Screening DNA Damage in the Rat Kidney and Liver by Untargeted DNA Adductomics.

Air pollution, tobacco smoke, and red meat are associated with renal cell cancer (RCC) risk in the United States and Western Europe; however, the chemicals that form DNA adducts and initiate RCC are mainly unknown. Aristolochia herbaceous plants are used for medicinal purposes in Asia and worldwide. They are a significant risk factor for upper tract urothelial carcinoma (UTUC) and RCC to a lesser extent. The aristolochic acid (AA) 8-methoxy-6-nitrophenanthro-[3,4-d]-1,3-dioxolo-5-carboxylic acid (AA-I), a component of Aristolochia herbs, contributes to UTUC in Asian cohorts and in Croatia, where AA-I exposure occurs from ingesting contaminated wheat flour. The DNA adduct of AA-I, 7-(2'-deoxyadenosin-N6-yl)-aristolactam I, is often detected in patients with UTUC, and its characteristic A:T-to-T:A mutational signature occurs in oncogenes and tumor suppressor genes in AA-associated UTUC. Identifying DNA adducts in the renal parenchyma and pelvis caused by other chemicals is crucial to gaining insights into unknown RCC and UTUC etiologies. We employed untargeted screening with wide-selected ion monitoring tandem mass spectrometry (wide-SIM/MS2) with nanoflow liquid chromatography/Orbitrap mass spectrometry to detect DNA adducts formed in rat kidneys and liver from a mixture of 13 environmental, tobacco, and dietary carcinogens that may contribute to RCC. Twenty DNA adducts were detected. DNA adducts of 3-nitrobenzanthrone (3-NBA), an atmospheric pollutant, and AA-I were the most abundant. The nitrophenanthrene moieties of 3-NBA and AA-I undergo reduction to their N-hydroxy intermediates to form 2'-deoxyguanosine (dG) and 2'-deoxyadenosine (dA) adducts. We also discovered a 2'-deoxycytidine AA-I adduct and dA and dG adducts of 10-methoxy-6-nitro-phenanthro-[3,4-d]-1,3-dioxolo-5-carboxylic acid (AA-III), an AA-I isomer and minor component of the herbal extract assayed, signifying AA-III is a potent kidney DNA-damaging agent. The roles of AA-III, other nitrophenanthrenes, and nitroarenes in renal DNA damage and human RCC warrant further study. Wide-SIM/MS2 is a powerful scanning technology in DNA adduct discovery and cancer etiology characterization.

The comprehensive prediction of carcinogenic potency and tumorigenic dose (TD50) for two problematic N-nitrosamines in food: NMAMPA and NMAMBA using toxicology in silico methods.

The identification and toxicological assessment of potential carcinogens is of paramount importance for public health and safety. This study aimed to predict the carcinogenic potency and tumorigenic dose (TD50) for two problematic N-nitrosamines (N-NAs) commonly found in food: N-2-methylpropyl-N-1-methylacetonylnitrosamine (NMAMPA, CAS: 93755-83-0) and N-3-Methylbutyl-N-1-methylacetonylnitrosamine (NMAMBA, CAS: 71016-15-4). To achieve this goal, in silico toxicology methods were employed due to their practical applications and potential in risk assessment. The justification for conducting these studies was incoherent results published by the European Food Safety Authority (EFSA). For this purpose, we applied various in silico tools, including qualitative methods (ToxTree, ProTox II and CarcinoPred-EL) and quantitative methods (QSAR Toolbox and LAZAR) were applied to predict the carcinogenic potency. These tools leverage computational approaches to analyze chemical structures for finding toxicophores and generating predictions, making them efficient alternatives to traditional in vivo experiments. The results obtained indicated that N-NAs are carcinogenic compounds, and quantitative data was obtained in the form of estimated doses of TD50 for the compounds tested.

Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards.

Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessment of these pollutants is crucial for chemical health evaluation and environmental risk assessments. Traditional experimental methods are expensive and time-consuming, prompting the development of alternative approaches such as in silico methods. In this regard, deep learning (DL) has shown potential but lacks optimal performance and interpretability. This study introduces an interpretable DL model called CarcGC for chemical carcinogenicity prediction, utilizing a graph convolutional neural network (GCN) that employs molecular structural graphs as inputs. Compared to existing models, CarcGC demonstrated enhanced performance, with the area under the receiver operating characteristic curve (AUCROC) reaching 0.808 on the test set. Due to air pollution is closely related to the incidence of lung cancers, we applied the CarcGC to predict the potential carcinogenicity of chemicals listed in the United States Environmental Protection Agency's Hazardous Air Pollutants (HAPs) inventory, offering a foundation for environmental carcinogenicity screening. This study highlights the potential of artificially intelligent methods in carcinogenicity prediction and underscores the value of CarcGC interpretability in revealing the structural basis and molecular mechanisms underlying chemical carcinogenicity.

Difference in carcinogenicities of two different vapor grown carbon fibers with different physicochemical characteristics induced by intratracheal instillation in rats.

BACKGROUND: Carbon fibers are high aspect ratio structures with diameters on the submicron scale. Vapor grown carbon fibers are contained within multi-walled carbon tubes, with VGCF™-H commonly applied as a conductive additive in lithium-ion batteries. However, several multi-walled carbon fibers, including MWNT-7, have been reported to induce lung carcinogenicity in rats. This study investigated the carcinogenic potential of VGCF™-H fibers in F344 rats of both sexes with the vapor grown carbon fibers VGCF™-H and MWNT-7 over 2 years. The carbon fibers were administered to rats by intratracheal instillation at doses of 0, 0.016, 0.08, and 0.4 mg/kg (total doses of 0, 0.128, 0.64, and 3.2 mg/kg) once per week for eight weeks and the rats were observed for up to 2 years after the first instillation. RESULTS: Histopathological examination showed the induction of malignant mesothelioma on the pleural cavity with dose-dependent increases observed at 0, 0.128, 0.64, and 3.2 mg/kg in rats of both sexes that were exposed to MWNT-7. On the other hand, only two cases of pleural malignant mesothelioma were observed in the VGCF™-H groups; both rats that received 3.2 mg/kg in male. The animals in the MWNT-7 groups either died or became moribund earlier than those in the VGCF™-H groups, which is thought related to the development of malignant mesothelioma. The survival rates were higher in the VGCF™-H group, and more carbon fibers were observed in the pleural lavage fluid (PLF) of the MWNT-7 groups. These results suggest that malignant mesothelioma is related to the transfer of carbon fibers into the pleural cavity. CONCLUSIONS: The intratracheal instillation of MWNT-7 clearly led to carcinogenicity in both male and female rats at all doses. The equivocal evidence for carcinogenic potential that was observed in male rats exposed to VGCF™-H was not seen in the females. The differences in the carcinogenicities of the two types of carbon fibers are thought due to differences in the number of carbon fibers reaching the pleural cavity. The results indicate that the carcinogenic activity of VGCF™-H is lower than that of MWNT-7.

High-Field Asymmetric Waveform Ion Mobility Spectrometry Analysis of Carcinogenic Aromatic Amines in Tobacco Smoke with an Orbitrap Tribrid Mass Spectrometer.

Smoking is a risk factor for bladder cancer (BC), although the specific chemicals responsible for BC remain uncertain. Considerable research has focused on aromatic amines (AAs), including o-toluidine (o-tol), o-anisidine (o-anis), 2-naphthylamine (2-NA), and 4-aminobiphenyl (4-ABP), which are linked to human BC based on elevated BC incidence in occupationally exposed factory workers. These AAs arise at nanogram levels per combusted cigarette. The unambiguous identification of AAs, particularly low-molecular-weight monocyclic AAs in tobacco smoke extracts, by liquid chromatography-mass spectrometry (LC-MS) is challenging due to their poor performance on reversed-phase columns and co-elution with isobaric interferences from the complex tobacco smoke matrix. We employed a tandem liquid-liquid and solid-phase extraction method to isolate AAs from the basic fraction of tobacco smoke condensate (TSC) and utilized high-field asymmetric waveform ion mobility spectrometry (FAIMS) coupled to high-resolution accurate mass (HRAM) Orbitrap LC-MS2 to assay AAs in TSC. The employment of FAIMS greatly reduced sample complexity by removing precursor co-isolation interfering species at the MS1 scan stage, resulting in dramatically improved signal-to-noise of the precursor ions and cleaner, high-quality MS2 spectra for unambiguous identification and quantification of AAs in TSC. We demonstrate the power of LC/FAIMS/MS2 by characterizing and quantifying two low-molecular-weight carcinogenic AAs, o-tol and o-anis, in TSC, using stable isotopically labeled internal standards. These results demonstrate the power of FAIMS in trace-level analyses of AA carcinogens in the complex tobacco smoke matrix.

