Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

First report on chemometric modeling of tilapia fish aquatic toxicity to organic chemicals: Toxicity data gap filling

The Toxic Substances Control Act (TSCA) mandates the Environmental Protection Agency (EPA) to document chemicals entering the US. Due to the vast range of toxicity endpoints, experimental toxicological study for all chemicals is impossible to conduct. To address this, in silico methods like QSAR and read-across are strategically used to prioritize testing for chemicals lacking ecotoxicity data. Aquatic toxicity is one of the most critical endpoints directly related to aquatic species, mainly fish, followed by direct to indirect effects on humans through drinking water and fish as food, respectively. Therefore, we have employed the ToxValDB database to curate acute LC50 toxicity data for three Tilapia species covering two different genera, an ideal species for aquatic toxicity testing. Employing the curated dataset, we have developed multiple robust and predictive QSAR and quantitative read-across structure-activity relationship (q-RASAR) models for Tilapia zillii, Oreochromis niloticus, and Oreochromis mossambicus which helped to understand the toxicological mode of action (MoA) of the modeled chemicals and predict the aquatic toxicity of new untested chemicals followed by toxicity data gap filling. The best three QSAR models showed encouraging statistical quality in terms of determination coefficient R2 (0.94, 0.74, and 0.77), cross-validated leave-one-out Q2 (0.90, 0.67 and 0.70), and predictive capability in terms of R2pred (0.95, 0.77, and 0.74) for T. zillii, O. niloticus, and O. mossambicus datasets, respectively. The developed best mathematical models were used for the prediction of aquatic toxicity in terms of pLC50 for 297 untested organic chemicals across three major Tilapia species ranging from 1.841 to 8.561 M in terms of environmental risk assessment.

Development of reliable quantitative structure-toxicity relationship models for toxicity prediction of benzene derivatives using semiempirical descriptors

The Health and environmental hazards of benzene and nitrobenzene (NB) derivatives have remained a topic of interest of researchers. In silico methods for prediction of toxicity of chemicals have proved their worth in accurate forecast of environmental as well as health toxicity and are strongly recommended by regulatory authorities. Two quantitative structure-toxicity relationship (QSTR) models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 NB and 392 benzene derivatives are developed using semiempirical quantum chemical parameters. The best constructed QSTR models have good fitting ability (R² = 0.8053, 0.7591, and 0.8283) and robustness (Q²_LOO = 0.7507, 0.7227, and 0.8194; Q²_LMO = 0.7338, 0.7153, and 0.8172). The external predictivity of all the models are quite good (R²_EXT = 0.8256, 0.9349, and 0.8698). Electronegativity, Cosmo volume, total energy, and molecular weight are responsible for the increase and decrease of toxicity of benzene derivatives against S. obliquus while electronegativity, electrophilicity index, the heat of formation, total energy, hydrophobicity, and cosmo volume are responsible for modulation of toxicity of NB and benzene derivatives toward T. pyriformis. These models fulfill the requirements of all the five OECD principles.

A Quantitative Structure-Activity Relationship Approach to Determine Biotoxicity of Amide Herbicides for Ecotoxicological Risk Assessment

Amide herbicides have been widely applied in agriculture and found to be widespread and affect nontarget organisms in the environment. To better understand the biotoxicity mechanisms and determine the toxicity to the nontarget organisms for the hazard and risk assessment, five QSAR models were developed for the biotoxicity prediction of amide herbicides toward five aquatic and terrestrial organisms (including algae, daphnia, fish, earthworm and avian species), based on toxicity concentration and quantitative molecular descriptors. The results showed that the developed models complied with OECD principles for QSAR validation and presented excellent performances in predictive ability. In combination, the investigated QSAR relationship led to the toxicity mechanisms that eleven electrical descriptors (E_HOMO, E_LUMO, α_xx, α_yy, α_zz, μ, qN^-, Q_xx, Q_yy, qH⁺, and q^-), four thermodynamic descriptors (Cv, S^θ, H^θ, and ZPVE), and one steric descriptor (V_m) were strongly associated with the biotoxicity of amide herbicides. Electrical descriptors showed the greatest impacts on the toxicity of amide herbicides, followed by thermodynamic and steric descriptors.

