Structure-activity relationship studies of chemical mutagens and carcinogens: mechanistic investigations and prediction approaches

Metabolic and electrochemical mechanisms of dimeric naphthoquinones cytotoxicity in breast cancer cells

Cancer cells reprogram their metabolism due to genetic alteration to compensate for increased energy demand and enhanced anabolism, cell proliferation, and protection from oxidative damage. Here, we assessed the cytotoxicity of three dimeric naphthoquinones against the glycolytic MCF-7 versus the oxidative MDA-453 breast carcinoma cell lines. Dimeric naphthoquinones 1 and 2 impaired MDA-453, but not MCF-7, cell growth at IC(50)=15 μM. Significant increase in reactive oxygen species, decrease in oxygen consumption and ATP production were observed in MDA-453 cells but not in MCF-7 cell. These findings suggest that oxidative stress and mitochondrial dysfunction are mechanisms by which these agents exert their cytotoxic effects. Cyclic voltammetry and semi-empirical molecular orbital calculations further characterized the electrochemical behavior of these compounds. These results also suggest that dimeric naphthoquinones may be used to selectively target cancer cells that depend on oxidative phosphorylation for energy production and macromolecular synthesis.

Two new parameters based on distances in a receiver operating characteristic chart for the selection of classification models

There are several indices that provide an indication of different types on the performance of QSAR classification models, being the area under a Receiver Operating Characteristic (ROC) curve still the most powerful test to overall assess such performance. All ROC related parameters can be calculated for both the training and test sets, but, nevertheless, neither of them constitutes an absolute indicator of the classification performance by themselves. Moreover, one of the biggest drawbacks is the computing time needed to obtain the area under the ROC curve, which naturally slows down any calculation algorithm. The present study proposes two new parameters based on distances in a ROC curve for the selection of classification models with an appropriate balance in both training and test sets, namely the following: the ROC graph Euclidean distance (ROCED) and the ROC graph Euclidean distance corrected with Fitness Function (FIT(λ)) (ROCFIT). The behavior of these indices was observed through the study on the mutagenicity for four genotoxicity end points of a number of nonaromatic halogenated derivatives. It was found that the ROCED parameter gets a better balance between sensitivity and specificity for both the training and prediction sets than other indices such as the Matthews correlation coefficient, the Wilk's lambda, or parameters like the area under the ROC curve. However, when the ROCED parameter was used, the follow-on linear discriminant models showed the lower statistical significance. But the other parameter, ROCFIT, maintains the ROCED capabilities while improving the significance of the models due to the inclusion of FIT(λ).

Challenges for computational structure-activity modelling for predicting chemical toxicity: future improvements?

INTRODUCTION

Structure-activity modelling for predicting toxicology as a discipline is now 50 years old, and great strides have been taken in developing methods for the physicochemical analysis of molecules and their toxicity evaluation, both essential stages in modelling. Computational toxicology also has huge potential for speeding up the screening and prioritisation of chemicals for further testing and for reducing the numbers of expensive and time-consuming conventional tests. Yet, the realisation of this potential has been largely stifled by many problems inherent in developing and validating new structure-activity models of toxicity.

AREAS COVERED

Problems of computational toxicology discussed include i) the use of inappropriate molecular descriptors and tools that are not transparent; ii) the undetected existence of chemicals that cause large changes in toxicity with only small differences in molecular structure (causing 'activity cliffs' in the structure-activity landscape); iii) spurious correlations between structure and activity; iv) lack of quality control of toxicity data; v) difficulties in determining predictivity for novel chemicals; and vi) an over-reliance on complex mathematics and statistics.

EXPERT OPINION

Greater emphasis needs to be placed on i) the selection of training and test sets of chemicals to enable both internal and external validation of models to be undertaken for more accurate assessment of model predictivity and ii) the use of recently developed techniques for characterising structure-activity landscapes.

Synthesis of 2,4-Diarylquinolines: Nickel-Catalysed Ligand-Free Cross-Couplings of 4-Chloro-2-Arylquinolines with Arylmagnesium Halides in 2-Methyltetrahydrofuran

A ligand-free and room temperature protocol for the synthesis of 2,4-diarylquinolines is described. Treatment of 4-chloro-2-arylquinolines with arylmagnesium halides in the presence of a catalytic amount of nickel(II) chloride without ligands in 2-methyltetrahydrofuran (2-MeTHF) afforded the corresponding cross-coupling products in good yields.