Maximizing use of existing carcinogenicity data to support acceptable intake levels for mutagenic impurities in pharmaceuticals: Learnings from N-nitrosamine case studies.

The unexpected finding of N-nitrosamine (NA) impurities in many pharmaceutical products raised significant challenges for industry and regulators. In addition to well-studied small molecular weight NAs, many of which are potent rodent carcinogens, novel NAs associated with active pharmaceutical ingredients have been found, many of which have limited or no safety data. A tiered approach to establishing Acceptable Intake (AI) limits for NA impurities has been established using chemical-specific data, read-across, or a class-specific TTC limit. There are ∼140 NAs with some rodent carcinogenicity data, but much of it is older and does not meet current guidelines for what constitutes a 'robust' bioassay. Nevertheless, these data are an important source of information to ensure the best science is used for assessing NA impurities and assuring consumer safety while minimizing impact that can lead to drug shortages. We present several strategies to maximize the use of imperfect data including using a lower confidence limit on a rodent TD50, and leveraging data from multiple NAs. Information on the chemical structure known to impact potency can also support development of an AI or potentially conclude that a particular NA does not fall in the cohort of concern for potent carcinogenicity.

Innovative screening for functional improved aromatic amine derivatives: Toxicokinetics, free radical oxidation pathway and carcinogenic adverse outcome pathway.

Aromatic amines, one of the most widely used low-cost antioxidants in rubbers, have been regarded as pollutants with human health concerns. To overcome this problem, this study developed a systematic molecular design, screening, and performance evaluation method to design functionally improved, environmentally friendly and synthesizable aromatic amine alternatives for the first time. Nine of 33 designed aromatic amine derivatives have improved antioxidant property (lower bond dissociation energy of N-H), and their environmental and bladder carcinogenicity impacts were evaluated through toxicokinetic model and molecular dynamics simulation. The environmental fate of the designed AAs-11-8, AAs-11-16, and AAs-12-2 after antioxidation (i.e., peroxyl radicals (ROO·), hydroxyl radicals (HO·), superoxide anion radicals (O2·-) and ozonation reaction) was also analyzed. Results showed that the by-products of AAs-11-8 and AAs-12-2 have less toxicity after antioxidation. In addition, human bladder carcinogenicity of the screened alternatives was also evaluated through adverse outcome pathway. The carcinogenic mechanisms were analyzed and verified through amino acid residue distribution characteristics, 3D-QSAR and 2D-QSAR models. AAs-12-2, with high antioxidation property, low environmental impacts and carcinogenicity, was screened as the optimum alternative for 3,5-Dimethylbenzenamine. This study provided theoretical support for designing environmentally friendly and functionally improved aromatic amine alternatives from toxicity evaluation and mechanism analysis.

Systematic Review of Roles of Arecoline and Arecoline N-Oxide in Oral Cancer and Strategies to Block Carcinogenesis.

