Nongenotoxic carcinogenicity of chemicals: mechanisms of action and early recognition through a new set of structural alerts

Prioritizing of potential environmental exposure carcinogens beyond IARC group 1-2B based on weight of evidence (WoE) approach

Environmental exposures are the main cause of cancer, and their carcinogenicity has not been fully evaluated, identifying potential carcinogens that have not been evaluated is critical for safety. This study is the first to propose a weight of evidence (WoE) approach based on computational methods to prioritize potential carcinogens. Computational methods such as read across, structural alert, (Quantitative) structure-activity relationship and chemical-disease association were evaluated and integrated. Four different WoE approach was evaluated, compared to the best single method, the WoE-1 approach gained 0.21 and 0.39 improvement in the area under the receiver operating characteristic curve (AUC) and Matthew's correlation coefficient (MCC) value, respectively. The evaluation of 681 environmental exposures beyond IARC list 1-2B prioritized 52 chemicals of high carcinogenic concern, of which 21 compounds were known carcinogens or suspected carcinogens, and eight compounds were identified as potential carcinogens for the first time. This study illustrated that the WoE approach can effectively complement different computational methods, and can be used to prioritize chemicals of carcinogenic concern.

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.

Toxicological Profiling of Potential Shikimate Kinase Inhibitors Against Mycobacterium tuberculosis

Over the last decade, Mycobacterium tuberculosis has mutated into a putative 'superbug', as treatments against it have failed due to increasing antimicrobial resistance. As a result, the rising incidence of multidrug-resistant tuberculosis (MDR-TB) is posing a significant public health threat, thus, the need to develop effective drugs for MDR-TB has become an urgent priority. To identify new drug candidates for the treatment of MDR-TB, the present study was based on mycobacterial shikimate kinase (MtSK) as the pharmacological target. One hundred potential MtSK inhibitors were identified from literature and database searches to identify compounds that were designed to specifically function as MtSK antagonists. The ADME properties of these compounds were evaluated by using the SwissADME web tool. ProTox-II software was also used to investigate any potential endocrine disrupting effects, mediated through their interaction with oestrogenic and/or androgenic receptors. This study also aimed to predict LD50 values of potential drug candidates that would be active against the standard H37Rv strain of M. tuberculosis, by using the ProTox-II in silico tool. The molecules for which no structural hazard alerts were identified with these software tools were further subjected to molecular docking analyses and molecular dynamic simulations to estimate their ability to interact with the MtSK enzyme. Preliminary results from SwissADME indicated that 30 molecules were drug-like, due to their physicochemical and pharmacokinetic properties. However, subsequent analysis with ToxTree and ProTox-II indicated that only three of these 30 drug-like molecules were suitable for taking forward into further in vitro experiments. This study, which is based on the use of commonly used open-source in silico tools, identified new MtSK ligands for potential use in the development of new drugs for the therapeutic management of tuberculosis. An initial prediction of their safety profile was also generated.

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

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

Special Issue "Advances in Genome Regulation in Cancer"

Cancer is globally increasing [...].

New approach methodologies to facilitate and improve the hazard assessment of non-genotoxic carcinogens-a PARC project

Carcinogenic chemicals, or their metabolites, can be classified as genotoxic or non-genotoxic carcinogens (NGTxCs). Genotoxic compounds induce DNA damage, which can be detected by an established in vitro and in vivo battery of genotoxicity assays. For NGTxCs, DNA is not the primary target, and the possible modes of action (MoA) of NGTxCs are much more diverse than those of genotoxic compounds, and there is no specific in vitro assay for detecting NGTxCs. Therefore, the evaluation of the carcinogenic potential is still dependent on long-term studies in rodents. This 2-year bioassay, mainly applied for testing agrochemicals and pharmaceuticals, is time-consuming, costly and requires very high numbers of animals. More importantly, its relevance for human risk assessment is questionable due to the limited predictivity for human cancer risk, especially with regard to NGTxCs. Thus, there is an urgent need for a transition to new approach methodologies (NAMs), integrating human-relevant in vitro assays and in silico tools that better exploit the current knowledge of the multiple processes involved in carcinogenesis into a modern safety assessment toolbox. Here, we describe an integrative project that aims to use a variety of novel approaches to detect the carcinogenic potential of NGTxCs based on different mechanisms and pathways involved in carcinogenesis. The aim of this project is to contribute suitable assays for the safety assessment toolbox for an efficient and improved, internationally recognized hazard assessment of NGTxCs, and ultimately to contribute to reliable mechanism-based next-generation risk assessment for chemical carcinogens.

