CagA C-terminal variations in Helicobacter pylori strains from Colombian patients with gastric precancerous lesions
L Sicinshci, P Correa, R Peek, M Camargo, M Piazuelo, Robertino Mera
Published in Clinical Microbiology and Infection, 2010, Vol 16, Issue 4
Abstract: The C-terminus of the Helicobacter pylori CagA protein is polymorphic, bearing different EPIYA sequences (EPIYA-A, B, C or D), and one or more CagA multimerization (CM) motifs. The number of EPIYA-C motifs is associated with precancerous lesions and gastric cancer (GC). The relationship between EPIYA, CM motifs and gastric lesions was examined in H. pylori-infected Colombian patients from areas of high and low risk for GC. Genomic DNA was extracted from H. pylori strains cultured from gastric biopsies from 80 adults with dyspeptic symptoms. Sixty-seven (83.8%) of 80 strains were cagA positive. The 3¢ region of cagA was sequenced, and EPIYA and CM motifs were identified. CagA proteins contained one (64.2%), two (34.3%) or three EPIYA-C motifs (1.5%), all with Western type CagA-specific sequences. Strains with one EPIYA-C motif were associated with less severe gastric lesions (non-atrophic and multifocal atrophic gastritis), whereas strains with multiple EPIYA-C motifs were associated with more severe lesions (intestinal metaplasia and dysplasia) (p <0.001). In 54 strains, the CM motifs were identical to those common in Western strains. Thirteen strains from the low-risk area contained two different CM motifs: one of Western type located within the EPIYA-C segment and another following the EPIYA-C segment and resembling the CM motif found in East Asian strains. These strains induced significantly shorter projections in AGS cells and an attenuated reduction in levels of CagA upon immunodepletion of SHP-2 than strains possessing Western/Western motifs. This novel finding may partially explain the difference in GC incidence in these populations.
Semi-automated Risk Estimation using large databases: Quinolones and Clostridium difficile associated diarrhea
Robertino Mera, K Beach, G Powell, E Pattishal
Published in Pharmacoepidemiology and Drug SafetyPublished Online in advance of print: Apr 29 2010.DOI: 10.1002/pds.1968
Abstract:
Purpose: The availability of large databases with person time information and appropriate statistical methods allow for relatively rapid pharmacovigilance analyses. A semi-automated method was used to investigate the effect of fluoroquinolones on the incidence of C. difficile associated diarrhea (CDAD).
Methods: Two US databases, an electronic medical record (EMR) and a large medical claims database for the period 2006–2007 were evaluated using a semi-automated methodology. The raw EMR and claims datasets were subject to a normalization procedure that aligns the drug exposures and conditions using ontologies; Snowmed for medications and MedDRA for conditions. A retrospective cohort design was used together with matching by means of the propensity score. The association between exposure and outcome was evaluated using a Poisson regression model after taking into account potential confounders.
Results: A comparison between quinolones as the target cohort and macrolides as the comparison cohort produced a total of 564 797 subjects exposed to a quinolone in the claims data and 233 090 subjects in the EMR. They were matched with replacement within six strata of the propensity score. Among the matched cohorts there were a total of 488 and 158 outcomes in the claims and the EMR respectively. Quinolones were found to be twice more likely to be significantly associated with CDAD than macrolides adjusting for risk factors (IRR 2.75, 95%CI 2.18–3.48).
Conclusions: Use of a semi-automated method was successfully applied to two observational databases and was able to rapidly identify a potential for increased risk of developing CDAD with quinolones.
Comparisons of minimisation and Atkinson’s algorithm
Stephen Senn, Vladimir Anisimov and Valerii Fedorov
Published in Statistics in Medicine, 2010, V. 29, 7/8, pp. 721-730
Abstract: Some general points regarding efficiency in clinical trials are made. Reasons as to why fitting many covariates to adjust the estimate of the treatment effect may be less problematic than commonly supposed are given. Two methods of dynamic allocation of patients based on covariates, minimization and Atkinson’s approach, are compared and contrasted for the particular case where all covariates are binary. The results of Monte Carlo simulations are also presented. It is concluded that in the cases considered, Atkinson’s approach is slightly more efficient than minimization although the difference is unlikely to be very important in practice. Both are more efficient than simple randomization, although it is concluded that fitting covariates may make a more valuable and instructive contribution to inferences about treatment effects than only balancing them.