Application of Aroma Precursors in Food Plant Raw Materials: Biotechnological Aspect

The article is devoted to the analysis of the main factors accompanying the use of aroma precursors, in particular, of a lipid nature, in food raw materials. The prerequisites for the impact on the precursors of aroma with the help of plant enzymes are given. The purpose of the article is to analyze the biotechnological aspect, which is based on enzymatic reactions with aroma precursors and enzymes of plant origin. Features of the mechanism of action of lipid precursors are highlighted, their diversity causing various characteristic reactions is analyzed, and possible end products of reactions with certain odors are noted. The attention is paid to the issue of the status of the naturalness of flavor precursors in food products, which varies in different countries. A scheme of factors influencing the formation of aroma from lipid precursors has been developed. The influence of pigments of carotenoid nature on the aroma is considered, namely: examples of instantaneous change of watermelon aroma to pumpkin one due to isomerization of carotenoids are given. The main factors of enzymatic formation of aroma from precursors of polyunsaturated fatty acids for their effective use by creating micromicelles are summarized. A way to overcome the barrier of interaction between lipid precursors of a hydrophobic nature and hydrophilic enzymes has been substantiated. It is proposed to accelerate enzymatic reactions under in vitro conditions and use the vacuum effect to overcome the barrier between enzymes and precursors. To explain the effect of vacuum in a system with enzymes, ideas about disjoining pressure and the reasonable expediency of its use are considered. A schematic process flow diagram for the restoration of aroma lost during the technological processing of raw materials is given; it demonstrates the factors for ensuring interfacial activation conditions for enzymes and aroma precursors.

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method

Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes.

Systematic QSAR and iQCCR modelling of fused/non-fused aromatic hydrocarbons (FNFAHs) carcinogenicity to rodents: reducing unnecessary chemical synthesis and animal testing

The prediction of new or untested FNFAHs will reduce unnecessary chemical synthesis and animal testing, and contribute to the design of safer chemicals for production activities.

Prediction of toxicity of secondary metabolites

The prediction of toxicological endpoints has gained broad acceptance; it is widely applied in early stages of drug discovery as well as for impurities obtained in the production of generic or equivalent products. In this work, we describe methodologies for the prediction of toxicological endpoints compounds, with a particular focus on secondary metabolites. Case studies include toxicity prediction of natural compound databases with anti-diabetic, anti-malaria and anti-HIV properties.

In silico genotoxicity and carcinogenicity prediction for food-relevant secondary plant metabolites

Humans are exposed to thousands of different secondary plant metabolites which may have beneficial health effects, but numerous compounds may also have toxic potential. In the present study we have examined the genotoxic and carcinogenic potential of 609 food-relevant phytochemicals by using computer models for toxicity prediction. We developed a scoring method and combined the results of different models to increase the predictive power. A combination of the VEGA models SARpy, KNN, ISS, and CAESAR, and of the LAZAR model "Salmonella typhimurium" for genotoxicity prediction performed better than the single models regarding specificity and accuracy. Statistical evaluation of the combined model for carcinogenicity prediction was not possible due to the low number of substances suitable for model validation. The in silico results of the present exercise will be useful for priority setting purposes regarding future risk assessment of secondary plant metabolites. Based on our analysis, (-)-asimilobine, aloin, annoretine, chrysothrone, coptisine, elymoclavine, and thalicminine were predicted to be genotoxic with high probability and may therefore be selected for subsequent experimental genotoxicity testing. Moreover, the class of pyrrolizidine alkaloids is suggested to be a high priority subject for further studies as these substances have been predicted to be carcinogenic with high probability.

