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

Genotoxicity assessments of N-nitrosoethylisopropylamine (NEIPA) and N-nitrosodiisopropylamine (NDIPA) in the C57BL/6J mouse

N-Nitrosamines, known as drug impurities and suspected carcinogens, have drawn significant public concern. In response to drug regulatory needs, the European Medicines Agency (EMA) has previously proposed a carcinogenic potency categorization approach based on the N-nitrosamine α-hydroxylation hypothesis, i.e., that N-nitrosamine mutagenicity increases with the number of α-hydrogen atoms. However, this structure-activity relationship has not been fully tested in vivo. NEIPA (N-nitrosoethylisopropylamine) and NDIPA (N-nitrosodiisopropylamine) are small N-Nitrosamines with similar structures, differing in that the former compound has an additional α-hydrogen atom. In this study, NEIPA and NEIPA doses, 25-100 mg/kg, were administered orally to C57BL/6 J mice for seven consecutive days, and their mutation and DNA damage effects were compared. Compared with NDIPA, the mutagenicity and DNA damage potencies of NEIPA (which contains one more α-hydrogen) were much greater. These differences may be related to their distinct metabolic pathways and target organs. This case study confirms the role of α-hydroxyl modification in the mutagenicity of nitrosamines, with oxidation at the α-hydrogen being a crucial step in the formation of mutagens from N-Nitrosamines, and can inform mutagenicity risk assessment and the formulation of regulatory standards for N-nitrosamine impurities.

Development of Liquid Organic Hydrogen Carriers for Hydrogen Storage and Transport

The storage and transfer of energy require a safe technology to mitigate the global environmental issues resulting from the massive application of fossil fuels. Fuel cells have used hydrogen as a clean and efficient energy source. Nevertheless, the storage and transport of hydrogen have presented longstanding problems. Recently, liquid organic hydrogen carriers (LOHCs) have emerged as a solution to these issues. The hydrogen storage technique in LOHCs is more attractive than those of conventional energy storage systems like liquefaction, compression at high pressure, and methods of adsorption and absorption. The release and acceptance of hydrogen should be reversible by LOHC molecules following favourable reaction kinetics. LOHCs comprise liquid and semi-liquid organic compounds that are hydrogenated to store hydrogen. These hydrogenated molecules are stored and transported and finally dehydrogenated to release the required hydrogen for supplying energy. Hydrogenation and dehydrogenation are conducted catalytically for multiple cycles. This review elaborates on the characteristics of different LOHC molecules, based on their efficacy as energy generators. Additionally, different catalysts used for both hydrogenation and dehydrogenation are discussed.

An Integrated Workflow Assisted by In Silico Predictions To Expand the List of Priority Polycyclic Aromatic Compounds

The limited information in existing mass spectral libraries hinders an accurate understanding of the composition, behavior, and toxicity of organic pollutants. In this study, a total of 350 polycyclic aromatic compounds (PACs) in 9 categories were successfully identified in fine particulate matter by gas chromatography high resolution mass spectrometry. Using mass spectra and retention indexes predicted by in silico tools as complementary information, the scope of chemical identification was efficiently expanded by 27%. In addition, quantitative structure-activity relationship models provided toxicity data for over 70% of PACs, facilitating a comprehensive health risk assessment. On the basis of extensive identification, the cumulative noncarcinogenic risk of PACs warranted attention. Meanwhile, the carcinogenic risk of 53 individual analogues was noteworthy. These findings suggest that there is a pressing need for an updated list of priority PACs for routine monitoring and toxicological research since legacy polycyclic aromatic hydrocarbons (PAHs) contributed modestly to the overall abundance (18%) and carcinogenic risk (8%). A toxicological priority index approach was applied for relative chemical ranking considering the environmental occurrence, fate, toxicity, and analytical availability. A list of 39 priority analogues was compiled, which predominantly consisted of high-molecular-weight PAHs and alkyl derivatives. These priority PACs further enhanced source interpretation, and the highest carcinogenic risk was attributed to coal combustion.

