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

Investigation of occurrence of aromatic amines in municipal wastewaters using passive sampling

Aromatic amines (AAs) are human-made compounds known for their mutagenic properties, entering surface waters from various sources, often originating as transformation products of dyes or pesticides. Despite their low concentrations in surface waters, AAs can exhibit mutagenicity. Our study focused on evaluating three passive samplers (PSs) for enriching these compounds from influent and effluent of a wastewater treatment plant (WWTP) in Brno, Czech Republic. The PSs tested included variants containing AttractSPE™ SDB-RPS sorbent disk, one with and one without a diffusive agarose hydrogel layer, and a modified Speedisk (Bakerbond Speedisk® H2O-Philic). PSs were deployed in wastewater (WW) for one to four weeks in various overlapping combinations, and the uptake of AAs to PSs was compared to their concentrations in 24-hour composite water samples. A targeted LC/MS analysis covered 42 amines, detecting 11 and 13 AAs in daily composite influent and effluent samples, respectively. In the influent, AAs ranged from 1.5 ng L-1 for 1-anilinonaphthalene to 1.0 μg L-1 for aniline, and the highest concentration among all measured amines was observed for cyclohexylamine at 2.9 μg L-1. In the effluent, concentrations ranged from 0.5 ng L-1 for 1-anilinonaphthalene to 88 ng L-1 for o-anisidine. PSs demonstrated comparable accumulation of amines, with integrative uptake up to 28 days in both influent and effluent and detection of up to 23 and 27 amines in influent and effluent, respectively; altogether 34 compounds were detected in the study. Sampling rates (Rs) were estimated for compounds present in at least 50 % of the samples and showing <40 % aqueous concentration variability, with robustness evaluated by comparing values for compounds in WWTP influent and effluent. Although all devices performed similarly, hydrogel-based PS exhibited superior performance in several criteria, including time integration and robustness of sampling rates, making it a suitable monitoring tool for AAs in WW.

Tracking Aromatic Amines from Sources to Surface Waters

This review examines the environmental occurrence and fate of aromatic amines (AAs), a group of environmental contaminants with possible carcinogenic and mutagenic effects. AAs are known to be partially responsible for the genotoxic traits of industrial wastewater (WW), and AA antioxidants are acutely toxic to some aquatic organisms. Still, there are gaps in the available data on sources, occurrence, transport, and fate in domestic WW and indoor environments, which complicate the prevention of adverse effects in aquatic ecosystems. We review key domestic sources of these compounds, including cigarette smoke and grilled protein-rich foods, and their presence indoors and in aquatic matrices. This provides a basis to evaluate the importance of nonindustrial sources to the overall environmental burden of AAs. Appropriate sampling techniques for AAs are described, including copper-phthalocyanine trisulfonate materials, XAD resins in solid-phase extraction, and solid-phase microextraction methods, which can offer insights into AA sources, transport, and fate. Further discussion is provided on potential progress in the research of AAs and their behavior in an aim to support the development of a more comprehensive understanding of their effects and potential environmental risks.

Multiple Instance Learning Improves Ames Mutagenicity Prediction for Problematic Molecular Species

The prediction of Ames mutagenicity continues to be a concern in both regulatory and pharmacological toxicology. Traditional quantitative structure-activity relationship (QSAR) models of mutagenicity make predictions based on molecular descriptors calculated on a chemical data set used in their training. However, it is known that molecules such as aromatic amines can be non-mutagenic themselves but metabolically activated by S9 rodent liver enzyme in Ames tests forming molecules such as iminoquinones or amine substituents that better stabilize mutagenic nitrenium ions in known pathways of mutagenicity. Modern in silico modeling methods can implicitly model these metabolites through consideration of the structural elements relevant to their formation but do not include explicit modeling of these metabolites' potential activity. These metabolites do not have a known individual mutagenicity label and, in their current state, cannot be fitted into a traditional QSAR model. Multiple instance learning (MIL) however can be applied to a group of metabolites and their parent under a single mutagenicity label. Here we trained MIL models on Ames data, first with an aromatic amines data set (n = 457), a class known to require metabolic activation, and subsequently on a larger data set (n = 6505) incorporating multiple molecular species. MIL was shown to be able to predict Ames mutagenicity with performance in line with previously established models (balanced accuracy = 0.778), suggesting its potential utility in Ames prediction applications. Furthermore, the MIL model predicted well on identified hard-to-predict molecule groups relative to the models in which these molecule groups were identified. These results are presumably due to the increased consideration of the metabolic contribution to the mutagenic outcome. Further exploration of MIL as a supplement to existing models could aid in the prediction of chemicals where implicit modeling of metabolites cannot fully grasp their characteristics. This paper demonstrates the potential of an MIL approach to modeling Ames tests with S9 and is particularly relevant to metabolically activated xenobiotic mutagens.

