Synthesis,antitumor testing and molecular modeling study of some new 6-substituted amido,azo or thioureido-quinazolin-4(3H)-ones

A new series of 6-substituted amido, azo or thioureido-quinazolin-4(3H)-one was synthesized and tested for their in-vitro antitumor activity. Compounds 21, 53 and 60 showed broad spectrum antitumor activity with average IC₅₀ values of 6.7, 7.6 and 9.1 μM, respectively compared with methotrexate (1, IC₅₀ 19.26 μM). As an attempt to reveal the mechanism of the antitumor potency, cell cycle analysis and DHFR inhibition were performed. Compounds 59 and 61 induced their cytotoxicity in Hela (IC₅₀ 10.6 μM) and HCT-116 (IC₅₀ 15.5 μM) cell lines, respectively through Pre-G1 apoptosis, inhibiting cell growth at G2-M phase. Compounds 29, 33, 59 and 61 showed DHFR inhibitory potency at IC₅₀ 0.2, 0.2, 0.3 and 0.3 μM, respectively. The active DHFR inhibitors showed high affinity binding toward the amino acid residues Thr56, Ser59 and Ser118. The active compounds obeyed Lipinski’s rule of five and could be used as template model for further optimization.     

Monitoring clinical trials with multiple arms

In order to benefit from the substantial overhead expenses of a large group sequential clinical trial, the simultaneous investigation of several competing treatments becomes more popular. If at some interim analysis any treatment arm reveals itself to be inferior to any other treatment under investigation, this inferior arm may be or may even need to be dropped for ethical and/or economic reasons. Recently proposed methods for monitoring and analysis of group sequential clinical trials with multiple treatment arms are compared and discussed. The main focus of the article is on the application and extension of (adaptive) closed testing procedures in the group sequential setting that strongly control the familywise error rate. A numerical example is given for illustration.     

Using pooled exposure assessment to improve efficiency in case-control studies

Assays can be so expensive that interesting hypotheses become impractical to study epidemiologically. One need not, however, perform an assay for everyone providing a biological specimen. We propose pooling equal-volume aliquots from randomly grouped sets of cases and randomly grouped sets of controls, and then assaying the smaller number of pooled samples. If an effect modifier is of concern, the pooling can be done within strata defined by that variable. For covariates assessed on individuals (e.g., questionnaire data), set-based counterparts are calculated by adding the values for the individuals in each set. The pooling set then becomes the unit of statistical analysis. We show that, with appropriate specification of a set-based logistic model, standard software yields a valid estimated exposure odds ratio, provided the multiplicative formulation is correct. Pooling minimizes the depletion of irreplaceable biological specimens and can enable additional exposures to be studied economically. Statistical power suffers very little compared with the usual, individual-based analysis. In settings where high assay costs constrain the number of people an investigator can afford to study, specimen pooling can make it possible to study more people and hence improve the study's statistical power with no increase in cost.     

Power of 22 microsatellite markers in fluorescent multiplexes for parentage testing in goats (Capra hircus)

Two multiplex systems, each containing 11 microsatellite loci, were developed for semiautomated parentage testing in goats. Eight of the loci originate from goats, nine from cattle and five from sheep. Eighteen of the loci have been mapped to 16 different autosomes (in goats and cattle). Parentage exclusion probabilities were computed from allele frequencies in approximately 30 unrelated individuals from each of four economically important breeds: Mongolian Native Cashmere, Turkish Angora, Swiss Saanen, and Spanish Murciana-Grenadina. In cases where genotypes are known for one parent and an offspring, the 22 markers will exclude an (erroneously) alleged parent with a probability of > 0.999999 in the cashmere breed, > 0.99999 in Angora and Murciana-Grenadina, and > 0.9999 in Saanen. The multiplexes provide very high power for individual identification as the probability of finding two identical genotypes for the 22 loci is < 1 in 1.10(15) in each of the four breeds. The multiplexes will also be useful for studies of population structure, history, and diversity in goats and also in wild Capra species that represent important resources for genetic improvement of domestic breeds.     

