Structural determinants of developmental toxicity in hamsters.

A CASE/MULTICASE structure activity relationship (SAR) model of developmental toxicity of chemicals in hamsters (HaDT) was developed. The model exhibited a predictive performance of 74%. The model's overall predictivity and informational content were similar to those of an SAR model of mutagenicity in Salmonella. However, unlike the Salmonella mutagenicity model, the HaDT model did not identify overtly chemically reactive moieties as associated with activity. Moreover, examination of the number and nature of significant structural determinants suggested that developmental toxicity in hamsters was not the result of a unique mechanism or attack on a specific molecular target. The analysis also indicated that the availability of experimental data on additional chemicals would improve the performance of the SAR model.     

Testing by artificial intelligence: computational alternatives to the determination of mutagenicity.

In order to develop methods for evaluating the predictive performance of computer-driven structure-activity methods (SAR) as well as to determine the limits of predictivity, we investigated the behavior of two Salmonella mutagenicity data bases: (a) a subset from the Genetox Program and (b) one from the U.S. National Toxicology Program (NTP). For molecules common to the two data bases, the experimental concordance was 76% when "marginals" were included and 81% when they were excluded. Three SAR methods were evaluated: CASE, MULTICASE and CASE/Graph Indices (CASE/GI). The programs "learned" the Genetox data base and used it to predict NTP molecules that were not present in the Genetox compilation. The concordances were 72, 80 and 47% respectively. Obviously, the MULTICASE version is superior and approaches the 85% interlaboratory variability observed for the Salmonella mutagenicity assays when the latter was carried out under carefully controlled conditions.     

Constructing plasma protein binding model based on a combination of cluster analysis and 4D-fingerprint molecular similarity analyses

Based on 2D-connectivity molecular similarity and cluster analyses, a dataset for HSA binding is divided into the training set and the test set. 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, and SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM), which only takes the most similar compound in the training set into consideration, predicts the binding affinity of a test compound. This scheme has relatively poor predictivity based on 4D-fingerprint similarity analyses. The other three algorithmic schemes (SM, SR, and SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. Further investigation shows that the NP/HA, HS/HA, and HA/HA IPE/IPE type measures predict the test set well. Moreover, these three IPE/IPE type similarity measures are very similar to one another for the particular training and test sets investigated. The 4D-fingerprints have relatively high predictivity for this particular dataset.     

Interlaboratory evaluation of a flow cytometric, high content in vitro micronucleus assay

An international, multi-lab trial was conducted to evaluate a flow cytometry-based method for scoring micronuclei in mouse lymphoma L5178Y cells [S.L. Avlasevich, S.M. Bryce, S.E. Cairns, S.D. Dertinger, In vitro micronucleus scoring by flow cytometry: differential staining of micronuclei versus apoptotic and necrotic chromatin enhances assay reliability, Environ. Mol. Mutagen. 47 (2006) 56-66]. A reference laboratory investigated the potential of six chemicals to induce micronuclei -- the genotoxicants mitomycin C (MMC), etoposide (ETOPO), and vinblastine (VB), and the non-genotoxicants sucrose (SUC), staurosporine (STS), and dexamethasone (DEX). The latter two non-genotoxicants were selected as extreme challenges to the assay because of their potent apoptogenic activity. Three collaborating laboratories were supplied with prototype In Vitro MicroFlow kits, and each was assigned one genotoxicant and one non-genotoxicant. Cells were treated continuously for 24h over a range of concentrations up to 5 mg/ml, or overtly cytotoxic concentrations. Micronuclei were scored via standard microscopy and flow cytometry. In addition to enumerating micronucleus frequencies, a cytotoxicity measurement that is simultaneously acquired with the flow cytometric micronucleus scoring procedure was evaluated (Flow-NBR). With this method, latex particles served as counting beads, and facilitated relative survival measurements that exclude the presence of dead/dying cells. For comparison purposes, additional cytotoxicity endpoints were measured, including several that are based on cell number, and others that reflect compromised membrane integrity, including dye permeability and/or phospholipid distribution. Key findings for this set of compounds include the following: (1) significant discrepancies in top concentration selection were found when cytotoxicity measurements were based on different methods, with the Flow-NBR approach tending to be the most sensitive, (2) both microscopy- and flow cytometry-based scoring methods detected concentration-dependent micronucleus formation for the three genotoxic agents studied, with good agreement between the reference laboratory and the collaborating laboratories, and (3) whereas flow cytometric analyses showed no significant increases for the non-genotoxicants when top concentration selection was based on Flow-NBR, significantly elevated micronucleus frequencies were observed for concentrations that were chosen based on less-sensitive cytotoxicity assays. Collectively, these results indicate that rapid assessment of genotoxicity can be accomplished with a relatively simple flow cytometric technique, and that the scoring system is transferable across laboratories. Furthermore, a concurrent assessment of cytotoxicity, Flow-NBR, may help reduce the occurrence of irrelevant positive results, as it may represent a more appropriate means for choosing top concentration levels. Finally, the data presented herein reinforce concerns about the manner in which cytotoxicity limits are described in guidance documents, since these recommendations tend to cite fixed cut-off values without reference to methodology.     