Prioritization of mycotoxins based on mutagenicity and carcinogenicity evaluation using combined in silico QSAR methods

Mycotoxins and their metabolites are a family of compounds that contains a great diversity of both structure and biological properties. Information on their toxicity is spread within several databases and in scientific literature. Due to the number of molecules and their structure diversity, the cost and time required for hazard evaluation of each compound is unrealistic. In that purpose, new approach methodologies (NAMs) can be applied to evaluate such large set of molecules. Among them, quantitative structure-activity relationship (QSAR) in silico models could be useful to predict the mutagenic and carcinogenic properties of mycotoxins. First, a complete list of 904 mycotoxins and metabolites was built. Then, some known mycotoxins were used to determine the best QSAR tools for mutagenicity and carcinogenicity predictions. The best tool was further applied to the whole list of 904 mycotoxins. At the end, 95 mycotoxins were identified as both mutagen and carcinogen and should be prioritized for further evaluation.

Inhibiting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants: Targeting the Spike and Envelope Proteins Using Nanomaterial Like Peptides

Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused significant death, economic crisis, and the world to almost completely shut down. This present study focused on targeting the novel SARS-CoV-2 envelope protein, which has not been frequently mutating, and the S protein with a much larger peptide capable of inhibiting virus-mammalian cell attraction. In doing so, molecular dynamics software was used here to model six peptides including: NapFFTLUFLTUTE, NapFFSLAFLTATE, NapFFSLUFLSUTE, NapFFTLAFLTATE, NapFFSLUFLSUSE, and NapFFMLUFLMUME. Results showed that two of these completely hydrophobic peptides (NapFFTLUFLTUTE and NapFFMLUFLMUME) had a strong ability to bind to the virus, preventing its binding to a mammalian cell membrane, entering the cell, and replicating by covering many cell attachment sites on SARS-CoV-2. Further cell modeling results demonstrated the low toxicity and suitable pharmacokinetic properties of both peptides making them ideal for additional in vitro and in vivo investigation. In this manner, these two peptides should be further explored for a wide range of present and future COVID-19 therapeutic and prophylactic applications.

In silico analytical toolset for predictive degradation and toxicity of hazardous pollutants in water sources

Different phenolic compounds, including multimeric lignin derivatives in the β-O-4 form, are among the most prevalent compounds in wastewater, often generated from paper industries. Relatively small concentrations of lignin are hazardous to aquatic organisms and can trigger severe environmental hazards. Herein, we present a predictive toolset to insight the induced toxic hazards prediction, and their Lignin peroxidase (LiP)-assisted degradation mechanism of selected multimeric lignin model compounds. T.E.ST and Toxtree toolset were deployed for toxic hazards estimation in different endpoints. To minimize the concerning hazards, we screened multimeric compounds for binding affinity with LiP. The binding affinity was found to be significantly lower than the reference compound. An Extra precision (XP) Glide score of -6.796 kcal/mol was found for dimer (guaiacyl 4-O-5 guaiacyl) complex as lowest compared to reference compound (-4.007 kcal/mol). The active site residues ASP-153, HIP-226, VAL-227, ARG-244, GLU-215, 239, PHE-261 were identified as site-specific key binding AA residues actively involved with corresponding ligands, forming Hydrophobic, H-Bond, π-Stacking, π-π type interactions. The DESMOND-assisted molecular dynamics simulation's (MDS) trajectories of protein-ligand revealed the considerable binding behavior and attained stability and system equilibrium state. Such theoretical and predictive conclusions indicted the feasibility of LiP assisted sustainable mitigation of lignin-based compounds, and such could be used to protect the environment from the potential hazards posed by recognized similar pollutants.

Evaluation of toxicological aspects of three new benzoxazole compounds with sunscreen photophysical properties using in silico and in vitro methods

Sunscreening chemicals protect against damage caused by sunlight most absorbing UVA or UVB radiations. In this sense, 2-(2'-hydroxyphenyl)benzoxazole derivatives with amino substituents in the 4' and 5' positions have an outstandingly high Sun Protection Factor and adequate photostability, but their toxicity is not yet known. This study aimed to evaluate the toxicity of three synthetic 2-(2'-hydroxyphenyl)benzoxazole derivatives for their possible application as sunscreens. In silico tools were used in order to assess potential risks regarding mutagenic, carcinogenic, and skin sensitizing potential. Bioassays were performed in L929 cells to assess cytotoxicity in MTT assay and genotoxic activities in the Comet assay and micronucleus test. Also, the Salmonella/microsome assay was performed to evaluate gene mutations. The in silico predictions indicate a low risk of mutagenicity and carcinogenicity of the compounds while the skin sensitizing potential was low or inconclusive. The 2-(4'-amino-2'-hydroxyphenyl)benzoxazol compound was the most cytotoxic and genotoxic among the compounds evaluated in L929 cells, but none induced mutations in the Salmonella/microsome assay. The amino substituted at the 4' position of the phenyl ring appears to have greater toxicological risks than substituents at the 5' position of 2-(phenyl)benzoxazole. The findings warrant further studies of these compounds in cosmetic formulations.

Quo vadis blood protein adductomics?

