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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.

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.

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.

The records contained further evidence that Soviet sympathizers had indeed infiltrated the State Department, beginning in the s. Included were letters from two U.

Roosevelt and a senior State Department official. Thanks to an official in the State department sympathetic to the Party, the confidential correspondence, concerning political and economic matters in Europe, ended up in the hands of Soviet intelligence.

Jacob Albam and the Sobles Jack and Myra were indicted on espionage charges by the FBI in ; all three were later convicted and served prison terms.

The Zlatovskis remained in Paris , France , where the laws did not allow their extradition to the United States for espionage.

Robert Soblen was sentenced to life in prison for his espionage work at Sandia National Laboratories , but jumped bail and escaped to Israel.

After being expelled from that country, he later committed suicide in Great Britain while awaiting extradition back to the United States.

During the Second World War , Soviet espionage agents obtained classified reports on electronic advances in radio-beacon artillery fuses by Emerson Radio , including a complete proximity fuse reportedly the same fuse design that was later installed on Soviet anti-aircraft missiles to shoot down Francis Gary Powers 's U-2 in Joseph Stalin directed Soviet intelligence officers to collect information in four main areas.

Pavel Fitin , the year-old chief of the KGB First Directorate, was directed to seek American intelligence concerning Hitler's plans for the war in Russia; secret war aims of London and Washington, particularly with regard to planning for Operation Overlord , the second front in Europe; any indications the Western Allies might be willing to make a separate peace with Hitler; and American scientific and technological progress, particularly in the development of an atomic weapon.

The United States Treasury Department was successfully penetrated by nearly a dozen Soviet agents or information sources, including Harold Glasser and his superior, Harry Dexter White , assistant secretary of the treasury and the second most influential official in the department.

White agreed to assist Soviet intelligence in any way he could. The principal function of White was to aid in the infiltration and placement of Soviet operatives within the government, and protecting sources.

White likewise was a purveyor of information and resources to assist Soviet aims, and agreed to press for the release of German occupation currency plates to the Soviet Union.

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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.

Objectives include learning about the untapped potential of older adults and the growing field of creative aging, including research and programming at the national, state and local levels; receiving handson training in creative programs you can take back to your community; learning about the resources and creative opportunities that exist for older adults locally, regionally and nationally; and connecting with other artists and aging service providers in your area.

Presented by the Center for Creative Aging, the symposium is open to artists and all aging service providers. Easy to make Easter bark is a great treat to make with the kids and a good way to make use of all the candy around the house.

Under and by virtue of the power vested in me by the laws of the State of North Carolina, particularly by Chapter Session Laws of and set forth in GS , and pursuant to the Commissioners of Guilford County, I hereby advertise all real estate properties described below for the unpaid Guilford County Taxes owing for the year Taxing unit may foreclose the tax liens and sell the real property subject to the liens in satisfaction of its claim for taxes.

Call , fax or email classads hpe. The Enterprise will assume no liability for omission of advertising material in whole or in part. Please check your ad the first day it runs.

Call give credit for only Friday before the first for Saturday, Sunday incorrect publication. Pre-payment is Wednesday.

Fax required for deadlines are one all individual ads and hour earlier. For Businesses may earn your convenience, lower rates by we accept Visa, advertising on a Mastercard, cash or regular basis.

Call for checks. Call to see if High Point Enterprise you can insure your you qualify for this sale against the rain!

Ask us for details! Furnished Apart. SERVICES Work All persons, firms and c o r p o r a t i o n s indebted to the said estate will please make immediate payment to the undersigned.

This the 23 March, All persons indebted to said estate please make immediate payment to the undersigned. Carroll, deceased late of Guilford County, this is to notify all per sons, fi rms, and corporations having cla ims agai nst said Estate to present t h e m t o t h e undersigned on or before the 9th day of June, , or this Notice will be pleaded in bar of their recovery.

Michael W. All persons, firms or corpo rations indebted to said estate will please make immediate payment to the undersigned.

Elizabeth M. Koonce Roberson Haworth Reese, P. Notice: This project will be financed with funding from the American Recovery and Reinvestment Act of A copy of the Contract Provisions required for Recovery Act funding will be provided to the bidder.