Betel quid and areca nut are complex mixture carcinogens, but little is known about whether their derived single-agent arecoline or arecoline N-oxide (ANO) is carcinogenic, and the underlying mechanisms remain unclear. In this systematic review, we analyzed recent studies on the roles of arecoline and ANO in cancer and strategies to block carcinogenesis. In the oral cavity, flavin-containing monooxygenase 3 oxidizes arecoline to ANO, and both alkaloids conjugate with N-acetylcysteine to form mercapturic acid compounds, which are excreted in urine, reducing arecoline and ANO toxicity. However, detoxification may not be complete. Arecoline and ANO upregulated protein expression in oral cancer tissue from areca nut users compared to expression levels in adjacent normal tissue, suggesting a causal relationship between these compounds and oral cancer. Sublingual fibrosis, hyperplasia, and oral leukoplakia were diagnosed in mice subjected to oral mucosal smearing of ANO. ANO is more cytotoxic and genotoxic than arecoline. During carcinogenesis and metastasis, these compounds increase the expression of epithelial-mesenchymal transition (EMT) inducers such as reactive oxygen species, transforming growth factor-ß1, Notch receptor-1, and inflammatory cytokines, and they activate EMT-related proteins. Arecoline-induced epigenetic markers such as sirtuin-1 hypermethylation, low protein expression of miR-22, and miR-886-3-p accelerate oral cancer progression. Antioxidants and targeted inhibitors of the EMT inducers used reduce the risk of oral cancer development and progression. Our review findings substantiate the association of arecoline and ANO with oral cancer. Both of these single compounds are likely carcinogenic to humans, and their mechanisms and pathways of carcinogenesis are useful indicators for cancer therapy and prognosis.

Mutagenic impurities in pharmaceuticals: A critical assessment of the cohort of concern with a focus on N-nitrosamines.

The TTC (Threshold of Toxicological Concern; set at 1.5 µg/day for pharmaceuticals) defines an acceptable patient intake for any unstudied chemical posing a negligible risk of carcinogenicity or other toxic effects. A group of high potency mutagenic carcinogens, defined solely by the presence of particular structural alerts, are referred to as the "cohort of concern" (CoC); aflatoxin-like-, N-nitroso-, and alkyl-azoxy compounds are considered to pose a significant carcinogenic risk at intakes below the TTC. Kroes et al. (2004) derived values for the TTC and CoC in the context of food components, employing a non-transparent dataset never placed in the public domain. Using a reconstructed all-carcinogen dataset from relevant publications, it is now clear that there are exceptions for all three CoC structural classes. N-Nitrosamines represent 62% of the N-nitroso class in the reconstructed dataset. Employing a contemporary dataset, 20% are negative in rodent carcinogenicity bioassays with less than 50% of all N-nitrosamines estimated to fall into the highest risk category. It is recommended that CoC nitrosamines are identified by compound-specific data rather than structural alerts. Thus, it should be possible to distinguish CoC from non-CoC N-nitrosamines in the context of mutagenic impurities described in ICH M7 (R1).

Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database.

Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96-18-4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.

New Aminobiphenylcysteine Derivatives in Globin and Urine of Rats Dosed with 4-Aminobiphenyl, a Tobacco Smoke Carcinogen.

The 4-biphenylnitrenium ion (BPN), a reactive metabolic intermediate of the tobacco smoke carcinogen 4-aminobiphenyl (4-ABP), can react with nucleophilic sulfanyl groups in glutathione (GSH) as well as in proteins. The main site of attack of these S-nucleophiles was predicted using simple orientational rules of aromatic nucleophilic substitution. Thereafter, a series of presumptive 4-ABP metabolites and adducts with cysteine were synthesized, namely, S-(4-amino-3-biphenyl)cysteine (ABPC), N-acetyl-S-(4-amino-3-biphenyl)cysteine (4-amino-3-biphenylmercapturic acid, ABPMA), S-(4-acetamido-3-biphenyl)cysteine (AcABPC), and N-acetyl-S-(4-acetamido-3-biphenyl)cysteine (4-acetamido-3-biphenylmercapturic acid, AcABPMA). Then, globin and urine of rats dosed with a single ip dose of 4-ABP (27 mg/kg b.w.) was analyzed by HPLC-ESI-MS2. ABPC was identified in acid-hydrolyzed globin at levels of 3.52 ± 0.50, 2.74 ± 0.51, and 1.25 ± 0.12 nmol/g globin (mean ± S.D.; n = 6) on days 1, 3, and 8 after dosing, respectively. In the urine collected on day 1 (0-24 h) after dosing, excretion of ABPMA, AcABPMA, and AcABPC amounted to 1.97 ± 0.88, 3.09 ± 0.75, and 3.69 ± 1.49 nmol/kg b.w. (mean ± S.D.; n = 6), respectively. On day 2, excretion of the metabolites decreased by one order of magnitude followed by a slower decrease on day 8. Regarding the possible formation of AcABPC from ABPC, N-acetylation of the amino group at the biphenyl moiety prior to that at cysteine appears to be very unlikely. Thus, the structure of AcABPC indicates the involvement of N-acetyl-4-biphenylnitrenium ion (AcBPN) and/or its reactive ester precursors in in vivo reactions with GSH and protein-bound cysteine. ABPC in globin might become an alternative biomarker of the dose of toxicologically relevant metabolic intermediates of 4-ABP.