In silico prediction of the mutagenicity of nitroaromatic compounds using correlation weights of fragments of local symmetry

Most quantitative structure-property/activity relationships (QSPRs/QSARs) techniques involve using different programs separately for generating molecular descriptors and separately for building models based on available descriptors. Here, the capabilities of the CORAL program are evaluated. A user of the program should apply as the basis for models the representation of the molecular structure by means of the simplified molecular input-line entry system (SMILES) as well as experimental data on the endpoint of interest. The local symmetry of SMILES is a novel composition of symmetrically represented symbols, which are three 'xyx', four 'xyyx', or five symbols 'xyzyx'. We updated our CORAL software using this optimal, new flexible descriptor, sensitive to the symmetric composition of a specific part of the molecule. Computational experiments have shown that taking account of these attributes of SMILES can improve the predictive potential of models for the mutagenicity of nitroaromatic compounds. In addition, the above computational experiments have confirmed the advantage of using the index of ideality of correlation (IIC) and the correlation intensity index (CII) for Monte Carlo optimization of the correlation weights for various attributes of SMILES, including the local symmetry. The average value of the coefficient of determination for the validation set (five different models) without fragments of local symmetry is 0.8589 ± 0.025, whereas using fragments of local symmetry improves this criterion of the predictive potential up to 0.9055 ± 0.010.

An Overview and Discussion of N-nitrosamine Considerations for Orally Inhaled Drug Products and Relevance to Other Dosage Forms

The presence of N-nitrosamines in drug products are currently an area of high regulatory and industry scrutiny, having been detected above acceptable regulatory levels in several solid oral drug products. For over 20 years, there has been an expectation that N-nitrosamines be eliminated or controlled to acceptable levels in orally inhaled and nasal drug products (OINDP). As a result, the OINDP industry has developed and implemented risk management processes and considerations to address N-nitrosamines in final drug product, including management and understanding of upstream supply particularly for OINDP device and container closure systems. We provide an overview of N-nitrosamine formation, discuss key current regulatory expectations worldwide for N-nitrosamines in drug products, discuss risk management approaches relevant for drug device combination products, and share analytical "tips" with respect to handling N-nitrosamines chemical assessments.

Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics

In this review, we describe the current status and challenges in applying machine-learning techniques to the analysis and prediction of pharmacokinetic data. The theory of pharmacokinetics has been developed over decades on the basis of physiology and reaction kinetics. Mathematical models allow the reduction of pharmacokinetic data to parameter values, giving insight and understanding into ADME processes and predicting the outcome of different dosing scenarios. However, much information hidden in the data is lost through conceptual simplification with models. It is difficult to use mechanistic models alone to predict diverse pharmacokinetic time profiles, including inter-drug and inter-individual differences, in a cross-sectional manner. Machine learning is a prediction platform that can handle complex phenomena through data-driven analysis. As a resule, machine learning has been successfully adopted in various fields, including image recognition and language processing, and has been used for over two decades in pharmacokinetic research, primarily in the area of quantitative structure-activity relationships for pharmacokinetic parameters. Machine-learning models are generally known to provide better predictive performance than conventional linear models. Owing to the recent success in deep learning, models with new structures are being consistently proposed. These models include transfer learning and generative adversarial networks, which contribute to the effective use of a limited amount of data by diverting existing similar models or generating pseudo-data. How to make such newly emerging machine learning technologies applicable to meet challenges in the pharmacokinetics/pharmacodynamics field is now the key issue.

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.

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.

Bacterial mutagenicity test data: collection by the task force of the Japan pharmaceutical manufacturers association

Background

Ames test is used worldwide for detecting the bacterial mutagenicity of chemicals. In silico analyses of bacterial mutagenicity have recently gained acceptance by regulatory agencies; however, current in silico models for prediction remain to be improved. The Japan Pharmaceutical Manufacturers Association (JPMA) organized a task force in 2017 in which eight Japanese pharmaceutical companies had participated. The purpose of this task force was to disclose a piece of pharmaceutical companies’ proprietary Ames test data.