Chemicals are measured regularly in air, food, the environment, and the workplace. Biomonitoring of chemicals in biological fluids is a tool to determine the individual exposure. Blood protein adducts of xenobiotics are a marker of both exposure and the biologically effective dose. Urinary metabolites and blood metabolites are short term exposure markers. Stable hemoglobin adducts are exposure markers of up to 120 days. Blood protein adducts are formed with many xenobiotics at different sites of the blood proteins. Newer methods apply the techniques developed in the field of proteomics. Larger adducted peptides with 20 amino acids are used for quantitation. Unfortunately, at present the methods do not reach the limits of detection obtained with the methods looking at single amino acid adducts or at chemically cleaved adducts. Therefore, to progress in the field new approaches are needed.

Towards prevention of ischemia-reperfusion kidney injury: Pre-clinical evaluation of 6-chromanol derivatives and the lead compound SUL-138✰

Acute kidney injury (AKI) is a global healthcare burden attributable to high mortality and staggering costs of dialysis. The underlying causes of AKI include hypothermia and rewarming (H/R), ischemia/reperfusion (I/R), mitochondrial dysfunction and reactive oxygen species production. Inspired by the mechanisms conferring organ protection in hibernating hamster, 6-chromanol derived compounds were developed to address the need of effective prevention and treatment of AKI. Here we report on the pre-clinical screening of 6-chromanol leads that confer protection during I/R to select compounds with favorable profiles for clinical testing in AKI. A library of 6-chromanols (n = 63) was screened in silico for pharmacochemical properties and druggability. Selected compounds (n = 15) were screened for the potency to protect HEK293 cells from H/R cell death and subjected to a panel of in vitro safety assays. Based on these parameters, SUL-138 was selected as the lead compound and was found to safeguard kidney function and decrease renal injury after I/R in rats. The compound was without cardiovascular or respiratory effects in vivo. SUL-138 pharmacokinetics of control animals (mouse, rat) and those undergoing I/R (rat) was identical, showing a two-phase elimination profile with terminal half-life of about 8 h. Collectively, our phenotype-based screening approach led to the identification of 3 candidates for pre-clinical studies (5%, 3/64). SUL-138 emerged from this small-scale library of 6-chromanols as a novel prophylactic for AKI. The presented efficacy and safety data provide a basis for future development and clinical testing. SECTION ASSIGNMENTS: : Drug discovery and translational medicine, renal, metabolism SIGNIFICANCE STATEMENT: : Based on in silico druggability parameters, a 63 compound 6-chromanol library was narrowed down to 15 compounds. These compounds were subjected to phenotypical screening of cell survival following hypothermia damage and hit compounds were identified. After subsequent assessment of in vivo efficacy, toxicity, pharmacokinetics, and cardiovascular and respiratory safety, SUL-138 emerged as a lead compound that prevented kidney injury after ischemia/reperfusion and demonstrated a favorable pharmacokinetic profile unaffected by renal ischemia.

Hazard prioritisation of substances in printing inks and adhesives applied to plastic food packaging

Thousands of intentionally added substances can be used in printing inks and adhesives applied to plastic food packaging. Some of them can be transferred to foodstuffs through a phenomenon called migration, arising concerns on the potential adverse health effects derived from the exposure to chemicals that have not yet been assessed for their risks to humans. The large number of the substances concerned and the lack of prioritisation strategies hamper the work of control authorities, since it is not clear which substances should be monitored as first priority. In this study, a hazard prioritisation strategy is proposed. An inventory listing more than 6,000 substances used in inks and adhesives applied to plastic food packaging was compiled and filtered using several exclusion criteria aimed to set apart those substances for which there is no apparent need for further evaluation or because fall into one of the exclusion categories of the Threshold of Toxicological Concern (TTC) approach. Additionally, substances with a molecular weight >1,000 Da were removed. Approximately 2,300 substances were retained, for which a comprehensive hazard profiling was conducted based on the general scheme for the application of the TTC approach. First, structural alerts for genotoxic and non-genotoxic carcinogenicity were investigated. If a substance was neither genotoxic nor belonging to the chemical classes of organophosphates and carbamates, the Cramer classification was used. Furthermore, the substances were searched for their presence in three so-called 'Substances of Concern' lists and RASFF notifications. Groups of high, medium and low priority substances were established, resulting in 1,660 substances classified as high and medium priority. A panel of five experts evaluated these substances with respect to their relevance for further risk evaluations. By applying this hazard prioritisation strategy, 696 substances were identified as 'Very High Priority Substances' (VHPS) for which further assessments should be performed.

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.

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.

Hazard assessment using an in-silico toxicity assessment of the transformation products of boscalid, pyraclostrobin, fenbuconazole and glyphosate generated by exposure to an advanced oxidative process

