The Carcinogenic Potency Database: analyses of 4000 chronic animal cancer experiments published in the general literature and by the U.S. National Cancer Institute/National Toxicology Program

A Review of the Applications, Benefits, and Challenges of Generative AI for Sustainable Toxicology

Sustainable toxicology is vital for living species and the environment because it guarantees the safety, efficacy, and regulatory compliance of drugs, treatments, vaccines, and chemicals in living organisms and the environment. Conventional toxicological methods often lack sustainability as they are costly, time-consuming, and sometimes inaccurate. It means delays in producing new drugs, vaccines, and treatments and understanding the adverse effects of the chemicals on the environment. To address these challenges, the healthcare sector must leverage the power of the Generative-AI (GenAI) paradigm. This paper aims to help understand how the healthcare field can be revolutionized in multiple ways by using GenAI to facilitate sustainable toxicological developments. This paper first reviews the present literature and identifies the possible classes of GenAI that can be applied to toxicology. A generalized and holistic visualization of various toxicological processes powered by GenAI is presented in tandem. The paper discussed toxicological risk assessment and management, spotlighting how global agencies and organizations are forming policies to standardize and regulate AI-related development, such as GenAI, in these fields. The paper identifies and discusses the advantages and challenges of GenAI in toxicology. Further, the paper outlines how GenAI empowers Conversational-AI, which will be critical for highly tailored toxicological solutions. This review will help to develop a comprehensive understanding of the impacts and future potential of GenAI in the field of toxicology. The knowledge gained can be applied to create sustainable GenAI applications for various problems in toxicology, ultimately benefiting our societies and the environment.

GCLmf: A Novel Molecular Graph Contrastive Learning Framework Based on Hard Negatives and Application in Toxicity Prediction

In silico methods for prediction of chemical toxicity can decrease the cost and increase the efficiency in the early stage of drug discovery. However, due to low accessibility of sufficient and reliable toxicity data, constructing robust and accurate prediction models is challenging. Contrastive learning, a type of self-supervised learning, leverages large unlabeled data to obtain more expressive molecular representations, which can boost the prediction performance on downstream tasks. While molecular graph contrastive learning has gathered growing attentions, current models neglect the quality of negative data set. Here, we proposed a self-supervised pretraining deep learning framework named GCLmf. We first utilized molecular fragments that meet specific conditions as hard negative samples to boost the quality of the negative set and thus increase the difficulty of the proxy tasks during pre-training to learn informative representations. GCLmf has shown excellent predictive power on various molecular property benchmarks and demonstrates high performance in 33 toxicity tasks in comparison with multiple baselines. In addition, we further investigated the necessity of introducing hard negatives in model building and the impact of the proportion of hard negatives on the model.

PARP Pioneers: Using BRCA1/2 Mutation-targeted Inhibition to Revolutionize Breast Cancer Treatment

Breast cancer stands on the second position in the world in being common and women happen to have it with high rate of about five-folds around the world. The causes of occurrence can matter with different humans be it external factors or the internal genetic ones. Breast cancer is primarily driven by mutations in the BRCA1 and BRCA2 susceptibility genes. These BC susceptibility genes encode proteins critical for DNA homologous recombination repair (HRR). Poly (ADP ribose) polymerases (PARP) are the essential enzymes involved in the repairing of the damaged DNA. So the inhibition of these inhibitors can be considered as the promising strategy for targeting cancers with defective damage in the deoxyribonucleic acid. Olaparib and talazoparib are PARP inhibitors (PARPi) are being employed for the monotherapies in case of the deleterious germline HER2-negative and BRCA-mutated breast cancer. The potency of PARP for trapping on DNA and causes cytotoxicity may have difference in the safety and efficacy with the PARPi. The PARPi have been found its place in the all different types of breast cancers and have shown potential benefits. The purpose of this review is to provide an update on the oral poly (ADP-ribose) polymerase (PARP) inhibitors for the improvement in the treatment and management of breast cancer.

Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals

The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effective, suffer from being costly and time-consuming. In response to this challenge, new alternative methodologies, notably machine learning and deep learning techniques, have attracted attention for their potential in developing carcinogenicity prediction models. This article reviews the progress in predicting carcinogenicity using various machine learning and deep learning algorithms. A comparative analysis on these developed models reveals that support vector machine, random forest, and ensemble learning are commonly preferred for their robustness and effectiveness in predicting chemical carcinogenicity. Conversely, models based on deep learning algorithms, such as feedforward neural network, convolutional neural network, graph convolutional neural network, capsule neural network, and hybrid neural networks, exhibit promising capabilities but are limited by the size of available carcinogenicity datasets. This review provides a comprehensive analysis of current machine learning and deep learning models for carcinogenicity prediction, underscoring the importance of high-quality and large datasets. These observations are anticipated to catalyze future advancements in developing effective and generalizable machine learning and deep learning models for predicting chemical carcinogenicity.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

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.

Nitrosamine acceptable intakes should consider variation in molecular weight: The implication of stoichiometric DNA damage

N-nitrosamines (NAs) are a class of compounds of which many, especially of the small dialkyl type, are indirect acting DNA alkylating mutagens. Their presence in pharmaceuticals is subject to very strict acceptable daily intake (AI) limits, which are traditionally expressed on a mass basis. Here we demonstrate that AIs that are not experimentally derived for a specific compound, but via statistical extrapolation or read across to a suitable analog, should be expressed on a molar scale or corrected for the target substance's molecular weight. This would account for the mechanistic aspect that each nitroso group can, at maximum, account for a single DNA mutation and the number of molecules per mass unit is proportional to the molecular weight (MW). In this regard we have re-calculated the EMA 18 ng/day regulatory default AI for unknown nitrosamines on a molar scale and propose a revised default AI of 163 pmol/day. In addition, we provide MW-corrected AIs for those nitrosamine drug substance related impurities (NDSRIs) for which EMA has pre-assigned AIs by read-across. Regulatory acceptance of this fundamental scientific tenet would allow one to derive nitrosamine limits for NDSRIs that both meet the health-protection goals and are technically feasible.

