Quantitative structure-activity relationships of mutagenic and carcinogenic aromatic amines

Insight into chemically reactive metabolites of aliphatic amine pollutants: A de novo prediction strategy and case study of sertraline

The uncommon metabolic pathways of organic pollutants are easily overlooked, potentially leading to idiosyncratic toxicity. Prediction of their biotransformation associated with the toxic effects is the very purpose that this work focuses, to develop a de novo method to mechanistically predict the reactive toxicity pathways of uncommon metabolites from start aliphatic amine molecules, which employed sertraline triggered by CYP450 enzymes as a model system, as there are growing concerns about the effects on human health posed by antidepressants in the aquatic environment. This de novo prediction strategy combines computational and experimental methods, involving DFT calculations upon sequential growth, in vitro and in vivo assays, dissecting chemically reactive mechanism relevant to toxicity, and rationalizing the fundamental factors. Significantly, desaturation and debenzylation-aromatization as the emerging metabolic pathways of sertraline have been elucidated, with the detection of DNA adducts of oxaziridine metabolite in mice, highlighting the potential reactive toxicity. Molecular orbital analysis supports the reactivity preference for toxicological-relevant C-N desaturation over N-hydroxylation of sertraline, possibly extended to several other aliphatic amines based on the Bell-Evans-Polanyi principle. It was further validated toward some other wide-concerned aliphatic amine pollutants involving atrazine, ε-caprolactam, 6PPD via in silico and in vitro assays, thereby constituting a complete path for de novo prediction from case study to general applications.

DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction

The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity testing of new compounds is very necessary before being put on the market. Currently, many methods have been used to predict the carcinogenicity of compounds. However, most methods have limited predictive power and there is still much room for improvement. In this study, we construct a deep learning model based on capsule network and attention mechanism named DCAMCP to discriminate between carcinogenic and non-carcinogenic compounds. We train the DCAMCP on a dataset containing 1564 different compounds through their molecular fingerprints and molecular graph features. The trained model is validated by fivefold cross-validation and external validation. DCAMCP achieves an average accuracy (ACC) of 0.718 ± 0.009, sensitivity (SE) of 0.721 ± 0.006, specificity (SP) of 0.715 ± 0.014 and area under the receiver-operating characteristic curve (AUC) of 0.793 ± 0.012. Meanwhile, comparable results can be achieved on an external validation dataset containing 100 compounds, with an ACC of 0.750, SE of 0.778, SP of 0.727 and AUC of 0.811, which demonstrate the reliability of DCAMCP. The results indicate that our model has made progress in cancer risk assessment and could be used as an efficient tool in drug design.

Finding a Novel Chalcone-Cinnamic Acid Chimeric Compound with Antiproliferative Activity against MCF-7 Cell Line Using a Free-Wilson Type Approach

In this work, we carried out the design and synthesis of new chimeric compounds from the natural cytotoxic chalcone 2',4'-dihydroxychalcone (2',4'-DHC, A) in combination with cinnamic acids. For this purpose, a descriptive and predictive quantitative structure-activity relationship (QSAR) model was developed to study the chimeric compounds' anti-cancer activities against human breast cancer MCF-7, relying on the presence or absence of structural motifs in the chalcone structure, like in a Free-Wilson approach. For this, we used 207 chalcone derivatives with a great variety of structural modifications over the α and β rings, such as halogens (F, Cl, and Br), heterocyclic rings (piperazine, piperidine, pyridine, etc.), and hydroxyl and methoxy groups. The multilinear equation was obtained by the genetic algorithm technique, using logIC₅₀ as a dependent variable and molecular descriptors (constitutional, topological, functional group count, atom-centered fragments, and molecular properties) as independent variables, with acceptable statistical parameter values (R2 = 86.93, Q2_LMO = 82.578, Q2_BOOT = 80.436, and Q2_EXT = 80.226), which supports the predictive ability of the model. Considering the aromatic and planar nature of the chalcone and cinnamic acid cores, a structural-specific QSAR model was developed by incorporating geometrical descriptors into the previous general QSAR model, again, with acceptable parameters (R2 = 85.554, Q2_LMO = 80.534, Q2_BOOT = 78.186, and Q2_EXT = 79.41). Employing this new QSAR model over the natural parent chalcone 2',4'-DHC (A) and the chimeric compound 2'-hydroxy,4'-cinnamate chalcone (B), the predicted cytotoxic activity was achieved with values of 55.95 and 17.86 µM, respectively. Therefore, to corroborate the predicted cytotoxic activity compounds A and B were synthesized by two- and three-step reactions. The structures were confirmed by ¹H and ¹³C NMR and ESI+MS analysis and further evaluated in vitro against HepG2, Hep3B (liver), A-549 (lung), MCF-7 (breast), and CasKi (cervical) human cancer cell lines. The results showed IC₅₀ values of 11.89, 10.27, 56.75, 14.86, and 29.72 µM, respectively, for the chimeric cinnamate chalcone B. Finally, we employed B as a molecular scaffold for the generation of cinnamate candidates (C-K), which incorporated structural motifs that enhance the cytotoxic activity (pyridine ring, halogens, and methoxy groups) according to our QSAR model. ADME/tox in silico analysis showed that the synthesized compounds A and B, as well as the proposed chalcones C and G, are the best candidates with adequate drug-likeness properties. From all these results, we propose B (as a molecular scaffold) and our two QSAR models as reliable tools for the generation of anti-cancer compounds over the MCF-7 cell line.

Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library

High-throughput screening (HTS) assays for bioactivity in the Tox21 program aim to evaluate an array of different biological targets and pathways, but a significant barrier to interpretation of these data is the lack of high-throughput screening (HTS) assays intended to identify non-specific reactive chemicals. This is an important aspect for prioritising chemicals to test in specific assays, identifying promiscuous chemicals based on their reactivity, as well as addressing hazards such as skin sensitisation which are not necessarily initiated by a receptor-mediated effect but act through a non-specific mechanism. Herein, a fluorescence-based HTS assay that allows the identification of thiol-reactive compounds was used to screen 7,872 unique chemicals in the Tox21 10K chemical library. Active chemicals were compared with profiling outcomes using structural alerts encoding electrophilic information. Random Forest classification models based on chemical fingerprints were developed to predict assay outcomes and evaluated through 10-fold stratified cross validation (CV). The mean CV Balanced Accuracy of the validation set was 0.648. The model developed shows promise as a tool to screen untested chemicals for their potential electrophilic reactivity based solely on chemical structural features.

Artificial intelligence uncovers carcinogenic human metabolites

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger malignant transformation of cells. Accurate prediction of carcinogens is an ever-challenging task owing to the limited information about bona fide (non-)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable and outperforms existing best-practice methods for carcinogenicity prediction. Metabokiller unraveled potential carcinogenic human metabolites. To cross-validate Metabokiller predictions, we performed multiple functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites, namely 4-nitrocatechol and 3,4-dihydroxyphenylacetic acid, and observed high synergy between Metabokiller predictions and experimental validations.

In Silico simulation of Cytochrome P450-Mediated metabolism of aromatic amines: A case study of N-Hydroxylation

Aromatic amines, the widely used raw materials in industry, cause long-term exposure to human bodies. They can be metabolized by cytochrome P450 enzymes to form active electrophilic compounds, which will potentially react with nucleophilic DNA to exert carcinogenesis. The short lifetime and versatility of the oxidant (a high-valent iron (IV)-oxo species, compound I) of P450 enzymes prompts us to use theoretical methods to investigate the metabolism of aromatic amines. In this work, the density functional theory (DFT) has been employed to simulate the hydroxylation metabolism through H-abstraction and to calculate the activation energy of this reaction for 28 aromatic amines. The results indicate that the steric effects, inductive effects and conjugative effects greatly contribute to the metabolism activity of the chemicals. The further correlation reveals that the dissociation energy of -NH₂ (BDE_N-H) can successfully predict the time-consuming calculated activation energy (R² for aromatic and heteroaromatic amines are 0.93 and 0.86, respectively), so BDE_N-H can be taken as a key parameter to characterize the relative stability of aromatic amines in P450 enzymes and further to quickly assess their potential toxicity. The validation results prove such relationship has good statistical performance (q_cv² for aromatic and heteroaromatic amines are 0.95 and 0.90, respectively) and can be used to other aromatic amines in the application domain, greatly reducing computational cost and providing useful support for experimental research.