The potential role of in silico approaches to identify novel bioactive molecules from natural resources

In recent years, integration of in silico approaches to natural product (NP) research reawakened the declined interest in NP-based drug discovery efforts. In particular, advancements in cheminformatics enabled comparison of NP databases with contemporary small-molecule libraries in terms of molecular properties and chemical space localizations. Virtual screening and target fishing approaches were successful in recognizing the untold macromolecular targets for NPs to exploit the unmet therapeutic needs. Developments in molecular docking and scoring methods along with molecular dynamics enabled to predict the target-ligand interactions more accurately taking into consideration the remarkable structural complexity of NPs. Hence, innovative in silico strategies have contributed valuably to the NP research in drug discovery processes as reviewed herein. [Formula: see text].

In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach

Certain drugs are nitroaromatic compounds, which are potentially toxic. As such, it is of practical importance to assess and predict their mutagenic potency in the process of drug discovery. A classical quantitative structure-activity relationship (QSAR) model was developed using the linear partial least square (PLS) scheme to understand the underline mutagenic mechanism and a non-classical QSAR model was derived using the machine learning-based hierarchical support vector regression (HSVR) to predict the mutagenicity of nitroaromatic compounds based on a series of mutagenicity data (TA98-S9). It was observed that HSVR performed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical validations. A mock test designated to mimic real challenges also confirmed the better performance of HSVR. Furthermore, HSVR exhibited superiority in predictivity, generalization capabilities, consistent performance, and robustness when compared with various published predictive models. PLS, conversely, revealed some mechanistically interpretable relationships between descriptors and mutagenicity. Thus, this two-QSAR approach using the predictive HSVR and interpretable PLS models in a synergistic fashion can be adopted to facilitate drug discovery and development by designing safer drug candidates with nitroaromatic moiety.

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals

The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (M_W), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development.

Development of Quantitative Structure-Activity Relationship Models for Predicting Chronic Toxicity of Substituted Benzenes to Daphnia Magna

The chronic toxicity of anthropogenic molecules such as substituted benzenes to Daphnia magna is a basic eco-toxicity parameter employed to assess their environmental risk. As the experimental methods are laborious, costly, and time-consuming, development in silico models for predicting the chronic toxicity is vitally important. In this study, on the basis of five molecular descriptors and 48 compounds, a quantitative structure-property relationship model that can predict the chronic toxicity of substituted benzenes were developed by employing multiple linear regressions. The correlation coefficient (R (2)) and root-mean square error (RMSE) for the training set were 0.836 and 0.390, respectively. The developed model was validated by employing 10 compounds tested in our lab. The R EXT (2) and RMSE EXT for the validation set were 0.736 and 0.490, respectively. To further characterizing the toxicity mechanism of anthropogenic molecules to Daphnia, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed.

Mind the Gap! A Journey towards Computational Toxicology

Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale. However, the predictive potential of these models presents strengths and weaknesses. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. On the other, the unconscious use of in silico methods can mislead end-users with elusive results. The focus of this review is on the basic scientific and regulatory recommendations in the derivation and application of computational models. Attention is paid to examine the interplay between computational toxicology and drug discovery and development. Avoiding the easy temptation of an overoptimistic future, we report our view on what can, or cannot, realistically be done. Indeed, studies of safety/toxicity represent a key element of chemical prioritization programs carried out by chemical industries, and primarily by pharmaceutical companies.

Assessing herbal products with health claims

Herbs, herbal extracts, or phytochemicals are broadly used as foods, drugs, and as traditional medicines. These are well regulated in Europe, with thorough controls on both safety and efficacy or validity of health claims. However, the distinction between medicines and foods with health claims is not always clear. In addition, there are several cases of herbal products that claim benefits that are not scientifically demonstrated. This review details the European Union (EU) legislative framework that regulates the approval and marketing of herbal products bearing health claims as well as the scientific evidence that is needed to support such claims. To illustrate the latter, we focus on phytoecdysteroid (PE)-containing preparations, generally sold to sportsmen and bodybuilders. We review the limited published scientific evidence that supports claims for these products in humans. In addition, we model the in silico binding between different PEs and human nuclear receptors and discuss the implications of these putative bindings in terms of the mechanism of action of this family of compounds. We call for additional research to validate the safety and health-promoting properties of PEs and other herbal compounds, for the benefit of all consumers.