Advancing chemical carcinogenicity prediction modeling: opportunities and challenges

Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challenges concerning the inadequacy of available experimentally validated (non)carcinogen datasets and variabilities within bioassays, which contribute to the compromised model training. Existing AI solutions that leverage classical chemistry-driven descriptors do not provide adequate biological interpretability involved in imparting carcinogenicity. This highlights the urgency to devise alternative AI strategies. We propose multiple strategies, including implementing data-driven (integrated databases) and known carcinogen-characteristic-derived features to overcome these apparent shortcomings. In summary, these next-generation approaches will continue facilitating robust chemical carcinogenicity prediction, concomitant with deeper mechanistic insights.

Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment

Recent withdrawal of several drugs from the market due to elevated levels of N-nitrosamine impurities underscores the need for computational approaches to assess the carcinogenicity risk of nitrosamines. However, current approaches are limited because robust animal carcinogenicity data are only available for a few simple nitrosamines, which do not represent the structural diversity of the many possible nitrosamine drug substance related impurities (NDSRIs). In this paper, we present a novel method that uses data on CYP-mediated metabolic hydroxylation of CH₂ groups in non-nitrosamine xenobiotics to identify structural features that may also help in predicting the likelihood of metabolic α-carbon hydroxylation in N-nitrosamines. Our approach offers a new avenue for tapping into potentially large experimental data sets on xenobiotic metabolism to improve risk assessment of nitrosamines. As α-carbon hydroxylation is the crucial rate-limiting step in nitrosamine metabolic activation, identifying and quantifying the influence of various structural features on this step can provide valuable insights into their carcinogenic potential. This is especially important considering the scarce information available on factors that affect NDSRI metabolic activation. We have identified hundreds of structural features and calculated their impact on hydroxylation, a significant advancement compared to the limited findings from the small nitrosamine carcinogenicity data set. While relying solely on α-carbon hydroxylation prediction is insufficient for forecasting carcinogenic potency, the identified features can help in the selection of relevant structural analogues in read across studies and assist experts who, after considering other factors such as the reactivity of the resulting electrophilic diazonium species, can establish the acceptable intake (AI) limits for nitrosamine impurities.

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 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).

EFSA Panel on Contaminants in the Food Chain (EFSA CONTAM Panel), Schrenk D, Bignami M, Bodin L, Chipman JK, del Mazo J, Hogstrand C, (Ron) Hoogenboom L, Leblanc J, Nebbia CS, Nielsen E, Ntzani E, Petersen A, Sand S, Schwerdtle T, Vleminckx C, Wallace H, Romualdo B, Cristina F, Stephen H, Marco I, Mosbach‐Schulz O, Riolo F, Christodoulidou A, Grasl‐Kraupp B. Risk assessment of N-nitrosamines in food

EFSA was asked for a scientific opinion on the risks to public health related to the presence of N-nitrosamines (N-NAs) in food. The risk assessment was confined to those 10 carcinogenic N-NAs occurring in food (TCNAs), i.e. NDMA, NMEA, NDEA, NDPA, NDBA, NMA, NSAR, NMOR, NPIP and NPYR. N-NAs are genotoxic and induce liver tumours in rodents. The in vivo data available to derive potency factors are limited, and therefore, equal potency of TCNAs was assumed. The lower confidence limit of the benchmark dose at 10% (BMDL10) was 10 μg/kg body weight (bw) per day, derived from the incidence of rat liver tumours (benign and malignant) induced by NDEA and used in a margin of exposure (MOE) approach. Analytical results on the occurrence of N-NAs were extracted from the EFSA occurrence database (n = 2,817) and the literature (n = 4,003). Occurrence data were available for five food categories across TCNAs. Dietary exposure was assessed for two scenarios, excluding (scenario 1) and including (scenario 2) cooked unprocessed meat and fish. TCNAs exposure ranged from 0 to 208.9 ng/kg bw per day across surveys, age groups and scenarios. 'Meat and meat products' is the main food category contributing to TCNA exposure. MOEs ranged from 3,337 to 48 at the P95 exposure excluding some infant surveys with P95 exposure equal to zero. Two major uncertainties were (i) the high number of left censored data and (ii) the lack of data on important food categories. The CONTAM Panel concluded that the MOE for TCNAs at the P95 exposure is highly likely (98-100% certain) to be less than 10,000 for all age groups, which raises a health concern.

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.

Diterpene Sonderianin isolated from Croton blanchetianus exhibits acetylcholinesterase inhibitory action and anxiolytic effect in adult zebrafish (Danio rerio) by 5-HT system

Croton blanchetianus is known as 'marmeleiro preto', a very widespread shrub in Northeast Brazil. Terpenoids, steroids and phenolic compounds are among the reported secondary metabolites of the Croton genus that are a potential source of bioactive compounds. This study evaluated the anxiolytic potential of clerodine-type diterpene, sonderianin (CBWS) isolated from the stem bark of C. blanchetianus and its mechanism of action in adult zebrafish (Danio rerio) (ZFa). The anticonvulsant and anti-acetylcholinesterase effects have also been explored. ZFa (n = 6/group) were treated intraperitoneally (ip; 20 µL) with CBWS (4, 12 and 40 mg/kg) and vehicle (3% DMSO; 20 µL) and subjected to locomotor activity tests, as well as toxicity acute 96 h. CBWS was also administered for analysis in the light/dark test. The involvement of the serotonergic system (5-HT) was investigated using 5-HTR₁, 5-HTR_2A/2C and 5-HTR_3A/3B receptor antagonists. Anxiolytic doses were tested for pentylenetetrazol-induced seizure in ZFa. The inhibitory activity of the enzyme acetylcholinesterase (AChE) was measured. CBWS was not considered toxic and reduced locomotor activity. The results of the present study identified for the first time the interaction of the diterpene sonderianina in the CNS. This study provides evidence that CBWS has an anxiolytic effect mediated by serotonergic (5-HT) involvement and anti-acetylcholinesterase action. The 5-HTR₁ and 5-HTR_2A/2C receptors may be implicated in the low anticonvulsant effect in CBWS.Communicated by Ramaswamy H. Sarma.

