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Aryl-amines also have a known mechanism for genotoxicity. In a previous article, we have shown that an in silico assessment of aryl-amines using quantum mechanics reaction energy calculations can provide excellent detection of mutagenic aryl-amines [ 2 ].

However, we were surprised that statistical models incorporating additional descriptors did not improve the performance of the single nitrenium formation energy parameter given the wealth of QSAR literature showing accuracy approaching or exceeding the known experimental error.

Additionally, we found that the set of Novartis aryl-amines was surprisingly challenging to model compared to those in the literature.

Our ultimate goal is to provide medicinal chemists with usable models to improve the chances of avoiding a toxicity trap that is often visible only after low-throughput tests come back.

The aryl-amines can be predicted reliably with the nitrenium formation energy calculation but comparing all-substructure external Ames results to our Novartis results, we found that these were also much harder.

Other groups in pharmaceutical companies have noted difficulties in predicting mutagenicity in aryl-amines [ 3 ], and in internal all-substructure data sets using commercial software [ 4 , 5 ].

Previous to this article, differences between data sets typically used in the literature for building mutagenicity predictive methods and the data at pharmaceutical companies have not been compared.

This is key to the disconnect from literature studies and pharmaceutical studies. The high level of performance of statistical models in this arena with constructed test sets is misleading and does not reflect performance in pharmaceutically relevant sets.

Here we show the relative difficulty in predicting the Ames test result in the Novartis aryl-amines and other substructures, in contrast to literature sets.

Many compounds in the environment released from industrial pollution and production are known to cause cancer [ 6 ].

Regulatory agencies around the world in cooperation with industry experts have adopted stringent test methods to identify and regulate the use of chemical mutagens that might be exposed to the environment or administered to humans directly as pharmaceuticals [ 7 ].

Carcinogenicity is usually determined by an array of in-vivo and in-vitro surrogate tests, which are specified by regulatory authorities before administration to man.

The Ames bacterial test is a simple experiment to perform and it is a mandatory regulatory test that has been in use for almost 40 years and correlates with life-time rodent carcinogenicity studies that require 2 years to complete [ 8 , 9 ].

At the molecular level, this test for mutagenicity [ 10 , 11 ] detects a substance's ability to cause mutations in engineered strains of Salmonella typhimurium by observing return of function by point mutations in an altered His operon gene.

The mutations in the His operon strains prevents histidine biosynthesis, thus random mutations or mutations due to an external agent must occur for colony growth on histidine-deficient medium.

Many compounds are converted to mutagenic compounds after metabolism, so the test is performed with and without pre-incubation of the compound with rat liver enzymes.

The bacterial strains used in the test have been further engineered to have permeable cell membranes, a reasonably high spontaneous mutation rate, and diminished DNA repair capacity [ 12 ].

Additionally, it has been shown that the qualitative carcinogenicity result is not improved by quantitative mutagenicity potency data [ 8 ].

An increase in number of colonies over control by at least a factor of 2 and a clear dose dependence in the mini-Ames screening test [ 13 ] is classified as a positive result.

Although high-throughput screening assays exist, they do not faithfully predict the result of the Ames test and at the same time require a significant investment [ 14 , 15 ].

Consequently, the volume of data available for the Ames test is fairly limited. The turnaround time and cost for Ames testing makes accurate in silico models quite useful.

There are some limitations of the Ames test that present a challenge to building accurate in silico models. Reproducibility both across and inside one laboratory conducting the test is another serious issue.

The test is sensitive and uses high concentrations of the test chemical, which can increase the effect of impurities including metals [ 17 ], degradation products, or reagents [ 18 , 19 ].

The chemical can also be toxic to the bacterial system, most notably antibacterials or cytotoxic compounds, but must still be tested to the maximum possible concentration [ 7 ].

The cause of cancer through the action of chemicals has been studied extensively, and the process typically begins with the chemical, or one of its metabolites, interacting with DNA, which subsequently leads to mutations [ 20 ].

The principle of mutagenicity through reaction of DNA with electrophiles has been especially useful in rationalizing and deriving "toxicophores," substructures that are strongly associated with mutagenicity [ 21 , 22 ].

Some of these mechanisms have been studied carefully in vitro and in vivo [ 23 ], and DNA or protein adducts can be measured and observed experimentally [ 24 , 25 ].

The first line of defense in avoiding carcinogenicity in drug design is through the use of alerts to chemicals commonly associated with carcinogenicity, mostly derived from environmental testing [ 22 , 26 ].

Kazius et al. Others such as aryl-amines and nitroaromatics are known to be converted to more reactive species through oxidation, reduction, and conjugation metabolism reactions [ 29 ].

However, despite the inclusion of detoxifying rules, these methods misclassify many of the Ames- compounds as positive. There is a long history of modeling mutagenicity on chemicals expected to be encountered from environmental and food exposure [ 9 , 33 — 37 ].

Recent reviews on statistical models of mutagenicity [ 9 , 33 , 38 , 39 ] and a recent collaborative head-to-head mutagenicity prediction challenge summarize the current state of the art for external sets [ 40 ].

A summary of some recent models is included in the Supporting Information Additional file 1 which provided an accuracy in test set molecules ranging from 0.

A few studies that could be described as using a hybrid approach by identifying the most applicable out of a selection of models have also been developed with extremely good performance [ 40 , 41 ].

Ames test data is available from a number of sources including literature reviews, regulatory agencies, and funding agencies [ 42 ].

For our analysis, we focused on an internal Novartis set and two literature sets combined into one for aryl-amines and four datasets covering all substructures as detailed in Table 1.

For the aryl-amine sets, molecules with other substructures associated with mutagenicity, such as nitroaromatic, nitrile oxide, N-nitroso substructures, were removed from the analysis.

Set A was from internal Novartis Ames screening test results tested in one laboratory up to Set B is the aryl-amine subset from compilations published by Hansen et al [ 38 , 43 ] and Kazius et al [ 21 ].

All Ames screening results at Novartis excluding those with discrepant values comprised Set C. The complete set of Hansen et al.

Set E represents a second pharmaceutically relevant set of marketed pharmaceuticals extracted from a recent review by Brambilla and Martelli [ 44 ].

The complete Kazius set, Set F, was included in the analysis to give a combined collection of molecules.

A basic summary of the sets is shown in Table 1. The random forest classification models used in this article were constructed using the randomForest package [ 54 ] for R [ 55 ] using the approach developed by Breiman [ 54 , 56 ].

The method was used by constructing unpruned trees using a random sample of sqrt N of the available predictors for each tree and a 0.

The remaining data was predicted using the tree and averaged to create the combined out-of-bag OOB predictions depicted in the receiver operator characteristic ROC plots.

Variables showing little variance among cases were removed using the nearZeroVar function in the caret [ 58 ] package and all variables were centered by the mean and divided by the standard deviation using the preProcess function in the caret package.

Averaging of model performances in the ROC plots was done with vertical averaging of performance at a given false-positive rate, and error bars give the standard deviation.

Variables with zero variance were removed prior to training thus removing variables for the Novartis set and for Set B, and variables were mean-centered and variance-scaled at each training step.

The aryl-amine data sets were constructed as previously described [ 2 ]. The all-substructure sets were combined using Pipeline Pilot [ 60 ] ignoring chirality due to a lack of chirality in our 2D descriptors and after generating a canonical tautomer.

It is also worth noting that absolute chirality determination cannot be done for all compounds and inevitable data entry errors can make this another source of error.

Substructure counts were calculated using a Pipeline Pilot [ 60 ] protocol with substructure queries that were able to closely reproduce the counts generated in the work of Kazius et al.

The queries used are provided as Additional file 2. The Self-Organizing Map [ 61 ] for the combined all-substructure set was generated in Schrodinger Canvas version 1.

The program uses Euclidean distance to measure similarity between compounds, and the internal Morgan[ 46 ]-type circular fingerprints [ 47 , 63 ] generated with radius 2 and functional atom types were used as descriptors ECFP4.

For the aryl-amine set, the 'kohonen' package [ 64 ] in R was used instead due to a discovered problem in Canvas with applying trained maps to new compounds.

In this case, RDKit was used to generate circular Morgan fingerprints hashed to count variables as described for the statistical modeling.

In the following results, the differences in the sets are examined in terms of their properties, presence of previously identified mutagenic substructures, and structural similarity and clustering visualized using Kohonen self-organized maps.

The difference in predictivity of multiple statistical methods and descriptors between pharmaceutically relevant data and literature compilations is analyzed firstly for aryl-amines and then for sets containing all substructures.

For aryl-amines, the quantum mechanically derived reaction energy for forming a known reactive intermediate was shown to be a more stable and accurate predictor than statistical models with more descriptors.

This low percentage is quite similar to other recent reports on Ames results at other pharmaceutical companies such as the recent report from Hillebrecht et al.

A paper by Leach et al. This range was nearly absent in the benchmark sets shown in the left plot of Figure 1 , but for the Novartis and marketed drugs sets in the right plot, there is a large percentage of the compounds.