Quantum-Mechanical Approach to Predicting the Carcinogenic Potency of N-Nitroso Impurities in Pharmaceuticals.

N-Nitroso contaminants in medicinal products are of concern due to their high carcinogenic potency; however, not all these compounds are created equal, and some are relatively benign chemicals. Understanding the structure-activity relationships (SARs) that drive hazards in one molecule versus another is key to both protecting human health and alleviating costly and sometimes inaccurate animal testing. Here, we report on an extension of the CADRE (computer-aided discovery and REdesign) platform, which is used broadly by the pharmaceutical and personal care industries to assess environmental and human health endpoints, to predict the carcinogenic potency of N-nitroso compounds. The model distinguishes compounds in three potency categories with 77% accuracy in external testing, which surpasses the reproducibility of rodent cancer bioassays and constraints imposed by limited (high-quality) data. The robustness of predictions for more complex pharmaceuticals is maximized by capturing key SARs using quantum mechanics, that is, by hinging the model on the underlying chemistry versus chemicals in the training set. To this end, the present approach can be leveraged in a quantitative hazard assessment and to offer qualitative guidance using electronic structure comparisons between well-studied analogues and unknown contaminants.

Quasi-intrinsic fluorescent probes for detecting the DNA adduct (ABPdG) based on an excited-state intermolecular charge transfer mechanism.

N'-(2'-Deoxyguanosin-8-yl)-4-aminobiphenyl (ABPdG) is one of the most representative carcinogenic DNA adducts formed by human exposure to 4-aminobiphenyl (4-ABP) during dye production, rubber-manufacturing processes and cigarette smoke. Accordingly, the ultrasensitive detection of ABP-derived adducts in DNA with minimal interference to the native structures becomes key for elucidating carcinogenesis mechanisms and mitigating the risk of cancer. In view of the lack of efficient optical emission in ABPG, we report a theoretical study on the photophysical properties of a set of quasi-intrinsic fluorescent C-analogues, which can form stable W-C base pairs with ABPG. It is found that fluorophore replacement and ring-expansion can bring a red-shifted absorption and bright photoluminescence due to additional π-conjugation. In particular, because the tricyclic cytosine analogue 1,3-diaza-2-oxophenoxazine (tCO) possesses distinct optical properties, it is proposed as a biosensor to identify ABPG. The TDDFT-calculated absorption maximum of tCO is red-shifted by 97 nm in comparison with that of the native C base, which contributes to selective excitation after incorporating into the nucleic acids. Although the fluorescence is insensitive to base pairing with natural guanine, the excited state intermolecular charge transfer (ESICT)-governed "OFF-ON" signal can be observed in the presence and absence of ABPG. Moreover, to evaluate the direct availability of the bright C-analogues with high selectivity for the deoxyguanosine adduct ABPG in DNA, we further investigated thoroughly the effects of its linking to deoxyribose on its absorption and emission, which shows little difference from that of experiment.

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method.

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

What Makes a Potent Nitrosamine? Statistical Validation of Expert-Derived Structure-Activity Relationships.

The discovery of carcinogenic nitrosamine impurities above the safe limits in pharmaceuticals has led to an urgent need to develop methods for extending structure-activity relationship (SAR) analyses from relatively limited datasets, while the level of confidence required in that SAR indicates that there is significant value in investigating the effect of individual substructural features in a statistically robust manner. This is a challenging exercise to perform on a small dataset, since in practice, compounds contain a mixture of different features, which may confound both expert SAR and statistical quantitative structure-activity relationship (QSAR) methods. Isolating the effects of a single structural feature is made difficult due to the confounding effects of other functionality as well as issues relating to determining statistical significance in cases of concurrent statistical tests of a large number of potential variables with a small dataset; a naïve QSAR model does not predict any features to be significant after correction for multiple testing. We propose a variation on Bayesian multiple linear regression to estimate the effects of each feature simultaneously yet independently, taking into account the combinations of features present in the dataset and reducing the impact of multiple testing, showing that some features have a statistically significant impact. This method can be used to provide statistically robust validation of expert SAR approaches to the differences in potency between different structural groupings of nitrosamines. Structural features that lead to the highest and lowest carcinogenic potency can be isolated using this method, and novel nitrosamine compounds can be assigned into potency categories with high accuracy.