An integrated QSAR-PBPK modelling approach for predicting the inhalation toxicokinetics of mixtures of volatile organic chemicals in the rat

The objective of this study was to predict the inhalation toxicokinetics of chemicals in mixtures using an integrated QSAR-PBPK modelling approach. The approach involved: (1) the determination of partition coefficients as well as V(max) and K(m) based solely on chemical structure for 53 volatile organic compounds, according to the group contribution approach; and (2) using the QSAR-driven coefficients as input in interaction-based PBPK models in the rat to predict the pharmacokinetics of chemicals in mixtures of up to 10 components (benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene, and styrene). QSAR-estimated values of V(max) varied compared with experimental results by a factor of three for 43 out of 53 studied volatile organic compounds (VOCs). K(m) values were within a factor of three compared with experimental values for 43 out of 53 VOCs. Cross-validation performed as a ratio of predicted residual sum of squares and sum of squares of the response value indicates a value of 0.108 for V(max) and 0.208 for K(m). The integration of QSARs for partition coefficients, V(max) and K(m), as well as setting the K(m) equal to K(i) (metabolic inhibition constant) within the mixture PBPK model allowed to generate simulations of the inhalation pharmacokinetics of benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene and styrene in various mixtures. Overall, the present study indicates the potential usefulness of the QSAR-PBPK modelling approach to provide first-cut evaluations of the kinetics of chemicals in mixtures of increasing complexity, on the basis of chemical structure.

QSARs of aromatic amines: identification of potent carcinogens

In previous investigations, we have developed Quantitative Structure-Activity Relationships (QSAR) models for a series of aromatic amines based on well defined physicochemical descriptors: these QSARs were aimed at: (a) describing the modulation of the carcinogenic potency among the active ones only; and (b) modeling the separation between carcinogens and non-carcinogens. In this analysis based on a larger range of chemicals, we checked and confirmed the validity and robustness of the previous models. Since the identification of high potency carcinogens (which pose the highest risk to human health) is particularly relevant to risk assessment, we also established a new QSAR model that points directly to aromatic amines likely to have high carcinogenic potency.

Quantitative structure-activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach

Worldwide, legislative and governmental efforts are focusing on establishing simple screening tools for identifying those chemicals most likely to cause adverse effects without experimentally testing all chemicals of regulatory concern. This is because even the most basic biological testing of compounds of concern, apart from requiring a huge number of test animals, would be neither resource nor time effective. Thus, alternative approaches such as the one proposed here, quantitative structure-activity relationship (QSAR) modelling, are increasingly being used for identifying the potential health hazards and subsequent regulation of new industrial chemicals. This paper follows up on our earlier work that demonstrated the use of the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach to QSAR modelling for predictions of the carcinogenic potency of nitroso compounds. The data set comprises 56 nitroso compounds which have been bio-assayed in female rats and administered by the oral water route. The QSAR model was able to account for about 81% of the variance in the experimental activity and exhibited good cross-validation statistics. A reasonable interpretation of the TOPS-MODE descriptors was achieved by means of bond contributions, which in turn afforded the recognition of structural alerts (SAs) regarding carcinogenicity. A comparison of the SAs obtained from different data sets showed that experimental factors, such as the sex and the oral administration route, exert a major influence on the carcinogenicity of nitroso compounds. The present and previous QSAR models combined together provide a reliable tool for estimating the carcinogenic potency of yet untested nitroso compounds and they should allow the identification of SAs, which can be used as the basis of prediction systems for the rodent carcinogenicity of these compounds.

The use of computer models in pharmaceutical safety evaluation

With the ever increasing volume of data available to scientists in drug discovery and development, the opportunity to leverage an increasing amount of these data in the assessment of drug safety is clear. The challenge in an environment of increasing data volume is in the structuring and the analysis of these data, such that decisions can be made without excluding information or overstating their meaning. Informatics and modelling play a crucial role in addressing this challenge in two basic ways: a) the data are structured and analysed in a transparent and objective way; and b) new experiments are designed with the model as part of the design process, much like modern experimental physics. Enhancing the use and impact of informatics and modelling on drug discovery is not simply a matter of increasing processor speed and memory capacity. The transformation of raw data to usable, and useful, information is a scientific, technical and, perhaps most importantly, cultural challenge within drug discovery. This review will highlight some of the history, current approaches and promising future directions in this rapidly expanding area.

Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling

The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures. Very few were judged to be strongly related to carcinogenicity, among substructures known to be responsible for carcinogens as revealed from biomedical studies. In order to develop QSAR models for the prediction of the carcinogenicities of a wide variety of chemicals with a satisfactory performance level, the relationship between the carcinogenicity data with improved reliability and a subset of significant descriptors selected from 1,504 Dragon descriptors was analyzed with a support vector machine (SVM) method: the classification function (SVC) for weighted data in LIBSVM program was used to classify chemicals into two carcinogenic categories (positive or negative), where weights were set depending on the reliabilities of the carcinogenicity data. The quality and stability of the models presented were tested by performing a dual cross-validation procedure. A single SVM model as the first step was developed for all the 911 chemicals using 250 selected descriptors, achieving an overall accuracy level, i.e., positive and negative correct estimate, of about 70%. In order to improve the accuracy of the final model, the 911 chemicals were classified into 20 mutually overlapping subgroups according to contained substructures, a specific SVM model was optimized for each subgroup, and the predicted carcinogenicities of the 911 chemicals were determined by the majorities of the outputs of the corresponding SVM models. The model developed on the basis of grouping of chemicals into 20 substructures predicts the carcinogenicities of a wide variety of chemicals with a satisfactory overall accuracy of approximately 80%.

Detection of halogenated organic compounds using immobilized thermophilic dehalogenase

Environmental pollutants containing halogenated organic compounds can cause a plethora of health problems. Detection, quantification, and eventual remediation of halogenated pollutants in the environment are important to human well-being. Toward this end, we previously identified a haloacid dehalogenase, L-HAD(ST), from the thermophile Sulfolobus tokodaii. This thermophilic enzyme is extremely stable and catalyzes, stereospecifically, the dehalogenation of L-2-haloacids. In the current study, we covalently linked L-HAD(ST) to an N-hydroxysuccinimidyl Sepharose resin to construct a highly specific sensor with long shelf life for the detection of L-2-haloacids. The enzyme-modified resin was packed into disposable columns. Samples containing L-2-haloacids were first incubated in the column, and were then collected to quantify the chloride produced through the breakdown of the substrate. The optimum pH of the immobilized enzyme is around 9.5, similar to that of the soluble protein. Its catalytic activity increased with temperature up to the highest temperature measured (50 degrees C). The resin could be fully regenerated after multiple reaction cycles and retained 70% of the initial activity after being stored at 4 degrees C for 6 months. The L-HAD(ST)-modified resin could be used to breakdown and quantify L-2-haloacids spiked in the simulated environmental samples, indicating dehalogenases from extremophiles can potentially be employed in the detection and decontamination of L-2-haloacids.

Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: Part C: use of QSAR and an expert system for the estimation of the mechanism of action of drug-induced hepatobiliary and urinary tract toxicities

This report describes an in silico methodology to predict off-target pharmacologic activities and plausible mechanisms of action (MOAs) associated with serious and unexpected hepatobiliary and urinary tract adverse effects in human patients. The investigation used a database of 8,316,673 adverse event (AE) reports observed after drugs had been marketed and AEs noted in the published literature that were linked to 2124 chemical structures and 1851 approved clinical indications. The Integrity database of drug patent and literature studies was used to find pharmacologic targets and proposed clinical indications. BioEpisteme QSAR software was used to predict possible molecular targets of drug molecules and Derek for Windows expert system software to predict chemical structural alerts and plausible MOAs for the AEs. AEs were clustered into five types of liver injury: liver enzyme disorders, cytotoxic injury, cholestasis and jaundice, bile duct disorders, and gall bladder disorders, and six types of urinary tract injury: acute renal disorders, nephropathies, bladder disorders, kidney function tests, blood in urine, and urolithiasis. Results showed that drug-related AEs were highly correlated with: (1) known drug class warnings, (2) predicted off-target activities of the drugs, and (3) a specific subset of clinical indications for which the drug may or may not have been prescribed.

Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity

The description of quantitative structure-activity relationship (QSAR) models has been a topic for scientific research for more than 40 years and a topic within the regulatory framework for more than 20 years. At present, efforts on QSAR development are increasing because of their promise for supporting reduction, refinement, and/or replacement of animal toxicity experiments. However, their acceptance in risk assessment seems to require a more standardized and scientific underpinning of QSAR technology to avoid possible pitfalls. For this reason, guidelines for QSAR model development recently proposed by the Organization for Economic Cooperation and Development (OECD) [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. OECD Environment Health and Safety Publications: Series on Testing and Assessment No. 69, Paris] are expected to help increase the acceptability of QSAR models for regulatory purposes. The guidelines recommend that QSAR models should be associated with (i) a defined end point, (ii) an unambiguous algorithm, (iii) a defined domain of applicability, (iv) appropriate measures of goodness-of-fit, robustness, and predictivity, and (v) a mechanistic interpretation, if possible [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. The present perspective provides an overview of these guidelines for QSAR model development and their rationale, as well as the promises and pitfalls of using QSAR approaches and these guidelines for predicting metabolism and toxicity of new and existing chemicals.

Additive SMILES-based carcinogenicity models: Probabilistic principles in the search for robust predictions

Optimal descriptors calculated with the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity as continuous values (logTD₅₀). These descriptors can be calculated using correlation weights of SMILES attributes calculated by the Monte Carlo method. A considerable subset of these attributes includes rare attributes. The use of these rare attributes can lead to overtraining. One can avoid the influence of the rare attributes if their correlation weights are fixed to zero. A function, limS, has been defined to identify rare attributes. The limS defines the minimum number of occurrences in the set of structures of the training (subtraining) set, to accept attributes as usable. If an attribute is present less than limS, it is considered “rare”, and thus not used. Two systems of building up models were examined: 1. classic training-test system; 2. balance of correlations for the subtraining and calibration sets (together, they are the original training set: the function of the calibration set is imitation of a preliminary test set). Three random splits into subtraining, calibration, and test sets were analysed. Comparison of abovementioned systems has shown that balance of correlations gives more robust prediction of the carcinogenicity for all three splits (split 1: r_test²=0.7514, s_test=0.684; split 2: r_test²=0.7998, s_test=0.600; split 3: r_test²=0.7192, s_test=0.728).

Machine learning of chemical reactivity from databases of organic reactions

Databases of chemical reactions contain knowledge about the reactivity of specific reagents. Although information is in general only explicitly available for compounds reported to react, it is possible to derive information about substructures that do not react in the reported reactions. Both types of information (positive and negative) can be used to train machine learning techniques to predict if a compound reacts or not with a specific reagent. The whole process was implemented with two databases of reactions, one involving BuNH2 as the reagent, and the other NaCNBH3. Negative information was derived using MOLMAP molecular descriptors, and classification models were developed with Random Forests also based on MOLMAP descriptors. MOLMAP descriptors were based exclusively on calculated physicochemical features of molecules. Correct predictions were achieved for approximately 90% of independent test sets. While NaCNBH3 is a selective reducing reagent widely used in organic synthesis, BuNH2 is a nucleophile that mimics the reactivity of the lysine side chain (involved in an initiating step of the mechanism leading to skin sensitization).

Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives

Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include Vitotox, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-throughput assays combined with innovative data-mining and in silico methods. Various initiatives in this regard have begun, including CAESAR, OSIRIS, CHEMOMENTUM, CHEMPREDICT, OpenTox, EPAA, and ToxCast. In silico methods can be used for priority setting, mechanistic studies, and to estimate potency. Ultimately, such efforts should lead to improvements in application of in silico methods for predicting carcinogenicity to assist industry and regulators and to enhance protection of public health.

Structural motifs modulating the carcinogenic risk of aromatic amines

The structure alerts (SA) for carcinogenicity/mutagenicity are a repository of the science on chemical biological interactions; in addition, they have a crucial role in practical applications for risk assessment. In predictive toxicology, it is crucial that knowledge of SAs is accompanied by knowledge of the structural motifs that modulate their effects. Recently, we have compiled an updated list of SAs implemented in the expert system Tox-tree 1.50 (open source, freely available). These SAs are aimed at discriminating between active and inactive chemicals, and include only modulating factors with a high probability of eliminating completely the effect of the SA. Here we have examined the factors that modulate carcinogenic potency: this is an additional piece of information that can have a role in fine-tuning a risk assessment. The case study selected is the carcinogenic potential of the aromatic amines in rats and mice. As the carcinogenic potency of these compounds is different in mice and rats (correlation coefficient = 0.546), there are both agreements and differences in the pattern of these motifs. Differences are observed mainly for the motifs that decrease the carcinogenic potency of aromatic amines. In mice, substitutions ortho and meta to the amino group tend to decrease the potency, as well as -NO(2) in any position. In rats, these motifs affect the potency to a more limited extent. On the other hand, increasing effects are quite similar in the two animals and are exerted mainly by additional rings, tricyclic systems, five-numbered rings, and N-heteroaromatic systems.