Agricultural pesticide use is ongoing and consumer concern regarding the safety of pesticide residues on produce has generated interest in techniques that can safely reduce residues post-harvest. Recently an advanced oxidative process has shown promise in substantial residue reduction on the surface of produce. Given the potential for oxidative transformation of pesticides to generate transformation products with greater toxicity than the parent residue, take for example the oxon products of the organophosphorus insecticides, it is important to consider what transformation products are generated by pesticide exposure to an oxidative process and their potential toxicity. In this study, previously published transformation products of boscalid, pyraclostrobin, fenbuconazole and glyphosate were identified after exposure to 3% hydrogen peroxide, UV-C irradiation or their combination in an advanced oxidative process on glass, their oral toxicity, carcinogenicity and developmental toxicity were identified in-silico and an initial tier hazard assessment was conducted. Of the 87 total structures that were searched for, 53 were detected by UPLC-QTOF-MS and identified by mass spectra: 15, 13, 22 and 3 structures for boscalid, pyraclostrobin, fenbuconazole and glyphosate respectively, including the parent residues. Oral toxicity of the transformation products of pyraclostrobin and glyphosate was similar to or lower than the parent residue. Several transformation products of boscalid and fenbuconazole were estimated to be significantly more orally toxic than their parent residues. While the majority of the transformation products of boscalid, pyraclostrobin and fenbuconazole were predicted to be carcinogenic there were 11 that were consistently identified to have carcinogenic potential by several assessments. 29 of the 53 molecules were predicted to be probable developmental toxicants. An initial tier hazard assessment was conducted for Cramer rules classification and mutagenicity using the threshold of toxicological concern approach and predicted rat oral LD50. Two exposure scenarios were considered, one highly protective considering each transformation product to be at the highest maximum residue limit (MRL) for the pesticide and whole produce consumption at the highest consumption rate from the USEPA Exposures Handbook, the other considering only apple consumption with the relevant MRL. As indicated by the hazard assessment, several transformation products of boscalid, pyraclostrobin and fenbuconazole should be strongly considered for further testing, either by quantifying their production or in-vivo and in-vitro toxicity tests due to their predicted toxicity and associated hazard.

Mushroom-derived bioactive compounds potentially serve as the inhibitors of SARS-CoV-2 main protease: An in silico approach

Background and aim

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has now become the world pandemic. There is a race to develop suitable drugs and vaccines for the disease. The anti-HIV protease drugs are currently repurposed for the potential treatment of COVID-19. The drugs were primarily screened against the SARS-CoV-2 main protease. With an urgent need for safe and effective drugs to treat the virus, we have explored natural products isolated from edible and medicinal mushrooms that have been reported to possess anti-HIV protease.

Experimental procedures

We have examined 36 compounds for their potential to be SARS-CoV-2 main protease inhibitors using molecular docking study. Moreover, drug-likeness properties including absorption, distribution, metabolism, excretion and toxicity were evaluated by in silico ADMET analysis.

Results

Our AutoDock study showed that 25 of 36 candidate compounds have the potential to inhibit the main viral protease based on their binding affinity against the enzyme's active site when compared to the standard drugs. Interestingly, ADMET analysis and toxicity prediction revealed that 6 out of 25 compounds are the best drug-like property candidates, including colossolactone VIII, colossolactone E, colossolactone G, ergosterol, heliantriol F and velutin.

Conclusion

Our study highlights the potential of existing mushroom-derived natural compounds for further investigation and possibly can be used to fight against SARS-CoV-2 infection.

Taxonomy classification by evise

Disease, Infectious Disease, Respiratory System Disease, Covid-19, Traditional Medicine, Traditional Herbal Medicine, Phamaceutical Analysis.

Comparative Metagenomic Screening of Aromatic Hydrocarbon Degradation and Secondary Metabolite-Producing Genes in the Red Sea, the Suez Canal, and the Mediterranean Sea

Marine and ecosystem pollution due to oil spills can be addressed by identifying the aromatic hydrocarbon (HC)-degrading microorganisms and their responsible genes for biodegradation. Moreover, screening for genes coding for secondary metabolites is invaluable for drug discovery. We report here, the first metagenomic study investigating the shotgun metagenome of the Suez Canal water sampled at Ismailia city concerning its aromatic HC degradation potential in comparison to the seawater sampled at Halayeb city at the Red Sea and Sallum city at the Mediterranean Sea. Moreover, for an in-depth understanding of marine biotechnology applications, we screened for the polyketide synthases (PKSs) and nonribosomal peptide synthetase (NRPS) domains in those three metagenomes. By mapping against functional protein databases, we found that 13, 6, and 3 gene classes from the SEED database; 2, 1, and 3 gene classes from the EgGNOG; and 5, 4, and 2 genes from the InterPro2GO database were identified to be differentially abundant among Halayeb, Ismailia, and Sallum metagenomes, respectively. Also, Halayeb metagenome in the Red Sea reported the highest number of PKS domains showing higher potential in secondary metabolite production in addition to the oil degradation potential.

Fumonisin B1 induces toxicity in human leukocytes at low concentrations: Are computational studies effective to determine biosafety?

Fumonisin B₁ is a mycotoxin produced by Fusarium verticillioides and Fusarium proliferatum found in various crops, particularly maize. Besides carcinogenicity, other manifestations have been registered in different animals and in humans. In the case of humans, epidemiological studies have reported high prevalence of esophageal cancer in populations exposed to fumonisins. This study aimed to evaluate the minimum concentration of FB₁ capable of inducing cytotoxicity (cell viability test), genotoxicity (comet assay) and mutagenicity (micronucleus) in cultured human leukocytes and to evaluate the effectiveness of in silico tests to predict FB₁ toxicity. All concentrations analyzed (200; 100; 50; 5; 0.5; 0.05; 0.005 μg/mL and 300; 30; 3; 1; 0.1; 0.01 fg/mL) except the lowest demonstrated dose-dependent toxicity in all parameters analyzed (p < 0.05 to p < 0.0001). As for predictions, only the Lazar software showed carcinogenicity of FB₁ for rats. Thus, it is evident that FB₁ is able to induce dose-dependent damage at low concentrations, and that computational tests, although desirable for prediction, are not effective as biological tests to determine toxicity, at least of FB ₁ and within the experimental conditions tested.