The bias towards larger molecules likely reflects that the Ames test has often been considered later in drug development, when molecules and their precursors have more complex structures.

In contrast, the median weight for Set D is about , with a slightly sharper distribution as shown in the left plot in Figure 1.

The set of marketed pharmaceuticals with Ames test results is shown in green in the right plot of Figure 1. Molecular weight distributions of Ames test data sets.

The literature sets are shown in the left plot, and the Novartis Sets A and C and the marketed drugs compilation Set E are shown on the right.

The fact that there is such an even distribution, including a large fraction of lower molecular weight compounds, in the Novartis set may reflect the importance of this class and the response to the issue of genotoxicity.

When an issue is identified, the typical medicinal chemistry approach is to synthesize dozens of molecules and test all of them. Building blocks that are components of larger molecules are often tested in case of trace genotoxic impurities and for internal guidelines are tested if used for a final clinical candidate.

Also drugs for different disease areas such as neuroscience may require smaller molecules. The "toxicophores" described in Kazius were used to construct a further comparison of two of the all-substructure sets, Set C Novartis and Set D Hansen.

Naturally, a number of these functional groups are less common in drug design because of their reactivity or under-represented in test results or in the compounds synthesized due to concerns for toxicity in the Ames test.

Nitroaromatics were not nearly as represented in this set and are well-established as having a high probability of being responsible for genotoxicity.

Building statistical models in the other data sets may benefit greatly from having a feature so strongly associated with genotoxicity. Mutagenic substructure distributions of Ames test data sets non-aryl-amine.

Comparison of Novartis Set C orange bars and the Hansen et al. Even within a distinct substructure, aryl-amines, the pharmaceutically relevant set is much different from the Ames test results typically presented in the literature.

The use of Kohonen, or Self-Organizing, Maps [ 61 ] SOMs was helpful for visualizing the differences between the sets using distances between molecular fingerprints of the molecules.

This technique clusters molecules with similar substructure with each other in the best matching cell while also maintaining a 2-dimensional grid of cells such that similar molecules appear in adjacent cells.

Multidimensional scaling and simple clustering was also investigated for visualization but yielded unsatisfactory neighbors in the first case, and a less useful visualization tool in the second.

A SOM map built with the aryl-amines found in all sets is shown in Figure 3 but colored by property. The left plot is colored by where the aryl-amine is from: whether the molecule is a Novartis aryl-amine orange or from the external sets blue.

Finally, some representative structures are shown in the approximate locations of the map in the right plot.

Cells with some of each class are colored as pie charts depicting the relative fraction of each class present. The approach knows nothing of the set membership of each compound, yet it shows a striking separation of the aryl-amines both by whether they are part of a drug company's tested compounds or from a literature Ames compilation.

Polyaromatic amines such as aminoacridines, aminophenazines, or aminochrysenes are not highly common in medicinal chemistry. However, they are quite common in the available literature sets.

This makes these sets easier to model. Self-organizing map of aryl-amine chemical space. Comparison of aryl-amines in Set C and D using a self-organizing map SOM based on circular Morgan fingerprints, the SOM cells are shown in the top two plots with coloring applied based on a.

Size of the marker conveys the number of compounds in the cell. In Figure 3 , we also show where commercial aryl-amines that have been calculated by our model lie in the map.

A significant population exists near CF 3 -substituted anilines in the top right, which have historically been Ames- 2 nd plot and have higher nitrenium formation energies.

The top left of the map contains mostly larger and more polar aryl-amines, which were purposely left out of the calculations because of the goal of identifying safer starting materials and the better performance of the predictor for lower molecular weight aryl-amines.

The center-right area of the map is where a large proportion of the commercially available aryl-amines are located avoiding some of the larger polyaromatic and triphenyl systems.

The nitrenium formation energy predictor can clarify which compounds in this area are safer bets as discussed in the next section. For the aryl-amine SOM, the population was somewhat uniform, but in the all-substructure plot, the number of molecules per cell varies from 1 to This is natural due to the more extensive differences in the set.

The bottom three plots then further characterize where certain substructures are distributed in the SOM.

The blue cells show the presence of a polyaromatic substructure in the bottom-left. The aryl-amines are distributed throughout the area and depicted in shades of red.

Those molecules with multiple aryl-amine substructures have an increasingly pink hue sector of the pie marker. Finally in the bottom-right plot, the nitroaromatics are highlighted in shades of green.

As in the case of the aryl-amines, multiple substructures are given as separate pie-chart sectors of increasing brightness. These are seen almost solely in the external set and in regions of high mutagenicity.

Self organizing map of the chemical space of compounds considered colored by properties. SOM for all compounds in Sets C, D, E, and F colored according to property with pie charts to represent the percentage of molecules in the cell matching a property.

The HOMO energy correlates with the ionization potential, or the energetic cost of losing an electron, while the LUMO correlates to electron affinity, or the gain of an electron.

Good performance using these descriptors has been achieved for small sets of aryl-amines with only a few terms in linear classification and regression models [ 35 ].

Beanplots of four QM descriptors considered in our study. The beanplot is a way to show all data while also conveying a sense of the distribution.

The mean of each distribution is given as a long dark line. Reaction energies are given relative to aniline. A number of groups have also studied the utility of studying the reactions of aryl-amines to understand mutagenicity [ 2 , 3 , 35 , 67 , 68 ].

It was determined that the most statistically significant factor for predicting Ames toxicity was the reaction energy for forming the reactive intermediate, the nitrenium ion, from the aryl-amine [ 2 , 3 ].

This simple descriptor alone can provide a useful prediction of mutagenicity [ 3 , 67 , 68 ]. These energies are dependent on 3D conformation and the electronic spin state of the reactive intermediate and thus require care to ensure the calculated value is accurate.

Using this reaction energy for all Novartis aryl-amines was initially disappointing since good to excellent performance was observed in previous reports for other datasets, in addition to our prediction of external sets gathered for our testing.

Upon closer examination, it was clear that most of the sets did not have a uniform distribution up to the range of molecular weight of final pharmaceutical compounds and natural products that comprise a significant portion of the Novartis set.

As shown in Figure 6 , the performance was much lower for molecules with higher molecular weight in Set A orange dotted line.

Considering that the principle toxicity mechanism of aryl-amines requires metabolic activation, one possible explanation is that larger molecules have more selectivity in metabolic enzymes.

Anecdotally, smaller molecular fragments that present themselves as impurities, degradation products or metabolic products were the most common aryl-amine Ames problem at Novartis.

Therefore, prediction of lower molecular weight, reagent-like aryl-amines were the principal interest. ROC curve for using the single parameter nitrenium formation energy for aryl-amine sets A and B.

Other groups have introduced other quantum mechanics descriptors for aryl-amines in addition to nitrenium forming reactions [ 2 , 3 , 67 — 70 ], including the charge on the nitrenium ion nitrogen [ 68 ], relative energy of anion formation and relative iron complexation energy in a CYP1A2 binding site model [ 70 ], and finally reaction energy for aminyl radical formation, another species that could be produced in the cytochrome systems and has been associated with DNA damage [ 71 ].

Some of the reactions that have been used are summarized below in Equations 1 -5 and these have been compared for Set A.

The Pearson correlation matrix in Table 3 shows that all of the nitrenium forming processes represented by Equations 1 -3 are closely correlated and all provide good discrimination.

Larger HOMO orbital energies of the amine and lower reaction energies for forming the reactive nitrenium ion would make it easier to form the intermediate.

As suggested in a recent article [ 70 ], we looked at the anion formation energy Equation 5 and though on its own it has little discrimination as shown in Figure 5 and its AUC in Table 3 , it appears to provide a useful complement to the nitrenium formation energy.

A PLS model using all of the quantum mechanical descriptors showed a large loading value in the first component for nitrenium formation energies and the anion formation energy had the largest loading value in the second component.

The starting geometries for the anions can be generated using the same procedure for generating the nitrenium ions from the B3LYP-optimized aryl-amines.

Out of all of the QM parameters, the most useful parameter by PLS loadings and Random Forest variable importance top ranked in all runs using all of the data was the nitrenium formation energy Equation 1.

This particular reaction is also the easiest to calculate out of Equations 1 -3 since it reduces the number of atoms in the system compared to losing -OH or -OAc as the leaving group Equations 2 and 3.

While HOMO energy has a high correlation with nitrenium formation energy 0. Multi-dimensional statistical models improving upon the performance of the nitrenium formation energy parameter alone were difficult to construct.

A comparison of these methods and other approaches to modeling Ames toxicity when all mutagens are included have already been presented in other studies [ 36 ].

We have chosen to focus on PLS and random forest analysis of the aryl-amine data for further discussion because of the interpretability of PLS, the ability to include a large number of correlated variables, and the straightforward assessment of the importance of variables.

As summarized in Table 4 , the performance of the method on the Novartis set, Set A, was highly variable and significantly poorer than for Set B.