Regulating chemicals: law, science, and the unbearable burdens of regulation

The challenges of regulating industrial chemicals remain unresolved in the United States. The Toxic Substances Control Act (TSCA) of 1976 was the first legislation to extend coverage to the regulation of industrial chemicals, both existing and newly registered. However, decisions related to both law and science that were made in passing this law inevitably rendered it ineffectual. Attempts to fix these shortcomings have not been successful. In light of the European Union's passage of innovative principles and requirements for chemical regulation, it is no longer possible to deny the opportunity and need for reform in US law and practice.

Bio/chemoinformatics in India: an outlook

With the advent of significant establishment and development of Internet facilities and computational infrastructure, an overview on bio/chemoinformatics is presented along with its multidisciplinary facts, promises and challenges. The Government of India has paved the way for more profound research in biological field with the use of computational facilities and schemes/projects to collaborate with scientists from different disciplines. Simultaneously, the growth of available biomedical data has provided fresh insight into the nature of redundant and compensatory data. Today, bioinformatics research in India is characterized by a powerful grid computing systems, great variety of biological questions addressed and the close collaborations between scientists and clinicians, with a full spectrum of focuses ranging from database building and methods development to biological discoveries. In fact, this outlook provides a resourceful platform highlighting the funding agencies, institutes and industries working in this direction, which would certainly be of great help to students seeking their career in bioinformatics. Thus, in short, this review highlights the current bio/chemoinformatics trend, educations, status, diverse applicability and demands for further development.

Donepezil + propargylamine + 8-hydroxyquinoline hybrids as new multifunctional metal-chelators, ChE and MAO inhibitors for the potential treatment of Alzheimer's disease

The synthesis, biochemical evaluation, ADMET, toxicity and molecular modeling of novel multi-target-directed Donepezil + Propargylamine + 8-Hydroxyquinoline (DPH) hybrids 1-7 for the potential prevention and treatment of Alzheimer's disease is described. The most interesting derivative was racemic α-aminotrile4-(1-benzylpiperidin-4-yl)-2-(((8-hydroxyquinolin-5-yl)methyl)(prop-2-yn-1-yl)amino) butanenitrile (DPH6) [MAO A (IC50 = 6.2 ± 0.7 μM; MAO B (IC50 = 10.2 ± 0.9 μM); AChE (IC50 = 1.8 ± 0.1 μM); BuChE (IC50 = 1.6 ± 0.25 μM)], an irreversible MAO A/B inhibitor and mixed-type AChE inhibitor with metal-chelating properties. According to docking studies, both DPH6 enantiomers interact simultaneously with the catalytic and peripheral site of EeAChE through a linker of appropriate length, supporting the observed mixed-type AChE inhibition. Both enantiomers exhibited a relatively similar position of both hydroxyquinoline and benzyl moieties with the rest of the molecule easily accommodated in the relatively large cavity of MAO A. For MAO B, the quinoline system was hosted at the cavity entrance whereas for MAO A this system occupied the substrate cavity. In this disposition the quinoline moiety interacted directly with the FAD aromatic ring. Very similar binding affinity values were also observed for both enantiomers with ChE and MAO enzymes. DPH derivatives exhibited moderate to good ADMET properties and brain penetration capacity for CNS activity. DPH6 was less toxic than donepezil at high concentrations; while at low concentrations both displayed a similar cell viability profile. Finally, in a passive avoidance task, the antiamnesic effect of DPH6 was tested on mice with experimentally induced amnesia. DPH6 was capable to significantly decrease scopolamine-induced learning deficits in healthy adult mice.