Synthesis and mode of action studies of novel {2-(3-R-1H-1,2,4-triazol-5-yl)phenyl}amines to combat pathogenic fungi

Due to their high specificity and efficacy, triazoles have become versatile antifungals to treat fungal infections in human healthcare and to control phytopathogenic fungi in agriculture. However, azole resistance is an emerging problem affecting human health as well as food security. Here we describe the synthesis of 10 novel {2-(3-R-1H-1,2,4-triazol-5-yl)phenyl}amines. Their structure was ascertained by liquid chromatography-mass spectrometry, ¹ H and ¹³ C NMR, and elemental analysis data. Applying an in vitro growth assay, these triazoles show moderate to significant antifungal activity against the opportunistic pathogen Aspergillus niger, 12 fungi (Fusarium oxysporum, Fusarium fujikuroi, Colletotrichum higginsianum, Gaeumannomyces graminis, Colletotrichum coccodes, Claviceps purpurea, Alternaria alternata, Mucor indicus, Fusarium graminearum, Verticillium lecanii, Botrytis cinerea, Penicillium digitatum) and three oomycetes (Phytophtora infestans GL-1, P. infestans 4/91; R+ and 4/91; R-) in the concentration range from 1 to 50 µg/ml (0.003-2.1 μM). Frontier molecular orbital energies were determined to predict their genotoxic potential. Molecular docking calculations taking into account six common fungal enzymes point to 14α-demethylase (CYP51) and N-myristoyltransferase as the most probable fungal targets. With respect to effectiveness, structure-activity calculations revealed the strong enhancing impact of adamantyl residues. The shown nonmutagenicity in the Salmonella reverse-mutagenicity assay and no violations of drug-likeness parameters suggest the good bioavailability and attractive ecotoxicological profile of the studied triazoles.

Prediction of ADME-Tox properties and toxicological endpoints of triazole fungicides used for cereals protection

Within this study we have considered 9 triazole fungicides that are approved to be used in European Union for protecting cereals: cyproconazole, epoxiconazole, flutriafol, metconazole, paclobutrazole, tebuconazole, tetraconazole, triadimenol and triticonazole. We have summarized the few available data that support their effects on humans and used various computational tools to obtain a widely view concerning their possible harmful effects on humans. The results of our predictive study reflect that all triazole fungicides considered in this study reveal good oral bioavailability, are envisaged as being able to penetrate the blood brain barrier and to interact with P-glycoprotein and with hepatic cytochromes. The predictions concerning the toxicological endpoints for the investigated triazole fungicides reveal that they. reflect potential of skin sensitization, of blockage of the hERG K+ channels and of endocrine disruption, that they have not mutagenic potential and their carcinogenic potential is not clear. Epoxiconazole and triadimenol are predicted to have the highest potentials of producing numerous harmful effects on humans and their use should be avoided or limited.

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.

Biological Safety Studies and Simultaneous Determination of Linagliptin and Synthetic Impurities by LC-PDA

A stability-indicating LC method was developed for quantification of linagliptin (LGT) and three synthetic impurities. The method utilizes a Thermo Scientific® RP-8 column (100 mm × 4.6 mm; 5 μm) with the PDA detector for quantitation of impurities. A mixture of 0.1% formic acid with pH 3.5 (A) and acetonitrile (B) was used as the mobile phase at a flow rate of 0.6 mL·min^-1 with gradient elution. The percentage of mobile phase B increases from 30% to 70% over 5 min and decreases from 70% to 30% between 5 and 8 min. The method was validated according to International Council for Harmonization (ICH) guidelines. The LOD values obtained were 0.0171 μg·mL^-1 and 0.015 μg·mL^-1 for LGT and impurities, respectively. The LOQ values were 0.06 μg·mL^-1 for LGT and impurities. In all cases, the correlation coefficients of LGT and impurities were >0.999, showing the linearity of the method. The % recovery of the LGT and added impurity were in the range of 92.92-99.79%. The precision of the method showed values less than 1.47% for LGT and less than 4.63% for impurities. The robustness was also demonstrated by small modifications in the chromatographic conditions. The selectivity was evidenced because the degradation products formed in stress conditions did not interfere in the determination of LGT and impurities. Toxicity prediction studies suggested toxicity potential of the impurities, which was confirmed using biological safety studies in vitro.