Results

Ames test data for 99 chemicals of various chemical classes were collected for disclosure in this study. These chemicals are related to the manufacturing process of pharmaceutical drugs, including reagents, synthetic intermediates, and drug substances. The structure-activity (mutagenicity) relationships are discussed in relation to structural alerts for each chemical class. In addition, in silico analyses of these chemicals were conducted using a knowledge-based model of Derek Nexus (Derek) and a statistics-based model (GT1_BMUT module) of CASE Ultra. To calculate the effectiveness of these models, 89 chemicals for Derek and 54 chemicals for CASE Ultra were selected; major exclusions were the salt form of four chemicals that were tested both in the salt and free forms for both models, and 35 chemicals called “known” positives or negatives for CASE Ultra. For Derek, the sensitivity, specificity, and accuracy were 65% (15/23), 71% (47/66), and 70% (62/89), respectively. The sensitivity, specificity, and accuracy were 50% (6/12), 60% (25/42), and 57% (31/54) for CASE Ultra, respectively. The ratio of overall disagreement between the CASE Ultra “known” positives/negatives and the actual test results was 11% (4/35). In this study, 19 out of 28 mutagens (68%) were detected with TA100 and/or TA98, and 9 out of 28 mutagens (32%) were detected with either TA1535, TA1537, WP2uvrA, or their combination.

Conclusion

The Ames test data presented here will help avoid duplicated Ames testing in some cases, support duplicate testing in other cases, improve in silico models, and enhance our understanding of the mechanisms of mutagenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41021-021-00206-1.

Exploring phenylcarbamoylazinane-1,2,4-triazole thioethers as lipoxygenase inhibitors supported with in vitro, in silico and cytotoxic studies

Searching small molecules as an auspicious approach to develop new anti-inflammatory drugs is a challenge for the researchers especially by modifying active pharmacophoric groups in the targeted molecules. In the current work, a series of new S-alkyl/aralky derivatives (8a-h; 9a-h) of 2-(4-ethyl/phenyl-5-(1-phenylcarbamoylpiperidine)-4H-1,2,4-triazol-3-ylthio)ether were synthesized and assessed for their inhibitory action against the 15-lipoxygenase from soybean (15-sLOX). The basic precursor ethyl piperidine-4-carboxylate (a) was consecutively changed into phenylcarbamoyl derivative (1), hydrazide (2), semicarbazides (3/4) and N-ethyl/phenyl-5-(1-phenylcarbamoylpiperidine)-1,2,4-triazoles (5/6), which further in association with electrophiles (7a-h) promoted to the final products (8a-h; 9a-h). The synthesized derivatives were characterized by FT-IR, ¹H-, ¹³C NMR spectroscopy, EI-MS, and HR-EI-MS spectrometry. Amongst these, 8a, 8c, and 9c, expressed potent inhibitory profiles against the 15-sLOX enzyme with IC₅₀ values of 12.52 ± 0.35 to 35.64 ± 0.29 µM, followed by the compounds 9b, 9g, 9d, 9a, 8b, 8e, 8d, 8g, 8h, 8f and 9h with IC₅₀ values in the range of 43.78 ± 0.43 to 108.65 ± 0.38 µM. All compounds exhibited variable cellular viability levels by MTT assay. Flow cytometric data demonstrated that 8f, 8g, 8h have maximal lymphocyte cellular viability and all compounds affected cells in the late apoptosis phase. In silico ADMET studies supported the drug-likeness of most of the molecules. These studies were supported by molecular docking against 15-sLOX, human 5-LOX (5-hLOX) and human 15-LOX (5-hLOX); that inhibitors of 15-sLOX docked-in the active pocket of either 5-hLOX or 15-hLOX and docking score remained constant for all three enzymes within a narrow range (-6.8 to -9.7) as did it for standard quercetin (-8.4 to -9.0). The most dominant bonding interactions were π-π, π-anion, and π-alkyl type along with the hydrogen bonding. The data collected altogether demonstrates the better possibility of some of these compounds as good LOX inhibitors in search for 'lead' as anti-inflammatory agents in the process of drug discovery and development.