A review of synergy concepts of nonlinear blending and dose-reduction profiles
John Peterson
Published in Frontiers of Bioscience, 2010, S2, 483–503
Abstract: This article presents a case-study review of synergy concepts of nonlinear blending and dose-reduction profiles. “Strong nonlinear blending” is a novel concept that provides a flexible paradigm for the assessment of combination drug synergy that is applicable to any shaped combination-drug dose-response surface; issues of varying relative potency, partial inhibitors, potentiation, or coalism pose no problems at all. Dose-reduction profiles are overlay plots created to show how much each drug can be reduced in amount and yet achieve the same efficacy as larger amounts of each drug used individually. This review applies these synergy concepts to two data sets from a previously published experiment. The previous publication had claimed a high degree of Loewe synergy for one of the data sets. However, a more penetrating analysis shows that with regard to strong nonlinear blending there is no reason to blend (for purposes of response enhancement) the two compounds studied. However, the dose-reduction profile plots show how Loewe synergy is present and provide further insight to the interaction of the two compounds (on the dose-concentration scale).
Effects of unstratified and centre-stratified randomization in multi-centre clinical trials
Vladimir Anisimov
Published in Pharmaceutical Statistics, 2010 (early view)
Abstract: This paper deals with the analysis of randomization effects in multi-centre clinical trials. The two randomization schemes most often used in clinical trials are considered: unstratified and centre-stratified block-permuted randomization. The prediction of the number of patients randomized to different treatment arms in different regions during the recruitment period accounting for the stochastic nature of the recruitment and effects of multiple centres is investigated.
A new analytic approach using a Poisson-gamma patient recruitment model (patients arrive at different centres according to Poisson processes with rates sampled from a gamma distributed population) and its further extensions is proposed. Closed-form expressions for corresponding distributions of the predicted number of the patients randomized in different regions are derived. In the case of two treatments, the properties of the total imbalance in the number of patients on treatment arms caused by using centre-stratified randomization are investigated and for a large number of centres a normal approximation of imbalance is proved. The impact of imbalance on the power of the study is considered. It is shown that the loss of statistical power is practically negligible and can be compensated by a minor increase in sample size. The influence of patient dropout is also investigated. The impact of randomization on predicted drug supply overage is discussed.
Spatiotemporal regulation of the cough motor pattern
Cheng Wang, Sourish Saha, Melanie Rose, Paul Davenport and Donald Bolser
Published in Cough, 2009, 5-12
Abstract: The purpose of this study was to identify the spatiotemporal determinants of the cough motor pattern. We speculated that the spatial and temporal characteristics of the cough motor pattern would be regulated separately. Electromyograms (EMG) of abdominal muscles (ABD, rectus abdominis or transversus abdominis), and parasternal muscles (PS) were recorded in anesthetized cats. Repetitive coughing was produced by mechanical stimulation of the lumen of the intrathoracic trachea. Cough inspiratory (CTI) and expiratory (CTE) durations were obtained from the PS EMG. The ABD EMG burst was confined to the early part of CTE and was followed by a quiescent period of varying duration. As such, CTE was divided into two segments with CTE1 defined as the duration of the ABD EMG burst and CTE2 defined as the period of little or no EMG activity in the ABD EMG. Total cough cycle duration (CTTOT) was strongly correlated with CTE2 (r2>0.8), weakly correlated with CTI (r2<0.3), and not correlated with CTE1 (r2<0.2). There was no significant relationship between CTI and CTE1 or CTE2. The magnitudes of inspiratory and expiratory motor drive during cough were only weakly correlated with each other (r2<0.36) and were not correlated with the duration of any phase of cough. The results support: a) separate regulation of CTI and CTE, b) two distinct subphases of CTE (CTE1 and CTE2), c) the duration of CTE2 is a primary determinant of CTTOT, and d) separate regulation of the magnitude and temporal features of the cough motor pattern.