Profiles of primary aromatic amines, nicotine, and cotinine in indoor dust and associated human exposure in China

Despite the widespread use of primary aromatic amines (AAs) in consumer products, little is known about their prevalence in house dust. In this study, we investigated the occurrence of 35 AAs and two tobacco chemical markers (nicotine and its breakdown product cotinine) in 119 samples of house dust collected from five provinces in China. Ten of the 35 AAs and [nicotine and cotinine] were found in >80% and 100% of the samples, respectively, at concentration ranges of 29.1-19,200 (median: 700 ng/g) and 23.2-22,400 (4600) ng/g, respectively. Aniline was the predominant AA found in all dust samples (median: 257 ng/g). Dust samples from Henan and Shanxi provinces contained higher summed concentrations of the 10 AAs than those from Sichuan and Shandong, although the concentrations did not vary significantly among the five provinces (p > 0.05). A significant (p = 0.048), positive correlation (r = 0.882) existed between concentrations of nicotine and cotinine in dust samples. Similarly, concentrations of AAs were significantly correlated with those of nicotine in dust samples. Dyestuffs, rubber products, polyurethane foam and tobacco smoke are the major sources of AAs in the indoor environment. The estimated daily intakes (EDI) through dust ingestion ranged from 0.349 (adults) to 6.62 ng/kg-bw/day (toddlers) for AAs and from 1.27 to 51.1 ng/kg-bw/day for nicotine which are well below the current tolerable daily intakes.

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.

Discovery and Optimization of Orally Bioavailable Phthalazone and Cinnolone Carboxylic Acid Derivatives as S1P2 Antagonists against Fibrotic Diseases

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease. Current treatments only slow down disease progression, making new therapeutic strategies compelling. Increasing evidence suggests that S1P2 antagonists could be effective agents against fibrotic diseases. Our compound collection was mined for molecules possessing substructure features associated with S1P2 activity. The weakly potent indole hit 6 evolved into a potent phthalazone series, bearing a carboxylic acid, with the aid of a homology model. Suboptimal pharmacokinetics of a benzimidazole subseries were improved by modifications targeting potential interactions with transporters, based on concepts deriving from the extended clearance classification system (ECCS). Scaffold hopping, as a part of a chemical enablement strategy, permitted the rapid exploration of the position adjacent to the carboxylic acid. Compound 38, with good pharmacokinetics and in vitro potency, was efficacious at 10 mg/kg BID in three different in vivo mouse models of fibrotic diseases in a therapeutic setting.

Mechanism-Based Insights into Removing the Mutagenicity of Aromatic Amines by Small Structural Alterations

Aromatic and heteroaromatic amines (ArNH₂) are activated by cytochrome P450 monooxygenases, primarily CYP1A2, into reactive N-arylhydroxylamines that can lead to covalent adducts with DNA nucleobases. Hereby, we give hands-on mechanism-based guidelines to design mutagenicity-free ArNH₂. The mechanism of N-hydroxylation of ArNH₂ by CYP1A2 is investigated by density functional theory (DFT) calculations. Two putative pathways are considered, the radicaloid route that goes via the classical ferryl-oxo oxidant and an alternative anionic pathway through Fenton-like oxidation by ferriheme-bound H₂O₂. Results suggest that bioactivation of ArNH₂ follows the anionic pathway. We demonstrate that H-bonding and/or geometric fit of ArNH₂ to CYP1A2 as well as feasibility of both proton abstraction by the ferriheme-peroxo base and heterolytic cleavage of arylhydroxylamines render molecules mutagenic. Mutagenicity of ArNH₂ can be removed by structural alterations that disrupt geometric and/or electrostatic fit to CYP1A2, decrease the acidity of the NH₂ group, destabilize arylnitrenium ions, or disrupt their pre-covalent transition states with guanine.

Preparation of K+ intercalated MnO2-rGO composite for the electrochemical detection of nitroaniline in industrial wastewater

This work reports the electrochemical detection of highly hazardous material 4-Nitroaniline (4-NA) based on the metal oxide-rGO composite materials. The potassium intercalated MnO₂-rGO composite material was prepared by a simple one-pot reduction method. The K⁺ intercalation on K-MnO₂-rGO was confirmed by X-ray photoelectron spectroscopy (XPS) and Raman analysis. The amorphous nature of prepared material was scrutinized by high-resolution transmission electron microscopy (HRTEM) and selected area electron diffraction (SAED) pattern analysis. The elemental compositions are done by energy dispersive X-ray Analysis (EDX) mapping. The prepared composite material K-MnO₂-rGO was used to determine the 4-NA by differential pulse voltammetry (DPV). The electroanalytical performances of fabricated K-MnO₂-rGO/SPCE were compared with the K-MnO₂ and rGO in pH 7. The developed 4-NA sensor showed good sensitivity (2.85 µA µM^-1 cm^-2), linear range (0.001-10.53 µM), and LOD (0.7 nM). Furthermore, the K-MnO₂-rGO/SPCE exhibited high selectivity with the other potential interfering nitro compounds in river water and pond water samples. Therefore the developed sensor can be applied for the determination of noxious pollutants in real-time monitoring devices.