Strategies for Assessing Acceptable Intakes for Novel N-Nitrosamines Derived from Active Pharmaceutical Ingredients

The detection of N-nitrosamines, derived from solvents and reagents and, on occasion, the active pharmaceutical ingredient (API) at higher than acceptable levels in drug products, has led regulators to request a detailed review for their presence in all medicinal products. In the absence of rodent carcinogenicity data for novel N-nitrosamines derived from amine-containing APIs, a conservative class limit of 18 ng/day (based on the most carcinogenic N-nitrosamines) or the derivation of acceptable intakes (AIs) using structurally related surrogates with robust rodent carcinogenicity data is recommended. The guidance has implications for the pharmaceutical industry given the vast number of marketed amine-containing drugs. In this perspective, the rate-limiting step in N-nitrosamine carcinogenicity, involving cytochrome P450-mediated α-carbon hydroxylation to yield DNA-reactive diazonium or carbonium ion intermediates, is discussed with reference to the selection of read-across analogs to derive AIs. Risk-mitigation strategies for managing putative N-nitrosamines in the preclinical discovery setting are also presented.

Search for the optimal genotoxicity assay for routine testing of chemicals: Sensitivity and specificity of conventional and new test systems

Many conventional in vitro tests that are currently widely used for routine screening of chemicals have a sensitivity/specificity in the range between 60 % and 80 % for the detection of carcinogens. Most procedures were developed 30-40 years ago. In the last decades several assays became available which are based on the use of metabolically competent cell lines, improvement of the cultivation conditions and development of new endpoints. Validation studies indicate that some of these models may be more reliable for the detection of genotoxicants (i.e. many of them have sensitivity and specificity values between 80 % and 95 %). Therefore, they could replace conventional tests in the future. The bone marrow micronucleus (MN) assay with rodents is at present the most widely used in vivo test. The majority of studies indicate that it detects only 5-6 out of 10 carcinogens while experiments with transgenic rodents and comet assays seem to have a higher predictive value and detect genotoxic carcinogens that are negative in MN experiments. Alternatives to rodent experiments could be MN experiments with hen eggs or their replacement by combinations of new in vitro tests. Examples for promising candidates are ToxTracker, TGx-DDI, multiplex flow cytometry, γH2AX experiments, measurement of p53 activation and MN experiments with metabolically competent human derived liver cells. However, the realization of multicentric collaborative validation studies is mandatory to identify the most reliable tests.

Practical and Science-Based Strategy for Establishing Acceptable Intakes for Drug Product N-Nitrosamine Impurities

The potential for N-nitrosamine impurities in pharmaceutical products presents a challenge for the quality management of medicinal products. N-Nitrosamines are considered cohort-of-concern compounds due to the potent carcinogenicity of many of the structurally simple chemicals within this structural class. In the past 2 years, a number of drug products containing certain active pharmaceutical ingredients have been withdrawn or recalled from the market due to the presence of carcinogenic low-molecular-weight N,N-dialkylnitrosamine impurities. Regulatory authorities have issued guidance to market authorization holders to review all commercial drug substances/products for the potential risk of N-nitrosamine impurities, and in cases where a significant risk of N-nitrosamine impurity is identified, analytical confirmatory testing is required. A key factor to consider prior to analytical testing is the estimation of the daily acceptable intake (AI) of the N-nitrosamine impurity. A significant proportion of N-nitrosamine drug product impurities are unique/complex structures for which the development of low-level analytical methods is challenging. Moreover, these unique/complex impurities may be less potent carcinogens compared to simple nitrosamines. In the present work, our objective was to derive AIs for a large number of complex N-nitrosamines without carcinogenicity data that were identified as potential low-level impurities. The impurities were first cataloged and grouped according to common structural features, with a total of 13 groups defined with distinct structural features. Subsequently, carcinogenicity data were reviewed for structurally related N-nitrosamines relevant to each of the 13 structural groups and group AIs were derived conservatively based on the most potent N-nitrosamine within each group. The 13 structural group AIs were used as the basis for assigning AIs to each of the structurally related complex N-nitrosamine impurities. The AIs of several N-nitrosamine groups were found to be considerably higher than those for the simple N,N-dialkylnitrosamines, which translates to commensurately higher analytical method detection limits.

DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation

Carcinogenicity testing plays an essential role in identifying carcinogens in environmental chemistry and drug development. However, it is a time-consuming and label-intensive process to evaluate the carcinogenic potency with conventional 2-years rodent animal studies. Thus, there is an urgent need for alternative approaches to providing reliable and robust assessments on carcinogenicity. In this study, we proposed a DeepCarc model to predict carcinogenicity for small molecules using deep learning-based model-level representations. The DeepCarc Model was developed using a data set of 692 compounds and evaluated on a test set containing 171 compounds in the National Center for Toxicological Research liver cancer database (NCTRlcdb). As a result, the proposed DeepCarc model yielded a Matthews correlation coefficient (MCC) of 0.432 for the test set, outperforming four advanced deep learning (DL) powered quantitative structure-activity relationship (QSAR) models with an average improvement rate of 37%. Furthermore, the DeepCarc model was also employed to screen the carcinogenicity potential of the compounds from both DrugBank and Tox21. Altogether, the proposed DeepCarc model could serve as an early detection tool (https://github.com/TingLi2016/DeepCarc) for carcinogenicity assessment.

Developing Structure-Activity Relationships for N-Nitrosamine Activity

The detection of N-nitrosodimethylamine (NDMA) in several marketed drugs led regulatory agencies to require that N-nitrosamine risk assessments be performed on all marketed medical products [EMA/351053/2019 rev 1 (2019)]. Regulation of N-nitrosamine impurity levels in pharmaceutical drug substances and products is described in the ICH M7(R1) guideline where they are referred to as "cohort-of-concern" compounds as several are potent rodent carcinogens [Kroes et. al. 2004]. EMA, U.S. FDA and other regulatory agencies have set provisional acceptable daily intake limits for N-nitrosamines calculated from rodent carcinogenicity TD₅₀ values for experimentally measured N-nitrosamines or the measured TD₅₀ values of close analogs. The class-specific limit can be adjusted based upon a structure activity relationship analysis (SAR) and comparison with analogs having established carcinogenicity data [EMA/369136/2020, (2020)]. To investigate whether improvements in SARs can more accurately predict N-nitrosamine carcinogenic potency, an ad hoc workgroup of 23 companies and universities was established with the goals of addressing several scientific and regulatory issues including: reporting and review of N-nitrosamine mutagenicity and carcinogenicity reaction mechanisms, collection and review of available, public relevant experimental data, development of structure-activity relationships consistent with mechanisms for prediction of N-nitrosamine carcinogenic potency categories, and improved methods for calculating acceptable intake limits for N-nitrosamines based upon mechanistic analogs. Here we describe this collaboration and review our progress to date towards development of mechanistically based structure-activity relationships. We propose improving risk assessment of N-nitrosamines by first establishing the dominant reaction mechanism prior to retrieving an appropriate set of close analogs for use in read-across exercises.