Prediction of chemical carcinogenicity by machine learning approaches

In this paper we report a successful application of machine learning approaches to the prediction of chemical carcinogenicity. Two different approaches, namely a support vector machine (SVM) and artificial neural network (ANN), were evaluated for predicting chemical carcinogenicity from molecular structure descriptors. A diverse set of 844 compounds, including 600 carcinogenic (CG+) and 244 noncarcinogenic (CG-) molecules, was used to estimate the accuracies of these approaches. The database was divided into two sets: the model construction set and the independent test set. Relevant molecular descriptors were selected by a hybrid feature selection method combining Fischer's score and Monte Carlo simulated annealing from a wide set of molecular descriptors, including physiochemical properties, constitutional, topological, and geometrical descriptors. The first model validation method was based a five-fold cross-validation method, in which the model construction set is split into five subsets. The five-fold cross-validation was used to select descriptors and optimise the model parameters by maximising the averaged overall accuracy. The final SVM model gave an averaged prediction accuracy of 90.7% for CG+ compounds, 81.6% for CG- compounds and 88.1% for the overall accuracy, while the corresponding ANN model provided an averaged prediction accuracy of 86.1% for CG+ compounds, 79.3% for CG- compounds and 84.2% for the overall accuracy. These results indicate that the hybrid feature selection method is very efficient and the selected descriptors are truly relevant to the carcinogenicity of compounds. Another model validation method, i.e. a hold-out method, was used to build the classification model using the selected descriptors and the optimised model parameters, in which the whole model construction set was used to build the classification model and the independent test set was used to test the predictive ability of the model. The SVM model gave a prediction accuracy of 87.6% for CG+ compounds, 79.1% for CG- compounds and 85.0% for the overall accuracy. The ANN model gave a prediction accuracy of 85.6% for CG+ compounds, 79.1% for CG- compounds and 83.6% for the overall accuracy. The results indicate that the built models are potentially useful for facilitating the prediction of chemical carcinogenicity of untested compounds.

Predictivity of QSAR

A range of good quality, local QSARs for mutagenicity and carcinogenicity have been assessed and challenged for their predictivity in respect to real external test sets (i.e., chemicals never considered by the authors while developing their models). The QSARs for potency (applicable only to toxic chemicals) generated predictions 30-70% correct, whereas the QSARs for discriminating between active and inactive chemicals were 70-100% correct in their external predictions: thus the latter can be used with good reliability for applicative purposes. On the other hand internal, statistical validation methods, which are often assumed to be good diagnostics for predictivity, did not correlate well with the predictivity of the QSARs when challenged in external prediction tests. Nonlocal models for noncongeneric chemicals were considered as well, pointing to the critical role of an adequate definition of the applicability domain.

Comparative QSTR studies for predicting mutagenicity of nitro compounds

Mutagenicity and carcinogenicity are toxicological endpoints which pose a great concern being the major determinants of cancers and tumours. Nitroarenes possess genotoxic properties as they can form various electrophilic intermediates and adducts with biological systems. Different QSTR techniques were employed to develop models for the prediction of mutagenicity of nitroarenes using a diverse set of 197 nitro aromatic and hetero aromatic molecules. The 2D and 3D QSTR methods used for model development gave statistically significant results. The alignment for 3D methods was obtained by maximum common substructures (MCS) approach, by taking the most mutagenic molecule of the dataset as the template. All the QSTR models were developed with the same set of training and test set molecules. The 3D contours and 2D contribution maps along with molecular fingerprints provide useful information about the mutagenic potentials of the molecules. The GFA based model shows thermodynamic and topological descriptors play an important role in characterizing mutagenicity of nitroarenes. Atomic-level thermodynamic descriptor namely AlogP throws light on hydrophobic features and helps to understand the bilinear model. Topological aspects of these classes of compounds were depicted by the fragment fingerprints and Balaban indices obtained from HQSAR and GFA models, respectively. The predictive abilities of 2D and 3D QSTR models may be useful as a vibrant predictive tool to screen out mutagenic nitroarenes and design safer non-mutagenic nitro compounds.