Interlaboratory evaluation of a flow cytometric, high content in vitro micronucleus assay

An international, multi-lab trial was conducted to evaluate a flow cytometry-based method for scoring micronuclei in mouse lymphoma L5178Y cells [S.L. Avlasevich, S.M. Bryce, S.E. Cairns, S.D. Dertinger, In vitro micronucleus scoring by flow cytometry: differential staining of micronuclei versus apoptotic and necrotic chromatin enhances assay reliability, Environ. Mol. Mutagen. 47 (2006) 56–66]. A reference laboratory investigated the potential of six chemicals to induce micronuclei—the genotoxicants mitomycin C (MMC), etoposide (ETOPO), and vinblastine (VB), and the non-genotoxicants sucrose (SUC), staurosporine (STS), and dexamethasone (DEX). The latter two non-genotoxicants were selected as extreme challenges to the assay because of their potent apoptogenic activity. Three collaborating laboratories were supplied with prototype In Vitro MicroFlow™ kits, and each was assigned one genotoxicant and one non-genotoxicant. Cells were treated continuously for 24 h over a range of concentrations up to 5 mg/ml, or overtly cytotoxic concentrations. Micronuclei were scored via standard microscopy and flow cytometry. In addition to enumerating micronucleus frequencies, a cytotoxicity measurement that is simultaneously acquired with the flow cytometric micronucleus scoring procedure was evaluated (Flow-NBR). With this method, latex particles served as counting beads, and facilitated relative survival measurements that exclude the presence of dead/dying cells. For comparison purposes, additional cytotoxicity endpoints were measured, including several that are based on cell number, and others that reflect compromised membrane integrity, including dye permeability and/or phospholipid distribution. Key findings for this set of compounds include the following: (1) significant discrepancies in top concentration selection were found when cytotoxicity measurements were based on different methods, with the Flow-NBR approach tending to be the most sensitive, (2) both microscopy- and flow cytometry-based scoring methods detected concentration-dependent micronucleus formation for the three genotoxic agents studied, with good agreement between the reference laboratory and the collaborating laboratories, and (3) whereas flow cytometric analyses showed no significant increases for the non-genotoxicants when top concentration selection was based on Flow-NBR, significantly elevated micronucleus frequencies were observed for concentrations that were chosen based on less-sensitive cytotoxicity assays. Collectively, these results indicate that rapid assessment of genotoxicity can be accomplished with a relatively simple flow cytometric technique, and that the scoring system is transferable across laboratories. Furthermore, a concurrent assessment of cytotoxicity, Flow-NBR, may help reduce the occurrence of irrelevant positive results, as it may represent a more appropriate means for choosing top concentration levels. Finally, the data presented herein reinforce concerns about the manner in which cytotoxicity limits are described in guidance documents, since these recommendations tend to cite fixed cut-off values without reference to methodology.     