Comparison of Designs for Response Surface Models with Random Block Effects
Sourish Saha and André Khuri
Published in Journal of Quality Technology and Quantitative Management, 2009, 6 (3), 219–234
Abstract: The purpose of this article is to compare designs for response surface models with a random block effect. To assess the quality of prediction associated with a given design, the scaled prediction variance is considered as a design criterion. The proposed approach is based on using quantiles of this design criterion on concentric surfaces within the experimental region. The dependence of these quantiles on the unknown value of the ratio of two variance components, namely, the ones for the block effect and the experimental error, is depicted by plotting the so-called quantile dispersion graphs (QDGs). These plots provide a clear assessment of the quality of prediction associated with a given design. A numerical example is presented to illustrate the proposed methodology.
A Bayesian Design Space Approach To Robustness and System Suitability for Pharmaceutical Assays and Other Processes
John Peterson and Mohammad Yahyah
Published in Statistics in Biopharmaceutical Research, 2009, 1 (4), 441–449
Abstract: The ICH Q2 (R1) Guidance on Validation of Analytical Procedures states that a robustness assessment for an analytical method should provide “an indication of its reliability during normal usage.” The concept of “design space” as specified in the ICH Q8 Guidance may be used to create a zone of reliable robustness for an analytical method or pharmaceutical process. A Bayesian approach to design space as outlined by Peterson (2004) accounts for model parameter uncertainty, correlation among the quality responses at each fixed operating condition, and method response multiplicity. Two examples are provided to illustrate the application of a Bayesian design space to assessing reliability/robustness. One example is about assessing the ability of an HPLC analytical method to meet system suitability criteria and the other deals with a crystallization process for an active pharmaceutical ingredient.
A Bayesian Reliability Approach to Multiple Response Optimization with Seemingly Unrelated Regression Models
John Peterson, Guillermo Miró-Quesada and Enrique del Castillo
Published in Journal of Quality Technology and Quantitative Management, 2009, 6 (4), 353–369
Abstract: This paper presents a Bayesian predictive approach to multiresponse optimization experiments. It generalizes the work of Peterson [33] in two ways that make it more flexible for use in applications. First, a multivariate posterior predictive distribution of seemingly unrelated regression models is used to determine optimum factor levels by assessing the reliability of a desired multivariate response. It is shown that it is possible for optimal mean response surfaces to appear satisfactory yet be associated with unsatisfactory overall process reliabilities. Second, the use of a multivariate normal distribution for the vector of regression error terms is generalized to that of the (heavier tailed) multivariate t-distribution. This provides a Bayesian sensitivity analysis with regard to moderate outliers. The effect of adding design points is also considered through a preposterior analysis. The advantages of this approach are illustrated with two real examples.
What Your ICH Q8 Design Space Needs: A Multivariate Predictive Distribution
John Peterson
Published in Pharmaceutical Manufacturing, 2009
Abstract: The ICH Q8 core definition of design space is by now somewhat familiar: “The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” [1]. This definition is ripe for interpretation. The phrase “multidimensional combination and interaction” underscores the need to utilize multivariate analysis and factorial design of experiments (DoE), while the words “input variables (e.g., material attributes) and process parameters” remind us of the importance of measuring the right variables.
However, in presentations and articles discussing design space, not much focus has been given to the key phrase, “assurance of quality”. This does not seem justified, given that guidance documents such as ICH Q8, Q9, Q10, PAT, etc. are inundated with the words “risk” and “risk-based.” For any ICH Q8 design space constructed, surely the core definition of design space begs the question, “How much assurance?” [2]. How do we know if we have a “good” design space if we do not have a method for quantifying “How much assurance?” in a scientifically coherent manner?