Prediction of PAH mutagenicity in human cells by QSAR classification

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants of high environmental concern. The experimental data of a mutagenicity test on human B-lymphoblastoid cells (alternative to the Ames bacterial test) for a set of 70 oxo-, nitro- and unsubstituted PAHs, detected in particulate matter (PM), were modelled by Quantitative Structure-Activity Relationships (QSAR) classification methods (k-NN, k-Nearest Neighbour, and CART, Classification and Regression Tree) based on different theoretical molecular descriptors selected by Genetic Algorithms. The best models were validated for predictivity both externally and internally. For external validation, Self Organizing Maps (SOM) were applied to split the original data set. The best models, developed on the training set alone, show good predictive performance also on the prediction set chemicals (sensitivity 69.2-87.1%, specificity 62.5-87.5%). The classification of PAHs according to their mutagenicity, based only on a few theoretical molecular descriptors, allows a preliminary assessment of the human health risk, and the prioritisation of these compounds.

Mechanistic QSAR of aromatic amines: new models for discriminating between homocyclic mutagens and nonmutagens, and validation of models for carcinogens

Because of its environmental and industrial importance, the aromatic amines are the single chemical class most studied for its ability to induce mutations and cancer. The large database of mutagenicity and carcinogenicity results has been studied with Quantitative Structure-Activity Relationship (QSAR) approaches by several authors, leading to models for the following: (a) the mutagenic potency in Salmonella thyphimurium; (b) the carcinogenic potency in rodents; and (c) the discrimination between rodent carcinogens and noncarcinogens. However, satisfactory models for the discrimination between mutagens and nonmutagens are lacking. The present work provides new QSARs for mutagenic/nonmutagenic homocyclic aromatic amines in S. typhimurium strains TA98 and TA100. The two new models are validated by checking their ability to predict the mutagenicity of further aromatic amines not included in the training set, and not used to generate the QSAR models. In addition, we also validated previous QSAR models for the carcinogenicity/noncarcinogenicity of the aromatic amines with external data. The mechanistic implications of the models are discussed in light of the other QSARs for the aromatic amines. The results of the analysis point to two QSAR models (one for mutagenicity and one for rodent carcinogenicity) as reliable tools for the in silico characterization of the risk posed by the aromatic amines.

Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR models

This report presents a comparison of the predictive performance of MC4PC and MDL-QSAR software as well as a method for combining the predictions from both programs to increase overall accuracy. The conclusions are based on 10 x 10% leave-many-out internal cross-validation studies using 1540 training set compounds with 2-year rodent carcinogenicity findings. The models were generated using the same weight of evidence scoring method previously developed [Matthews, E.J., Contrera, J.F., 1998. A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Regul. Toxicol. Pharmacol. 28, 242-264.]. Although MC4PC and MDL-QSAR use different algorithms, their overall predictive performance was remarkably similar. Respectively, the sensitivity of MC4PC and MDL-QSAR was 61 and 63%, specificity was 71 and 75%, and concordance was 66 and 69%. Coverage for both programs was over 95% and receiver operator characteristic (ROC) intercept statistic values were above 2.00. The software programs had complimentary coverage with none of the 1540 compounds being uncovered by both MC4PC and MDL-QSAR. Merging MC4PC and MDL-QSAR predictions improved the overall predictive performance. Consensus sensitivity increased to 67%, specificity to 84%, concordance to 76%, and ROC to 4.31. Consensus rules can be tuned to reflect the priorities of the user, so that greater emphasis may be placed on predictions with high sensitivity/low false negative rates or high specificity/low false positive rates. Sensitivity was optimized to 75% by reclassifying all compounds predicted to be positive in MC4PC or MDL-QSAR as positive, and specificity was optimized to 89% by reclassifying all compounds predicted negative in MC4PC or MDL-QSAR as negative.

Antioxidant and antimutagenic activities of Randia echinocarpa fruit

We report for the first time the antioxidant and antimutagenic activities of fractions from Randia echinocarpa fruit, which is a Rubiaceae plant native to Sinaloa, Mexico. This fruit has been traditionally used in the prevention or treatment of cancer, among other diseases. The pulp of the fruit was sequentially extracted with solvents of different polarity (i.e. hexane, chloroform, methanol and water). A high extraction yield was obtained with methanol (72.17% d.w.). The aqueous extract showed the highest content of phenolics (2.27 mg/g as ferulic acid equivalents) and the highest antioxidant activity based on the beta-carotene bleaching method (486.15). The commercial antioxidant BHT was used as control (835.05). Antimutagenic activity of the aqueous extract (0-500 microg/tube) was evaluated using the Salmonella microsuspension assay (YG1024 strain) and 1-NP as the mutagen (50 and 100 ng/tube). The aqueous extract was neither toxic nor mutagenic and the percentage of inhibition on 1-NP mutagenicity was 32 and 53% at doses of 50 and 100 ng/tube, respectively. The results of the double incubation assay suggest that the extract inhibited the mutagenicity of 1-NP by a combination of desmutagenic and bioantimutagenic effects.

Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds

Prevention of environmentally induced cancers is a major health problem of which solutions depend on the rapid and accurate screening of potential chemical hazards. Lately, theoretical approaches such as the one proposed here - Quantitative Structure-Activity Relationship (QSAR) - are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the Topological Substructural Molecular Design (TOPS-MODE) approach, aiming at predicting the rodent carcinogenicity of a set of nitroso-compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises nitrosoureas (14 chemicals), N-nitrosamines (18 chemicals) C-nitroso-compounds (1 chemical), nitrosourethane (1 chemical) and nitrosoguanidine (1 chemical), which have been bioassayed in male rat using gavage as the route of administration. Here we are especially concerned in gathering the role of both parameters on the carcinogenic activity of this family of compounds. First, the regression model was derived, upon removal of one identified nitrosamine outlier, and was able to account for more than 84% of the variance in the experimental activity. Second, the TOPS-MODE approach afforded the bond contributions -- expressed as fragment contributions to the carcinogenic activity -- that can be interpreted and provide tools for better understanding the mechanisms of carcinogenesis. Finally, and most importantly, we demonstrate the potentialities of this approach towards the recognition of structural alerts for carcinogenicity predictions.

Trypanocidal nitroimidazole derivatives: Relationships among chemical structure and genotoxic activity

Human American trypanosomiasis is resurgent in Latin Americans, and new drugs are urgently required as current medications suffer from a number of drawbacks. Some nitroheterocycles have been demonstrated to exert a potent activity against trypanosomes. However, host toxicity issues halted their development as trypanocides. As part of the efforts to develop new compounds in order to treat parasitic infections, it is important to define their structure-activity relationship. In this study, 5-nitromegazol and two of its analogues, 4-nitromegazol, and 1-methyl-5-nitro-2-imidazolecarboxaldehyde 5-nitroimidazole-thiosemicarbazone, were tested and compared for in vitro induction of DNA damage in human leukocytes by the comet assay, performed at different pHs to better identify the types of damage. Specific oxidatively generated damage to DNA was also measured by using the comet assay with endonucleases. DNA damage was found in 5-nitromegazol-treated cells: oxidative stress appeared as the main source of DNA damage. 4-Nitromegazol did not produce any significant effect, thus confirming that 4-nitroimidazoles isomers have no important biological activity. The 5-nitroimidazole-thiosemicarbazone induced DNA damage with a higher efficiency than 5-nitromegazol. The central role in the reduction process played by the acidic hydrazine proton present in the thiosemicarbazone group but not in the cyclic (thiadiazole) form can contribute to rationalise our results. Given its versatility, thiosemicarbazone moiety could be involved in different reactions with nitrogenous bases (nucleophilic and/or electrophilic attacks).

Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines

The study of prediction of toxicity is very important and necessary because measurement of toxicity is typically time-consuming and expensive. In this paper, Recursive Partitioning (RP) method was used to select descriptors. RP and Support Vector Machines (SVM) were used to construct structure-toxicity relationship models, RP model and SVM model, respectively. The performances of the two models are different. The prediction accuracies of the RP model are 80.2% for mutagenic compounds in MDL's toxicity database, 83.4% for compounds in CMC and 84.9% for agrochemicals in in-house database respectively. Those of SVM model are 81.4%, 87.0% and 87.3% respectively.

A comprehensive model for reproductive and developmental toxicity hazard identification: I. Development of a weight of evidence QSAR database

A weight of evidence (WOE) reproductive and developmental toxicology (reprotox) database was constructed that is suitable for quantitative structure-activity relationship (QSAR) modeling and human hazard identification of untested chemicals. The database was derived from multiple publicly available reprotox databases and consists of more than 10,000 individual rat, mouse, or rabbit reprotox tests linked to 2134 different organic chemical structures. The reprotox data were classified into seven general classes (male reproductive toxicity, female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity), and 90 specific categories as defined in the source reprotox databases. Each specific category contained over 500 chemicals, but the percentage of active chemicals was low, generally only 0.1-10%. The mathematical WOE model placed greater significance on confirmatory observations from repeat experiments, chemicals with multiple findings within a category, and the categorical relatedness of the findings. Using the weighted activity scores, statistical analyses were performed for specific data sets to identify clusters of categories that were correlated, containing similar profiles of active and inactive chemicals. The analysis revealed clusters of specific categories that contained chemicals that were active in two or more mammalian species (trans-species). Such chemicals are considered to have the highest potential risk to humans. In contrast, some specific categories exhibited only single species-specific activities. Results also showed that the rat and mouse were more susceptible to dysmorphogenesis than rabbits (6.1- and 3.6-fold, respectively).