(Eco)toxicological maps: A new risk assessment method integrating traditional and in silico tools and its application in the Ledra River (Italy)

Contaminants giving rise to emerging concern like pharmaceuticals, personal care products, pesticides and Endocrine Disrupting Chemicals (EDCs) have been detected in wastewaters, as reported in the literature, but little is known about their (eco)toxicological effects and consequent human health impact. The present study aimed at overcoming this lack of information through the use of in silico methods integrated with traditional toxicological risk analysis. This is part of a pilot project involving the management of wastewater treatment plants in the Ledra River basin (Italy). We obtained data to work up a global risk assessment method combining the evaluations of health risks to humans and ecological receptors from chemical contaminants found in this specific area. The (eco)toxicological risk is expressed by a single numerical value, permitting the comparison of different sampling sites and the evaluation of future environmental and technical interventions.

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.

Mutagenicity, anticancer activity and blood brain barrier: similarity and dissimilarity of molecular alerts

The aim of the present work is an attempt to define computable measure of similarity between different endpoints. The similarity of structural alerts of different biochemical endpoints can be used to solve tasks of medicinal chemistry. Optimal descriptors are a tool to build up models for different endpoints. The optimal descriptor is calculated with simplified molecular input-line entry system (SMILES). A group of elements (single symbol or pair of symbols) can represent any SMILES. Each element of SMILES can be represented by so-called correlation weight i.e. coefficient that should be used to calculate descriptor. Numerical data on the correlation weights are calculated by the Monte Carlo method, i.e. by optimization procedure, which gives maximal correlation coefficient between the optimal descriptor and endpoint for the training set. Statistically stable correlation weights observed in several runs of the optimization can be examined as structural alerts, which are promoters of the increase or the decrease of a biochemical activity of a substance. Having data on several runs of the optimization correlation weights, one can extract list of promoters of increase and list of promoters of decrease for an endpoint. The study of similarity and dissimilarity of the above lists has been carried out for the following pairs of endpoints: (i) mutagenicity and anticancer activity; (ii) mutagenicity and blood brain barrier; and (iii) blood brain barrier and anticancer activity. The computational experiment confirms that similarity and dissimilarity for pairs of endpoints can be measured.

Integrating computational methods to predict mutagenicity of aromatic azo compounds

Azo dyes have several industrial uses. However, these azo dyes and their degradation products showed mutagenicity, inducing damage in environmental and human systems. Computational methods are proposed as cheap and rapid alternatives to predict the toxicity of azo dyes. A benchmark dataset of Ames data for 354 azo dyes was employed to develop three classification strategies using knowledge-based methods and docking simulations. Results were compared and integrated with three models from the literature, developing a series of consensus strategies. The good results confirm the usefulness of in silico methods as a support for experimental methods to predict the mutagenicity of azo compounds.

Hemoglobin Adducts and Urinary Metabolites of Arylamines and Nitroarenes

Arylamines and nitroarenes are intermediates in the production of pharmaceuticals, dyes, pesticides, and plastics and are important environmental and occupational pollutants. N-Hydroxyarylamines are the toxic common intermediates of arylamines and nitroarenes. N-Hydroxyarylamines and their derivatives can form adducts with hemoglobin (Hb-adducts), albumin, DNA, and tissue proteins in a dose-dependent manner. Most of the arylamine Hb-adducts are labile and undergo hydrolysis in vitro, by mild acid or base, to form the arylamines. According to current knowledge of arylamine adduct-formation, the hydrolyzable fraction is derived from the reaction products of the arylnitroso derivatives that yield arylsulfinamide adducts with cysteine. Hb-adducts are markers for the bioavailability of N-hydroxyarylamines. Hb-adducts of arylamines and nitroarenes have been used for many biomonitoring studies for over 30 years. Hb-adducts reflect the exposure history of the last four months. Biomonitoring of urinary metabolites is a less invasive process than biomonitoring blood protein adducts, and urinary metabolites have served as short-lived biomarkers of exposure to these hazardous chemicals. However, in case of intermittent exposure, urinary metabolites may not be detected, and subjects may be misclassified as nonexposed. Arylamines and nitroarenes and/or their metabolites have been measured in urine, especially to monitor the exposure of workers. This review summarizes the results of human biomonitoring studies involving urinary metabolites and Hb-adducts of arylamines and nitroarenes. In addition, studies about the relationship between Hb-adducts and diseases are summarized.

Screening of commercial cyclic peptide conjugated to HIV-1 Tat peptide as inhibitor of N-terminal heptad repeat glycoprotein-2 ectodomain Ebola virus through in silico analysis

Ebola Hemorrhagic Fever (EHF) is a disease caused by viruses from genus Ebolavirus. Zaire ebolavirus (EBOV) is the deadliest species which has 76% case fatality rate. Up until now, there is no U.S. Food and Drug Administration (FDA) approved drugs to treat EHF. Antiviral drug based on EBOV N-terminal heptad repeat glycoprotein-2 (NHR GP2) Ectodomain inhibitor is one kind of treatment that has not well developed. NHR GP2 Ectodomain has an important role in the process of EBOV entry into the cell through endocytosis mechanism. In this study, we used in silico methods to investigate the activity of commercial cyclic peptide conjugated to Human Immunodeficiency Virus type 1 Trans-activator of the transcription (HIV-1 Tat) peptide as ligands which act as an inhibitor of EBOV NHR GP2 Ectodomain. The commercial cyclic peptides which we used in this study were obtained from the selected chemical companies. Conjugation of the commercial cyclic peptides to HIV-1 Tat peptide was done in order to accumulate it inside the endosome. The ligands which had the best inhibition properties were screened using molecular docking and molecular dynamics simulation. Prediction of pharmacological properties of the peptides was done to choose the best drug candidate. The result of screening processes shows that Ligand 023 has the highest potency as the drug lead. The ligand needs to undergo further analysis through in vitro, in vivo, and a clinical trial to ensure that this ligand has a therapeutic ability as an antiviral drug for Ebola virus infection.