Computational approach towards the design of artemisinin-thymoquinone hybrids against main protease of SARS-COV-2

Background

The sanitary emergency installed in the world, generated by the pandemic of COVID-19, instigates the search for scientific strategies to mitigate the damage caused by the disease to different sectors of society. The disease caused by the coronavirus, SARS-CoV-2, reached 216 countries/territories, where about 199 million people were reported with the infection. Of these, more than 4 million died. In this sense, strategies involving the development of new antiviral molecules are extremely important. The main protease (Mpro) from SARS-CoV-2 is an important target, which has been widely studied for antiviral treatment. This work aims to perform a screening of pharmacodynamics and pharmacokinetics of synthetic hybrids from thymoquinone and artemisin (THY-ART) against COVID-19.

Results

Molecular docking studies indicated that hybrids of artemisinin and thymoquinone showed a relevant interaction with the active fraction of the enzyme Mpro, when compared to the reference drugs. Furthermore, hybrids show an improvement in the interaction of substances with the enzyme, mainly due to the higher frequency of interactions with the Thr199 residue. ADMET studies indicated that hybrids tend to permeate biological membranes, allowing good human intestinal absorption, with low partition to the central nervous system, potentiation for CYP-450 enzyme inhibitors, low risk of toxicity compared to commercially available drugs, considering mainly mutagenicity and cardiotoxicity, low capacity of hybrids to permeate the blood–brain barrier, high absorption and moderate permeability in Caco-2 cells. In addition, T1–T7 tend to have a better distribution of their available fractions to carry out diffusion and transport across cell membranes, as well as increase the energy of interaction with the SARS-CoV-2 target.

Conclusions

Hybrid products of artemisinin and thymoquinone have the potential to inhibit Mpro, with desirable pharmacokinetic and toxicity characteristics compared to commercially available drugs, being indicated for preclinical and subsequent clinical studies against SARS-CoV-2. Emphasizing the possibility of synergistic use with currently used drugs in order to increase half-life and generate a possible synergistic effect. This work represents an important step for the development of specific drugs against COVID-19.

The numerical probability of carcinogenicity to humans of some antimicrobials: Nitro-monoaromatics (including 5-nitrofurans and 5-nitroimidazoles), quinoxaline-1,4-dioxides (including carbadox), and chloramphenicol

Many substances are already tested in the long-term rodent bioassay (RCB). Nonetheless, statements such as the following are common in the regulatory literature: "the significance of the carcinogenicity findings in rodents relative to the therapeutic use of drugs in humans is unknown." (U.S. FDA prescribing information for nitrofurantoin). In the absence of epidemiological data, chemicals carcinogenic in RCBs are typically classified as either possibly or probably carcinogenic to humans, particularly without the -numerical probability for the carcinogenicity to humans- (PPV) of the classified substance. Through the biostatistics-based and regulatorily pertinent -predictive values approach- (PVA), the present study investigated the PPV of several antimicrobials relevant to human or veterinary medicine. A combination of structure-activity relationship, mutagenicity, and tumor-related histopathology was used to resolve reliable and pertinent PPVs. For 62 specific antimicrobials (e.g., carbadox), a 97.9% (or more) probability of carcinogenicity to humans was estimated. For nitrofurantoin, a 99.9% probability of carcinogenicity to humans was reckoned. Therefore, a risk-benefit evaluation on the in-force authorization of nitrofurantoin for uncomplicated human urinary infections is needed. A discussion was provided on the involved mechanisms of carcinogenic action and some regulatory implications of the findings. Neither this study nor the PVA aimed to encourage indiscriminate animal testing but the contrary, to reduce unnecessary or redundant in vivo testing by powering the predictivity of nonclinical toxicology.

Virtual Screening of Citrus Flavonoid Tangeretin: A Promising Pharmacological Tool for the Treatment and Prevention of Zika fever and COVID-19

It is of great importance for the pharmaceutical industry to find therapeutic substances extracted from natural sources, which are abundant, obtained with low costs and presenting the antiviral potential for the treatment of Zika virus (ZIKV) and COVID-19. Tangeretin (TAN) is a citrus polymethoxyflavone from Citrus reticulata peel oil with known antiviral activities, whose physico-chemical properties are not reported. The present study aimed to investigate by a theoretical screening of electronic, structural properties and pharmacodynamic and pharmacokinetic parameters that characterize TAN as a therapeutic drug in the treatment and prevention of zika fever and COVID-19. The molecule reached its minimum energy-forming state of [Formula: see text]795.85747[Formula: see text]kJ/mol and the HOMO and LUMO boundary orbitals reactivity descriptors suggest that the compound is stable and does not tend to be reactive in intermolecular interactions. The ligand connects to the NS1 ZIKV receptor with strong H-bond interactions, also connects with the NS5 ZIKV receptor in a competitive effect with the SAM inhibitor and acts in a supplementary effect with the N3 inhibitor and the BRT drug in the Mpro SARS-CoV-2 receptor. The properties of ADMET shows that the compound suffers few amounts of drug alterations because it inhibits the metabolic enzymes CYP2C9 and CYP3A4 and penetrates the central nervous system, without accumulation of drug residues in the blood or in the lumen in the gastrointestinal tract, without risk of toxicity to the patient. With the results obtained, it is possible to identify TAN as a promising pharmacological tool for the treatment and prevention of Zika fever and COVID-19.