Naturalness and predictivity of classifications

It is shown for binary (1, 0) data that there is a direct relationship between a new measure of predictivity in classification and the Simple Matching Coefficient. This predictivity measure is related to the variance of a cluster in phenetic space and to the mean variance of the proportion of 1 states in the members of the cluster. It can be considered a form of 'squared predictivity'. Maximizing the measure leads to Variance Clustering, and if weighted by the number of cluster members, to Sums of Squares Clustering. The relations to the Unweighted Average Pair Group and Unweighted Centroid Pair Group methods are also elucidated.     

QSAR studies on CCR2 antagonists with chiral sensitive hologram descriptors

Chemokines are small molecular weight water-soluble proteins playing a key role in immunomodulation and host-defense mechanisms. CCR2 receptor is targeted for diseases like arthritis, multiple sclerosis, vascular disease, obesity, and type 2 diabetes. Reported, herein are the QSAR studies performed on a diverse set of enantiopure analogues reported as CCR2 antagonists by hologram analysis. The best model highlights the importance of chirality feature in comparison with the other models developed without the chirality. The validated model showed high internal and external predictive power. The robustness of the model was achieved with good statistical r(2) of 0.945 and cross-validated r(cv)(2) of 0.837. The challenging test predictivity of the model was confirmed with r(pred)(2) of 0.807. The fragment fingerprints help in understanding essential pharmacophoric features for CCR2 antagonism and provide basis for SAR of the molecules. The 2D contribution maps with fragment information will be useful for the design of novel CCR2 antagonists having improved efficacy.     

Deuterium Oxide Enhances <Emphasis Type="Italic">Escherichia coli</Emphasis> SOS Response Induced by Genotoxicants

It has been demonstrated that deuterium oxide enhances the SOS response of Escherichia coli cells induced by chemical genotoxicants and mutagens. This demonstrates that the heavy nonradioactive hydrogen isotope deuterium can be considered to be a comutagen.     

Modified Treatment Approach Using Cardiovascular Disease Risk Calculator for Primary Prevention

Background

The recent guidelines for preventing atherosclerotic cardiovascular events are an important advancement. For primary prevention, statins are recommended if the ten-year risk is ≥ 5% (consideration for therapy) or ≥ 7.5% (definitive treatment unless contraindication after discussion). We rationalized that a significant cohort with ten-year risk below the treatment thresholds would predictably surpass them within the recommended 4–6 year window for reassessing the ten-year risk. As atherosclerosis is a progressive disease, these individuals may therefore benefit with more aggressive therapies even at baseline.

Methods and Findings

We used publicly available NHANES dataset for ten-year risk calculation. There were 1805 participants. To evaluate the ten-year risk change at five years, we considered two scenarios: no change in the baseline parameters except increased age by five (No Change) and alternatively 10% improvement in systolic BP, total and HDL-c, no smoking with five-year increase in age (Reduced Risk Profile). Amongst non-diabetics with <5% risk at baseline, 35% reached or exceeded 5% risk in five years (5% reached or exceed the 7.5% risk) with No Change and 9% reached or exceeded 5% risk in five years (none reached 7.5% risk) with Reduced Risk Profile; furthermore, 94% of the non-diabetic cohort with baseline risk between 3.5%–5% would exceed the 5% and/or 7.5% boundary limit with No Change. Amongst non-diabetics with 5–7.5% baseline risks, 87% reached or exceeded 7.5% with No Change while 30% reached or exceeded 7.5% risk with Reduced Risk Profile.

Conclusions

A significant population cohort at levels below the treatment thresholds will predictably exceed these limits with time with or without improvement in modifiable risk factors and may benefit with more aggressive therapy at baseline. We provide an improved risk calculator that allows for integrating expected risk modification into discussion with an individual. This needs to be prospectively tested in clinical trials.     

New insights into Trimezieae (Iridaceae) phylogeny: what do molecular data tell us?