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

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

Modeling and insights into the structural basis of chemical acute aquatic toxicity

It has become a top global regulatory priority to prevent and control pollution from the release of synthetic chemicals, which continues to affect the aquatic communities. In the past decades, computational tools were largely used to significantly reduce the budget and time cost of chemical acute aquatic toxicity assessment. But the structural basis of toxic compounds was rarely analyzed. In the present study, we collected 1438, 485 and 961 chemicals with acute toxicity data records for three representative aquatic species, including Tetrahymena pyriformis, Daphnia magna, and Fathead minnow, respectively. A series of artificial intelligence models were developed using OCHEM tools. For each aquatic toxicity endpoint, a consensus model was developed based on the top performed individual models. The consensus models provided good performance on external validation sets with total accuracy values 96.88 %, 90.63 %, and 84.90 % for Tetrahymena pyriformis toxicity (TPT), Daphnia magna toxicity (DMT), and Fathead minnow toxicity (FMT), respectively. The models can be freely accessed via https://ochem.eu/article/146910. Moreover, the analysis of physical-chemical properties suggested that several key molecular properties of aquatic toxic compounds were significantly different with those of non-toxic compounds. Thus, these descriptors may be associated to chemical acute aquatic toxicity, and may be useful for the understand of chemical aquatic toxicity. Besides, in this study, the structural alerts for aquatic toxicity were detected using f-score and frequency ratio analysis of predefined substructures. A total of 112, 58 and 33 structural alerts were identified responsible for TPT, DMT, and FMT, respectively. These structural alerts could provide useful information for the mechanisms of chemical aquatic toxicity and visual alerts for environmental assessment. All the structural alerts were integrated in the web-server SApredictor (www.sapredictor.cn).

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts

The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be “black box”, which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.

Current Therapeutic Landscape and Safety Roadmap for Targeting the Aryl Hydrocarbon Receptor in Inflammatory Gastrointestinal Indications

Target modulation of the AhR for inflammatory gastrointestinal (GI) conditions holds great promise but also the potential for safety liabilities both within and beyond the GI tract. The ubiquitous expression of the AhR across mammalian tissues coupled with its role in diverse signaling pathways makes development of a “clean” AhR therapeutically challenging. Ligand promiscuity and diversity in context-specific AhR activation further complicates targeting the AhR for drug development due to limitations surrounding clinical translatability. Despite these concerns, several approaches to target the AhR have been explored such as small molecules, microbials, PROTACs, and oligonucleotide-based approaches. These various chemical modalities are not without safety liabilities and require unique de-risking strategies to parse out toxicities. Collectively, these programs can benefit from in silico and in vitro methodologies that investigate specific AhR pathway activation and have the potential to implement thresholding parameters to categorize AhR ligands as “high” or “low” risk for sustained AhR activation. Exploration into transcriptomic signatures for AhR safety assessment, incorporation of physiologically-relevant in vitro model systems, and investigation into chronic activation of the AhR by structurally diverse ligands will help address gaps in our understanding regarding AhR-dependent toxicities. Here, we review the role of the AhR within the GI tract, novel therapeutic modality approaches to target the AhR, key AhR-dependent safety liabilities, and relevant strategies that can be implemented to address drug safety concerns. Together, this review discusses the emerging therapeutic landscape of modalities targeting the AhR for inflammatory GI indications and offers a safety roadmap for AhR drug development.