The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology

Since 2009, the Tox21 project has screened ∼8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 "10K" chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure-activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical-activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology

Since 2009, the Tox21 project has screened ∼8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 "10K" chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure-activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical-activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

Bruce Nathan Ames - Paradigm shifts inside the cancer research revolution

Dr. Bruce Ames turned 92 on December 16, 2020. He considers his most recent work linking adequate consumption of 30 known vitamins and minerals with successful aging to be his most important contribution. With the passage of time, it is not uncommon for the accomplishments of a well-known scientist to undergo a parsimonious reductionism in the public mind - Pasteur's vaccine, Mendel's peas, Pavlov's dogs, Ames' test. Those of us in the research generation subsequent to Dr. Ames' are undoubtedly affected by our own unconscious tendencies toward accepting the outstanding achievements of the past as commonplace. In doing so, seminal advances made by earlier investigators are often inadvertently subsumed into common knowledge. But having followed Ames' work since the mid-1970s, we are cognizant that the eponymous Ames Test is but a single chapter in a long and rich narrative. That narrative begins with Ames' classic studies on the histidine operon of Salmonella, for which he was elected to the National Academy of Sciences. A summary of the historical progression of the understanding of chemical carcinogenesis to which Ames and his colleagues contributed is provided. Any summary of a topic as expansive and complex as the ongoing unraveling of the mechanisms underlying chemical carcinogenesis will only touch upon some of the major conceptual advances to which Ames and his colleagues contributed. We hope that scientists of all ages familiar with Ames only through the eponymous Ames Test will further investigate the historical progression of the conceptualization of cancer caused by chemical exposure. As the field of chemical carcinogenesis gradually moves away from primary reliance on animal testing to alternative protocols under the rubric of New Approach Methodologies (NAM) an understanding of where we have been might help to guide where we should go.

Novel Acaricidal Drug Fluazuron Causes Immunotoxicity via Selective Depletion of Lymphocytes T CD8

Fluazuron is one of the newest veterinary antitick medicines. Belonging to the benzoylphenylureas group, its mechanism of action acts by the interference of the formation of the chitin of the tick, which is responsible for the hardening of its exoskeletons. In addition to taking care of the health of the animal so that it receives the medication in the doses and the correct form, it is important to analyze the safety of the operator. Reduced resistance to infectious disease was a well-documented consequence of primary and acquired immunodeficiencies, but a novel finding following xenobiotic exposure. The awareness of the consequences of altered immune function is the most likely outcome of inadvertent exposure. The human health implications of studies in which chemical exposure reduced resistance to infection drove an early focus on immunosuppression within the toxicology community. The main objective is to perform the evaluation by computational platforms and in cell culture, searching for data that can serve as a foundation for a better understanding of the toxic effects involved with the accidental contamination of Fluazuron and, thus, to assist the medical community and users to understand the risks inherent in its use. As far as we can determine in the literature, our work has unmistakably demonstrated that the Fluazuron can cause genotoxicity by probable chromatin rearrangement and immunodepleting by specific reduction of the CD8 T lymphocyte subpopulation, mediated by the decrease in gamma interferon production. Although the use of Fluazuron is a necessity for tick control and for cattle management, we must bear in mind that the imminent risks to its application exist. Careless use can damage the immune system which in turn carries a gigantic hazard by opening a door to diseases and pathogens and leaving us defenseless.

Potential Breast Anticancer Drug Targets Revealed by Differential Gene Regulatory Network Analysis and Molecular Docking: Neoadjuvant Docetaxel Drug as a Case Study

Understanding gene-gene interaction and its causal relationship to protein-protein interaction is a viable route for understanding drug action at the genetic level, which is largely hindered by inability to robustly map gene regulatory networks. Here, we use biological prior knowledge of family-to-family gene interactions available in the KEGG database to reveal individual gene-to-gene interaction networks that underlie the gene expression profiles of 2 cell line data sets, sensitive and resistive to neoadjuvant docetaxel breast anticancer drug. Comparison of the topology of the 2 networks revealed that the resistant network is highly connected with 2 large domains of connectivity: one in which the RAF1 and MAP2K2 genes form hubs of connectivity and another in which the RAS gene is highly connected. On the contrary, the sensitive network is highly disrupted with a lower degree of connectivity. We investigated the interactions of the neoadjuvant docetaxel drug with the protein chains encoded by gene-gene interactions that underlie the disruption of the sensitive network topology using protein-protein and drug-protein docking techniques. We found that the sensitive network is likely to be disrupted by interaction of the neoadjuvant docetaxel drug with the DAXX and FGR1 proteins, which is consistent with the observed accumulation of cytoplasmic DAXX and overexpression of FGR1 precursors in cancer cell lines. This indicates that the DAXX and FGR1 proteins could be potential targets for the neoadjuvant docetaxel drug. The work, therefore, provides a new route for understanding the effect of the drug mode of action from the viewpoint of the change in the topology of gene-gene regulatory networks and provides a new avenue for bridging the gap between gene-gene interactions and protein-protein interactions which could have deep implications on mainstream drug development protocols.

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.

Consensus Diversity Plots: a global diversity analysis of chemical libraries

Background

Measuring the structural diversity of compound databases is relevant in drug discovery and many other areas of chemistry. Since molecular diversity depends on molecular representation, comprehensive chemoinformatic analysis of the diversity of libraries uses multiple criteria. For instance, the diversity of the molecular libraries is typically evaluated employing molecular scaffolds, structural fingerprints, and physicochemical properties. However, the assessment with each criterion is analyzed independently and it is not straightforward to provide an evaluation of the “global diversity”.

Results

Herein the Consensus Diversity Plot (CDP) is proposed as a novel method to represent in low dimensions the diversity of chemical libraries considering simultaneously multiple molecular representations. We illustrate the application of CDPs to classify eight compound data sets and two subsets with different sizes and compositions using molecular scaffolds, structural fingerprints, and physicochemical properties.