Maid Service seeks honest, mature, hardworking women. Weekday hours. Apply W. Parris Ave. Candidate must have good verbal skills and be very organized.

This position will be answering incoming calls as well as calling past and current subscribers to The High Point Enterprise. Position hours are Saturday 6amam and Sunday 6ampm.

Must be flexible in scheduling. No phone calls please. Snack Bar Position Cooking exp. Weekend hours reqd.

Pay plus tips. Apply in peron. Main St. No Phone Calls Please. Tire Technician Needed for L. Apply in person only at: N. Needing Experienced Upholstery Sewers and Upholsterers with a minimum of 3 years experience.

Apply in person, Select Furniture, South Rd. HP NC or call Multi-Family HUD experience a must, tax credit preferred, not required.

Basic computer skills, and a good attitude a must. Fax resume with desired salary to You are required to make defense to such pleading not later than MAY 17, , and upon your failure to do so the party seeking service against you will apply to the Court for the relief sought.

Curtis Howe, Attorney for Plaintiff N. Stove, refrig. No pets. No Section 8. WE have section 8 approved apartments. Call day or night Well located in High Point.

Well located. Reasonable rent. Call day or night. Excellent industrial building. Lots of offices at Shore Drive.

Industrial McWay Dr, sf. N High Point. Can not Live Without? Kearns St Not pets. Spring Dep. Limited Time! Call Roger or Philip Today.

Stylist seeking immediate clientele. Great Pay plus Benefits. Call This the 17th March, Clinical Team Leader: FT position for RN with strong leadership abilities to manage the home health and hospice nursing home teams.

The qualified candidate will have acute care pediatric experience, ability to work collaboratively within the interdisciplinary team and communicate effectively with referral sources and families.

Take notice that you are required to make defense to such pleading no later than forty 40 days after the date of the first publication of this notice, exclusive of such date.

Upon your failure to do so, the Petitioner will apply to the Court for relief sought in the Petition. Any parental rights that you may have will be terminated upon the entry of the decree of adoption.

Approx months old. House Broken, Very Friendly. Found in High Point Area. Call to identify Applicants will be selected based on qualifications and ability to provide the necessary services described in the scope of services to be provided.

Thomasville City Schools reserves the right to reject any and all bids. This project will need to begin construction around mid June of Construction will need to be completed before the beginning of the school year in August of Exact dates are to be determined by selected applicant and Thomasville City Schools.

For a copy of the scope of services, Contact: Greg Miller Maint. Cream Colored. Jack a Poo. All persons, firms and corporations indebted to the said estate will please make immediate payment to the undersigned.

Hamilton St. Cent Air. Must show employment proof. Good Location. Main Travel Inn Express, HP no sec. No Alcohol or Drugs. Incld Util.. HPU rooming hse.

Hamilton Davidson County, Conrad Realtors 30, sq ft warehouse, loading docks, plenty of parking. Call dy or night Repainted inside refinished beautiful hardwood floors, this is like new.

Mow, trim, aerate, fert. Reasonable Rates. Excellent Condition Call leave message. Great Outdoors Pet.. Call for more information. Sale or TradeNeeds restoring.

Call 98 Kawasaki Vulcan. Lots of Chrome. Very good cond. Back-up camera. Lots of Chromes. Less than 18K.

Good cond. Call Southwind Motorhome. More wooded lots available. Plus much, much more…. Home features 3 bedrooms, 3 full baths, sunroom, brick landscaped patio, hardwired sound system, 4 car carport, covered breezeway.

A Must See! Beautiful home set on 3 acres, New cabinets, corian countertops, hardwood, carpet, appliances, deck, roof. Home has 3 bedrooms, 2 full baths, formal living room, dining room, great room.

Great for starter home or rental investment. All Brick Exterior Built Paved Parking. Many Upgrades and new appliances, floor coverings, cabinets, paint.

For additional information call Owner Financing. Directions: Westchester to West Lexington, south on Hwy. Eight Flexible floorplans!