The performance on the test set decreased dramatically when adding a second component leading to a decrease in average AUC of 0. The same approach for the external set, Set B, resulted in a significantly higher AUC performance in the test set of 0.

This was higher than the test set performance of Set A by 0. The random forest out-of-bag model performance on the test set for Set B averaged over runs was significantly better than the 2-component PLS model.

Performance using 1, 2, or 3 components using randomly sampled test and training sets is shown with Set A on the left and Set B on right.

Error bars represent standard deviation. For both Set A and Set B, the multiple-variable PLS models offered an improved prediction over using nitrenium formation energy alone dashed line in the training set but not in the test set.

The performance of the Set A PLS model on the test set was much worse on average than using this single parameter.

The model in Set B was slightly better but unfortunately, most of the performance increase over the nitrenium formation energy 0.

These results are frustrating but provoked thought about why the molecules commonly used in the literature are different and easier to model.

In an attempt to address the problem of overfitting in this PLS model, a smaller selection of variables was chosen guided by the PLS loading weights, Pearson correlation between variables, and variable importances from a random forest model of the set.

The weights were averaged over the models and the largest 30 mean loading weights were used. Table 5 shows the variable loading and jack-knife significance testing run in the PLS cross-validation as well as the mean decrease in Gini coefficient over all trees for the random forest model built with the widest selection of parameters.

Two additional descriptors the Balaban j index [ 72 ] and density are given, which were suggested by random forest importance measures and their low correlation with the other descriptors.

The Balaban index was also identified as a discriminating variable in a previous investigation of aryl-amines and depends partly on the number of rings [ 3 ].

The first principal component included the nitrenium formation energy and other descriptors relating to electrostatics, hydrophobicity, and indirect properties such as the number of atoms.

The number of oxygen atoms and a fingerprint bit associated with an aryl-amine substructure was also significant.

Using just the first component parameters shown in bold in Table 5 resulted in less decrease in performance between training and test sets and decreased performance by less than 0.

Fitting all data led to an intermediate performance between the training and test sets as would be expected. A random forest model using only these descriptors performed much better than one using all of the potential descriptors for Set A, and for Set B this approach had similar but slightly lower performance.

The likely overfitting in the random forest model was quite surprising and indicates a tendency for many of the parameters to introduce conflicting results.

The single parameter nitrenium formation energy can met or exceeded the performance of PLS models that were given far more information. It was also able to perform well on the challenging Novartis set.

The plot on the left is for the model built with a full descriptor set and the plot on the right is for a limited descriptor set.

The right plot uses only 9 descriptors including nitrenium formation energy. The plot on the left uses a full descriptor set while the plot on the right is for a model using a limited descriptor set.

In a further attempt to characterize the differences in Set A and Set B, the sets were used as a test set for a model built from the other set.

The results of this experiment are shown in Table 7 and Figure The difference in performance was quite instructive and shows that the performance of Set B is less able to extrapolate to the aryl-amines in Set A than vice versa.

The performance of the Set A model was actually better for Set B data than for the data used to train it while a model based on Set B had clear difficulty in predicting Set A.

The performance of the Set A model on Set B 0. In fact even the 9-descriptor Set A model gave a performance of 0. However, when these models were applied to Set A, the performance was markedly worse and the 9-descriptor model performed much better than the model with all of the descriptors.

The unscaled PLS scores are shown in Figure 11 in the form of a boxplot for each model. Extrapolation from one aryl-amine data set to another: cross-set performance.

Comparison of the performance obtained when a PLS 1-component model is fitted using all of the data in Set A and used to predict Set B left plot, solid red line to that obtained when fitting to the data in Set B and using that model to predict the data in Set A right plot, solid orange line.

The performance for the training set is also shown in dashed lines and the performance of using only the nitrenium formation energy is shown with a dot-dashed line.

Extrapolation from one aryl-amine data set to another: cross-set Tukey Boxplot of the distribution of scores. Distribution of scores obtained for Set A orange box and Set B red box when applying a model trained on all of the data in Set A left or a model trained on all of the data in Set B right as a measure of outliers and domain.

The pre-built mutagenicity prediction model available to us in TopKat [ 80 ] was explored as a possible prediction method. The model provides the Tanimoto similarity with the most similar compound used to construct the model as one way to assess model applicability.

During the s Soviet intelligence focused on military and industrial espionage in Britain, France, Germany, and the United States, specifically in the aircraft and munitions industries, in order to industrialize and compete with Western powers, as well as strengthening the Soviet armed forces.

The processes is identified by Yuri Bezmenhof, a defector from the Soviet KGB, who put the process into the four stages "destabilize, demoralize, crisis, normalization" where an enemy country would be transitioned to communism over several decades.

Browder later stated that "by the mid-thirties, the Party was not putting its principal emphasis on recruiting members. Browder advocated the use of a United Front involving other members of the left, both to strengthen advocacy of pro-Soviet policy and to enlarge the pool of potential recruits for espionage work.

She discontinued this work only when Browder himself requested her release from duty, fearful that her work would compromise his position as General Secretary.

In the s, the chief Soviet espionage organization operating in the U. Peters headed the secret apparatus that supplied internal government documents from the Ware group to the GRU.

Browder assisted Peters in building a network of operatives in the administration of President Franklin D. Courier for the group at the time was Whittaker Chambers.

Browder oversaw the efforts of Jacob Golos and his girlfriend, Elizabeth Bentley , whose network of agents and sources included two key figures at the Department of Treasury, Nathan Gregory Silvermaster and Harry Dexter White.

One early Soviet spy ring was headed by Jacob Golos. He was also a longtime senior official of the CPUSA involved in covert work and cooperation with Soviet intelligence agencies.

He took over an existing network of agents and intelligence sources from Earl Browder. He had worked with Soviet intelligence from the mids, and probably earlier.

The firm, which posed as a travel agency, was used to facilitate international travel to and from the United States by Soviet agents and CPUSA members.

World Tourists was also involved in manufacturing fake passports, as Browder used such a false passport on covert trips to the Soviet Union in Soviet intelligence did not like Golos' refusal to allow Soviet contact with his sources a measure implemented by Golos to protect himself and to ensure his continued retention by the NKVD.

But even then, he did not reveal his agent network. After Browder went to prison in , Golos took over running Browder's agents. In , Golos set up a commercial forwarding enterprise, called the US Shipping and Service Corporation, with Elizabeth Bentley , his lover, as one of its officers.

Sometime in November , Golos met in New York City with key figures of the Perlo group , a group working in several government departments and agencies in Washington, D.

The group was already in the service of Browder. Later that same month, after a series of heart attacks over the previous two years, Golos died in bed in Bentley's arms.

Bentley then took over his operations thus the reference in the decrypts to him as a "former" colleague.

Whittaker Chambers later testified that the plans for a tank design with a revolutionary new suspension invented by J. Walter Christie then being tested in the U.

The records provide an irrefutable record of Soviet intelligence and cooperation provided by those in the radical left in the United States from the s through the s.

Some documents revealed that the CPUSA was actively involved in secretly recruiting party members from African-American groups and rural farm workers.

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He was 19 when taken captive. A few wore diapers. Some appeared several months old, while the Xinhua News Agency said the bodies included fetuses.

But he acknowledged he still lacks full support at the United Nations. Obama said. On the U. The Justice Department investigates complaints that administrators ignored racial bullying in a Philadelphia school.

Barbara Coloroso, a nationally known antibullying consultant, had. She spent a day there in September, training teachers and administrators on how to recognize and deal with bullying.

Coloroso said school officials made mistakes by failing to stop the bullying and, after Phoebe hanged herself, by allowing at least some of the students involved to continue to attend classes and a school dance with no visible signs of discipline.

Obviously not. And, did they deal effectively with the bullies? Coloroso told The Associated Press on Tuesday. Both domestic priorities came in one bill, pushed through by Democrats in the House and Senate and signed into law by a beaming president.

The new law makes a series of changes to the massive health insurance reform bill that he signed into law with even greater fanfare last week. Those fixes included removing some specials deals that had angered the public and providing more money for poorer and middle-.

But during an appearance at a community college in suburban Virginia, he emphasized the overshadowed part of the bill: education. In this final piece of health reform, Democrats added in a restructuring of the way the government handles loans affecting millions of students.

The law strips banks of their role as middlemen in federal student loans and puts the government in charge. We are studying an investigational medication for the treatment of keloids.

If you are at least 18 years of age and are not pregnant or nursing, you may qualify to participate. If you are interested in this clinical trial, please call the number below and leave a message as to how we can best reach you.

This same Congress has apparently cajoled its members into believing you can add 30 million insureds without increasing costs or rationing benefits.

Incidentally, there are no CPAs in the U. As an unwashed conservative, I have attempted to read and understand parts of the health care bill and, while not a scholar, I have concluded many in Congress have not read the bill and even more do not understand it.

Our president postponed his trip to Indonesia to shepherd the health care bill through Congress. Silly me! I am 49 years old and live in the Reedy Creek area.

I am married to Robin Whitley Allen and have two step-daughters and six grandchildren. I am a member of Christ Moravian Church. I believe in conservative values and responsible government.