A descriptor-based analysis to highlight the mechanistic rationale of mutagenicity

Cancer is a main concern for human health and there is a need of alternative methodologies to rapidly screen large quantitative of compounds that may represent a toxicological risk. Here a statistical analyses is performed on a benchmark database of experimental Ames data to identify chemical descriptors discriminating mutagens and non-mutagens. A total of 53 activating and deactivating modulators are identified, that flagged respectively a percentage of mutagen and non-mutagen up to 87%. Modulators are further combined to form synergistic cross-terms, accounting for the effect that combined properties may have on the final toxicity. Exclusion rules are defined as exception to the modulators. Synergistic cross-terms and exclusion rules improve the enrichment of mutagens/non-mutagens with respect of the original abundance in the dataset to values higher than 95%. The external predictivity of modulators and cross-terms reach balanced accuracy up to 0.775 that is analogous to other mutagenicity models from the literature, confirming the suitability of the rules to real-life screening of chemicals. Modulators are discussed for their mechanistic link to mutagenicity. This analysis confirms the key role of some properties (polarizability, shape, mass, presence of reactive functional groups or unsaturated planar systems) as driving elements for the initiation of the mutagenicity.

Improved performance of immobilized laccase on Fe3O4@C-Cu2+ nanoparticles and its application for biodegradation of dyes

An effective strategy for enhancement of catalytic activity and stability of immobilized laccase via metal affinity adsorption on Fe₃O₄@C-Cu²⁺ nanoparticles was developed, which involved the fabrication of hydroxyl and carboxyl functionalized Fe₃O₄@C nanoparticles via a simple hydrothermal process and the subsequent chelation with Cu²⁺ for the immobilization of laccase under a mild condition. Our results revealed that the Fe₃O₄@C-Cu²⁺ nanoparticles possess a high loading amount of bovine serum albumin (BSA, 436 mg/g support) and laccase activity recovery of 82.3 % after immobilization. Laccase activity assays indicated that thermal and pH stabilities, and resistances to organic solvents and metal ions of the immobilized laccase were relatively higher than those of the free enzyme. The immobilized laccase maintained more than 61 % of its original activity after 10 consecutive reuses. Most importantly, the immobilized laccase possessed excellent degradation of diverse synthetic dyes. The degradation rates of malachite green (MG), brilliant green (BG), crystal violet (CV), azophloxine, Procion red MX-5B, and reactive blue 19 (RB19) was approximately 99, 93, 79, 88, 75 and 81 (%) in the first cycle. Even after 10 consecutive reuses, the removal efficiencies of the six dyes were found to be 94, 80, 71, 78, 60, and 65 (%), respectively.

Characterization of N-(2,6-dimethylphenyl)hydroxylamine adducts of 2'-deoxyguanosine under weakly basic conditions

Aromatic amines are a class of chemical carcinogens that are activated by cytochrome P450 enzymes to form arylhydroxylamines that are conjugated to form N-acetoxyarylamines or N-sulfonyloxyarylamines. These conjugates undergo N-O bond cleavage to become reactive nitrenium ions that may form DNA adducts. Numerous studies in the past using N-acetoxyarylamines to investigate DNA adduct formation were conducted, however, less is known in regard to DNA adduct formation directly from arylhydroxylamines - especially under conditions that mimic the physiological conditions of cells such as weakly basic conditions. In this study, 2'-deoxyguanosine (dG) was exposed to N-(2,6-dimethylphenyl)hydroxylamine (2,6-DMPHA) and N-phenylhydroxylamine (PHA) at pH 7.4 without enzymes and analyzed by liquid chromatography high resolution mass spectrometry (LC-HRMS). 2,6-DMPHA exposure resulted in the production of relatively low amounts of adducts however the identities of at least six different adducts that were formed through reactions with carbon, nitrogen and oxygen of 2'-deoxyguanosine were proposed based upon different analytical approaches including HRMS CID fragmentation and NMR analyses. Contrastively, PHA exposure under identical conditions resulted in one adduct at the C8 position. It was concluded from these results and results of theoretical calculations that nitrenium ions produced from 2,6-DMPHA were relatively more stable resulting in longer nitrenium ion lifetimes which ultimately led to greater potential for 2,6-DMPHA nitrenium ions to react with multiple sites on dG.