PASS: identification of probable targets and mechanisms of toxicity

Toxicity of chemical compound is a complex phenomenon that may be caused by its interaction with different targets in the organism. Two distinct types of toxicity can be broadly specified: the first one is caused by the strong compound's interaction with a single target (e.g. AChE inhibition); while the second one is caused by the moderate compound's interaction with many various targets. Computer program PASS predicts about 2500 kinds of biological activities based on the structural formula of chemical compounds. Prediction is based on the robust analysis of structure-activity relationships for about 60,000 biologically active compounds. Mean accuracy exceeds 90% in leave-one-out cross-validation. In addition to some kinds of adverse effects and specific toxicity (e.g. carcinogenicity, mutagenicity, etc.), PASS predicts approximately 2000 kinds of biological activities at the molecular level, that providing an estimated profile of compound's action in biological space. Such profiles can be used to recognize the most probable targets, interaction with which might be a reason of compound's toxicity. Applications of PASS predictions for analysis of probable targets and mechanisms of toxicity are discussed.

The expanding role of predictive toxicology: an update on the (Q)SAR models for mutagens and carcinogens

Different regulatory schemes worldwide, and in particular, the preparation for the new REACH (Registration, Evaluation and Authorization of CHemicals) legislation in Europe, increase the reliance on estimation methods for predicting potential chemical hazard. To meet the increased expectations, the availability of valid (Q)SARs becomes a critical issue, especially for endpoints that have complex mechanisms of action, are time-and cost-consuming, and require a large number of animals to test. Here, findings from the survey on (Q)SARs for mutagenicity and carcinogenicity, initiated by the European Chemicals Bureau (ECB) and carried out by the Istituto Superiore di Sanita' are summarized, key aspects are discussed, and a broader view towards future needs and perspectives is given.

Approaches for externally validated QSAR modelling of Nitrated Polycyclic Aromatic Hydrocarbon mutagenicity

Nitrated Polycyclic Aromatic Hydrocarbons (nitro-PAHs), ubiquitous environmental pollutants, are recognized mutagens and carcinogens. A set of mutagenicity data (TA100) for 48 nitro-PAHs was modeled by the Quantitative Structure-Activity Relationships (QSAR) regression method, and OECD principles for QSAR model validation were applied. The proposed Multiple Linear Regression (MLR) models are based on two topological molecular descriptors. The models were validated for predictivity by both internal and external validation. For the external validation, three different splitting approaches, D-optimal Experimental Design, Self Organizing Maps (SOM) and Random Selection by activity sampling, were applied to the original data set in order to compare these methodologies and to select the best descriptors able to model each prediction set chemicals independently of the splitting method applied. The applicability domain was verified by the leverage approach.

Integration of Structure−Activity Relationship and Artificial Intelligence Systems To Improve in Silico Prediction of Ames Test Mutagenicity

The Ames mutagenicity test in Salmonella typhimurium is a bacterial short-term in vitro assay aimed at detecting the mutagenicity caused by chemicals. Mutagenicity is considered as an early alert for carcinogenicity. After a number of decades, several (Q)SAR studies on this endpoint yielded enough evidence to make feasible the construction of reliable computational models for prediction of mutagenicity from the molecular structure of chemicals. In this study, we propose a combination of a fragment-based SAR model and an inductive database. The hybrid system was developed using a collection of 4337 chemicals (2401 mutagens and 1936 nonmutagens) and tested using 753 independent compounds (437 mutagens and 316 nonmutagens). The overall error of this system on the external test set compounds is 15% (sensitivity = 15%, specificity = 15%), which is quantitatively similar to the experimental error of Ames test data (average interlaboratory reproducibility determined by the National Toxicology Program). Moreover, each single prediction is provided with a specific confidence level. The results obtained give confidence that this system can be applied to support early and rapid evaluation of the level of mutagenicity concern.