Design, synthesis, pharmacological evaluation, QSAR analysis, molecular modeling and ADMET of novel donepezil-indolyl hybrids as multipotent cholinesterase/monoamine oxidase inhibitors for the potential treatment of Alzheimer's disease

The design, synthesis, and pharmacological evaluation of donepezil-indolyl based amines 7-10, amides 12-16, and carboxylic acid derivatives 5 and 11, as multipotent ASS234 analogs, able to inhibit simultaneously cholinesterase (ChE) and monoamine oxidase (MAO) enzymes for the potential treatment of Alzheimer's disease (AD), is reported. Theoretical studies using 3D-Quantitative Structure-Activity Relationship (3D-QSAR) was used to define 3D-pharmacophores for inhibition of MAO A/B, AChE, and BuChE enzymes. We found that, in general, and for the same substituent, amines are more potent ChE inhibitors (see compounds 12, 13 versus 7 and 8) or equipotent (see compounds 14, 15 versus 9 and 10) than the corresponding amides, showing a clear EeAChE inhibition selectivity. For the MAO inhibition, amides were not active, and among the amines, compound 14 was totally MAO A selective, while amines 15 and 16 were quite MAO A selective. Carboxylic acid derivatives 5 and 11 showed a multipotent moderate selective profile as EeACE and MAO A inhibitors. Propargylamine 15 [N-((5-(3-(1-benzylpiperidin-4-yl)propoxy)-1-methyl-1H-indol-2-yl)methyl)prop-2-yn-1-amine] resulted in the most potent hMAO A (IC50 = 5.5 ± 1.4 nM) and moderately potent hMAO B (IC50 = 150 ± 31 nM), EeAChE (IC50 = 190 ± 10 nM), and eqBuChE (IC50 = 830 ± 160 nM) inhibitor. However, the analogous N-allyl and the N-morpholine derivatives 16 and 14 deserve also attention as they show an attractive multipotent profile. To sum up, donepezil-indolyl hybrid 15 is a promising drug for further development for the potential prevention and treatment of AD.

Reassessing the two-year rodent carcinogenicity bioassay: a review of the applicability to human risk and current perspectives

The 2-year rodent carcinogenicity test has been the regulatory standard for the prediction of human outcomes for exposure to industrial and agro-chemicals, food additives, pharmaceuticals and environmental pollutants for over 50 years. The extensive experience and data accumulated over that time has spurred a vigorous debate and assessment, particularly over the last 10 years, of the usefulness of this test in terms of cost and time for the information obtained. With renewed interest in the United States and globally, plus new regulations in the European Union, to reduce, refine and replace sentinel animals, this review offers the recommendation that reliance on information obtained from detailed shorter-term, 6 months rodent studies, combined with genotoxicity and chemical mode of action can realize effective prediction of human carcinogenicity instead of the classical two year rodent bioassay. The aim of carcinogenicity studies should not be on the length of time, and by obligation, number of animals expended but on the combined systemic pathophysiologic influence of a suspected chemical in determining disease. This perspective is in coordination with progressive regulatory standards and goals globally to utilize effectively resources of animal usage, time and cost for the goal of human disease predictability.

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes.

Mutagenicity, carcinogenicity, and other end points

Aiming at understanding the structural and physical chemical basis of the biological activity of chemicals, the science of structure-activity relationships has seen dramatic progress in the last decades. Coarse-grain, qualitative approaches (e.g., the structural alerts), and fine-tuned quantitative structure-activity relationship models have been developed and used to predict the toxicological properties of untested chemicals. More recently, a number of approaches and concepts have been developed as support to, and corollary of, the structure-activity methods. These approaches (e.g., chemical relational databases, expert systems, software tools for manipulating the chemical information) have dramatically expanded the reach of the structure-activity work; at present, they are powerful and inescapable tools for computer chemists, toxicologists, and regulators. This chapter, after a general overview of traditional and well-known approaches, gives a detailed presentation of the latter more recent support tools freely available in the public domain.

First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines

Different regulatory agencies in food and drug administration and environmental protection worldwide are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. Carcinogenicity is a toxicity endpoint of major concern in recent times. Interspecies toxicity correlations may provide a tool for estimating sensitivity towards toxic chemical exposure with known levels of uncertainty for a diversity of wildlife species. In this background, we have developed quantitative interspecies structure-carcinogenicity correlation models for rat and mouse [rodent species according to the Organization for Economic Cooperation and Development (OECD) guidelines] based on the carcinogenic potential of 166 organic chemicals with wide diversity of molecular structures, spanning a large number of chemical classes and biological mechanisms. All the developed models have been assessed according to the OECD principles for the validation of QSAR models. Consensus predictions for carcinogenicity of the individual compounds are presented here for any one species when the data for the other species are available. Informative illustrations of the contributing structural fragments of chemicals which are responsible for specific carcinogenicity endpoints are identified by the developed models. The models have also been used to predict mouse carcinogenicities of 247 organic chemicals (for which rat carcinogenicities are present) and rat carcinogenicities of 150 chemicals (for which mouse carcinogenicities are present). Discriminatory features for rat and mouse carcinogenicity values have also been explored.