Antibacterial and Antifungal Activities and Toxicity of the Essential Oil from Callistemon viminalis Complexed with β-Cyclodextrin

The essential oil from Callistemon viminalis (EOC) is rich in monoterpenes, with a variety of biological properties: antibacterial, antifungal, insecticide, and antioxidant. Inclusion complexes (ICs) with cyclodextrins (CDs) is an alternative to prevent toxicity, improve the activity, and reduce the concentration to be used. Thus, the objective of this work was to prepare an IC (EOC/β-CD) and evaluate the antibacterial, antifungal and phospholipase activities, as well as the toxicity. Antimicrobial activity used the agar diffusion test and antifungal activity the disc diffusion test. Toxicity tests were carried out using Lactuca sativa L. The inhibition of phospholipase activity using the venom of Bothrops atrox as an inducer was performed. Antibacterial and antifungal tests demonstrated a decrease in the minimum inhibitory concentration (MIC) of the IC. It was most significantly observed for the bacterium Listeria monocytogenes, for which there was a decrease in the MIC from 250 µg mL^-1 to 62.5 µg mL^-1 after complexation, and for the fungus Aspergillus flavus, with a decrease in MIC from 125 µg mL^-1 to 62.5 µg mL^-1 after complexation. Toxicity tests with Lactuca sativa showed a decrease in toxicity after complexation in all parameters analyzed, with no statistical difference from the negative control. Inhibition of phospholipase activity induced by Bothrops atrox venom was more expressive in the highest proportion studied (1:10 m:m), exerting 23% inhibition. The assays demonstrated that the complexation between the EOC and β-CD is a promising alternative for use in different branches, especially in food industry, to fully exploit its application potential.

Multiple biological active 4-aminopyrazoles containing trifluoromethyl and their 4-nitroso-precursors: Synthesis and evaluation

4-Nitroso-3-trifluoromethyl-5-alkyl[(het)aryl]pyrazoles were synthesized via one-pot nitrosation of 1,3-diketones or their lithium salts followed by treatment of hydrazines. Reduction of nitroso-derivatives made it possible to obtain 4-amino-3-trifluoromethylpyrazoles chlorides. According to computer-aided calculations, all synthesized compounds are expected to have acceptable ADME profile for drug design. Tuberculostatic, antibacterial, antimycotic, antioxidant and cytotoxic activities of the compounds were evaluated in vitro, while their analgesic and anti-inflammatory action was tested in vivo along with acute toxicity studies. N-Unsubstituted 4-nitrosopyrazoles were the most effective tuberculostatics (MIC to 0.36 μg/ml) and antibacterial agents against Streptococcus pyogenes (MIC to 7.8 μg/ml), Staphylococcus aureus,S. aureus MRSA and Neisseria gonorrhoeae (MIC to 15.6 μg/ml). 4-Nitroso-1-methyl-5-phenylpyrazole had the pronounced antimycotic action against a wide range of fungi (Trichophytonrubrum, T. tonsurans, T. violaceum, T. interdigitale, Epidermophytonfloccosum, Microsporumcanis with MIC 0.38-12.5 μg/ml). N-Unsubstituted 4-aminopyrazoles shown high radical-scavenging activity in ABTS test, ORAC/AAPH and oxidative erythrocyte hemolysis assays. 1-Methyl-5-phenyl-3-trifluoromethylpyrazol-4-aminium chloride revealed potential anticancer activity against HeLa cells (SI > 1351). The pronounced analgesic activity was found for 4-nitroso- and 4-aminopyrazoles having phenyl fragment at the position 5 in "hot plate" test. The most of the obtained pyrazoles had a moderate acute toxicity.

Statistical treatment of cytotoxicity in Ames bacterial reverse mutation assays can provide additional structure–activity relationship information

The bacterial reverse mutation assay, that is, the Ames test, measures mutations that reverse the inactivation of a gene involved in the synthesis of either histidine in Salmonella bacteria or tryptophan in Escherichia coli. The classic dose–response curve of an Ames assay plots number of reverse mutations (“revertants”) on the y-axis versus dose of the test chemical on the x-axis. Frequently, the dose–response curve resembles a parabola with a linear initial slope resulting from the accumulation of mutations, which transitions to a downward curvature resulting from cell killing (cytotoxicity) at increasingly higher doses of the test chemical. For regulatory purposes, a positive Ames test is usually considered as induction of twice the number of reverse mutations above background levels. For research purposes, the potency of the mutagenic response can be calculated from measuring the initial slope of the mutagenic response. This initial slope can be calculated in a manner that disentangles the downward pull on the initial slope value provided by the initiation of cytotoxicity. For a dose–response curve resembling a parabola, both the initial positive slope representing mutagenicity and the secondary negative slope representing cytotoxicity can be calculated from the same dose–response curve. The Ames test is the most commonly conducted genotoxicity assay. When a series of molecular congeners are assayed in the Ames test for mutagenicity, additional consideration of the cytotoxicity can provide important structure–activity relationship information.