Background and Aims

The Neotropical tribe Trimezieae are taxonomically difficult. They are generally characterized by the absence of the features used to delimit their sister group Tigridieae. Delimiting the four genera that make up Trimezieae is also problematic. Previous family-level phylogenetic analyses have not examined the monophyly of the tribe or relationships within it. Reconstructing the phylogeny of Trimezieae will allow us to evaluate the status of the tribe and genera and to examine the suitability of characters traditionally used in their taxonomy.

Methods

Maximum parsimony and Bayesian phylogenetic analyses are presented for 37 species representing all four genera of Trimezieae. Analyses were based on nrITS sequences and a combined plastid dataset. Ancestral character state reconstructions were used to investigate the evolution of ten morphological characters previously considered taxonomically useful.

Key Results

Analyses of nrITS and plastid datasets strongly support the monophyly of Trimezieae and recover four principal clades with varying levels of support; these clades do not correspond to the currently recognized genera. Relationships within the four clades are not consistently resolved, although the conflicting resolutions are not strongly supported in individual analyses. Ancestral character state reconstructions suggest considerable homoplasy, especially in the floral characters used to delimit Pseudotrimezia.

Conclusions

The results strongly support recognition of Trimezieae as a tribe but suggest that both generic- and species-level taxonomy need revision. Further molecular analyses, with increased sampling of taxa and markers, are needed to support any revision. Such analyses will help determine the causes of discordance between the plastid and nuclear data and provide a framework for identifying potential morphological synapomorphies for infra-tribal groups. The results also suggest Trimezieae provide a promising model for evolutionary research.     

Synergy between systemic toxicity and genotoxicity: relevance to human cancer risk

The health risk manager and policy analyst must frequently make recommendations based upon incomplete toxicity data. This is a situation which is encountered in the evaluation of human carcinogenic risks as animal cancer bioassay results are often not available. In this study, in order to assess the relevance of other possible indicators of carcinogenic risks, we used the "chemical diversity approach" to estimate the magnitude of the human carcinogenic risk based upon Salmonella mutagenicity and systemic toxicity data of the "universe of chemicals" to which humans have the potential to be exposed. Analyses of the properties of 10,000 agents representative of the "universe of chemicals" suggest that chemicals that have genotoxic potentials as well as exhibiting greater systemic toxicity are more likely to be carcinogens than non-genotoxicants or agents that exhibit lesser toxicity. Since "genotoxic" carcinogenicity is a hallmark of recognized human carcinogens, these findings are relevant to human cancer risk assessment.     

Profiting from pilot studies: Analysing mortality using Bayesian models with informative priors

Pilot studies are often used to help design ecological studies. Ideally the pilot data are incorporated into the full-scale study data, but if the pilot study's results indicate a need for major changes to experimental design, then pooling pilot and full-scale study data is difficult. The default position is to disregard the preliminary data. But ignoring pilot study data after a more comprehensive study has been completed forgoes statistical power or costs more by sampling additional data equivalent to the pilot study's sample size. With Bayesian methods, pilot study data can be used as an informative prior for a model built from the full-scale study dataset. We demonstrate a Bayesian method for recovering information from otherwise unusable pilot study data with a case study on eucalypt seedling mortality. A pilot study of eucalypt tree seedling mortality was conducted in southeastern Australia in 2005. A larger study with a modified design was conducted the following year. The two datasets differed substantially, so they could not easily be combined. Posterior estimates from pilot dataset model parameters were used to inform a model for the second larger dataset. Model checking indicated that incorporating prior information maintained the predictive capacity of the model with respect to the training data. Importantly, adding prior information improved model accuracy in predicting a validation dataset. Adding prior information increased the precision and the effective sample size for estimating the average mortality rate. We recommend that practitioners move away from the default position of discarding pilot study data when they are incompatible with the form of their full-scale studies. More generally, we recommend that ecologists should use informative priors more frequently to reap the benefits of the additional data.     