Multi-DNA Adduct and Abasic Site Quantitation In Vivo by Nano-Liquid Chromatography/High-Resolution Orbitrap Tandem Mass Spectrometry: Methodology for Biomonitoring Colorectal DNA Damage.

Epidemiological and mechanistic studies suggest that processed and red meat consumption and tobacco smoking are associated with colorectal cancer (CRC) risk. Several classes of carcinogens, including N-nitroso compounds (NOCs) in processed meats and heterocyclic aromatic amines (HAAs) and polycyclic aromatic hydrocarbons (PAHs) in grilled meats and tobacco smoke, undergo metabolism to reactive intermediates that may form mutation-inducing DNA adducts in the colorectum. Heme iron in red meat may contribute to oxidative DNA damage and endogenous NOC formation. However, the chemicals involved in colorectal DNA damage and the paradigms of CRC etiology remain unproven. There is a critical need to establish physicochemical methods for identifying and quantitating DNA damage induced by genotoxicants in the human colorectum. We established robust nano-liquid chromatography/high-resolution accurate mass Orbitrap tandem mass spectrometry (LC/HRAMS2) methods to measure DNA adducts of nine meat and tobacco-associated carcinogens and lipid peroxidation products in the liver, colon, and rectum of carcinogen-treated rats employing fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissues. Some NOCs form O6-carboxymethyl-2'-deoxyguanosine, O6-methyl-2'-deoxyguanosine, and unstable quaternary N-linked purine/pyrimidine adducts, which generate apurinic/apyrimidinic (AP) sites. AP sites were quantitated following derivatization with O-(pyridin-3-yl-methyl)hydroxylamine. DNA adduct quantitation was conducted with stable isotope-labeled internal standards, and method performance was validated for accuracy and reproducibility. Limits of quantitation ranged from 0.1 to 1.1 adducts per 108 bases using 3 µg of DNA. Adduct formation in animals ranged from ∼1 in 108 to ∼1 in 105 bases, occurring at comparable levels in fresh-frozen and FFPE specimens for most adducts. AP sites increased by 25- to 75-fold in the colorectum and liver, respectively. Endogenous lipid peroxide-derived 3-(2-deoxy-ß-d-erythro-pentofuranosyl)pyrimido[1,2-α]purin-10(3H)-one (M1dG) and 6-oxo-M1dG adduct levels were not increased by carcinogen dosing but increased in FFPE tissues. Human biomonitoring studies can implement LC/HRAMS2 assays for DNA adducts and AP sites outlined in this work to advance our understanding of CRC etiology.

Formation of Carcinogens in Processed Meat and Its Measurement with the Usage of Artificial Digestion-A Review.

Meat is a rich source of various nutrients. However, it needs processing before consumption, what in turn generates formation of carcinogenic compounds, i.a., polycyclic aromatic hydrocarbons (PAH), nitrosamines (NOCs), and the most mutagenic heterocyclic aromatic amines (HAAs). It was widely found that many factors affect the content of carcinogens in processed meat. However, it has recently been discovered that after digestion free HAAs are released, which are not detectable before enzymatic treatment. It was established that the highest percentage of carcinogens is released in the small intestine and that its amount can be increased up to 6.6-fold. The change in free HAAs content in analyzed samples was dependent on many factors such as meat type, doneness, particle size of meat, and the enzyme concentration used for digestion. In turn, introduction of bacteria naturally occurring in the human digestive tract into the model significantly decreases total amount of HAAs. Contrary, the addition of food ingredients rich in polyphenols, fiber, and water (pepper powder, onions, apples) increases free HAAs' release up to 56.06%. Results suggests that in vitro digestion should be an integral step of sample preparation. Artificial digestion introduced before chromatographic analysis will allow to estimate accurately the content of carcinogens in processed meat.