Evaluation of acetone as a solvent for the Ames test

Background

Acetone is a common alternative solvent used in the Ames test when test chemicals are unstable or poorly soluble in water or dimethyl sulfoxide (DMSO). Yet, there is a very limited number of studies evaluating acetone as a solvent in the modified Ames test with preincubation (preincubation test).

Results

We evaluated the acetone as a solvent for the preincubation test. Fourteen mutagens dissolved in acetone was added each to the reaction mixture at 2 different volumes (25 or 50 μL) to examine mutagenicity using bacterial test strains recommended in the Organization for Economic Cooperation and Development (OECD) test guideline 471, and compared with DMSO (100 μL). Cytotoxicity of acetone was also examined in these bacterial strains. TA1537 was most sensitive to the cytotoxicity of acetone, the degree of which was moderate and similar to DMSO in TA1537 without S9 mix. In other strains, cytotoxicity was limited to a mild degree with or without S9 mix. Cytotoxicity of acetone did not affect detection of mutagenicity of any mutagens; many of them being comparable or less mutagenic than those with DMSO.

Conclusions

These findings indicate that acetone is a viable candidate as a solvent for the preincubation test in the 5 bacterial strains.

Design and Synthesis of a Novel and Selective Kappa Opioid Receptor (KOR) Antagonist (BTRX-335140)

κ opioid receptor (KOR) antagonists are potential pharmacotherapies for the treatment of migraine and stress-related mood disorders including depression, anxiety, and drug abuse, thus the development of novel KOR antagonists with an improved potency/selectivity profile and medication-like duration of action has attracted the interest of the medicinal chemistry community. In this paper, we describe the discovery of 1-(6-ethyl-8-fluoro-4-methyl-3-(3-methyl-1,2,4-oxadiazol-5-yl)quinolin-2-yl)- N-(tetrahydro-2 H-pyran-4-yl)piperidin-4 amine (CYM-53093, BTRX-335140) as a potent and selective KOR antagonist, endowed with favorable in vitro ADMET and in vivo pharmacokinetic profiles and medication-like duration of action in rat pharmacodynamic experiments. Orally administered CYM-53093 showed robust efficacy in antagonizing KOR agonist-induced prolactin secretion and in tail-flick analgesia in mice. CYM-53093 exhibited a broad selectivity over a panel of off-target proteins. This compound is in phase 1 clinical trials for the treatment of neuropsychiatric disorders wherein dynorphin is thought to contribute to the underlying pathophysiology.

Nitro-Group-Containing Drugs

The nitro group is considered to be a versatile and unique functional group in medicinal chemistry. Despite a long history of use in therapeutics, the nitro group has toxicity issues and is often categorized as a structural alert or a toxicophore, and evidence related to drugs containing nitro groups is rather contradictory. In general, drugs containing nitro groups have been extensively associated with mutagenicity and genotoxicity. In this context, efforts toward the structure-mutagenicity or structure-genotoxicity relationships have been undertaken. The current Perspective covers various aspects of agents that contain nitro groups, their bioreductive activation mechanisms, their toxicities, and approaches to combat their toxicity issues. In addition, recent advances in the field of anticancer, antitubercular and antiparasitic agents containing nitro groups, along with a patent survey on hypoxia-activated prodrugs containing nitro groups, are also covered.