My conservative views while serving as commissioner are a matter of public record. We need to get Davidson County back to work. During this election cycle, there will.

The High Point Enterprise is committed to this community What precautions have you taken or should you take to prepare for such an event in the future?

In 30 words or less no name, address required , e-mail us your thoughts to letterbox hpe. Storms smash property but not lives M he devastating tornadoes that slashed through High Point, Davidson County and the Triad Sunday evening damaged more than homes throughout our local area alone, dozens of them perhaps beyond repair.

As the EF3 tornado with plus mph winds spun through the skies and residential areas of north High Point, it left a path of torn and twisted trees, downed power lines and scars that will take days and weeks and months to heal.

Many fine things were damaged or lost, some likely even irreplaceable, precious family heirlooms. But nothing — absolutely nothing — was more precious than the lives that were spared.

In High Point and throughout this area, only a few minor injuries were reported as a result of this powerful force of nature, the strongest tornadoes reported in the Piedmont Triad area in 12 years.

Similar storm conditions in the past have been deadly. This area is additionally fortunate to have the quality of individuals, emergency services and public servants that it has.

Hundreds, maybe thousands, of people went into action to help their neighbors in distress. Such a display in human caring makes us proud.

But still, we must remain on guard for those who might come here to prey upon those suffering. When vulnerable people need help, there always are unscrupulous people ready to try to take advantage.

I will work diligently to promote and market Davidson County. We cannot continue to rely on just the current economic development efforts or think federal or state government will produce jobs for our county.

We also need to be very careful on over-extending public debt on capital expenditures in the current economy while maintaining the best possible county services to the citizens of Davidson County.

I am a strong supporter of public safety, law enforcement and education. I believe in equal representation for all areas of Davidson County.

I believe that with a low tax rate, good schools, a business friendly geographical location and a work force ready to go to work, Davidson County has a lot to offer businesses.

We need to market these facts better. We need to let the world know that Davidson County is open for business!

Please, if you feel that you must include a column from a far-left, radical Democrat, kindly consider giving your readers a break by replacing the ranting, racist and sexist, Leonard Pitts.

In almost every column, he spews hatred for somebody, especially if the person is Caucasian. He cannot look to the future because he is so fixated on the past.

Likewise, blacks who did not vote for John McCain must also be racists. Sarah Palin is stupid. What a stupid remark!

I do not see doctorate on his resume. What has Pitts accomplished? A Pulitzer Prize for writing bigoted columns? Similarly, like Obama who received the Nobel Peace Prize for accomplishing nothing.

Botox, however, is administered by plastic surgeons. Plastic surgeons have the dough to hire expensive lobbyists. Politicians found it much easier to tax tanning salons instead.

For starters, tanning lamps, much like the sun, generate ultraviolet rays. Overexposure to ultraviolet rays can cause skin cancer. Besides, tanning salons are easy targets.

If the Web site The Medical News newsmedical. They have fewer funds to pay for expensive lobbyists, which made it easy for politicians to slap a 10 percent sales tax on them — one that goes into effect this July.

And my family and I are taking the whole thing personally — because we are cursed with fair, freckly skin that burns easily in the sun. One year, in the s, our parents took us to the ocean for the first time.

The temperature was in the upper 90s that week. My father begged us to be wary of the hot sun. Nonetheless, we raced to the beach as soon as we arrived.

We got scorched so badly the first day, we spent the rest of the. But that was long ago — before the government overhauled our health care system.

The 10 percent sales tax is intended to dissuade us from doing so. Tanning at a salon was an unneeded expense in a good economy.

In this economy, it is a costly extravagance — and that was before it got 10 percent more expensive. Thus, as the summer nears, my family and I have one less weapon in our arsenal to fend off a nasty burn.

My mother, desperate to try something to achieve a tan look, purchased a can of spray-on tan. Now you know why my family and I are taking it personally.

Visit him on the Web at www. An independent newspaper Founded in Michael B. Starn Publisher Thomas L. The Enterprise welcomes letters.

The editor reserves the right to edit letters for length and clarity and decorum. Writers are limited to words and to no more than one letter every two weeks.

Please include name, home address and daytime phone number. Mail to: Enterprise Letter Box P. Restless as usual, I also was clinging to my phone and the computer.

Anxiously communicating with all the friends I could think of who were in the most impacted areas, I hoped to confirm the news reports that the only major damage had been done to properties.

In the midst of the wind, rain and uncertainty, the idea of a sunrise seemed foreign to me. For a moment, I wondered if the clouds would even pass in time for the sun to shine through them.

Yet then the news stations switched gears and began to report on stories other than the storms. As though the little orphan Annie had invaded my thoughts, I realized that, despite the interruption, the day would continue as usual, and, in the morning, the sun would come out.

Everyone faces storms at some point, even those daredevils who stood outside filming the funnels Sunday night. At some point in each of our lives, we are consumed by the storms of grief over lost loved ones, worries about the future, struggles with illness,.

Storms invade our realities. When a storm surrounds us, the idea of soaking up the rays of the sun seems cruel and daunting.

Yet concentration on the sunshine to come is what makes the storms bearable. We all have our own way of reminding ourselves of the sunrise or of being reminded by others.

Though other methods are valid, this week the reminder I am most thankful for is my religion. Despite any storms that may have happened this year, Easter is our greatest reminder that the Son will rise and that storms are never as terrible as they seem in the moment.

You can bet your bottom dollar. Shop our large line of Rainbow sandals for the entire family! The panel was established five years ago and told to report back to the General Assembly before the end of We agree.

Climate change has not disappeared. The planet continues to warm. The warming deniers are squawking, of course,. But the totality of world research is unchanged.

Come in and see our new fabrics and trims for spring Aging Happens to Us All! We offer solutions to everything from sun damaged skin to wrinkles and saggy skin.

A personalized custom consultation with our staff will give you the information and options to allow you to look your best.

Virgil V. Willard, II, M. Piedmont Plastic Surgery, P. Suspected members of a group that called itself Hutaree were allegedly plotting to kill police officers.

The suspects were captured over the weekend in Michigan, Ohio, and Indiana. Miami Fire Rescue and fair personnel rescued the two people after nearly an hour with a cherry picker.

Centennial St. No Flavoring. But officials said Steele knew nothing of the club visit. The Republican National Committee fired a staffer who helped organize the Jan.

It said it will recoup the money from a donor who paid the tab and was reimbursed by the party on Feb. A copy of the bulletin was obtained by The Associated Press.

City officials said that upward of volunteers had given their time through Tuesday afternoon to help residents in areas of north High Point affected by the tornado.

An updated assessment of the damage reflects the need for help — resi-. Mel Watt, Dth, visited the area hit by the tornado Tuesday, and Rep.

Howard Coble, R-6th, is scheduled to visit the area today. By the time the assessment is done, up to structures may have sustained damages.

So far 28 structures have been ruled uninhabitable, she said. The last time a confirmed tornado struck in the city limit was 53 years ago, according to National.

Tim Barnhardt gets a cup of coffee from this tent set up in the yard of Maplewood Drive in the Blairwood Estates subdivision Tuesday. Free doughnuts, coffee, and biscuits were available to anyone either working or residents.

Do you know anyone who deserves some extra attention? Box , High Point, NC E-mail versions with an attached color photograph can be sent to whosnews hpe.

Main Street. Main from the school. They were turned upright to prevent fluids from spilling out. Weather Service records. A tornado on a similar track cut through High Point on April 5, A tornado that hit two years ago near the Piedmont Triad Farmers Market was just outside the city limit.

Boynton said power had been restored to virtually all residences and businesses by Tuesday afternoon. Volunteers from churches, civic clubs and schools have played a critical role in helping residents and the city make progress, Boynton said.

City and Guilford County officials continue to coordinate with federal and state representatives about disaster relief. Mel Watt, Dth, visited the area Tuesday, and Sen.

Hagan and Gov. Beverly Perdue got in touch with local officials, Boynton said. Boynton pledged that the parts of the city hit by the tornado will return to normal, but it will take at least several weeks.

One variable that could come into play is damage caused by falling trees. Some residents of the neighborhood said insurance adjusters had told them all of the damage to their homes would be covered.

Sherry Stephens of Silverstone Court, whose home was rendered uninhabitable by the tornado, said her insurance company was going to provide accommodations for her and her husband at a local hotel for the time being.

Mattie Carter of Hampton Park Drive reported the same thing will come into play for her Hampton Park Drive home while contractors do repairs.

At the new hpe. Visit the redesigned hpe. Rodney Ball High Point Robert Baxley High Point Louise Cecil Thomasville Steven Clark Thomasville Darren Collins..

Winston-Salem John Davis Jr High Point Kris Garner Linwood Don George Salisbury Ike Grubbs Lexington Crystal Grimes Trinity Jean Hargrave Lexington Gracie Harris High Point Billy Hooker Thomasville Herman Johnston..