Half basement, 2-stall garage, also detached garage. Spacious bedrooms and closets. Garden tub in the master bath. Tray ceilings and crown molding in the living room.

Private balcony overlooking a wooded area. Shown by appointment only. Low taxes. Over 4, Sq. Have other homes to finance. Will trade for land.

Vinyl Siding, Large Lot. Will trade for Land. Other Homes for sale with Owner Financing from. We can handle all most any job that you need done outside!

Senior citizen and Veteran discounts! This was a totally different situation, team and people.

We started clicking on all cylinders and started getting some stops. We won all year by fighting and scrapping. We accomplished what he wanted to do.

It made all the hard work worthwhile when the practices were tough and the time in the weight room was tough. They had more talent. Freeman, who started out as a power forward and started as a wing forward at times later in the year, proved she is a vital cog in the improvement of the program.

She averaged She scored in double figures 20 times and scored the most five times, with a high of 21 coming against Samford in the regular season.

She led the team in rebounding 19 times, with a game-high of The competition is bigger, fast and stronger. At East, I was usually bigger than anyone I played against.

You are going up against grown women who work on basketball for 10 months. But I love in-sync with each other. It was they went over the last 19 playing basketball.

The explosion began with very disappointing. We thought back and change anything. Jack Jensen, who led basketball and golf teams at Guilford College to national titles during a year coaching career, has died.

He was Sports Information Director Dave Walters said that Jensen died of an apparent heart attack last Sunday after returning from a college golf tournament.

Jensen coached future NBA players M. Carr, World B. Free and Greg Jackson. Jensen is survived by his wife, Marsha, two children and one grandson.

A memorial service is scheduled for Thursday at 2 p. Mounts got in under the tag. See prep roundup on 3D. The Bulldogs earned their trip home for the Final Four.

Butler carries a game winning streak into the national semis. The Bulldogs play. That sounds a lot like the Hickory Huskers — except for the 3-point shooting as there was no 3-point line in the s.

So forgive me for indulging in a few lines from my all-time favorite sports movie. Yankees 7 p. West 9 p. Mexico 10 p. Totals Halftime—Dayton Johnson , M.

Fouled Out—M. Rebounds—Dayton 45 Wright 11 , Mississippi 38 Holloway 7. Total Fouls—Dayton 18, Mississippi Commonwealth 68, St.

Louis 56, VCU leads Commonwealth at St. Louis , 8 p. VCU at St. Louis, 8 p. Butler , 6 p. West Virginia vs. Duke , approximately p. Stanford 73, Georgia 36 Xavier 74, Gonzaga Baylor Stanford vs.

Kansas City champion. Clayton 4, Monroe 15, Gray 0, Harvin 10, Ward 3, Deluzio 9, Hunnicutt 2, Davis 4, Bravard 3. Halftime—Connecticut Fouled Out—Monroe.

Rebounds—Florida St. Assists—Florida St. Total Fouls—Florida St. Rhode Island , p. Johnson 22, Wright 9, Huelsman 6, Warren 7, M. Johnson 12, Perry 2, Lowery 3, Williams 5, Fabrizius 0, Searcy 2, Benson 0.

Clippers at Milwaukee, 8 p. Washington at Houston, p. Lakers at Atlanta, 7 p. Philadelphia at Charlotte, 7 p.

Milwaukee at Cleveland, 7 p. Clippers at Toronto, 7 p. Oklahoma City at Boston, p. Miami at Detroit, p. Phoenix at New Jersey, p.

Dallas at Memphis, 8 p. Sacramento at Minnesota, 8 p. Washington at New Orleans, 8 p. Houston at San Antonio, p. New York at Portland, 10 p.

Golden State at Utah, 10 p. Durant 26, J. Green 16, Krstic 14, Westbrook 10, Sefolosha 9, Collison 5, Harden 12, Ibaka 10, Maynor 9, Weaver 0, Ollie 0.

Green 6, Speights 10, Carney 10, Meeks 7, Smith 0. Fouled Out—None. Total Fouls—Oklahoma City 18, Philadelphia A—14, 20, Landry 17, Greene 5, Hawes 4, Udrih 18, Evans 17, Nocioni 9, Thompson 18, Udoka 5, Brockman 2.

