, that influence the release rate of the model drug carvedilol from hydrophilic matrix tablets during the entire dissolution profile. The following FRCs were examined: particle size distribution, degree of substitution, and viscosity. Eight different HPMC samples were used to create a suitable design space. Multiple linear regression (MLR) and partial least squares regression (PLSR) analyses were used to create models for each time point. The PLSR results show that the first part of the drug release profiles is mainly regulated by the HPMC particle size. Apparent viscosity and hydroxypropoxy content (%HP) become important in later stages of the drug release profile, when the influence of particle size distribution decreases. These findings make it possible to better understand the importance of FRCs. Larger HPMC particles increase drug release in the first part of the drug release profile, whereas decreased apparent viscosity and a higher degree of %HP increase the drug release rate in the later part of the drug release profile.     

Evaluation of alternative statistical models for crossover studies to demonstrate caffeine adjuvancy in the treatment of tension headache

This paper discusses alternative statistical models for the analysis of six crossover studies to determine whether better relief of tension headache occurs from treatment with an analgesic plus caffeine (C) than with the analgesic alone (A) or with placebo (P). Each patient in these crossover studies randomly received a pair of distinct medications in such a way as to treat the first two of four headaches with the initial medication in the pair and to treat the third and fourth headaches with the last medication in the pair. In order to have greater power for the C versus A comparison, three times as many patients were randomly assigned to the A:C and C:A sequence groups as to the A:P, C:P, P:A, and P:C sequence groups.

An issue of statistical interest for these crossover studies is the extent to which the possibility of unequal carryover effects of the three medications influences the roles of alternative models for data analysis and the interpretation of results. When carryover effects for all three medications are equal, univariate analysis of variance for the difference scores between the average response for the first two headaches and the average response for the third and fourth headaches for each patient provides nearly the same power for pairwise treatment comparisons as more comprehensive multivariate methods for all four headaches. However, for comparisons concerning carryover effects and for treatment comparisons with adjustment for carryover effects, multivariate methods encompassing all four headaches jointly can provide greater power than univariate analysis for difference scores, particularly when there is low intraclass correlation for responses within the same patient. Another noteworthy role for multivariate methods in situations with potentially unequal carryover effects is their capacity to clarify whether multiple types of carryover effects occur across the second, third, and fourth headaches in the respective sequence groups.

Multivariate models with alternative specifications of carryover effects are fit to the data from the six crossover studies to compare C, A, and P by weighted least squares. The role of potential variation among centers is addressed in these analyses by the use of stratified proportional means over centers, means of center means, and means ignoring centers. The primary focus of attention in the respective analyses is the evaluation of treatment comparisons with and without adjustment for potential differences among carryover effects of the treatments. Comparisons among carryover effects are assessed as well, but they mainly serve a background purpose since the principal issue is the extent to which findings for treatment comparisons are similar across alternative ways of accounting for potential carryover effects.

For all models, the average predicted response across all headaches treated with C was significantly better than that for A or P. For models that adjusted treatment effects for carryover effects in a statistically efficient way, the adjusted direct treatment effect of C was significantly better than that of A or P. Thus, the superiority of C over A found robust support from models both with and without adjustment for potential differences among carryover effects of the treatments.     

Conditional Weighted Residuals (CWRES): A Model Diagnostic for the FOCE Method

Purpose Population model analyses have shifted from using the first order (FO) to the first-order with conditional estimation (FOCE) approximation to the true model. However, the weighted residuals (WRES), a common diagnostic tool used to test for model misspecification, are calculated using the FO approximation. Utilizing WRES with the FOCE method may lead to misguided model development/evaluation. We present a new diagnostic tool, the conditional weighted residuals (CWRES), which are calculated based on the FOCE approximation. Materials and Methods CWRES are calculated as the FOCE approximated difference between an individual’s data and the model prediction of that data divided by the root of the covariance of the data given the model. Results Using real and simulated data the CWRES distributions behave as theoretically expected under the correct model. In contrast, in certain circumstances, the WRES have distributions that greatly deviate from the expected, falsely indicating model misspecification. CWRES/WRES comparisons can also indicate if the FOCE estimation method will improve the results of an FO model fit to data. Conclusions Utilization of CWRES could improve model development and evaluation and give a more accurate picture of if and when a model is misspecified when using the FO or FOCE methods.     