DNA Damage and Oxidative Stress of Tobacco Smoke Condensate in Human Bladder Epithelial Cells.

Smoking is a major risk factor for bladder cancer (BC), with up to 50% of BC cases being attributed to smoking. There are 70 known carcinogens in tobacco smoke; however, the principal chemicals responsible for BC remain uncertain. The aromatic amines 4-aminobiphenyl (4-ABP) and 2-naphthylamine (2-NA) are implicated in BC pathogenesis of smokers on the basis of the elevated BC risk in factory workers exposed to these chemicals. However, 4-ABP and 2-NA only occur at several nanograms per cigarette and may be insufficient to induce BC. In contrast, other genotoxicants, including acrolein, occur at 1000-fold or higher levels in tobacco smoke. There is limited data on the toxicological effects of tobacco smoke in human bladder cells. We have assessed the cytotoxicity, oxidative stress, and DNA damage of tobacco smoke condensate (TSC) in human RT4 bladder cells. TSC was fractionated by liquid-liquid extraction into an acid-neutral fraction (NF), containing polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs, phenols, and aldehydes, and a basic fraction (BF) containing aromatic amines, heterocyclic aromatic amines, and N-nitroso compounds. The TSC and NF induced a time- and concentration-dependent cytotoxicity associated with oxidative stress, lipid peroxide formation, glutathione (GSH) depletion, and apurinic/apyrimidinic (AP) site formation, while the BF showed weak effects. LC/MS-based metabolomic approaches showed that TSC and NF altered GSH biosynthesis pathways and induced more than 40 GSH conjugates. GSH conjugates of several hydroquinones were among the most abundant conjugates. RT4 cell treatment with synthetic hydroquinones and cresol mixtures at levels present in tobacco smoke accounted for most of the TSC-induced cytotoxicity and the AP sites formed. GSH conjugates of acrolein, methyl vinyl ketone, and crotonaldehyde levels also increased owing to TSC-induced oxidative stress. Thus, TSC is a potent toxicant and DNA-damaging agent, inducing deleterious effects in human bladder cells at concentrations of <1% of a cigarette in cell culture media.

Carcinogenicity prediction using the index of ideality of correlation.

Carcinogenicity testing is necessary to protect human health and comply with regulations, but testing it with the traditionally used two-year rodent studies is time-consuming and expensive. In certain cases, such as for impurities, alternative methods may be convenient. Thus there is an urgent need for alternative approaches for reliable and robust assessments of carcinogenicity. The Monte Carlo technique with CORAL software is a tool to tackle this task for unknown compounds using available experimental data for a representative set of compounds. The models can be constructed with the simplified molecular input line entry system without additional physicochemical descriptors. We describe here a model based on a data set of 1167 substances. Matthew's correlation coefficient values for calibration and validation sets are 0.747 and 0.577, respectively. Double bonds between carbon atoms and double bonds of oxygen atoms are the molecular features that indicate the carcinogenic potential of a compound.

Multiscale computational investigations of the translesion synthesis bypass of tobacco-derived DNA adducts: critical insights that complement experimental biochemical studies.

Among the numerous agents that damage DNA, tobacco products remain one of the most lethal and result in the most diverse set of DNA lesions. This perspective aims to provide an overview of computational work conducted to complement experimental biochemical studies on the mutagenicity of adducts derived from the most potent tobacco carcinogen, namely 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (nicotine-derived nitrosaminoketone or NNK). Lesions ranging from the smallest methylated thymine derivatives to the larger, flexible pyridyloxobutyl (POB) guanine adducts are considered. Insights are obtained from density functional theory (DFT) calculations and molecular dynamics (MD) simulations into the damaged nucleobase and nucleoside structures, the accommodation of the lesions in the active site of key human polymerases, the intrinsic base pairing potentials of the adducts, and dNTP incorporation opposite the lesions. Overall, the computational data provide atomic level information that can rationalize the differential mutagenic properties of tobacco-derived lesions and uncover important insights into the impact of adduct size, nucleobase, position, and chemical composition of the bulky moiety.