Jones 4. Rebounds—Sacramento 56 Thompson 11 , Indiana 46 Murphy Total Fouls—Sacramento 22, Indiana Technicals—Sacramento defensive three second.

A—13, 18, Rangers76 N. Louis 75 36 30 9 81 Columbus 77 32 32 13 77 Vania King, United States, , NOTE: Two points for a win, one point for overtime loss.

Michigan , 7 p. Thursday, April 1 California at Illinois State , p. Championship Saturday, April 3 Semifinal winners.

Rangers 4, N. Los Angeles at Nashville, 8 p. Phoenix at Vancouver, 10 p. Carolina at Montreal, 7 p. Tampa Bay at Pittsburgh, p.

Chicago at Minnesota, 8 p. San Jose at Dallas, p. Anaheim at Colorado, 9 p. Phoenix at Calgary, p. Buffalo at Toronto, 7 p. Philadelphia at N.

Islanders, 7 p. Atlanta at Washington, 7 p. Carolina at Ottawa, p. Columbus at Detroit, p. Louis at Nashville, 8 p. Vancouver at Los Angeles, p.

PGA champions five years : Y. British Amateur champion: a-Matteo Manassero. Amateur Public Links champion: aBrad Benjamin.

Mid-Amateur champion: a-Nathan Smith. Top eight players and ties from U. Matt Jones Fredrik Jacobson Jason Bohn Angel Cabrera Jeff Quinney Mark Wilson Kevin Stadler Michael Allen Points Jeff Maggert Jimmy Walker Chad Campbell Webb Simpson Brian Stuard Scott Verplank Kenny Perry Davis Love III Mathew Goggin Andres Romero Rod Pampling Josh Teater Joe Durant Michael Bradley Greg Chalmers David Toms Rich S.

Johnson Michael Connell Scott Piercy Jeff Klauk Ted Purdy Chris Tidland Ben Curtis Justin Leonard Steve Wheatcroft Boo Weekley Jeev M.

Singh John Merrick Alex Cejka Adam Scott Troy Matteson James Nitties Tom Pernice, Jr. Blake Adams Brett Quigley Skip Kendall Graham DeLaet Troy Merritt Henrik Bjornstad Brian Davis Tom Lehman Michael Letzig Jeff Overton Omar Uresti Will MacKenzie Martin Flores Chez Reavie Fred Couples Jason Day Chris DiMarco Rich Beem Woody Austin Rory McIlroy Rich Barcelo Bill Lunde Matt Bettencourt Nich Thompson Calcavecchia Henrik Stenson Tim Wilkinson.

World Golf Ranking Through March 28 1. Tiger Woods USA Steve Stricker USA 7. Phil Mickelson USA 7.

Lee Westwood Eng 6. Jim Furyk USA 6. Paul Casey Eng 6. Ernie Els SAf 6. Ian Poulter Eng 6. Martin Kaymer Ger 5. Pad Harrington Irl 4. Rory McIlroy NIr 4.

Camilo Villegas Col 4. Geoff Ogilvy Aus 4. Henrik Stenson Swe 4. Retief Goosen SAf 4. Robert Allenby Aus 3.

Sergio Garcia Esp 4. Kenny Perry USA 3. Luke Donald Eng 3. Hunter Mahan USA 3. Stewart Cink USA 3. Robert Karlsson Swe 3.

Lucas Glover USA 3. In trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test.

Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy.

Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor.

External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set.

The performance gap between Novartis and external sets persists across different descriptors and learning methods.

The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints.

The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight.

Despite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary.

This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.

In the field of drug-discovery, a positive Ames test can halt development of a particular chemotype and possibly work on an entire drug target because genotoxicity of a potential therapeutic would be a serious issue that needs to be avoided.

Sufficiently nuanced rules do not exist to fix such a problem while maintaining the careful balance of potency and properties. Thus, prediction of whether a starting material, degradation product, or drug will be mutagenic in the Ames genotoxicity test is our primary goal.

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.

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