An Improved Method with High Anti-interference Ability for R Peak Detection in Wearable Devices

The rapid development of the wearable electrocardiogram monitoring equipment increases the requirements for R peak detection in wearable devices. An improved method called ISC algorithm is proposed with high anti-interference ability for R peak detection in wearable devices based on a simple basic algorithm called SC algorithm. The proposed method is characterized by using the updated amplitude selection threshold, updated slope comparison threshold and RR interval judgement to reduce false positives and false negatives. For data from MIT-BIH Arrhythmia Database, the positive predictivity P+ of ISC algorithm can reach 99.12%, and the sensitivity Se of ISC algorithm is more than 95%. For MIT-BIH Noise Stress Test Database, the accuracy of ISC algorithm for both sensitivity Se and positive predictivity P+ can exceed 94% under three common noise, baseline wander, muscle artifact, and electrode motion artifact, where the positive predictivity P+ of ISC algorithm is 44.46% higher than that of SC algorithm on average. For wearable devices in exercise, even under the exercise intensity of 7 km per hour, the average positive predictivity P+ of ISC algorithm is 99.32%, which is 60.93% higher than that of SC algorithm. The high anti-interference ability shows that ISC algorithm is suitable for R peak detection in wearable devices.     

Structure-activity relationship study of human liver microsomes-catalyzed hydrolysis rate of ester prodrugs of MENT by comparative molecular field analysis (CoMFA)

A series of MENT esters (3-71) was designed, prepared and tested to study the structure-activity relationship (SAR) of the hydrolysis rate with human liver microsomes of these prodrugs. Compounds were obtained covering a wide range of metabolic stability. The results are useful for the proper selection of prodrugs for different pharmaceutical formulations to deliver the potent and prostate-sparing androgen MENT. The MENT esters can especially be administered for male hormone replacement therapy and male contraception. Comparative molecular field analysis (CoMFA) was applied to a dataset of 28 esters, for which ED50 values could be obtained. The CoMFA model where the electrostatic and H-bond molecular fields were combined turned out to be most predictive. Despite the limited size of the dataset, CoMFA can help to rationalize the SAR of the ester hydrolysis rate of ester prodrugs of MENT.     

Will the real disease gene please stand up?

A common dilemma arising in linkage studies of complex genetic diseases is the selection of positive signals, their follow-up with association studies and discrimination between true and false positive results. Several strategies for overcoming these issues have been devised. Using the Genetic Analysis Workshop 14 simulated dataset, we aimed to apply different analytical approaches and evaluate their performance in discerning real associations. We considered a) haplotype analyses, b) different methods adjusting for multiple testing, c) replication in a second dataset, and d) exhaustive genotyping of all markers in a sufficiently powered, large sample group. We found that haplotype-based analyses did not substantially improve over single-point analysis, although this may reflect the low levels of linkage disequilibrium simulated in the datasets provided. Multiple testing correction methods were in general found to be over-conservative. Replication of nominally positive results in a second dataset appears to be less stringent, resulting in the follow-up of false positives. Performing a comprehensive assay of all markers in a large, well-powered dataset appears to be the most effective strategy for complex disease gene identification.     

Influence of dentures on SAR in the visible Chinese human head voxel phantom exposed to a mobile phone at 900 and 1800 MHz

To investigate the influence of dentures on electromagnetic energy absorption during the daily use of a mobile phone, a high-resolution head phantom based on the Visible Chinese Human dataset was reconstructed. Simulations on phantoms with various dentures were performed by using the finite-difference time-domain method with a 0.47 wavelength dipole antenna and a mobile phone model as radiation sources at 900 and 1800 MHz. The Specific energy Absorption Rate (SAR) values including 1 and 10 g average SAR values were assessed. When the metallic dental crowns with resonance lengths of approximately one-third to one-half wavelength in the tissue nearby are parallel to the radiation source, up to 121.6% relative enhancement for 1 g average SAR and 17.1% relative enhancement for 10 g average SAR are observed due to the resonance effect in energy absorption. When the radiation sources operate in the normal configuration, the 10 g average SAR values are still in compliance with the basic restrictions established by the Institute of Electrical and Electronic Engineers (IEEE) and the International Commission on Non-Ionizing Radiation Protection (ICNIRP), indicating that the safety limits will not be challenged by the usage of dentures.     