RIFM fragrance ingredient safety assessment, phenylethyl anthranilate, CAS Registry Number 133-18-6

The existing information supports the use of this material as described in this safety assessment. Phenylethyl anthranilate was evaluated for genotoxicity, repeated dose toxicity, developmental and reproductive toxicity, local respiratory toxicity, phototoxicity/photoallergenicity, skin sensitization, and environmental safety. Data from phenylethyl anthranilate and the read-across analog cinnamyl anthranilate (CAS # 87-29-6) show that phenylethyl anthranilate is not expected to be genotoxic. The skin sensitization endpoint was completed using the DST for non-reactive materials (900 μg/cm²); exposure is below the DST. The reproductive and local respiratory toxicity endpoints were evaluated using the TTC for a Cramer Class II material, and the exposure to phenylethyl anthranilate is below the TTC (0.009 mg/kg/day and 0.47 mg/day, respectively). Data on read-across analogs phenethyl alcohol (CAS # 60-12-8) and anthranilic acid (CAS # 118-92-3) provide a calculated MOE >100 for the repeated dose and developmental toxicity endpoints. The phototoxicity/photoallergenicity endpoints were evaluated based on UV spectra; phenylethyl anthranilate is not expected to be phototoxic/photoallergenic. The environmental endpoints were evaluated; phenylethyl anthranilate was found not to be PBT as per the IFRA Environmental Standards, and its risk quotients, based on its current volume of use in Europe and North America (i.e., PEC/PNEC), are <1.

Degradation of the anticancer drug flutamide by solar photo-Fenton treatment at near-neutral pH: Identification of transformation products and in silico (Q)SAR risk assessment

Flutamide (FLUT) is a non-steroidal drug mainly used in the treatment of prostate cancer and has been detected in the aquatic environment at ng L^-1 levels. The environmental fate and effects of FLUT have not yet been studied. Conventional treatment technologies fail to completely remove pharmaceuticals, so the solar photo-Fenton process (SPF) has been proposed as an alternative. In this study, the degradation of FLUT, at two different initial concentrations in ultra-pure water, was carried out by SPF. The initial SPF conditions were pH₀ 5, [Fe²⁺]₀ = 5 mg L^-1, and [H₂O₂]₀ = 50 mg L^-1. Preliminary elimination rates of 53.4% and 73.4%. The kinetics of FLUT degradation could be fitted by a pseudo-first order model and the k_obs were 6.57 × 10^-3 and 9.13 × 10^-3 min^-1 t_30W and the half-life times were 95.62 and 73.10 min t_30W were achieved for [FLUT]₀ of 5 mg L^-1 and 500 μg L^-1, respectively. Analysis using LC-QTOF MS identified thirteen transformation products (TPs) during the FLUT degradation process. The main degradation pathways proposed were hydroxylation, hydrogen abstraction, demethylation, NO₂ elimination, cleavage, and aromatic ring opening. Different in silico (quantitative) structure-activity relationship ((Q)SAR) freeware models were used to predict the toxicities and environmental fates of FLUT and the TPs. The in silico predictions indicated that these substances were not biodegradable, while some TPs were classified near the threshold point to be considered as PBT compounds. The in silico (Q)SAR predictions gave positive alerts concerning the mutagenicity and carcinogenicity endpoints. Additionally, the (Q)SAR toolbox software provided structural alerts corresponding to the positive alerts obtained with the different mutagenicity and carcinogenicity models, supporting the positive alerts with more proactive information.

Quantitative Structure-activity Relationship Analysis for Predicting Lipophilicity of Aniline Derivatives (Including some Pharmaceutical Compounds)

BACKGROUND

In this study, we used a hierarchical approach to develop quantitative structureactivity relationship (QSAR) models for modeling lipophilicity of a set of 81 aniline derivatives containing some pharmaceutical compounds.

OBJECTIVE

The multiple linear regression (MLR), principal component regression (PCR) and partial least square regression (PLSR) methods were utilized to construct QSAR models.

MATERIALS AND METHODS

Quantum mechanical calculations at the density functional theory level and 6- 311++G** basis set were carried out to obtain the optimized geometry and then, the comprehensive set of molecular descriptors was computed by using the Dragon software. Genetic algorithm (GA) was applied to select suitable descriptors which have the most correlation with lipophilicity of the studied compounds.

RESULTS

It was identified that such descriptors as Barysz matrix (SEigZ), hydrophilicity factor (Hy), Moriguchi octanol-water partition coefficient (MLOGP), electrophilicity (ω/eV) van der Waals volume (vWV) and lethal concentration (LC50/molkg-1) are the best descriptors for QSAR modeling. The high correlation coefficients and the low prediction errors for MLR, PCR and PLSR methods confirmed good predictability of the three models.