Conclusions

CDPs are general data mining tools that represent in two-dimensions the global diversity of compound data sets using multiple metrics. These plots can be constructed using single or combined measures of diversity. An online version of the CDPs is freely available at: https://consensusdiversityplots-difacquim-unam.shinyapps.io/RscriptsCDPlots/.Graphical Abstract

Pig-a gene mutation and micronucleated reticulocyte induction in rats exposed to tumorigenic doses of the leukemogenic agents chlorambucil, thiotepa, melphalan, and 1,3-propane sultone

To evaluate whether blood-based genotoxicity endpoints can provide temporal and dose-response data within the low-dose carcinogenic range that could contribute to carcinogenic mode of action (MoA) assessments, we evaluated the sensitivity of flow cytometry-based micronucleus and Pig-a gene mutation assays at and below tumorigenic dose rate 50 (TD50) levels. The incidence of micronucleated reticulocytes (MN-RET) was used to evaluate chromosomal damage, and the frequency of CD59-negative reticulocytes (RET(CD59-) ) and erythrocytes (RBC(CD59-) ) served as phenotypic reporters of mutation at the X-linked Pig-a gene. Several leukemogenic agents with a presumed genotoxic MoA were studied. Specifically, male Sprague Dawley rats were treated via oral gavage for 28 days with chlorambucil, thiotepa, melphalan, and 1,3-propane sultone at doses corresponding to 0.33x, 1x, and 3x TD50, as well as at the maximum tolerated dose. Frequencies of MN-RET were determined at Days 4 and 29, and RET(CD59-) and RBC(CD59-) data were collected pretreatment as well as Days 15/16, 29, and 56/57. Dose-related increases were observed for each endpoint, and time to maximal effect was consistently: MN-RET < RET(CD59-)  < RBC(CD59-) . For each of the chemicals studied, the genotoxic events occurred long before tumors or preneoplastic lesions would be expected. Furthermore, in the case of Pig-a gene mutation, the responses were observed at or below the TD50 dose for three out of the four chemicals studied. These data illustrate the potential for quantitative blood-based analyses to provide dose-response and temporality information that relates genetic damage to cancer induction.

Response to Morfeld (2013): Second commentary to Gebel 2012-established use of cancer potency indices and biological plausibility

The evaluations in Gebel (Arch Toxicol 86(7):995-1007, 2012) were carried out according to established procedures in regulatory toxicology. The variability in the available data was taken into account. The quality of the underlying data set should not be overestimated. The relevant conclusion in Gebel (Arch Toxicol 86(7):995-1007, 2012) is that the difference in carcinogenic potency comparing nanosized to microsized respirable granular biodurable particles without known significant specific toxicity (GBP) is low and lower than previously estimated.

Estragole: a weak direct-acting food-borne genotoxin and potential carcinogen

We evaluated the genotoxicity of the food-flavouring agent estragole in V79 cells using the sister chromatid exchange (SCE) assay and the alkaline comet assay. Unexpectedly, we observed an increase in SCE without an exogenous biotransformation system (S9) and a decrease in its presence. Positive results were also observed in the alkaline comet assay without S9, indicating DNA strand breakage. To ascertain repair of damage, we performed the comet assay in V79 cells after two hours of recovery, and observed a reduction of the genotoxic response. Estragole did not produce strand breaks in plasmid DNA in vitro. We then evaluated the formation of DNA adducts in V79 cells by use of the (32)P-postlabelling assay and detected a dose-dependent formation of DNA adducts, which may be responsible for its genotoxicity. We then assayed estragole in the comet assay with two CHO cell lines, a parental AA8 cell line, and an XRCC1-deficient cell line, EM9. Results confirmed the genotoxicity of estragole without biotransformation in both cell lines, although the genotoxicity in EM9 cells compared with that in AA8 cells was not significantly different, suggesting that the XRCC1 protein is not involved in the repair of estragole-induced lesions. Estragole induces apoptosis, but only with high doses (2000μM), and after long treatment periods (24h). Overall, our results suggest that estragole, besides being metabolized to genotoxic metabolites, is a weak direct-acting genotoxin that forms DNA adducts.

Can carcinogenic potency be predicted from in vivo genotoxicity data?: a meta-analysis of historical data

Genotoxicity is generally a parameter used for hazard identification, however, the applicability of using in vivo genotoxicity tests for hazard characterization has never been thoroughly investigated in a quantitative manner. Genotoxicity assays could be useful for the determination of cancer potency parameters given that genotoxicty tests measure mutations and/or chromosomal aberrations which are strongly associated with carcinogenesis. A detailed literature survey was performed in search for dose-response data in various in vivo genotoxicity and carcinogenicity studies. The benchmark dose (BMD) approach was applied using the dose-response modeling program PROAST. Dose-response data were available from 18 compounds in the micronucleus assay (MN), the in vivo transgenic rodent mutation assay (TG) and the comet assay, and their BMD(10) values were compared to the BMD(10) from carcinogenicity studies in mice. Of the 18 compounds, 15 had acceptable dose-response data from the MN and the TG, but only 4 from the comet assay. A major limitation in our analysis was the lack of proper dose-response studies using the recommended protocols. Nevertheless, our findings are promising because even with these suboptimal studies, a positive correlation was observed when the lowest BMD(10) from the genotoxicity tests (MN and TG) was compared to the tissue-matched carcinogenicity BMD(10) . It is evident that more compounds need to be analyzed with proper dose-response schemes to further validate our initial findings. Experimental designs of genotoxicity assays need to shift from focusing only on hazard identification where positive and negative results are reported, to a more quantitative, dose-response assessment.

Overall impact of the regulatory requirements for genotoxic impurities on the drug development process

In the last decade a considerable effort has been made both by the regulators and the pharmaceutical industry to assess genotoxic impurities (GTI) in pharmaceutical products. Though the control of impurities in drug substances and products is a well established and consolidated procedure, its extension to GTI has given rise to a number of problems, both in terms of setting the limits and detecting these impurities in pharmaceutical products. Several papers have dealt with this issue, discussing available regulations, providing strategies to evaluate the genotoxic potential of chemical substances, and trying to address the analytical challenge of detecting GTI at trace levels. In this review we would like to discuss the available regulations, the toxicological background for establishing limits, as well as the analytical approaches used for GTI assessment. The final aim is that of providing a complete overview of the topic with updated available information, to address the overall GTI issue during the development of new drug substances.

A strategy for the risk assessment of human genotoxic metabolites

The role of metabolism in genotoxicity and carcinogenicity of many chemicals is well established. Accordingly, both in vitro metabolic activation systems and in vivo assays are routinely utilized for genotoxic hazard identification of drug candidates prior to clinical investigations. This should, in most cases provide a high degree of confidence that the genotoxic potential of the parent and associated metabolites have been characterized. However, it is well known that significant differences can exist between human metabolism and that which occurs with in vitro and in vivo genotoxicity tests. This poses challenges when considering the adequacy of hazard identification and cancer risk assessment if a human metabolite of genotoxic concern is identified during the course of drug development. Since such challenges are particularly problematic when recognized in the later stages of drug development, a framework for conducting a carcinogenic risk assessment for human genotoxic metabolites is desirable. Here, we propose a risk assessment method that is dependent upon the availability of quantitative human and rodent ADME (absorption, distribution, metabolism, excretion) data, such that exposures to a metabolite of genotoxic concern can be estimated at the intended human efficacious dose and the maximum dose used in the 2-year rodent bioassay(s). The exposures are then applied to the risk assessment framework, based on known cancer potencies, that allows one to understand the probability of a known or suspect genotoxic metabolite posing a carcinogenic risk in excess of 1 in 100,000. Practical case examples are presented to illustrate the application of the risk assessment method within the context of drug development and to highlight its utility and limitations.