Re-evaluation of potassium nitrite (E 249) and sodium nitrite (E 250) as food additives

The Panel on Food Additives and Nutrient Sources added to Food (ANS) provided a scientific opinion re‐evaluating the safety of potassium nitrite (E 249) and sodium nitrite (E 250) when used as food additives. The ADIs established by the SCF (1997) and by JECFA (2002) for nitrite were 0–0.06 and 0–0.07 mg/kg bw per day, respectively. The available information did not indicate in vivo genotoxic potential for sodium and potassium nitrite. Overall, an ADI for nitrite per se could be derived from the available repeated dose toxicity studies in animals, also considering the negative carcinogenicity results. The Panel concluded that an increased methaemoglobin level, observed in human and animals, was a relevant effect for the derivation of the ADI. The Panel, using a BMD approach, derived an ADI of 0.07 mg nitrite ion/kg bw per day. The exposure to nitrite resulting from its use as food additive did not exceed this ADI for the general population, except for a slight exceedance in children at the highest percentile. The Panel assessed the endogenous formation of nitrosamines from nitrites based on the theoretical calculation of the NDMA produced upon ingestion of nitrites at the ADI and estimated a MoE > 10,000. The Panel estimated the MoE to exogenous nitrosamines in meat products to be < 10,000 in all age groups at high level exposure. Based on the results of a systematic review, it was not possible to clearly discern nitrosamines produced from the nitrite added at the authorised levels, from those found in the food matrix without addition of external nitrite. In epidemiological studies there was some evidence to link (i) dietary nitrite and gastric cancers and (ii) the combination of nitrite plus nitrate from processed meat and colorectal cancers. There was evidence to link preformed NDMA and colorectal cancers.

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R ²) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

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

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

Antioxidant and antimutagenic potential of Psidium guajava leaf extracts

Fruits, vegetables and medicinal herbs rich in phenolics antioxidants contribute toward reduced risk of age-related diseases and cancer. In this study, Psidium guajava leaf extract was fractionated in various organic solvents viz. petroleum ether, benzene, ethyl acetate, ethanl and methanol and tested for their antioxidant and antimutagenic properties. Methanolic fraction showed maximum antioxidant activity comparable to ascorbic acid and butylated hydroxyl toluene (BHT) as tested by DPPH free radical scavenging, phosphomolybdenum, FRAP (Fe3 + reducing power) and CUPRAC (cupric ions (Cu²⁺) reducing ability) assays. The fraction was analyzed for antimutagenic activities against sodium azide (NaN₃), methylmethane sulfonate (MMS), 2-aminofluorene (2AF) and benzo(a)pyrene (BP) in Ames Salmonella tester strains. The methanol extracted fraction at 80 μg/ml concentration inhibited above 70% mutagenicity. Further, phytochemical analysis of methanol fraction that was found to be most active revealed the presence of nine major compounds by gas chromatography-mass spectrometry (GC-MS). This data suggests that guava contains high amount of phenolics responsible for broad-spectrum antimutagenic and antioxidant properties in vitro and could be potential candidates to be explored as modern phytomedicine.

Modeling of Toxicity-Relevant Electrophilic Reactivity for Guanine with Epoxides: Estimating the Hard and Soft Acids and Bases (HSAB) Parameter as a Predictor

According to the electrophilic theory in toxicology, many chemical carcinogens in the environment and/or their active metabolites are electrophiles that exert their effects by forming covalent bonds with nucleophilic DNA centers. The theory of hard and soft acids and bases (HSAB), which states that a toxic electrophile reacts preferentially with a biological macromolecule that has a similar hardness or softness, clarifies the underlying chemistry involved in this critical event. Epoxides are hard electrophiles that are produced endogenously by the enzymatic oxidation of parent chemicals (e.g., alkenes and PAHs). Epoxide ring opening proceeds through a SN2-type mechanism with hard nucleophile DNA sites as the major facilitators of toxic effects. Thus, the quantitative prediction of chemical reactivity would enable a predictive assessment of the molecular potential to exert electrophile-mediated toxicity. In this study, we calculated the activation energies for reactions between epoxides and the guanine N7 site for a diverse set of epoxides, including aliphatic epoxides, substituted styrene oxides, and PAH epoxides, using a state-of-the-art density functional theory (DFT) method. It is worth noting that these activation energies for diverse epoxides can be further predicted by quantum chemically calculated nucleophilic indices from HSAB theory, which is a less computationally demanding method than the exacting procedure for locating the transition state. More importantly, the good qualitative/quantitative correlations between the chemical reactivity of epoxides and their bioactivity suggest that the developed model based on HSAB theory may aid in the predictive hazard evaluation of epoxides, enabling the early identification of mutagenicity/carcinogenicity-relevant SN2 reactivity.