一种用于制剂学材料可压性评价的新方程研究(英文)

本文提出了一种新的压缩方程用于评价制剂粉体的可压性。该方程基于假设粉体可压缩体积随着压力的变化率与可压缩体积成正比。采用该模型方程将几种辅料的压缩数据进行非线性拟合,达到了良好的拟合效果,且方程中的参数可较好的反映粉末的压缩行为。该方程被证实对粉体压缩性的评价结果与川北方程一致。     

A new nonlinear mixture response surface paradigm for the study of synergism: a three drug example

A flexible approach to response surface modeling for the study of the joint action of three active anticancer agents is used to model a complex pattern of synergism, additivity and antagonism in an in vitro cell growth assay. The method for determining a useful nonlinear response surface model depends upon a series of steps using appropriate scaling of drug concentrations and effects, raw data modeling, and hierarchical parameter modeling. The method is applied to a very large in vitro study of the combined effect of Trimetrexate (TMQ), LY309887 (LY), and Tomudex (TDX) on inhibition of cancer cell growth. The base model employed for modeling dose-response effect is the four parameter Hill equation [1]. In the hierarchical aspect of the final model, the base Hill model is treated as a function of the total amount of the three drug mixture and the Hill parameters, background B, dose for 50% effect D50, and slope m, are understood as functions of the three drug fractions. The parameters are modeled using the canonical mixture polynomials from the mixture experiment methodologies introduced by Scheff [2]. We label the model generated a Nonlinear Mixture Amount model with control observations, or zero amounts, an "NLMAZ" model. This modeling paradigm provides for the first time an effective statistical approach to modeling complex patterns of local synergism, additivity, and antagonism in the same data set, the possibility of including additional experimental components beyond those in the mixture, and the capability of modeling three or more drugs.     

Beliefs and attitudes regarding drug treatment: Application of the Theory of Planned Behavior in African-American cocaine users

Background

The Theory of Planned Behavior (TPB) can provide insights into perceived need for cocaine treatment among African American cocaine users.

Methods

A cross-sectional community sample of 400 (50% rural) not-in-treatment African-American cocaine users was identified through respondent-driven sampling in one urban and two rural counties in Arkansas. Measures included self-reports of attitudes and beliefs about cocaine treatment, perceived need and perceived effectiveness of treatment, and positive and negative cocaine expectancies. Normative beliefs were measured by perceived stigma and consequences of stigma regarding drug use and drug treatment. Perceived control was measured by readiness for treatment, prior drug treatment, and perceived ability to cut down on cocaine use without treatment.

Findings

Multiple regression analysis found that older age (standardized regression coefficient β = 0.15, P < 0.001), rural residence (β = − 0.09, P = 0.025), effectiveness of treatment (β = 0.39, P < 0.001), negative cocaine expectancies (β = 0.138, P = 0.003), experiences of rejection (β = 0.18, P < 0.001), need for secrecy (β = 0.12, P = 0.002), and readiness for treatment (β = 0.15, P < 0.001) were independently associated with perceived need for cocaine treatment.

Conclusions

TPB is a relevant model for understanding perceived need for treatment among African-American cocaine users. Research has shown perceived need to be a major correlate of treatment participation. Study results should be applicable for designing interventions to encourage treatment participation.     

Characterizing uncertainty and variability in physiologically based pharmacokinetic models: state of the science and needs for research and implementation.

Physiologically based pharmacokinetic (PBPK) models are used in mode-of-action based risk and safety assessments to estimate internal dosimetry in animals and humans. When used in risk assessment, these models can provide a basis for extrapolating between species, doses, and exposure routes or for justifying nondefault values for uncertainty factors. Characterization of uncertainty and variability is increasingly recognized as important for risk assessment; this represents a continuing challenge for both PBPK modelers and users. Current practices show significant progress in specifying deterministic biological models and nondeterministic (often statistical) models, estimating parameters using diverse data sets from multiple sources, using them to make predictions, and characterizing uncertainty and variability of model parameters and predictions. The International Workshop on Uncertainty and Variability in PBPK Models, held 31 Oct-2 Nov 2006, identified the state-of-the-science, needed changes in practice and implementation, and research priorities. For the short term, these include (1) multidisciplinary teams to integrate deterministic and nondeterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through improved documentation of model structure(s), parameter values, sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include (1) theoretical and practical methodological improvements for nondeterministic/statistical modeling; (2) better methods for evaluating alternative model structures; (3) peer-reviewed databases of parameters and covariates, and their distributions; (4) expanded coverage of PBPK models across chemicals with different properties; and (5) training and reference materials, such as cases studies, bibliographies/glossaries, model repositories, and enhanced software. The multidisciplinary dialogue initiated by this Workshop will foster the collaboration, research, data collection, and training necessary to make characterizing uncertainty and variability a standard practice in PBPK modeling and risk assessment.     