In vivo studies in the mouse to define a threshold for the genotoxicity of EMS and ENU

The presence of ethyl methanesulfonate (EMS) in tablets of a HIV medication triggered non-clinical studies into the dose response for mutation analysis after chronic dosing. Although there are a multitude of in vitro and in vivo studies on the genotoxic activity of EMS, no lifetime carcinogenicity studies, repeat dose mutation data or exposure analysis are available to serve as a solid basis for risk assessment. For alkylators like EMS it is generally assumed that the dose response for mutagenicity (and by default for carcinogenicity) is linear - indicating that no 'safe' dose does exist. A recent in vitro genotoxicity study [S.H. Doak, G.J. Jenkins, G.E. Johnson, E. Quick, E.M. Parry, J.M. Parry, Mechanistic influences for mutation induction curves after exposure to DNA-reactive carcinogens, Cancer Res. 67 (2007) 3904-3911] provided evidence, however, that the dose-response curve for mutagenic and clastogenic activity of EMS was thresholded - in contrast to ethylnitrosourea (ENU) tested in parallel. For risk assessment we sought to verify the existence of a threshold for mutagenic and clastogenic activity in vivo using the micronucleus test (MNT) and gene mutation test (MutaMouse), with the aim to provide reassurance to the patients that their exposure to EMS did not carry a toxicological risk. Dose levels ranging from 1.25 to 260mg/(kgday) were applied for up to 28 days. As reference we included ENU at doses of 1.1-22mg/(kgday). Our studies showed that daily doses of EMS up to 25mg/(kgday) (bone marrow, GI tract) and 50mg/(kgday) (liver) did not induce mutations in the lacZ gene in the three organs tested. Doses of EMS up to 80mg/(kgday) did not induce micronuclei in mouse bone marrow. Only at higher dose levels the genotoxic activity of EMS became apparent. Dose fractionation of EMS (28 times 12.5mg/kg versus a single high dose 380mg/kg) in the MutaMouse study provided further convincing evidence for the thresholded dose response of EMS and showed that no accumulation below the threshold was occurring. For ENU no threshold was apparent and dose fractionation indicated additivity. However, there are arguments that a threshold in the dose region of about 0.4mg/(kgday) ENU might exist.

Accurate and interpretable computational modeling of chemical mutagenicity

We describe a method for modeling chemical mutagenicity in terms of simple rules based on molecular features. A classification model was built using a rule-based ensemble method called RuleFit, developed by Friedman and Popescu. We show how performance compares favorably against literature methods. Performance was measured through the use of cross-validation and testing on external test sets. All data sets used are publicly available. The method automatically generated transparent rules in terms of molecular structure that agree well with known toxicology. While we have focused on chemical mutagenicity in demonstrating this method, we anticipate that it may be more generally useful in modeling other molecular properties such as other types of chemical toxicity.

A combination of 3D-QSAR, docking, local-binding energy (LBE) and GRID study of the species differences in the carcinogenicity of benzene derivatives chemicals

A combination of 3D-QSAR, docking, local-binding energy (LBE) and GRID methods was applied as a tool to study and predict the mechanism of action of 100 carcinogenic benzene derivatives. Two 3D-QSAR models were obtained: (i) model of mouse carcinogenicity on the basis of 100 chemicals (model 1) and (ii) model of the differences in mouse and rat carcinogenicity on the basis of 73 compounds (model 2). 3D-QSAR regression maps indicated the important differences in species carcinogenicity, and the molecular positions associated with them. In order to evaluate the role of P450 metabolic process in carcinogenicity, the following approaches were used. The 3D models of CYP2E1 for mouse and rat were built up. A docking study was applied and the important ligand-protein residues interactions and oxidation positions of the molecules were identified. A new approach for quantitative assessment of metabolism pathways was developed, which enabled us to describe the species differences in CYP2E1 metabolism, and how it can be related to differences in the carcinogenic potential for a subset of compounds. The binding energies of the important substituents (local-binding energy-LBE) were calculated, in order to quantitatively demonstrate the contribution of the substituents in metabolic processes. Furthermore, a computational procedure was used for determining energetically favourable binding sites (GRID examination) of the enzymes. The GRID procedure allowed the identification of some important differences, related to species metabolism in CYP2E1. Comparing GRID, 3D-QSAR maps and LBE results, a similarity was identified, indicating a relationship between P450 metabolic processes and the differences in the carcinogenicity.

Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity

BACKGROUND

To develop efficient approaches for rapid evaluation of chemical toxicity and human health risk of environmental compounds, the National Toxicology Program (NTP) in collaboration with the National Center for Chemical Genomics has initiated a project on high-throughput screening (HTS) of environmental chemicals. The first HTS results for a set of 1,408 compounds tested for their effects on cell viability in six different cell lines have recently become available via PubChem.

OBJECTIVES

We have explored these data in terms of their utility for predicting adverse health effects of the environmental agents.

METHODS AND RESULTS

Initially, the classification k nearest neighbor (kNN) quantitative structure-activity relationship (QSAR) modeling method was applied to the HTS data only, for a curated data set of 384 compounds. The resulting models had prediction accuracies for training, test (containing 275 compounds together), and external validation (109 compounds) sets as high as 89%, 71%, and 74%, respectively. We then asked if HTS results could be of value in predicting rodent carcinogenicity. We identified 383 compounds for which data were available from both the Berkeley Carcinogenic Potency Database and NTP-HTS studies. We found that compounds classified by HTS as "actives" in at least one cell line were likely to be rodent carcinogens (sensitivity 77%); however, HTS "inactives" were far less informative (specificity 46%). Using chemical descriptors only, kNN QSAR modeling resulted in 62.3% prediction accuracy for rodent carcinogenicity applied to this data set. Importantly, the prediction accuracy of the model was significantly improved (72.7%) when chemical descriptors were augmented by HTS data, which were regarded as biological descriptors.