CONCLUSION

In present study, the high correlation between experimental and predicted logP values of aniline derivatives indicated the validation and the good quality of the resulting three regression methods, but MLR regression procedure was a little better than the PCR and PLSR methods. It was concluded that the studied aniline derivatives are not hydrophilic compounds and this means these compounds hardly dissolve in water or an aqueous solvent.

Binder-Free Modification of a Glassy Carbon Electrode by Using Porous Carbon for Voltammetric Determination of Nitro Isomers

In this study, Liquidambar formosana tree leaves have been used as a renewable biomass precursor for preparing porous carbons (PCs). The PCs were produced by pyrolysis of natural waste of leaves after 10% KOH activation under a nitrogen atmosphere and characterized by a variety of state-of-the-art techniques. The PCs possess a large surface area, micro-/mesoporosity, and functional groups on its surface. A glassy carbon electrode modified with high PCs was explored as an efficient binder-free electrocatalyst material for the voltammetric determination of nitro isomers such as 3-nitroaniline (3-NA) and 4-nitroaniline (4-NA). Under optimal experimental conditions, the electrochemical detection of 3-NA and 4-NA was found to have a wide linear range of 0.2-115.6 and 0.5-120 μM and a low detection limit of 0.0551 and 0.0326 μM, respectively, with appreciable selectivity. This route not only enhanced the benefit from biomass wastes but also reduced the cost of producing electrode materials for electrochemical sensors. Additionally, the sensor was successfully applied in the determination of nitro isomers even in the presence of other common electroactive interference and real samples analysis (beverage and pineapple jam solutions). Therefore, the proposed method is simple, rapid, stable, sensitive, specific, reproducible, and cost-effective and can be applicable for real sample detection.

Mechanism-based structure-activity relationship (SAR) analysis of aromatic amines and nitroaromatics carcinogenicity via statistical analyses based on CPDB

Cancer is a leading cause of human mortality around the globe. In this study, mechanism-based SAR (Structure-Activity Relationship) was employed to investigate the carcinogenicity of aromatic amines and nitroaromatics based on CPDB. Principal component analysis and cluster analysis were used to construct the SAR model. Principle component analysis generated three principal components from 12 mechanism-based descriptors. The extracted principal components were later used for cluster analysis, which divided the selected 55 chemicals into six clusters. The three principal components were proposed to describe the "transport", "reactivity" and "electrophilicity" properties of the chemicals. Cluster analysis indicated that the relevant "transport" properties positively correlated with the carcinogenic potential and were contributing factors in determining the carcinogenicity of the studied chemicals. The mechanism-based SAR analysis suggested the electron donating groups, electron withdrawing groups and planarity are significant factors in determining the carcinogenic potency for studied aromatic compounds. The present study may provide insights into the relationship between the three proposed properties and the carcinogenesis of aromatic amines and nitroaromatics.

CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods

Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models (http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/).

A knowledge-based expert rule system for predicting mutagenicity (Ames test) of aromatic amines and azo compounds

Cancer is one of the main causes of death in Western countries, and a major issue for human health. Prolonged exposure to a number of chemicals was observed to be one of the primary causes of cancer in occupationally exposed persons. Thus, the development of tools for identifying hazardous chemicals and the increase of mechanistic understanding of their toxicity is a major goal for scientific research. We constructed a new knowledge-based expert system accounting the effect of different substituents for the prediction of mutagenicity (Ames test) of aromatic amines, a class of compounds of major concern because of their widespread application in industry. The herein presented model implements a series of user-defined structural rules extracted from a database of 616 primary aromatic amines, with their Ames test outcomes, aimed at identifying mutagenic and non-mutagenic chemicals. The chemical rationale behind such rules is discussed. Besides assessing the model's ability to correctly classify aromatic amines, its predictivity was further evaluated on a second database of 354 azo dyes, another class of chemicals of major concern, whose toxicity has been predicted on the basis of the toxicity of aromatic amines potentially generated from the metabolic reduction of the azo bond. Good performance in classification on both the amine (MCC, Matthews Correlation Coefficient=0.743) and the azo dye (MCC=0.584) datasets confirmed the predictive power of the model, and its suitability for use on a wide range of chemicals. Finally, the model was compared with a series of well-known mutagenicity predicting software. The good performance of our model compared with other mutagenicity models, especially in predicting azo dyes, confirmed the usefulness of this expert system as a reliable support to in vitro mutagenicity assays for screening and prioritization purposes. The model has been fully implemented as a KNIME workflow and is freely available for downstream users.