Fragment Prioritization on a Large Mutagenicity Dataset

The identification of structural alerts is one of the simplest tools used for the identification of potentially toxic chemical compounds. Structural alerts have served as an aid to quickly identify chemicals that should be either prioritized for testing or for elimination from further consideration and use. In the recent years, the availability of larger datasets, often growing in the context of collaborative efforts and competitions, created the raw material needed to identify new and more accurate structural alerts. This work applied a method to efficiently mine large toxicological dataset for structural alert showing a strong statistical association with mutagenicity. In details, we processed a large Ames mutagenicity dataset comprising 14,015 unique molecules obtained by joining different data sources. After correction for multiple testing, we were able to assign a probability value to each fragment. A total of 51 rules were identified, with p-value < 0.05. Using the same method, we also confirmed the statistical significance of several mutagenicity rules already present and largely recognized in the literature. In addition, we have extended the application of our method by predicting the mutagenicity of an external data set.

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.

In silico inhibition of GABARAP activity using antiepileptic medicinal derived compounds

Epilepsy is a neurological disorder affecting more than 50 million people worldwide. It can be controlled by antiepileptic drugs (AEDs) but more than 30% patients are still resistant to AEDs. To overcome this problem, researchers are trying to develop novel approaches to treat epilepsy including the use of herbal medicines. The γ-amino butyric acid type-A receptor associated protein (GABARAP) is ubiquitin-like modifier implicated in the intracellular trafficking of GABAAR. An in silico mutation was created at 116 amino acid position G116A, and an in silico study was carried out to identify the potential binding inhibitors (with antiepileptic properties) against the active sites of GABARAP. Five different plant derived compounds namely (a) Aconitine (b) Berberine (c) Montanine (d) Raubasine (e) Safranal were selected, and their quantitative structure-activity relationships (QSAR) have been conducted to search the inhibitory activity of the selected compounds. The results have shown maximum number of hydrogen bond (H-bond) interactions of Raubasine with highest interaction energy among all of the five compounds. So, Raubasine could be the best fit ligand of GABARAP but in vitro, and in vivo studies are necessary for further confirmation.

Alternative Toxicity Testing: Analyses on Skin Sensitization, ToxCast Phases I and II, and Carcinogenicity Provide Indications on How to Model Mechanisms Linked to Adverse Outcome Pathways

This article studies alternative toxicological approaches, with new (skin sensitization, ToxCast) and previous (carcinogenicity) analyses. Quantitative modeling of rate-limiting steps in skin sensitization and carcinogenicity predicts the majority of toxicants. Similarly, successful (Quantitative) Structure-Activity Relationships models exploit the quantification of only one, or few rate-limiting steps. High-throughput assays within ToxCast point to promising associations with endocrine disruption, whereas markers for pathways intermediate events have limited correlation with most endpoints. Since the pathways may be very different (often not simple linear chains of events), quantitative analysis is necessary to identify the type of mechanism and build the appropriate model.

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.

In silico studies of medicinal compounds against hepatitis C capsid protein from north India

Hepatitis viral infection is a leading cause of chronic hepatitis, cirrhosis, and hepatocellular carcinoma (HCC). Over one million people are estimated to be persistently infected with hepatitis C virus (HCV) worldwide. As capsid core protein is the key element in spreading HCV; hence, it is considered to be the superlative target of antiviral compounds. Novel drug inhibitors of HCV are in need to complement or replace the current treatments such as pegylated interferon’s and ribavirin as they are partially booming and beset with various side effects. Our study was conducted to predict 3D structure of capsid core protein of HCV from northern part of India. Core, the capsid protein of HCV, handles the assembly and packaging of HCV RNA genome and is the least variable of all the ten HCV proteins among the six HCV genotypes. Therefore, we screened four phytochemicals inhibitors that are known to disrupt the interactions of core and other HCV proteins such as (a) epigallocatechin gallate (EGCG), (b) ladanein, (c) naringenin, and (d) silybin extracted from medicinal plants; targeted against active site of residues of HCV-genotype 3 (G3) (Q68867) and its subtypes 3b (Q68861) and 3g (Q68865) from north India. To study the inhibitory activity of the recruited flavonoids, we conducted a quantitative structure–activity relationship (QSAR). Furthermore, docking interaction suggests that EGCG showed a maximum number of hydrogen bond (H-bond) interactions with all the three modeled capsid proteins with high interaction energy followed by naringenin and silybin. Thus, our results strongly correlate the inhibitory activity of the selected bioflavonoid. Finally, the dynamic predicted capsid protein molecule of HCV virion provides a general avenue to target structure-based antiviral compounds that support the hypothesis that the screened inhibitors for viral capsid might constitute new class of potent agents but further confirmation is necessary using in vitro and in vivo studies.