Thomasville Karen Jones High Point Melvin Kiger Thomasville Adelaide Liano Ramseur Ruth Lyerly Thomasville C. New Bern Estelle Seagraves Thomasville Lester Sports Tony, Wisc.

The High Point Enterprise publishes death notices without charge. Additional information is published for a fee.

Obituary information should be submitted through a funeral home. Clodfelter Rd. Pete was a resident of Davidson County most of his life.

He was a veteran of the U. Army Air Force, and during that time he was an airplane mechanic. Pete graduated from Wallburg High School and was an avid gardener, hunter and fisherman.

He was known for his perfectly kept garden, and loved sharing the fruits of his labors with others. His main objective was to provide for his family, and his extreme generosity will not be forgotten.

In addition to his parents, Pete was preceded in death by four sisters, his devoted wife Sue Clodfelter Kiger who died in , and his beloved daughter Sandra Kiger Johnson who died in March of Funeral service will be held at 2 p.

Lynn Upchurch and Rev. Mike Lee officiating. Interment will follow in the church cemetery. The family will receive friends following the service and committal at the cemetery.

The Kiger family would like to say a special thank you to the staff of Britthaven of Davidson and Hospice of Davidson County for their care, compassion and concern for Mr.

Green and Sons Funeral Home in Thomasville is assisting the family. Online condolences may be sent to the Kiger family at www.

He worked with Ms. He was instrumental in many of the programs being successful. Chris was very involved in the forming and continued excitement of Special Needs Baseball.

There was never any task too small or too big for Chris to give it his full attention. Chris was a loving husband, a devoted father and a loyal friend.

Although his career was much of his life, he loved his family deeply. A memorial service will be held at pm Saturday, April 3, at St.

Crystal was born January 23, , in Guilford County and had lived in the High Point and Trinity areas all of her life. Crystal was of the Baptist faith.

She enjoyed going to the beach, going to drag races and enjoyed life and lived it to the fullest. Crystal was loved my many, especially her mom, dad, daughter, extended family and friends.

Funeral will be 2 p. Raleigh Hayden and Rev. Jimmy Hayden. The family will receive friends Wednesday night from 6 until 8 at the funeral home.

A private memorial service will be held at a later date. Johnston was a veteran of the U. He was a member of Johnsontown United Methodist Church.

In addition to his parents he was preceded in death by thirteen brothers and sisters, a grandson, Keith and a great-grandson Matt. On November 7, , he was married to Rachel Smith, who survives of the home.

A Memorial Service will be held Saturday, April 3, , at 3 p. Wesley C. Smith officiating. The family will receive friends immediately following the service and other times at the home of his daughter, Dena Everhart, J.

Tysinger Road, Thomasville. Hobert Franklin Guillon 11 a. Steven Roger Clark 11 a. Zion United Church of Christ Mr.

Herman Roger Johnston 3 p. Wallburg Baptist Church 1 p. Margaret Kindle Alexander 1 p. Clifton Grove Baptist Church Visitation: 12 noon to 1 p.

Charles Ray Rowland Jr. Since Louise Clonts Cecil 2 p. John Rowe Davis Jr. Myrtle Markel Wilson 11 a. Rodney Rhett Ball No Services planned.

Ruth Brinkley Lyerly 2 p. Don Wayne George 3 p. Nelda Dodson Vernon 7 p. Magurite Allred 11 a. Beverly Wade 4 p.

John R. Davis, Jr. He was the fifth of six children born to the late John R. Davis, Sr. John was also preceded in death by his brother Holland and his sister Lucille Bowen.

After graduating from Wallburg High School, John enlisted in the army in He retired after thirty-nine years with Norton.

He especially enjoyed his years as an account executive because he loved people. He also was an active member of Covenant Church United Methodist until his health declined.

He sang in the choir, gave devotionals in the Sunday school class, and served on the board for several years. He also helped in his community.

When his children were in school, John worked at the ballgames serving concessions, and he served on the local school boards.

He and his wife also helped at the voting polls. John is survived by his loving wife Joyce Ward Davis. They shared sixtytwo years of marriage. He is survived by a daughter Jane and her husband Larry Greene of Winterville,.

He is also blessed with several nieces. The family will receive friends and family at the chapel one hour before the service. The graveside service will be private at a later time.

Online condolences may be made through www. Thursday at Ebenezer United Methodist Church. Arrangements by Davidson Funeral Home Lexington. Adelaide Ann Simonetti Liano, 97, died March 29, Funeral will be held at 2 p.

Visitation will be following the service. Ruth B. Lyerly went home to be with Jesus on March 27, On May 26, , Ruth married Johnny Lyerly, the love of her life.

Ruth and Johnny were married 57 years before his death in September Morton of Raleigh. She was a warm and encouraging mamaw to her granddaughter, Suzanne Morton Montgomery and her husband, Robert C.

Montgomery of Raleigh. She was a special blessing to her greatgrandson, Carson Montgomery of Raleigh. Brinkley and wife, Carolyn Bulla, of Raleigh.

Ruth began working as a bookkeeper and office clerk for Anvil Brand, Inc. In , she became the bookkeeper and office manager for Royals, Inc.

She worked there until when she retired at the age of Grubbs, 79, of Druid Hills Drive died March 29, For the past 14, he has been a stay-at-home dad.

He took on the primary job of raising our two kids, now ages 13 and 16, while my career soared.

The problem is, we never agreed to this arrangement. Roy left his job at a critical time out of anger and missed out on some major retraining. He also never made up for the loss in skills.

Instead, he stayed home, moped about, and now at 56 would have serious difficulty finding a job in his field if he wanted to.

Roy is not happy or fulfilled being at home and does nothing to get going on anything else. But each deadline I set passes with no change.

Should I leave him? Your husband may be chronically depressed, which is why he mopes around and has given up on establishing himself independent of you.

If your children are living up to their potential, his time has been well spent being a nurturing parent. While I understand your frustration at being the sole breadwinner, recognize that you are not alone in that role these days.

Many women are the heads of households, and they are not dumping their husbands en masse. So before making any hard-and-fast decision about leaving, consult an attorney and gain some insight about divorce laws in Minnesota, because regardless of what you decide, you could find yourself supporting Roy for an extended period of time.

When I became pregnant by him at 16, I lied to my family and told them the child was a result of a onenight stand. I am no longer involved with this man, although we parted on good terms and he continues to support our child.

What the man did was predatory and statutory rape. By staying silent, you may be enabling him to continue. If you are doing it for the money, there are other ways of getting support for your child.

Please rethink this. Write Dear Abby at www. Box , Los Angeles, CA In , Ruth moved to Piedmont Crossing. She served as Treasurer of the Piedmont Association until In September , she moved to the Hall of Piedmont, where she was given love and incredible care.

She ate all meals in the Courtyard Dining Room with her precious new friends. In lieu of flowers, the family requests that all memorials be sent to Piedmont Crossing, Hedrick Dr.

This request was made to honor Momma and show gratitude to the Staff and wonderful residents of the Hall.

Momma loved her Lord Jesus, her family, friends and jigsaw puzzles. She was a giver and not a taker. She has left her family a legacy of Faith, Hope, and Love.

A graveside service will be held on Friday, April 2, , at 2 p. On Saturday, April 17, , at p. Both services will be held by Sarah Snell, Chaplain. He had worked many years in the Trucking Industry with several companies and was a member of Zion United Church of Christ, where he served on the Consistory, as president and in other committees within the church.

He was a dog lover and all neighborhood strays gravitated to him. He was preceded in death by his father.

A funeral service will be held on Thursday, April 1, , at a. James Simonds and Rev. Joe D. Coltrane officiating.

Clark will remain at the J. The family will be at the funeral home on Wednesday from p. Suite , New York, NY On-line condolences may be sent to www.

Thursday at Salem Baptist Church. Visitation will be from 6 to 8 tonight at the church. Arrangements are in the care of Roberts Funeral Service.

For any oil over 6 qts. Komen for the Cure. The award was given to recognize her passion and commitment in eradicating breast cancer as a life-threatening disease by advancing research, education, screening and treatment.

Farmer was one of the. Special art Six framed photographs of students from the University of North Carolina School of the Arts are displayed in the office of U.

Kenan Institute for the Arts. They will be on display for one year. April 21 at Messiah Too, Bonnie Place. Reservation deadline is April 9.

For more information or a registration form, call or visit the Website www. Brittain of Shelby. Includes tea or coffee, grits, gravy or hashbrowns.

Army Pfc. Christopher A. Recently, Farmer. Sign up on the Web site www. Donohue: Please say something about lupus. What are the symptoms? What causes it?

Is it fatal? When illnesses are classified, lupus is put in the same group of conditions to which rheumatoid arthritis is assigned.

Among them are joints, skin, blood cells, kidneys, nerves, heart and the nervous system. It wages war on involved organs.

Evidence of the immune attack is seen in the strange antibodies found in the blood. Signs and symptoms include painful joints, muscle aches and weakness, kidney involvement as demonstrated on lab tests, a drop in infectionfighting white blood cells, a similar drop in clotforming platelets, disturbances of the heart and heart valves, and inflammation of blood vessels.