Acute toxicity and n-octanol/water partition coefficients of substituted thiophenols: determination and QSAR analysis

The acute toxicity (-log EC(50)) to Photobacterium phosphoreum and the n-octanol/water partition coefficient (log K(ow)) of 31 kinds of substituted thiophenols were determined at 298.15K. The -log EC(50) values of studied chemicals are between 4.26 and 5.89. Their log K(ow) values are between 1.34 and 4.02. Comparative molecular field (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models established were successful in predicting -log EC(50) and log K(ow) values of halogenated, methylic, amino and methoxy thiophenols. The size of molecule is the main factor influencing the properties. No correlation was found between the properties and their structural and thermodynamic descriptors from DFT calculation.

Efficient approach for simultaneous estimation of multiple health-promoting effects of foods

The investigation of new food constituents for purposes of disease prevention or health promotion is an area of increasing interest in food science. This paper proposes a new system that allows for simultaneous estimation of the multiple health-promoting effects of food constituents using informatics. The model utilizes expression data of intracellular marker proteins as descriptors that reply to stimulation of a constituent. To estimate three health-promoting effects, namely, cancer cell growth suppression activity, antiviral activity, and antioxidant stress activity, each model was constructed using expression data of marker proteins as input data and health-promoting effects as the output value. When prediction performances of three types of mathematical models constructed by simple, multiple regressions, or artificial neural network (ANN), were compared, the most adequate model was the one constructed using an ANN. There were no statistically significant differences between the actual data and estimated values calculated by the ANN models. This system was able to simultaneously estimate health-promoting effects with reasonable precision from the same expression data of marker proteins. This novel system should prove to be an interesting platform for evaluation of the health-promoting effects of food.

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.

Vanilloid derivatives as tyrosinase inhibitors driven by virtual screening-based QSAR models

A number of vanilloids have been tested as tyrosinase inhibitors using Ligand-Based Virtual Screening (LBVS) driven by QSAR (Quantitative Structure-Activity Relationship) models as the multi-agent classification system. A total of 81 models were used to screen this family. Then, a preliminary cluster analysis of the selected chemicals was carried out based on their bioactivity to detect possible similar substructural features among these compounds and the active database used in the QSAR model construction. The compounds identified were tested in vitro to corroborate the results obtained in silico. Among them, two chemicals, isovanillin (K(M) (app) = 1.08 mM) near to kojic acid (reference drug) in one cluster and isovanillyl alcohol (K(M) (app) = 0.88 mM) at the same distance as hydroquinone (reference drug) in another cluster showed inhibitory activity against tyrosinase. The algorithm proposed here could result in a suitable approach for faster and more effective identification of hit and/or lead compounds with tyrosinase inhibitory activity, helping to shorten the long pipeline in the research of novel depigmenting agents to treat skin disorders.

Toxicity prediction of compounds from turmeric (Curcuma longa L)

Turmeric belongs to the ginger family Zingiberaceae. Currently, cheminformatics approaches are not employed in any of the spices to study the medicinal properties traditionally attributed to them. The aim of this study is to find the most efficacious molecule which does not have any toxic effects. In the present study, toxicity of 200 chemical compounds from turmeric were predicted (includes bacterial mutagenicity, rodent carcinogenicity and human hepatotoxicity). The study shows out of 200 compounds, 184 compounds were predicted as toxigenic, 136 compounds are mutagenic, 153 compounds are carcinogenic and 64 compounds are hepatotoxic. To cross validate our results, we have chosen the popular curcumin and found that curcumin and its derivatives may cause dose dependent hepatotoxicity. The results of these studies indicate that, in contrast to curcumin, few other compounds in turmeric which are non-mutagenic, non-carcinogenic, non-hepatotoxic, and do not have any side-effects. Hence, the cost-effective approach presented in this paper could be used to filter toxic compounds from the drug discovery lifecycle.

QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors

One of the major economic alternatives to experimental toxicity testing is the use of quantitative structure-activity relationships (QSARs) which are used in formulating regulatory decisions of environmental protection agencies. In this background, we have modeled a large diverse group of 297 chemicals for their toxicity to Daphnia magna using mechanistically interpretable descriptors. Three-dimensional (3D) (electronic and spatial) and two-dimensional (2D) (topological and information content indices) descriptors along with physicochemical parameter logK(o/w) (n-octanol/water partition coefficient) and structural descriptors were used as predictor variables. The QSAR models were developed by stepwise multiple linear regression (MLR), partial least squares (PLS), genetic function approximation (GFA), and genetic PLS (G/PLS). All the models were validated internally and externally. Among several models developed using different chemometric tools, the best model based on both internal and external validation characteristics was a PLS equation with 7 descriptors and three latent variables explaining 67.8% leave-one-out predicted variance and 74.1% external predicted variance. The PLS model suggests that higher lipophilicity and electrophilicity, less negative charge surface area and presence of ether linkage, hydrogen bond donor groups and acetylenic carbons are responsible for greater toxicity of chemicals. The developed model may be used for prediction of toxicity, safety and risk assessment of chemicals to achieve better ecotoxicological management and prevent adverse health consequences.

Assessment of herbal medicinal products: challenges, and opportunities to increase the knowledge base for safety assessment

Although herbal medicinal products (HMP) have been perceived by the public as relatively low risk, there has been more recognition of the potential risks associated with this type of product as the use of HMPs increases. Potential harm can occur via inherent toxicity of herbs, as well as from contamination, adulteration, plant misidentification, and interactions with other herbal products or pharmaceutical drugs. Regulatory safety assessment for HMPs relies on both the assessment of cases of adverse reactions and the review of published toxicity information. However, the conduct of such an integrated investigation has many challenges in terms of the quantity and quality of information. Adverse reactions are under-reported, product quality may be less than ideal, herbs have a complex composition and there is lack of information on the toxicity of medicinal herbs or their constituents. Nevertheless, opportunities exist to capitalise on newer information to increase the current body of scientific evidence. Novel sources of information are reviewed, such as the use of poison control data to augment adverse reaction information from national pharmacovigilance databases, and the use of more recent toxicological assessment techniques such as predictive toxicology and omics. The integration of all available information can reduce the uncertainty in decision making with respect to herbal medicinal products. The example of Aristolochia and aristolochic acids is used to highlight the challenges related to safety assessment, and the opportunities that exist to more accurately elucidate the toxicity of herbal medicines.

Computational Toxicology Approaches at the US Food and Drug Administration

For over a decade, the United States Food and Drug Administration (US FDA) has been engaged in the applied research, development, and evaluation of computational toxicology methods used to support the safety evaluation of a diverse set of regulated products. The basis for evaluating computational toxicology methods is multi-factorial, including the potential for increased efficiency, reduction in the numbers of animals used, lower costs, and the need to explore emerging technologies that support the goals of the US FDA's Critical Path Initiative (e.g. to make decision support information available early in the drug review process). The US FDA's efforts have been facilitated by agency-approved data-sharing agreements between government and commercial software developers. This commentary review describes former and current scientific initiatives at the agency, in the area of computational toxicology methods. In particular, toxicology-based QSAR models, ToxML databases and knowledgebases will be addressed. Notably, many of the computational toxicology tools available are commercial products — however, several are emerging as non-commercial products, which are freely-available to the public, and which will facilitate the understanding of how these programs work and avoid the “black box” paradigm. Through productive collaborations, the US FDA Center for Drug Evaluation and Research, and the Center for Food Safety and Applied Nutrition, have worked together to evaluate, develop and apply these methods to chemical toxicity endpoints of regulatory interest.

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.