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.

Label-free and real-time imaging of dehydration-induced DNA conformational changes in cellular nucleus using second harmonic microscopy

Dehydration-induced DNA conformational changes have been probed for the first time with the use of second harmonic microscopy. Unlike conventional approaches, second harmonic microscopy provides a label-free and real-time approach to detect DNA conformational changes. Upon dehydration, cellular DNA undergoes a transition from B- to A-form, whereas cellular nuclei change from invisible to visible under second harmonic microscopy. These results showed that DNA is a second order nonlinear optical material. We further confirmed this by characterizing the nonlinear optical properties of extracted DNA from human cells. Our findings open a new path for SHG imaging. DNA can change its conformations under many circumstances. For example: normal cells turning into cancerous cells and drug molecules binding with DNA. Therefore, the detection of DNA conformational changes with second harmonic microscopy will be a useful tool in cancer therapy and new drug discovery.

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.

Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

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

Mutagenicity, carcinogenicity, and other end points

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

In silico physicochemical parameter predictions

Drug discovery is a complex process with the aim of discovering efficacious molecules where their potency and selectivity are balanced against ADMET properties to set the appropriate dose and dosing interval. The link between physicochemical properties and molecular structure are well established. The subsequent connections between physicochemical properties and a drug's biological behavior provide an indirect link back to structure, facilitating the prediction of a biological property as a consequence of a particular molecular manipulation. Due to this understanding, during early drug discovery in vitro physicochemical property assays are commonly performed to eliminate compounds with properties commensurate with high attrition risks. However, the goal is to accurately predict physicochemical properties to prevent the synthesis of high risk compounds and hence minimize wasted drug discovery efforts. This paper will review the relevance to ADMET behaviors of key physicochemical properties, such as ionization, aqueous solubility, hydrogen bonding strength and hydrophobicity, and the in silico methodology for predicting them.

Prediction of mutagenic activity of nitrophenanthrene and nitroanthracene isomers by simulated IR and Raman spectra

This paper expands upon our original work on nitroanthracenes in (Alparone, A., Librando, V., 2012. Spectrochim. Acta A 89, 129-136) on the series of nitrophenanthrene isomers. Geometries, electric properties, IR and Raman spectra of 1-, 2-, 3-, 4- and 9-nitrophenanthrene (1-NP, 2-NP, 3-NP, 4-NP and 9-NP) were obtained and analyzed using Density Functional Theory calculations. The balance between steric and π-conjugative interactions determines the order of stability 4-NP<1-NP~9-NP<2-NP∼3-NP. IR and Raman spectral zones between 1000 and 1600 cm(-1) show intense bands noticeably affected by the position of the substituent, being potentially useful to discriminate and monitor the investigated isomers. Dipole moments, summations of IR intensity (ΣI(IR)) and Raman activity (ΣA(Raman)) over the 3N-6 vibrational modes are sensitive to the structure, increasing steadily from the non-planar to the planar isomers. Good linear relationships between the ΣI(IR) (r=0.90) and ΣA(Raman) (r=0.99) against the Salmonella typhimurium strain TA98 mutagenic activity of nitrophenanthrenes and isomeric nitroanthracenes are found. On the basis of the structural and vibrational properties, 4-NP seems to have not mutagenic activity, while the unknown TA98 mutagenic potency of 1-nitroanthracene is predicted to be between that of 9-NP and 3-NP. Calculated ΣI(IR) and ΣA(Raman) values could be used as molecular descriptors for QSARs applications of series of isomers.