Several different rashes might appear on the skin. One typical rash is the lupus butterfly rash. The cheeks become red, and those red patches are connected by a wide red line that crosses the bridge of the nose and produces a silhouette resembling a butterfly.

Lupus patients lose their energy. This all sounds very. However, not every patient has all these signs and symptoms.

Prolonged exposure to the sun can trigger an interval of worsening symptoms. Lupus used to be fatal. It is rarely still fatal, but medicines have made this illness one that most endure without making huge changes in their lives.

The year survival rate for lupus patients approaches 90 percent. Take my word. There are many effective ones. Dear Dr.

Donohue: I am a year-old female and recently had a bone density test. The test showed that I have osteopenia. I know about osteoporosis.

However, I have never heard of osteopenia. Will you provide some information on it? On the journey to osteoporosis, osteopenia is a step behind osteoporosis.

Exercise is one. That means you have to support body weight in moving the body. Walking is weight-bearing. For arms, weightlifting is the exercise to do — as well as for legs.

Vitamin D and calcium are two other elements of an osteopenia program. Get a daily dose of 1, IU of vitamin D and a daily calcium intake of 1, mg.

Donohue: My daughter, who is in her mids, takes birthcontrol pills on a cycle devised by her doctor. She had had very heavy periods that resulted in blood-loss anemia.

She also suffered from severe premenstrual syndrome. Is it safe to go a full year without having periods? She has been her best this past year, both physically and emotionally.

One such pill is Lybrel, which is taken for an entire year without any breaks for a period. The pattern is almost balanced, and the paucity of high-card values would suggest that we should play for nine tricks, not ten at spades.

Players often take such risks at matchpoint duplicate scoring. Bramley clearly hoped the deal would produce as many tricks at notrump as at spades, especially with the opening lead coming around to the South hand.

Fallenius won with the ten, picked up the spades and finished with three overtricks and most of the matchpoints. In fact, South or North could take 12 tricks at notrump on any lead with a complex squeeze.

A creative approach to whatever you face will lead you in directions you never thought possible. You will be able to make decisions that are based on what you want and need instead of what everyone else wants.

Treat each opportunity with an open mind. Let your intentions be known and your determination seen. The more active you are, the more attention you will attract and help you will receive.

Social events and romance are favored and can enhance your relationship or lead to a new one. An opportunity to make a change does look positive but only if you can make it work personally as well as professionally.

Consider the effects of any change you make. Take a look at what you are up against so you will be ready for whatever competition you face.

If you handle people with dignity, you will be well received. Your integrity and patience will pay off when it comes to money matters. You will be noticed by someone who can alter your future.

An interesting turn of events will unfold. Use a little intrigue coupled with your magnetic charm to capture the attention of someone who can orchestrate positive transformations.

A change at home may be unexpected but, in the end, you will benefit. Look out for your own interests. Put yourself first and get some rest.

Take care of emotional issues quickly and a love connection will take a serious turn. Say no and move along.

You are the dealer. What is your opening call? ANSWER: The hand has 14 points, but the defensive values are lacking, the spots are poor and there is no spade length.

East dealer Neither side vulnerable. Three stars: If you focus, you will reach your goals. Four stars: You can pretty much do as you please, a good time to start new projects.

Five stars: Nothing can stop you now. Go for the gold. The park with more than 2, garden gnomes is visited by more than , people each year.

This version borrows the concept of a layered mascarpone cream and ladyfinger dessert, but adapts it with spring and Easter in mind.

Mascarpone cheese can be found in the specialty cheese section of most grocers. Organic edible flowers can be found with the herbs in the produce section.

Heat until simmering and the sugar is dissolved. Set aside to cool. To make the mascarpone cream, in a medium stainless steel bowl, whisk together the egg yolks, sugar and both liqueurs.

Set aside. Place the bowl of the egg mixture over the pan. The bowl should rest over the water without touching it.

Whisk the yolk mixture continuously until thickened, lightened in color and hot to the touch, about 10 minutes. In the bowl of a stand mixer, combine the mascarpone cheese and the yolk mixture.

Beat together on medium-low until thoroughly mixed. Increase speed to medium then beat for 30 seconds. It should be thickened and hold peaks. In an 8-byinch pan, arrange a layer of ladyfingers across the bottom.

The number that will. Sprinkle evenly with the syrup. You should use half the syrup. Spread half of the mascarpone cream over the top of the ladyfingers.

Evenly distribute 1 package of the raspberries over the cream, gently pressing them in. Arrange a second layer of ladyfingers, drizzle with the remaining syrup, then top with the remaining mascarpone cream and raspberries.

Refrigerate at least 4 hours or overnight before serving. To make sugared flowers, you can choose to use small flowers whole or pull the petals off larger flowers.

Beat the egg white and water together until bubbly. Set aside on a wire rack to dry. Sprinkle over the top of the tiramisu before serving.

Candy coating chocolates can be found at most craft stores in the candy and cake decorating aisle, as well as in the baking aisle of most grocers.

You can get a variety of colors, including pastels and white, as well as milk and dark chocolates; mix and match as desired. Close the bag and shake until the coconut is evenly green.

Line a rimmed baking sheet with waxed or parchment paper. One at a time, place each color of candy coating chocolate in a microwave-safe bowl and heat on high in second bursts, stirring between, until melted and smooth.

Spoon the melted chocolate onto the prepared baking sheet in a random pattern. Use a small spoon to swirl the colors together. Let cool for 5 minutes, then top with the jelly beans, sprinkles, other candies and green coconut as desired.

Set aside to harden completely, about 20 to 30 minutes, then break into chunks. Greene St. Davie St.

The growing awareness of human potential in later life provides a unique opportunity for artists and health-care professionals to proactively incorporate creative programs into the lives of older adults.

The Creative Aging Symposium offers people in both professions ways to embrace creativity and advance culture change in aging service environments.

The highly interactive twoday symposium will raise awareness of resources available at the national, state and local levels during the general session on May 6.

The May 7 workshops will offer experiential concurrent sessions providing valuable handson tools to encourage innovative thought and implementation of new creative programs.

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Substructure counts were calculated using a Pipeline Pilot [ 60 ] protocol with substructure queries that were able to closely reproduce the counts generated in the work of Kazius et al.

The queries used are provided as Additional file 2. The Self-Organizing Map [ 61 ] for the combined all-substructure set was generated in Schrodinger Canvas version 1.

The program uses Euclidean distance to measure similarity between compounds, and the internal Morgan[ 46 ]-type circular fingerprints [ 47 , 63 ] generated with radius 2 and functional atom types were used as descriptors ECFP4.

For the aryl-amine set, the 'kohonen' package [ 64 ] in R was used instead due to a discovered problem in Canvas with applying trained maps to new compounds.

In this case, RDKit was used to generate circular Morgan fingerprints hashed to count variables as described for the statistical modeling.

In the following results, the differences in the sets are examined in terms of their properties, presence of previously identified mutagenic substructures, and structural similarity and clustering visualized using Kohonen self-organized maps.

The difference in predictivity of multiple statistical methods and descriptors between pharmaceutically relevant data and literature compilations is analyzed firstly for aryl-amines and then for sets containing all substructures.

For aryl-amines, the quantum mechanically derived reaction energy for forming a known reactive intermediate was shown to be a more stable and accurate predictor than statistical models with more descriptors.

This low percentage is quite similar to other recent reports on Ames results at other pharmaceutical companies such as the recent report from Hillebrecht et al.

A paper by Leach et al. This range was nearly absent in the benchmark sets shown in the left plot of Figure 1 , but for the Novartis and marketed drugs sets in the right plot, there is a large percentage of the compounds.

The bias towards larger molecules likely reflects that the Ames test has often been considered later in drug development, when molecules and their precursors have more complex structures.

In contrast, the median weight for Set D is about , with a slightly sharper distribution as shown in the left plot in Figure 1.

The set of marketed pharmaceuticals with Ames test results is shown in green in the right plot of Figure 1. Molecular weight distributions of Ames test data sets.

The literature sets are shown in the left plot, and the Novartis Sets A and C and the marketed drugs compilation Set E are shown on the right. The fact that there is such an even distribution, including a large fraction of lower molecular weight compounds, in the Novartis set may reflect the importance of this class and the response to the issue of genotoxicity.

When an issue is identified, the typical medicinal chemistry approach is to synthesize dozens of molecules and test all of them. Building blocks that are components of larger molecules are often tested in case of trace genotoxic impurities and for internal guidelines are tested if used for a final clinical candidate.

Also drugs for different disease areas such as neuroscience may require smaller molecules. The "toxicophores" described in Kazius were used to construct a further comparison of two of the all-substructure sets, Set C Novartis and Set D Hansen.

Naturally, a number of these functional groups are less common in drug design because of their reactivity or under-represented in test results or in the compounds synthesized due to concerns for toxicity in the Ames test.

Nitroaromatics were not nearly as represented in this set and are well-established as having a high probability of being responsible for genotoxicity.