2015 Philip S. Portoghese Medicinal Chemistry Lectureship. Curing Hepatitis C Virus Infection with Direct-Acting Antiviral Agents: The Arc of a Medicinal Chemistry Triumph

The development of direct-acting antiviral agents that can cure a chronic hepatitis C virus (HCV) infection after 8-12 weeks of daily, well-tolerated therapy has revolutionized the treatment of this insidious disease. In this article, three of Bristol-Myers Squibb's HCV programs are summarized, each of which produced a clinical candidate: the NS3 protease inhibitor asunaprevir (64), marketed as Sunvepra, the NS5A replication complex inhibitor daclatasvir (117), marketed as Daklinza, and the allosteric NS5B polymerase inhibitor beclabuvir (142), which is in late stage clinical studies. A clinical study with 64 and 117 established for the first time that a chronic HCV infection could be cured by treatment with direct-acting antiviral agents alone in the absence of interferon. The development of small molecule HCV therapeutics, designed by medicinal chemists, has been hailed as "the arc of a medical triumph" but may equally well be described as "the arc of a medicinal chemistry triumph".

Predicting carcinogenicity of organic compounds based on CPDB

Cancer is a major killer of human health and predictions for the carcinogenicity of chemicals are of great importance. In this article, predictive models for the carcinogenicity of organic compounds using QSAR methods for rats and mice were developed based on the data from CPDB. The models was developed based on the data of specific target site liver and classified according to sex of rats and mice. Meanwhile, models were also classified according to whether there is a ring in the molecular structure in order to reduce the diversity of molecular structure. Therefore, eight local models were developed in the final. Taking into account the complexity of carcinogenesis and in order to obtain as much information, DRAGON descriptors were selected as the variables used to develop models. Fitting ability, robustness and predictive power of the models were assessed according to the OECD principles. The external predictive coefficients for validation sets of each model were in the range of 0.711-0.906, and for the whole data in each model were all greater than 0.8, which represents that all models have good predictivity. In order to study the mechanism of carcinogenesis, standardized regression coefficients were calculated for all predictor variables. In addition, the effect of animal sex on carcinogenesis was compared and a trend that female showed stronger tolerance for cancerogen than male in both species was appeared.

QSAR-assisted virtual screening of lead-like molecules from marine and microbial natural sources for antitumor and antibiotic drug discovery

A Quantitative Structure-Activity Relationship (QSAR) approach for classification was used for the prediction of compounds as active/inactive relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1746 compounds from PubChem with empirical CDK descriptors and semi-empirical quantum-chemical descriptors. A data set of 183 active pharmaceutical ingredients was additionally used for the external validation of the best models. The best classification models for antibiotic and antitumor activities were used to screen a data set of marine and microbial natural products from the AntiMarin database-25 and four lead compounds for antibiotic and antitumor drug design were proposed, respectively. The present work enables the presentation of a new set of possible lead like bioactive compounds and corroborates the results of our previous investigations. By other side it is shown the usefulness of quantum-chemical descriptors in the discrimination of biologically active and inactive compounds. None of the compounds suggested by our approach have assigned non-antibiotic and non-antitumor activities in the AntiMarin database and almost all were lately reported as being active in the literature.

Multicriteria approach to select polyaromatic river mutagen candidates

The identification of unknown compounds remains one of the most challenging tasks to link observed toxic effects in complex environmental mixtures to responsible toxicants in effect-directed analysis (EDA). Here, a workflow is presented based on nontarget liquid chromatography-high resolution mass spectrometry (LC-HRMS) starting with molecular formulas determined in a previous study. A compound database search (ChemSpider) was performed to retrieve candidates for each formula. Subsequently, the number of candidates was reduced by applying MS-, physical-chemical, and chromatography-based selection criteria including HRMS/MS fragmentation and plausibility, ionization efficiency with different ion sources and detection modes, acid/base behavior, octanol/water partitioning, retention time prediction and finally toxic effects (mutagenicity caused by aromatic amines). The workflow strongly decreased the number of possible candidates and resulted in the tentative identification of possible mutagens and the positive identification of the nonmutagen benzyl(diphenyl) phosphine oxide in a mutagenic fraction. The positive identification of mutagens was hampered by a lack of commercially available standards. The workflow is an innovative and promising approach and forms an excellent basis for possible further advancements.