The Use of (Q)SAR Methods in the Context of REACH

ABSTRACT The new European REACH (Registration, Evaluation, Authorisation of Chemicals) legislation requires new information on chemicals, which is tiered according to production volume. The need for data promotes the use of (quantitative) structure-activity relationships ([Q]SARs) in order to meet the European political goal to protect the lives of animals. Within the preparation for REACH, the EU Commission set up REACH implementation projects (RIPs). They are aimed at giving guidance to industry and regulators on how to interpret the legislation. Within the framework of REACH, (Q)SARs are expected to have specific applications in several fields such as raising concerns for chemicals with low production volume for which no (eco)toxicological data have to be submitted, supporting the grouping of chemicals (family approach), and supporting the classification of chemicals as PBT (persistent/bioaccumulative/toxic) or vPvB (very persistent, very bioaccumulative) substances. Currently, the use of (Q)SARs in European regulatory decision making is limited. However, a new European tool (Toxtree) will be made available along with training courses to promote the use of (Q)SAR approaches under REACH. It is expected that the experimental data, to be submitted by industry and made publicly available under REACH, will provide opportunities to improve existing prediction tools and to develop new approaches for (Q)SAR analysis. In the political perspective, the tools will provide support as a rational basis for the intelligent design of new chemicals with sustainable (eco)toxicological properties.

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.

Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities

The toxicological assessment of genotoxic impurities is important in the regulatory framework for pharmaceuticals. In this context, the application of promising computational methods (e.g. Quantitative Structure-Activity Relationships (QSARs), Structure-Activity Relationships (SARs) and/or expert systems) for the evaluation of genotoxicity is needed, especially when very limited information on impurities is available. To gain an overview of how computational methods are used internationally in the regulatory assessment of pharmaceutical impurities, the current regulatory documents were reviewed. The software recommended in the guidelines (e.g. MCASE, MC4PC, Derek for Windows) or used practically by various regulatory agencies (e.g. US Food and Drug Administration, US and Danish Environmental Protection Agencies), as well as other existing programs were analysed. Both statistically based and knowledge-based (expert system) tools were analysed. The overall conclusions on the available in silico tools for genotoxicity and carcinogenicity prediction are quite optimistic, and the regulatory application of QSAR methods is constantly growing. For regulatory purposes, it is recommended that predictions of genotoxicity/carcinogenicity should be based on a battery of models, combining high-sensitivity models (low rate of false negatives) with high-specificity ones (low rate of false positives) and in vitro assays in an integrated manner.

Molecular mechanisms underlying chemical liver injury

The liver is necessary for survival. Its strategic localisation, blood flow and prominent role in the metabolism of xenobiotics render this organ particularly susceptible to injury by chemicals to which we are ubiquitously exposed. The pathogenesis of most chemical-induced liver injuries is initiated by the metabolic conversion of chemicals into reactive intermediate species, such as electrophilic compounds or free radicals, which can potentially alter the structure and function of cellular macromolecules. Many reactive intermediate species can produce oxidative stress, which can be equally detrimental to the cell. When protective defences are overwhelmed by excess toxicant insult, the effects of reactive intermediate species lead to deregulation of cell signalling pathways and dysfunction of biomolecules, leading to failure of target organelles and eventual cell death. A myriad of genetic factors determine the susceptibility of specific individuals to chemical-induced liver injury. Environmental factors, lifestyle choices and pre-existing pathological conditions also have roles in the pathogenesis of chemical liver injury. Research aimed at elucidating the molecular mechanism of the pathogenesis of chemical-induced liver diseases is fundamental for preventing or devising new modalities of treatment for liver injury by chemicals.

Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity

The knowledge-based Toxtree expert system (SAR approach) was integrated with the statistically based counter propagation artificial neural network (CP ANN) model (QSAR approach) to contribute to a better mechanistic understanding of a carcinogenicity model for non-congeneric chemicals using Dragon descriptors and carcinogenic potency for rats as a response. The transparency of the CP ANN algorithm was demonstrated using intrinsic mapping technique specifically Kohonen maps. Chemical structures were represented by Dragon descriptors that express the structural and electronic features of molecules such as their shape and electronic surrounding related to reactivity of molecules. It was illustrated how the descriptors are correlated with particular structural alerts (SAs) for carcinogenicity with recognized mechanistic link to carcinogenic activity. Moreover, the Kohonen mapping technique enables one to examine the separation of carcinogens and non-carcinogens (for rats) within a family of chemicals with a particular SA for carcinogenicity. The mechanistic interpretation of models is important for the evaluation of safety of chemicals.