Building statistical models in the other data sets may benefit greatly from having a feature so strongly associated with genotoxicity.

Mutagenic substructure distributions of Ames test data sets non-aryl-amine. Comparison of Novartis Set C orange bars and the Hansen et al.

Even within a distinct substructure, aryl-amines, the pharmaceutically relevant set is much different from the Ames test results typically presented in the literature.

The use of Kohonen, or Self-Organizing, Maps [ 61 ] SOMs was helpful for visualizing the differences between the sets using distances between molecular fingerprints of the molecules.

This technique clusters molecules with similar substructure with each other in the best matching cell while also maintaining a 2-dimensional grid of cells such that similar molecules appear in adjacent cells.

Multidimensional scaling and simple clustering was also investigated for visualization but yielded unsatisfactory neighbors in the first case, and a less useful visualization tool in the second.

A SOM map built with the aryl-amines found in all sets is shown in Figure 3 but colored by property. The left plot is colored by where the aryl-amine is from: whether the molecule is a Novartis aryl-amine orange or from the external sets blue.

Finally, some representative structures are shown in the approximate locations of the map in the right plot. Cells with some of each class are colored as pie charts depicting the relative fraction of each class present.

The approach knows nothing of the set membership of each compound, yet it shows a striking separation of the aryl-amines both by whether they are part of a drug company's tested compounds or from a literature Ames compilation.

Polyaromatic amines such as aminoacridines, aminophenazines, or aminochrysenes are not highly common in medicinal chemistry. However, they are quite common in the available literature sets.

This makes these sets easier to model. Self-organizing map of aryl-amine chemical space. Comparison of aryl-amines in Set C and D using a self-organizing map SOM based on circular Morgan fingerprints, the SOM cells are shown in the top two plots with coloring applied based on a.

Size of the marker conveys the number of compounds in the cell. In Figure 3 , we also show where commercial aryl-amines that have been calculated by our model lie in the map.

A significant population exists near CF 3 -substituted anilines in the top right, which have historically been Ames- 2 nd plot and have higher nitrenium formation energies.

The top left of the map contains mostly larger and more polar aryl-amines, which were purposely left out of the calculations because of the goal of identifying safer starting materials and the better performance of the predictor for lower molecular weight aryl-amines.

The center-right area of the map is where a large proportion of the commercially available aryl-amines are located avoiding some of the larger polyaromatic and triphenyl systems.

The nitrenium formation energy predictor can clarify which compounds in this area are safer bets as discussed in the next section.

For the aryl-amine SOM, the population was somewhat uniform, but in the all-substructure plot, the number of molecules per cell varies from 1 to This is natural due to the more extensive differences in the set.

The bottom three plots then further characterize where certain substructures are distributed in the SOM. The blue cells show the presence of a polyaromatic substructure in the bottom-left.

The aryl-amines are distributed throughout the area and depicted in shades of red. Those molecules with multiple aryl-amine substructures have an increasingly pink hue sector of the pie marker.

Finally in the bottom-right plot, the nitroaromatics are highlighted in shades of green. As in the case of the aryl-amines, multiple substructures are given as separate pie-chart sectors of increasing brightness.

These are seen almost solely in the external set and in regions of high mutagenicity. Self organizing map of the chemical space of compounds considered colored by properties.

SOM for all compounds in Sets C, D, E, and F colored according to property with pie charts to represent the percentage of molecules in the cell matching a property.

The HOMO energy correlates with the ionization potential, or the energetic cost of losing an electron, while the LUMO correlates to electron affinity, or the gain of an electron.

Good performance using these descriptors has been achieved for small sets of aryl-amines with only a few terms in linear classification and regression models [ 35 ].

Beanplots of four QM descriptors considered in our study. The beanplot is a way to show all data while also conveying a sense of the distribution.

The mean of each distribution is given as a long dark line. Reaction energies are given relative to aniline. A number of groups have also studied the utility of studying the reactions of aryl-amines to understand mutagenicity [ 2 , 3 , 35 , 67 , 68 ].

It was determined that the most statistically significant factor for predicting Ames toxicity was the reaction energy for forming the reactive intermediate, the nitrenium ion, from the aryl-amine [ 2 , 3 ].

This simple descriptor alone can provide a useful prediction of mutagenicity [ 3 , 67 , 68 ]. These energies are dependent on 3D conformation and the electronic spin state of the reactive intermediate and thus require care to ensure the calculated value is accurate.

Using this reaction energy for all Novartis aryl-amines was initially disappointing since good to excellent performance was observed in previous reports for other datasets, in addition to our prediction of external sets gathered for our testing.

Upon closer examination, it was clear that most of the sets did not have a uniform distribution up to the range of molecular weight of final pharmaceutical compounds and natural products that comprise a significant portion of the Novartis set.

As shown in Figure 6 , the performance was much lower for molecules with higher molecular weight in Set A orange dotted line.

Considering that the principle toxicity mechanism of aryl-amines requires metabolic activation, one possible explanation is that larger molecules have more selectivity in metabolic enzymes.

Anecdotally, smaller molecular fragments that present themselves as impurities, degradation products or metabolic products were the most common aryl-amine Ames problem at Novartis.

Therefore, prediction of lower molecular weight, reagent-like aryl-amines were the principal interest.

ROC curve for using the single parameter nitrenium formation energy for aryl-amine sets A and B. Other groups have introduced other quantum mechanics descriptors for aryl-amines in addition to nitrenium forming reactions [ 2 , 3 , 67 — 70 ], including the charge on the nitrenium ion nitrogen [ 68 ], relative energy of anion formation and relative iron complexation energy in a CYP1A2 binding site model [ 70 ], and finally reaction energy for aminyl radical formation, another species that could be produced in the cytochrome systems and has been associated with DNA damage [ 71 ].

Some of the reactions that have been used are summarized below in Equations 1 -5 and these have been compared for Set A.

The Pearson correlation matrix in Table 3 shows that all of the nitrenium forming processes represented by Equations 1 -3 are closely correlated and all provide good discrimination.

Larger HOMO orbital energies of the amine and lower reaction energies for forming the reactive nitrenium ion would make it easier to form the intermediate.

As suggested in a recent article [ 70 ], we looked at the anion formation energy Equation 5 and though on its own it has little discrimination as shown in Figure 5 and its AUC in Table 3 , it appears to provide a useful complement to the nitrenium formation energy.

A PLS model using all of the quantum mechanical descriptors showed a large loading value in the first component for nitrenium formation energies and the anion formation energy had the largest loading value in the second component.

The starting geometries for the anions can be generated using the same procedure for generating the nitrenium ions from the B3LYP-optimized aryl-amines.

Out of all of the QM parameters, the most useful parameter by PLS loadings and Random Forest variable importance top ranked in all runs using all of the data was the nitrenium formation energy Equation 1.

This particular reaction is also the easiest to calculate out of Equations 1 -3 since it reduces the number of atoms in the system compared to losing -OH or -OAc as the leaving group Equations 2 and 3.

While HOMO energy has a high correlation with nitrenium formation energy 0. Multi-dimensional statistical models improving upon the performance of the nitrenium formation energy parameter alone were difficult to construct.

A comparison of these methods and other approaches to modeling Ames toxicity when all mutagens are included have already been presented in other studies [ 36 ].

We have chosen to focus on PLS and random forest analysis of the aryl-amine data for further discussion because of the interpretability of PLS, the ability to include a large number of correlated variables, and the straightforward assessment of the importance of variables.

As summarized in Table 4 , the performance of the method on the Novartis set, Set A, was highly variable and significantly poorer than for Set B.

The performance on the test set decreased dramatically when adding a second component leading to a decrease in average AUC of 0.

The same approach for the external set, Set B, resulted in a significantly higher AUC performance in the test set of 0.

This was higher than the test set performance of Set A by 0. The random forest out-of-bag model performance on the test set for Set B averaged over runs was significantly better than the 2-component PLS model.

Performance using 1, 2, or 3 components using randomly sampled test and training sets is shown with Set A on the left and Set B on right.

Error bars represent standard deviation. For both Set A and Set B, the multiple-variable PLS models offered an improved prediction over using nitrenium formation energy alone dashed line in the training set but not in the test set.

The performance of the Set A PLS model on the test set was much worse on average than using this single parameter.

The model in Set B was slightly better but unfortunately, most of the performance increase over the nitrenium formation energy 0. These results are frustrating but provoked thought about why the molecules commonly used in the literature are different and easier to model.

In an attempt to address the problem of overfitting in this PLS model, a smaller selection of variables was chosen guided by the PLS loading weights, Pearson correlation between variables, and variable importances from a random forest model of the set.

The weights were averaged over the models and the largest 30 mean loading weights were used. Table 5 shows the variable loading and jack-knife significance testing run in the PLS cross-validation as well as the mean decrease in Gini coefficient over all trees for the random forest model built with the widest selection of parameters.

Two additional descriptors the Balaban j index [ 72 ] and density are given, which were suggested by random forest importance measures and their low correlation with the other descriptors.