A Computational Approach for Respiratory Hazard Identification of Flavor Chemicals in Tobacco Products

Flavor chemicals contribute to the appeal and toxicity of tobacco products, including electronic nicotine delivery systems (ENDS). The assortment of flavor chemicals available for use in tobacco products is extensive. In this study, a chemistry-driven computational approach was used to evaluate flavor chemicals based on intrinsic hazardous structures and reactivity of chemicals. A large library of 3012 unique flavor chemicals was compiled from publicly available information. Next, information was computed and collated based on their (1) physicochemical properties, (2) global harmonization system (GHS) health hazard classification, (3) structural alerts linked to the chemical's reactivity, instability, or toxicity, and (4) common substructure shared with FDA's harmful and potentially harmful constituents (HPHCs) flavor chemicals that are respiratory toxicants. Computational analysis of the constructed flavor library flagged 638 chemicals with GHS classified respiratory health hazards, 1079 chemicals with at least one structural alert, and 2297 chemicals with substructural similarity to FDA's established and proposed list of HPHCs. A subsequent analysis was performed on a subset of 173 chemicals in the flavor library that are respiratory health hazards, contain structural alerts as well as flavor HPHC substructures. Four general toxicophore structures with an increased potential for respiratory toxicity were then identified. In summary, computational methods are efficient tools for hazard identification and understanding structure-toxicity relationship. With appropriate context of use and interpretation, in silico methods may provide scientific evidence to support toxicological evaluations of chemicals in or emitted from tobacco products.

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

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

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Integration of Epigenetic Mechanisms into Non-Genotoxic Carcinogenicity Hazard Assessment: Focus on DNA Methylation and Histone Modifications

Epigenetics involves a series of mechanisms that entail histone and DNA covalent modifications and non-coding RNAs, and that collectively contribute to programing cell functions and differentiation. Epigenetic anomalies and DNA mutations are co-drivers of cellular dysfunctions, including carcinogenesis. Alterations of the epigenetic system occur in cancers whether the initial carcinogenic events are from genotoxic (GTxC) or non-genotoxic (NGTxC) carcinogens. NGTxC are not inherently DNA reactive, they do not have a unifying mode of action and as yet there are no regulatory test guidelines addressing mechanisms of NGTxC. To fil this gap, the Test Guideline Programme of the Organisation for Economic Cooperation and Development is developing a framework for an integrated approach for the testing and assessment (IATA) of NGTxC and is considering assays that address key events of cancer hallmarks. Here, with the intent of better understanding the applicability of epigenetic assays in chemical carcinogenicity assessment, we focus on DNA methylation and histone modifications and review: (1) epigenetic mechanisms contributing to carcinogenesis, (2) epigenetic mechanisms altered following exposure to arsenic, nickel, or phenobarbital in order to identify common carcinogen-specific mechanisms, (3) characteristics of a series of epigenetic assay types, and (4) epigenetic assay validation needs in the context of chemical hazard assessment. As a key component of numerous NGTxC mechanisms of action, epigenetic assays included in IATA assay combinations can contribute to improved chemical carcinogen identification for the better protection of public health.

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

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

In silico assessment of genotoxicity. Combinations of sensitive structural alerts minimize false negative predictions for all genotoxicity endpoints and can single out chemicals for which experimentation can be avoided

Genotoxicity assessment of chemicals has a crucial role in most regulations. Due to labor, time, cost, and animal welfare issues, attention is being given to (Q)SAR methods. A strategic application of alternative methods is to first use a sequence of conservative (very sensitive) (Q)SARs and/or in vitro models to arrive at the conclusion that no further testing is necessary for negatives, and to use mechanistically based, Weight-Of-Evidence approach to evaluate the chemicals showing positive results. The ICH M7 guideline to detect DNA-reactive impurities in drugs follows these lines (recommending solely (Q)SAR in step 1). However, ICH M7 focuses only on Ames test. Here a large database of more than 6000 chemicals positive in at least one endpoint (in vitro gene mutations or chromosomal aberrations, in vivo micronucleus, aneugenicity) were analyzed with structural alerts implemented in the OECD QSAR Toolbox, resulting in maximum 3% false negatives. These promising results indicate that it may be possible to extend the approach to the whole range of genotoxicity endpoints required by regulations. Since structural alerts may generate false positives, cautious follow-up of positives is recommended (with e.g., statistically based QSARs, read across of similar chemicals, expert judgement, and experimentation when necessary).