N-[6-(4-butanoyl-5-methyl-1H-pyrazol-1-yl)pyridazin-3-yl]-5-chloro-1-[2-(4-methylpiperazin-1-yl)-2-oxoethyl]-1H-indole-3-carboxamide (SAR216471), a novel intravenous and oral, reversible, and directly acting P2Y12 antagonist

In the search of a potential backup for clopidogrel, we have initiated a HTS campaign designed to identify novel reversible P2Y12 antagonists. Starting from a hit with low micromolar binding activity, we report here the main steps of the optimization process leading to the identification of the preclinical candidate SAR216471. It is a potent, highly selective, and reversible P2Y12 receptor antagonist and by far the most potent inhibitor of ADP-induced platelet aggregation among the P2Y12 antagonists described in the literature. SAR216471 displays potent in vivo antiplatelet and antithrombotic activities and has the potential to differentiate from other antiplatelet agents.

Discovery of daclatasvir, a pan-genotypic hepatitis C virus NS5A replication complex inhibitor with potent clinical effect

The discovery and development of the first-in-class hepatitis C virus (HCV) NS5A replication complex inhibitor daclatasvir (6) provides a compelling example of the power of phenotypic screening to identify leads engaging novel targets in mechanistically unique ways. HCV NS5A replication complex inhibitors are pan-genotypic in spectrum, and this mechanistic class provides the most potent HCV inhibitors in vitro that have been described to date. Clinical trials with 6 demonstrated a potent effect on reducing plasma viral load and, in combination with mechanistically orthogonal HCV inhibitors, established the ability to cure even the most difficult infections without the need for immune stimulation. In this Drug Annotation, we describe the discovery of the original high-throughput screening lead 7 and the chemical conundrum and challenges resolved in optimizing to 6 as a clinical candidate and finally we summarize the results of select clinical studies.

Electron-correlation based externally predictive QSARs for mutagenicity of nitrated-PAHs in Salmonella typhimurium TA100

In quantitative modeling, there are two major aspects that decide reliability and real external predictivity of a structure-activity relationship (SAR) based on quantum chemical descriptors. First, the information encoded in employed molecular descriptors, computed through a quantum-mechanical method, should be precisely estimated. The accuracy of the quantum-mechanical method, however, is dependent upon the amount of electron-correlation it incorporates. Second, the real external predictivity of a developed quantitative SAR (QSAR) should be validated employing an external prediction set. In this work, to analyze the role of electron-correlation, QSAR models are developed for a set of 51 ubiquitous pollutants, namely, nitrated monocyclic and polycyclic aromatic hydrocarbons (nitrated-AHs and PAHs) having mutagenic activity in TA100 strain of Salmonella typhimurium. The quality of the models, through state-of-the-art external validation procedures employing an external prediction set, is compared to the best models known in the literature for mutagenicity. The molecular descriptors whose electron-correlation contribution is analyzed include total energy, energy of HOMO and LUMO, and commonly employed electron-density based descriptors such as chemical hardness, chemical softness, absolute electronegativity and electrophilicity index. The electron-correlation based QSARs are also compared with those developed using quantum-mechanical descriptors computed with advanced semi-empirical (SE) methods such as PM6, PM7, RM1, and ab initio methods, namely, the Hartree-Fock (HF) and the density functional theory (DFT). The models, developed using electron-correlation contribution of the quantum-mechanical descriptors, are found to be not only reliable but also satisfactorily predictive when compared to the existing robust models. The robustness of the models based on descriptors computed through advanced SE methods, is also observed to be comparable to those developed with the electron-correlation based descriptors. The work emphasizes that the correlation-energy can serve as a reliable descriptor to explore the origin of biological activities at the level of electron-dynamics.

Discovery and development of hepatitis C virus NS5A replication complex inhibitors

Lead inhibitors that target the function of the hepatitis C virus (HCV) nonstructural 5A (NS5A) protein have been identified by phenotypic screening campaigns using HCV subgenomic replicons. The demonstration of antiviral activity in HCV-infected subjects by the HCV NS5A replication complex inhibitor (RCI) daclatasvir (1) spawned considerable interest in this mechanistic approach. In this Perspective, we summarize the medicinal chemistry studies that led to the discovery of 1 and other chemotypes for which resistance maps to the NS5A protein and provide synopses of the profiles of many of the compounds currently in clinical trials. We also summarize what is currently known about the NS5A protein and the studies using NS5A RCIs and labeled analogues that are helping to illuminate aspects of both protein function and inhibitor interaction. We conclude with a synopsis of the results of notable clinical trials with HCV NS5A RCIs.