The Balaban index was also identified as a discriminating variable in a previous investigation of aryl-amines and depends partly on the number of rings [ 3 ].

The first principal component included the nitrenium formation energy and other descriptors relating to electrostatics, hydrophobicity, and indirect properties such as the number of atoms.

The number of oxygen atoms and a fingerprint bit associated with an aryl-amine substructure was also significant. Using just the first component parameters shown in bold in Table 5 resulted in less decrease in performance between training and test sets and decreased performance by less than 0.

Fitting all data led to an intermediate performance between the training and test sets as would be expected.

A random forest model using only these descriptors performed much better than one using all of the potential descriptors for Set A, and for Set B this approach had similar but slightly lower performance.

The likely overfitting in the random forest model was quite surprising and indicates a tendency for many of the parameters to introduce conflicting results.

The single parameter nitrenium formation energy can met or exceeded the performance of PLS models that were given far more information.

It was also able to perform well on the challenging Novartis set. The plot on the left is for the model built with a full descriptor set and the plot on the right is for a limited descriptor set.

The right plot uses only 9 descriptors including nitrenium formation energy. The plot on the left uses a full descriptor set while the plot on the right is for a model using a limited descriptor set.

In a further attempt to characterize the differences in Set A and Set B, the sets were used as a test set for a model built from the other set.

The results of this experiment are shown in Table 7 and Figure The difference in performance was quite instructive and shows that the performance of Set B is less able to extrapolate to the aryl-amines in Set A than vice versa.

The performance of the Set A model was actually better for Set B data than for the data used to train it while a model based on Set B had clear difficulty in predicting Set A.

The performance of the Set A model on Set B 0. In fact even the 9-descriptor Set A model gave a performance of 0. However, when these models were applied to Set A, the performance was markedly worse and the 9-descriptor model performed much better than the model with all of the descriptors.

The unscaled PLS scores are shown in Figure 11 in the form of a boxplot for each model. Extrapolation from one aryl-amine data set to another: cross-set performance.

Comparison of the performance obtained when a PLS 1-component model is fitted using all of the data in Set A and used to predict Set B left plot, solid red line to that obtained when fitting to the data in Set B and using that model to predict the data in Set A right plot, solid orange line.

The performance for the training set is also shown in dashed lines and the performance of using only the nitrenium formation energy is shown with a dot-dashed line.

Extrapolation from one aryl-amine data set to another: cross-set Tukey Boxplot of the distribution of scores. Distribution of scores obtained for Set A orange box and Set B red box when applying a model trained on all of the data in Set A left or a model trained on all of the data in Set B right as a measure of outliers and domain.

The pre-built mutagenicity prediction model available to us in TopKat [ 80 ] was explored as a possible prediction method.

The model provides the Tanimoto similarity with the most similar compound used to construct the model as one way to assess model applicability.

Set A has an average closest Tanimoto distance of 0. Although it could be argued that these models require retraining when applied to data far from the training set, such data are often not available.

A simple retraining using a three-fold cross-validation experiment, resulted in only marginal improvement in performance for the Novartis set with AUCs 0.

The ROC curves for these investigations are shown in Figure The good performance for the aryl-amines in Set B suggests that the aryl-amine substructure alone is not problematic in developing these models.

Previous publications have not separated the performance by substructure, so it was unclear that this would be true.

Performance of the TopKat Ames mutagenicity prediction module on aryl-amines. Additionally, the vertically averaged performance of a 3-fold random cross-validated retraining of the TopKat model using Set A is shown in brown with standard deviation error bars.

Given the difficulty of addressing aryl-amines, we began to search for reasons the set would be more difficult and if the result would be true for more than just this subspace.

Literature reports have provided excellent results for benchmark sets containing all mutagens and small collections of aryl-amines or nitroaromatics.

Even better performance could be obtained using multiple models based on the applicability domain of a mutagen under consideration such as Sushko et al.

Though surveys of the poor performance of pre-built commercial model performance on proprietary sets has been presented, reports on models of large proprietary sets and delineation of substructure seemed to be lacking.

A classification model given a collection of distinct features strongly associated with mutagenicity would be expected to perform better than a model missing such clear-cut mutagenic features such as nitroaromatics mentioned previously.

Table 8 and Figure 13 describes the performance of 2 global models, the TopKat pre-built commercial model and a random forest model built from all data in Sets C, D, E, and F.

Removing molecules with the typically mutagenic polyaromatic, aryl-amine, and nitroaromatic substructures resulted in significant performance decreases in both models in both Set C Novartis, orange, solid line to orange, dashed line and Set D Hansen et al.

The decreases in performance were greater for the TopKat model and for Set D. The global random forest model contained more training data which improved the performance on Set D compared to TopKat, and Set C had fewer of these mutagenic substructures as was presented in Figure 2.

However, the nitroaromatic subset in the random forest global model and the aryl-amine and polyaromatic mutagenic substructure performance in both models were equivalent or slightly worse than the overall performance.

ROC curve performance for models on all-substructure data sets. The left plot shows the performance of the default TopKat Ames mutagenicity model on Sets C orange , D red , E green , and F black as well as the performance for particular substructures in all sets blue : nitroaromatics purple , aryl-amines brown , or polyaromatic gray.

Dotted lines for Sets C and D show performance after removing these substructures. The center plot shows the out-of-bag performance of random forest models built on Sets C orange , D red , and E green and the global model blue when they are used as the training set.

The right plot shows the Set C, D, or E subsets of the global out-of-bag performance blue of a random forest model built on all of the data.

The performance of the random forest model built on the global set was similar to the performance of local random forest models built on the individual sets for Sets C and D.

It is important to note the extremely high performance for the Kazius set which is a large portion of the training data in the TopKat method.

The random forest model constructed from all of the data also did well on this set which again indicates that it is inherently a simpler set to model using the commonly used descriptors.

This all-data model has good performance across the entire chemical space map as detailed in Figure 14 where greens indicate a successful prediction over trees while yellow indicates an equivocal prediction and oranges and reds would be expected to give the wrong prediction.

In fact, a random forest model built with just Set D provided a fairly good prediction but gave more equivocal results in the regions occupied mainly by Novartis compounds.

As might be expected from the good performance of TopKat on set D and poor performance on the Novartis set, Figure 14 shows that most of the cells with Novartis compounds would be misclassified by the TopKat model red cells but also gets other regions wrong such as the lower left-hand corner.

Unlike the case of the aryl-amines, the Novartis all-substructure model does not perform well on regions occupied mostly by the other sets. The performance map provides almost a perfect opposite to TopKat, though the lower-left-hand corner is still difficult to predict with many equivocal cells.

Performance of global models mapped to chemical space. In the right box, the SOM is colored by the mean absolute error of the predictions in the cell for the indicated model.

Dark green indicates correct classification, yellow an equivocal prediction, and red an incorrect prediction.

Cell mean absolute error is defined as the difference between the predicted probability of being mutagenic and the experimental class 0 or 1.

In this article we have shown that there are significant differences in the physicochemical and biological properties of compounds used in drug discovery and those in compiled Ames test results from the literature.

This includes molecular weight, substructure distribution, and the percentage of mutagenic compounds in the data set.

This is important to communicate, as much of the literature data is being used to test prediction methods as well as playing a role in current testing strategy debates.

The compounds in the Novartis test results are mostly drug precursor molecules, while literature mutagenicity results are often petrochemicals and pesticides of primary concern as environmental pollutants.

The size and complexity of the molecules tested at Novartis was significantly larger on average than that of molecules included in external sets, as visualized by distributions in molecular weight.

Chemical functional groups or substructures that have a high association with mutagenicity determined from the literature data are largely absent in the Novartis set taking away a valuable discrimination feature.

As a result of these factors, many drug discovery molecules are outside the applicability domain of pre-built commercial models. The data is also more difficult due to lack of strongly associated structural features and would lead to worse performance of these statistical models if they were included in the training set.

Therefore these models cannot provide adequate performance to predict, let alone, avoid a positive Ames test. The Ames test, as well as other genotoxicity tests, continue to be a significant problem in drug discovery, and companies should work together to share data with the wider community of scientists and organizations.

The best-validated and best-performing prediction available for low molecular weight aryl-amines is still a quantum-mechanics reaction energy representing the formation of the nitrenium ion.

Effective predictive models could be built for all-substructure sets using the random forest methodology and commonly available 2D descriptors and chemical fingerprints.

Performance was still significantly lower for molecules from Novartis and marketed pharmaceuticals. Despite extensive work in the area of predicting this particular toxicity, work in designing more difficult test sets and more adaptable models is still necessary.

Eur J Med Chem. Leach AG, Cann R, Tomasi S: Reaction energies computed with density functional theory correspond with a whole organism effect; modelling the Ames test for mutagenicity.

Chem Commun. Google Scholar. Chem Res Toxicol. Epxert Opin Drug Metab Toxicol. Waldron HA: A brief history of scrotal cancer. Br J Ind Med.

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