脑创伤治疗的新靶点：脑-肠轴

脑创伤是致死、致残率较高的疾病。创伤后的原发性损伤重在预防,继发性损伤才是临床治疗的重点。 脑创伤后会出现肠道出血、渗透性增加和菌群失调等;而改善肠道内环境状态可以促进神经功能恢复。脑和肠道之 间存在多种方式的双向信息交流,即脑-肠轴。针对脑-肠轴的治疗可以改善包括脑卒中在内的多种神经系统疾病 的继发性损伤。对脑-肠轴及其在神经系统疾病中的功能作用进行综述,以期为寻找脑创伤治疗的新靶点开拓 思路。     

Structural models and binding site prediction of the C-terminal domain of human Hsp90: a new target for anticancer drugs

Heat shock protein 90 is a valuable target for anticancer drugs because of its role in the activation and stabilization of multiple oncogenic signalling proteins. While several compounds inhibit heat shock protein 90 by binding the N-terminal domain, recent studies have proved that the C-terminal domain is important for dimerization of the chaperone and contains an additional binding site for inhibitors. Heat shock protein 90 inhibition achieved with molecules binding to the C-terminal domain provides an additional and novel opportunity to design and develop drugs. Therefore, for the first time, we have investigated the structure and the dynamic behaviour of the C-terminal domain of human heat shock protein 90 with and without the small-middle domain, using homology modelling and molecular dynamics simulations. In addition, secondary structure predictions and peptide folding simulations proved useful to investigate a putative additional alpha-helix located between H18 and beta20 of the C-terminal domain. Finally, we used the structural information to infer the location of the binding site located in the C-terminal domain by using a number of computational tools. The predicted pocket is formed by two grooves located between helix H18, the loop downstream of H18 and the loop connecting helices H20 and H21 of each monomer of the C-terminal domain, with only two amino acids contributing from each middle domain.     

Conditioned defensive burying: A new paradigm for the study of anxiolytic agents

Behavioral paradigms that have been designed to mimic forms of learning that are important for the survival of animals in the wind, rather than to minimize the contributions of adaptive predispositions, may prove to be particularly useful for studying the behavioral effects of drugs. In the present experiments, the propensity of rats to bury sources of aversive stimulation was disrupted in a dose-dependent fashion by a single injection of the anxiolytic drug, diazepam. This suggested that the conditioned defensive burying paradigm could prove to be a valuable addition to the paradigms available for studying anxiolytic effects. Supporting this view were two additional observations. First, the relative potencies of diazepam, chlordiazepoxide, and pentobarbital in the burying paradigm compared favorably with their relative potencies in clinical settings. Second, the effects of anxiolytics on conditioned burying appeared to be dissociable from the effects of other drugs that disrupt this behavior.     

Evaluating Diagnostic Tests for Chlamydia trachomatis in the Absence of a Gold Standard: A Comparison of Three Statistical Methods

Studies designed to evaluate diagnostic tests for Chlamydia trachomatis typically involve a panel of new and established tests. Statistical analysis of these studies has proven challenging as no gold standard reference test is available. We illustrate a novel multiple latent variable model (MLVM), which improves over earlier methods by recognizing that different diagnostic tests for C. trachomatis may be measuring different targets. For example, nucleic acid amplification tests (NAATs) are designed to measure C. trachomatis DNA, while cell culture is designed to measure the presence of current C. trachomatis infection. The MLVM does not arbitrarily assume any test is perfect. Further, it provides separate sensitivity and specificity estimates with respect to each latent target. Using simulated and real data, we will contrast the performance of MLVM with two other methods for evaluating C. trachomatis tests: (i) the composite reference standard (CRS) approach, and (ii) the standard latent class model (TLCM). We show that the tests involved in the definition of the CRS are arbitrarily assumed to have perfect specificity, and that both the CRS and the TLCM assume that all tests are measuring the same latent variable, the “current infection.” When these assumptions are not justified, as is frequently the case, the resulting estimates of sensitivity and specificity may be seriously biased. The MLVM attempts to address these problems.