CONCLUSIONS

Our studies suggest that combining NTP-HTS profiles with conventional chemical descriptors could considerably improve the predictive power of computational approaches in toxicology.

Analysis of 2,3,7,8-tetrachlorodibenzo-p-dioxin-induced proteome changes in 5L rat hepatoma cells reveals novel targets of dioxin action including the mitochondrial apoptosis regulator VDAC2

As part of a comprehensive survey of the impact of the environmental pollutant and hepatocarcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the proteome of hepatic cells, we have performed a high resolution two-dimensional gel electrophoresis study on the rat hepatoma cell line 5L. 78 protein species corresponding to 73 different proteins were identified as up- or down-regulated following exposure of the cells to 1 nm TCDD for 8 h. There was an overlap of only nine proteins with those detected as altered by TCDD in our recent study using the non-gel-based isotope-coded protein label method (Sarioglu, H., Brandner, S., Jacobsen, C., Meindl, T., Schmidt, A., Kellermann, J., Lottspeich, F., and Andrae, U. (2006) Quantitative analysis of 2,3,7,8-tetrachlorodibenzo-p-dioxin-induced proteome alterations in 5L rat hepatoma cells using isotope-coded protein labels. Proteomics 6, 2407-2421) indicating a strong complementarity of the two approaches. For the majority of the altered proteins, an effect of TCDD on their abundance or posttranslational modifications had not been known before. Several observations suggest that a sizable fraction of the proteins with altered abundance was induced as an adaptive response to TCDD-induced oxidative stress that was demonstrated using the fluorescent probe dihydrorhodamine 123. A prominent group of these proteins comprised various enzymes for which there is evidence that their expression is regulated via the Keap1/Nrf2/antioxidant response element pathway. Other proteins included several involved in the maintenance of mitochondrial energy production and the regulation of the mitochondrial apoptotic pathway. A particularly intriguing finding was the up-regulation of the mitochondrial outer membrane pore protein, voltage-dependent anion channel-selective protein 2 (VDAC2), which was dependent on the presence of a functional aryl hydrocarbon receptor. The regulatability of VDAC2 protein abundance has not been described previously. In view of the recently discovered central role of VDAC2 as an inhibitor of the activation of the proapoptotic protein BAK and the mitochondrial apoptotic pathway, the present data point to a hitherto unrecognized mechanism by which TCDD may affect cellular homeostasis and survival.

Risk assessment of substances that are both genotoxic and carcinogenic

The European Food Safety Authority (EFSA) and the World Health Organization (WHO), with the support of the International Life Sciences Institute, European Branch (ILSI Europe), organized an international conference on 16-18 November 2005 to discuss how regulatory and advisory bodies evaluate the potential risks of the presence in food of substances that are both genotoxic and carcinogenic. The objectives of the conference were to discuss the possible approaches for risk assessment of such substances, how the approaches may be interpreted and whether they meet the needs of risk managers. ALARA (as low as reasonably achievable) provides advice based solely on hazard identification and does not take into account either potency or human exposure. The use of quantitative low-dose extrapolation of dose-response data from an animal bioassay raises numerous scientific uncertainties related to the selection of mathematical models and extrapolation down to levels of human exposure. There was consensus that the margin of exposure (MOE) was the preferred approach because it is based on the available animal dose-response data, without extrapolation, and on human exposures. The MOE can be used for prioritisation of risk management actions but the conference recognised that it is difficult to interpret it in terms of health risk.

Quantitative analysis of 2,3,7,8-tetrachlorodibenzo-p-dioxin-induced proteome alterations in 5L rat hepatoma cells using isotope-coded protein labels

In an effort to contribute to a better understanding of the hepatic toxicity of the ubiquitous environmental pollutant and hepatocarcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a comprehensive quantitative proteome analysis was performed on 5L rat hepatoma cells exposed to 1 nM TCDD for 8 h. Changes in the abundances of individual protein species in TCDD-treated cells as compared to untreated cells were analysed using the nongel-based isotope-coded protein label (ICPL) method [Schmidt, A., Kellermann, J., Lottspeich, F., Proteomics 2005, 5, 4-15]. 89 proteins were identified as up- or down-regulated by TCDD. For the majority of the altered proteins, an impact of TCDD on their abundance had not been known before. Due to the physicochemical properties or the translational regulation of a large number of the affected proteins, their alteration would have escaped detection by gel-based methods for proteome analysis and by standard mRNA expression profiling, respectively. The identified proteins with TCDD-altered abundance include several proteins implicated in cell cycle regulation, growth factor signalling and the control of apoptosis. The results thus provide new starting-points for the investigation of specific aspects of the toxicity and carcinogenicity of dioxin in liver.

Animal carcinogenicity studies: 2. Obstacles to extrapolation of data to humans

Due to limited human exposure data, risk classification and the consequent regulation of exposure to potential carcinogens has conventionally relied mainly upon animal tests. However, several investigations have revealed animal carcinogenicity data to be lacking in human predictivity. To investigate the reasons for this, we surveyed 160 chemicals possessing animal but not human exposure data within the US Environmental Protection Agency chemicals database, but which had received human carcinogenicity assessments by 1 January 2004. We discovered the use of a wide variety of species, with rodents predominating, and of a wide variety of routes of administration, and that there were effects on a particularly wide variety of organ systems. The likely causes of the poor human predictivity of rodent carcinogenicity bioassays include: 1) the profound discordance of bioassay results between rodent species, strains and genders, and further, between rodents and human beings; 2) the variable, yet substantial, stresses caused by handling and restraint, and the stressful routes of administration common to carcinogenicity bioassays, and their effects on hormonal regulation, immune status and predisposition to carcinogenesis; 3) differences in rates of absorption and transport mechanisms between test routes of administration and other important human routes of exposure; 4) the considerable variability of organ systems in response to carcinogenic insults, both between and within species; and 5) the predisposition of chronic high dose bioassays toward false positive results, due to the overwhelming of physiological defences, and the unnatural elevation of cell division rates during ad libitum feeding studies. Such factors render profoundly difficult any attempts to accurately extrapolate human carcinogenic hazards from animal data.