SAR modelling of complex phenomena: probing methodological limitations

The increased acceptance of the use of structure-activity relationship (SAR) approaches to toxicity modelling has necessitated an evaluation of the limitations of the methodology. In this study, the limit of the capacity of the MULTICASE SAR program to model complex biological and toxicological phenomena was assessed. It was estimated that, provided the data set consists of at least 300 chemicals, divided equally between active and inactive compounds, the program is capable of handling phenomena that are even more "complex" than those modelled up to now (for example, allergic contact dermatitis, Salmonella mutagenicity, biodegradability, inhibition of tubulin polymerisation). However, within the data sets currently used to generate SAR models, there are limits to the complexity that can be handled. This may be the situation with regard to the modelling of systemic toxicity (for example, the LD50).     

The utility of morphological characters in gastropod phylogenetics: an example from the Calyptraeidae

Organismal taxonomy is often based on a single or a small number of morphological characters. When they are morphologically simple or known to be plastic, we may not have great confidence in the taxonomic conclusions of analyses based on these characters. For example, calyptraeid gastropod shells are well known for their simplicity and plasticity, and appear to be subject to frequent evolutionary convergences, but are nevertheless the basis for calyptraeid taxonomy. In a case like this, knowing how the pattern of relationships inferred from morphological features used in traditional taxonomy compares to the patterns of relationships inferred from other morphological characters or DNA sequence data would be useful. In this paper, I examine the relative utility of traditional taxonomic characters (shell characters), anatomical characters and molecular characters for reconstructing the phylogeny of calyptraeid gastropods. The results of an ILD test and comparisons of the recovered tree topologies suggest that there is conflict between the DNA sequence data and the morphological data. Very few of the nodes recovered by the morphological data were recovered by any other dataset. Despite this conflict, the inclusion of morphological data increased the resolution and support of nodes in the topology recovered from a combined dataset. The RIs and CIs of the morphological data on the best estimate topology were not any worse than these indices for the other datasets. This analysis demonstrates that although analyses can be misled by these convergences if morphological characters are used alone, these characters contribute significantly to the combined dataset.  © 2003 The Linnean Society of London . Biological Journal of the Linnean Society , 2003, 78 , 541–593.     

An overall strategy for the testing of chemicals for human hazard and risk assessment under the EU REACH system

In its White Paper, "Strategy for a Future Chemicals Policy," published in 2001, the European Commission (EC) proposed the REACH (Registration, Evaluation and Authorisation of CHemicals) system to deal with both existing and new chemical substances. This system is based on a top-down approach to toxicity testing, in which the degree of toxicity information required is dictated primarily by production volume (tonnage). If testing is to be based on traditional methods, very large numbers of laboratory animals could be needed in response to the REACH system, causing ethical, scientific and logistical problems that would be incompatible with the time-schedule envisaged for testing. The EC has emphasised the need to minimise animal use, but has failed to produce a comprehensive strategy for doing so. The present document provides an overall scheme for predictive toxicity testing, whereby the non-animal methods identified and discussed in a recent and comprehensive ECVAM document, could be used in a tiered approach to provide a rapid and scientifically justified basis for the risk assessment of chemicals for their toxic effects in humans. The scheme starts with a preliminary risk assessment process (involving available information on hazard and exposure), followed by testing, based on physicochemical properties and (Q)SAR approaches. (Q)SAR analyses are used in conjunction with expert system and biokinetic modelling, and information on metabolism and identification of the principal metabolites in humans. The resulting information is then combined with production levels and patterns of use to assess potential human exposure. The nature and extent of any further testing should be based strictly on the need to fill essential information gaps in order to generate adequate risk assessments, and should rely on non-animal methods, as far as possible. The scheme also includes a feedback loop, so that new information is used to improve the predictivity of computational expert systems. Several recommendations are made, the most important of which is that the European Union (EU) should actively promote the improvement and validation of (Q)SAR models and expert systems, and computer-based methods for biokinetic modelling, since these offer the most realistic and most economical solution to the need to test large numbers of chemicals.