Assessment of arbovirus vector infection rates using variable size pooling

Pool testing of vector samples for arboviruses is widely used in surveillance programmes. The proportion of infected mosquitoes (Diptera: Culicidae) is often estimated from the minimum infection rate (MIR), based on the assumption of only one infected mosquito per positive pool. This assumption becomes problematic when pool size is large and/or infection rate is high. By relaxing this constraint, maximum likelihood estimation (MLE) is more useful for a wide range of infection levels that may be encountered in the field. We demonstrate the difference between these two estimation approaches using West Nile virus (WNV) surveillance data from vectors collected by gravid traps in Chicago during 2002. MLE of infection rates of Culex mosquitoes was as high as 60 per 1000 at the peak of transmission in August, whereas MIR was less than 30 per 1000. More importantly, we demonstrate roles of various pooling strategies for better estimation of infection rates based on simulation studies with hypothetical mosquito samples of 18 pools. Variable size pooling (with a serial pool sizes of 5, 10, 20, 30, 40 and 50 individuals) performed consistently better than a constant size pooling of 50 individuals. We conclude that variable pool size coupled with MLE is critical for accurate estimates of mosquito infection rates in WNV epidemic seasons.     

Comparison of parameters from rhythmometric models with multiple components on hybrid data

Population multiple components is a statistical tool useful for the analysis of time-dependent hybrid data. With a small number of parameters, it is possible to model and to predict the periodic behavior of a population. In this article, we propose two methods to compare among populations rhythmometric parameters obtained by multiple component analysis. The first is a parametric method based in the usual statistical techniques for comparison of mean vectors in multivariate normal populations. The method, through MANOVA analysis, allows comparison of the MESOR and amplitude-acrophase pair of each component among two or more populations. The second is a nonparametric method, based in bootstrap techniques, to compare parameters from two populations. This test allows one to compare the MESOR, the amplitude, and the acrophase of each fitted component, as well as the global amplitude, orthophase, and bathyphase estimated when all fitted components are harmonics of a fundamental period. The idea is to calculate a confidence interval for the difference of the parameters of interest. If this interval does not contain zero, it can be concluded that the parameters from the two models are different with high probability. An estimation of p-value for the corresponding test can also be calculated. Both methods are illustrated with an example, based on clinical data. The nonparametric test can also be applied to paired data, a special situation of great interest in practice. By the use of similar bootstrap techniques, we illustrate how to construct confidence intervals for any rhythmometric parameter estimated from population multiple components models, including the orthophase, bathyphase, and global amplitude. These tests for comparison of parameters among populations are a needed tool when modeling the nonsinusoidal rhythmic behavior of hybrid data by population multiple component analysis.     

Model-free Analysis of Protein Dynamics: Assessment of Accuracy and Model Selection Protocols Based on Molecular Dynamics Simulation

The popular model-free approach to analyze NMR relaxation measurements has been examined using artificial amide (15)N relaxation data sets generated from a 10 nanosecond molecular dynamics trajectory of a dihydrofolate reductase ternary complex in explicit water. With access to a detailed picture of the underlying internal motions, the efficacy of model-free analysis and impact of model selection protocols on the interpretation of NMR data can be studied. In the limit of uncorrelated global tumbling and internal motions, fitting the relaxation data to the model-free models can recover a significant amount of quantitative information on the internal dynamics. Despite a slight overestimation, the generalized order parameter is quite accurately determined. However, the model-free analysis appears to be insensitive to the presence of nanosecond time scale motions with relatively small magnitude. For such cases, the effective correlation time can be significantly underestimated. As a result, proteins appear to be more rigid than they really are. The model selection protocols have a major impact on the information one can reliably obtain. The commonly employed protocol based on step-up hypothesis testing has severe drawbacks of oversimplification and underfitting. The consequences are that the order parameter is more severely overestimated and the correlation time more severely underestimated. Instead, model selection based on Bayesian Information Criteria (BIC), recently introduced to the model-free analysis by d'Auvergne and Gooley (2003), provides a better balance between bias and variance. More appropriate models can be selected, leading to improved estimate of both the order parameter and correlation time. In addition, the computational cost is significantly reduced and subjective parameters such as the significance level are unnecessary.