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

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

Threshold of Toxicological Concern—An Update for Non-Genotoxic Carcinogens

The Threshold of Toxicological Concern (TTC) concept can be applied to organic compounds with the known chemical structure to derive a threshold for exposure, below which a toxic effect on human health by the compound is not expected. The TTC concept distinguishes between carcinogens that may act as genotoxic and non-genotoxic compounds. A positive prediction of a genotoxic mode of action, either by structural alerts or experimental data, leads to the application of the threshold value for genotoxic compounds. Non-genotoxic substances are assigned to the TTC value of their respective Cramer class, even though it is recognized that they could test positive in a rodent cancer bioassay. This study investigated the applicability of the Cramer classes specifically to provide adequate protection for non-genotoxic carcinogens. For this purpose, benchmark dose levels based on tumor incidence were compared with no observed effect levels (NOELs) derived from non-, pre- or neoplastic lesions. One key aspect was the categorization of compounds as non-genotoxic carcinogens. The recently finished CEFIC LRI project B18 classified the carcinogens of the Carcinogenicity Potency DataBase (CPDB) as either non-genotoxic or genotoxic compounds based on experimental or in silico data. A detailed consistency check resulted in a dataset of 137 non-genotoxic organic compounds. For these 137 compounds, NOEL values were derived from high quality animal studies with oral exposure and chronic duration using well-known repositories, such as RepDose, ToxRef, and COSMOS DB. Further, an effective tumor dose (ETD10) was calculated and compared with the lower confidence limit on benchmark dose levels (BMDL10) derived by model averaging. Comparative analysis of NOEL/EDT10/BMDL10 values showed that potentially bioaccumulative compounds in humans, as well as steroids, which both belong to the exclusion categories, occur predominantly in the region of the fifth percentiles of the distributions. Excluding these 25 compounds resulted in significantly higher but comparable fifth percentile chronic NOEL and BMDL10 values, while the fifth percentile EDT10 value was slightly higher but not statistically significant. The comparison of the obtained distributions of NOELs with the existing Cramer classes and their derived TTC values supports the application of Cramer class thresholds to all non-genotoxic compounds, such as non-genotoxic carcinogens.

Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity

Introduction: Genotoxicity is an imperative component of the human health safety assessment of chemicals. Its secure forecast is of the utmost importance for all health prevention strategies and regulations.Areas covered: We surveyed several types of alternative, animal-free approaches ((quantitative) structure-activity relationship (Q)SAR, read-across, Adverse Outcome Pathway, Integrated Approaches to Testing and Assessment) for genotoxicity prediction within the needs of regulatory frameworks, putting special emphasis on data quality and uncertainties issues.Expert opinion: (Q)SAR models and read-across approaches for in vitro bacterial mutagenicity have sufficient reliability for use in prioritization processes, and as support in regulatory decisions in combination with other types of evidence. (Q)SARs and read-across methodologies for other genotoxicity endpoints need further improvements and should be applied with caution. It appears that there is still large room for improvement of genotoxicity prediction methods. Availability of well-curated high-quality databases, covering a broader chemical space, is one of the most important needs. Integration of in silico predictions with expert knowledge, weight-of-evidence-based assessment, and mechanistic understanding of genotoxicity pathways are other key points to be addressed for the generation of more accurate and trustable results.