Screening of commercial cyclic peptide as inhibitor NS5 methyltransferase of Dengue virus through Molecular Docking and Molecular Dynamics Simulation

Dengue has become a major global health threat, especially in tropical and subtropical regions. The development of antiviral agent targeting viral replication is really needed at this time. NS5 methyltransferase presents as a novel antiviral target. This enzyme plays an important role in the methylation of 5'-cap mRNA. Inhibition of the NS5 methyltransferase could inhibit dengue virus replication. In this research, two sites of NS5 methyltransferase (S-Adenosyl methionine/SAM binding site and RNA-cap site) were used as targets for inhibition. As much as 300 commercial cyclic peptides were screened to these target sites by means of molecular docking. Analysis of ligand-enzyme binding free energy and pharmacological prediction revealed two best ligands, namely [Tyr123] Prepro Endothelin (110-130), amide, human and Urotensin II, human. According to molecular dynamic simulation, both ligands maintain a stable complex conformation between enzyme and ligand at temperature 310 K and 312 K. Hence, Urotensin II, human is more reactive at 312 K than at 310 K. However, both ligands can be used as potential inhibitor candidates against NS5 methyltransferase of dengue virus with Urotensin II, human exposes more promising activity at 312 K.

Establishing the level of safety concern for chemicals in food without the need for toxicity testing

There is demand for methodologies to establish levels of safety concern associated with dietary exposures to chemicals for which no toxicological data are available. In such situations, the application of in silico methods appears promising. To make safety statement requires quantitative predictions of toxicological reference points such as no observed adverse effect level and carcinogenic potency for DNA-reacting chemicals. A decision tree (DT) has been developed to aid integrating exposure information and predicted toxicological reference points obtained with quantitative structure activity relationship ((Q)SAR) software and read across techniques. The predicted toxicological values are compared with exposure to obtain margins of exposure (MoE). The size of the MoE defines the level of safety concern and should account for a number of uncertainties such as the classical interspecies and inter-individual variability as well as others determined on a case by case basis. An analysis of the uncertainties of in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. It can be used when fast decision making is required.

Determining chemical reactivity driving biological activity from SMILES transformations: the bonding mechanism of anti-HIV pyrimidines

Assessing the molecular mechanism of a chemical-biological interaction and bonding stands as the ultimate goal of any modern quantitative structure-activity relationship (QSAR) study. To this end the present work employs the main chemical reactivity structural descriptors (electronegativity, chemical hardness, chemical power, electrophilicity) to unfold the variational QSAR though their min-max correspondence principles as applied to the Simplified Molecular Input Line Entry System (SMILES) transformation of selected uracil derivatives with anti-HIV potential with the aim of establishing the main stages whereby the given compounds may inhibit HIV infection. The bonding can be completely described by explicitly considering by means of basic indices and chemical reactivity principles two forms of SMILES structures of the pyrimidines, the Longest SMILES Molecular Chain (LoSMoC) and the Branching SMILES (BraS), respectively, as the effective forms involved in the anti-HIV activity mechanism and according to the present work, also necessary intermediates in molecular pathways targeting/docking biological sites of interest.

Comparison of in silico models for prediction of mutagenicity

Using a dataset with more than 6000 compounds, the performance of eight quantitative structure activity relationships (QSAR) models was evaluated: ACD/Tox Suite, Absorption, Distribution, Metabolism, Elimination, and Toxicity of chemical substances (ADMET) predictor, Derek, Toxicity Estimation Software Tool (T.E.S.T.), TOxicity Prediction by Komputer Assisted Technology (TOPKAT), Toxtree, CEASAR, and SARpy (SAR in python). In general, the results showed a high level of performance. To have a realistic estimate of the predictive ability, the results for chemicals inside and outside the training set for each model were considered. The effect of applicability domain tools (when available) on the prediction accuracy was also evaluated. The predictive tools included QSAR models, knowledge-based systems, and a combination of both methods. Models based on statistical QSAR methods gave better results.

Chemoinformatics and chemical genomics: potential utility of in silico methods

Computational life sciences and informatics are inseparably intertwined and they lie at the heart of modern biology, predictive quantitative modeling and high-performance computing. Two of the applied biological disciplines that are poised to benefit from such progress are pharmacology and toxicology. This review will describe in silico chemoinformatics methods such as (quantitative) structure-activity relationship modeling and will overview how chemoinformatic technologies are considered in applied regulatory research. Given the post-genomics era and large-scale repositories of omics data that are available, this review will also address potential applications of in silico techniques in chemical genomics. Chemical genomics utilizes small molecules to explore the complex biological phenomena that may not be not amenable to straightforward genetic approach. The reader will gain the understanding that chemoinformatics stands at the interface of chemistry and biology with enabling systems for mapping, statistical modeling, pattern recognition, imaging and database tools. The great potential of these technologies to help address complex issues in the toxicological sciences is appreciated with the applied goal of the protection of public health.