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

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

MutagenPred-GCNNs: A Graph Convolutional Neural Network-Based Classification Model for Mutagenicity Prediction with Data-Driven Molecular Fingerprints

An important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In this research, we used an advanced graph convolutional neural network (GCNN) architecture to identify the molecular representation and develop predictive models based on these representations. The predictive model based on features extracted by GCNNs can not only predict the mutagenicity of compounds but also identify the structure alerts in compounds. In fivefold cross-validation and external validation, the highest area under the curve was 0.8782 and 0.8382, respectively; the highest accuracy (Q) was 80.98% and 76.63%, respectively; the highest sensitivity was 83.27% and 78.92%, respectively; and the highest specificity was 78.83% and 76.32%, respectively. Additionally, our model also identified some toxicophores, such as aromatic nitro, three-membered heterocycles, quinones, and nitrogen and sulfur mustard. These results indicate that GCNNs could learn the features of mutagens effectively. In summary, we developed a mutagenicity classification model with high predictive performance and interpretability based on a data-driven molecular representation trained through GCNNs.

Genotoxicity and subchronic toxicity studies of Lipocet®, a novel mixture of cetylated fatty acids

Cetylated fatty acids (CFAs) are a group of fats that contain a single ester group within a hydrocarbon chain, which are reported to have beneficial biological effects. A novel mixture of CFAs produced by combining fatty acids derived from refined olive oil with cetyl esters (Lipocet®) is proposed for use as a food ingredient and was therefore subjected to a nonclinical safety assessment. The safety of Lipocet® was evaluated in a bacterial reverse mutation test and an in vitro mammalian cell micronucleus test, followed by a 90-day oral (gavage) toxicity study. In the 90-day study, Sprague-Dawley rats were administered with the vehicle (corn oil) or Lipocet® at 1,500, 3,000, or 4,500 mg/kg body weight/day for 90 days. A comparator reference control group received noncetylated fatty acids derived from olive oil at 4,500 mg/kg body weight/day to identify any effects that may be expected following consumption of high doses of fat. Lipocet® was nongenotoxic in vitro. In the 90-day study, changes observed in hematological and clinical biochemistry parameters were minor in nature and/or showed poor dose dependency. Histopathology findings in the gastrointestinal tract and lungs were noted but were not associated with a clear dose response and were likely incidental. Moreover, Lipocet® was just as well tolerated as the reference control. Therefore, 4,500 mg/kg body weight/day (the highest dose tested) was considered the no-observed-adverse-effect-level. These results support the safety of Lipocet® for use as a food ingredient.

Scopy: an integrated negative design python library for desirable HTS/VS database design

BACKGROUND

High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery.

RESULTS

In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram.

CONCLUSION

Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

Prediction of hERG potassium channel blockage using ensemble learning methods and molecular fingerprints

The human ether-a-go-go-related gene (hERG) encodes a tetrameric potassium channel called Kv11.1. This channel can be blocked by certain drugs, which leads to long QT syndrome, causing cardiotoxicity. This is a significant problem during drug development. Using computer models to predict compound cardiotoxicity during the early stages of drug design will help to solve this problem. In this study, we used a dataset of 1865 compounds exhibiting known hERG inhibitory activities as a training set. Thirty cardiotoxicity classification models were established using three machine learning algorithms based on molecular fingerprints and molecular descriptors. Through using these models as the base classifier, a new cardiotoxicity classification model with better predictive performance was developed using ensemble learning method. The accuracy of the best base classifier, which was generated using the XGBoost method with molecular descriptors, was 84.8 %, and the area under the receiver-operating characteristic curve (AUC) was 0.876 in the five fold cross-validation. However, all of the ensemble models that we developed had higher predictive performance than the base classifiers in the five fold cross-validation. The best predictive performance was achieved by the Ensemble-Top7 model, with accuracy of 84.9 % and AUC of 0.887. We also tested the ensemble model using external validation data and achieved accuracy of 85.0 % and AUC of 0.786. Furthermore, we identified several hERG-related substructures, which provide valuable information for designing drug candidates.

KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development

Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.

Evaluation of two in silico programs for predicting mutagenicity and carcinogenicity potential for 4-methylimidazole (4-MeI) and known metabolites

4-Methylimidazole (4-MeI) is a nitrogen-containing heterocyclic compound that is used in the manufacture of chemicals, dyes and pharmaceuticals and may be found in a variety of foods following formation during heating. The purpose of this study was to use two different in silico programs, CASE Ultra and Toxtree, to investigate potential structure-activity relationships in 4-MeI and its metabolites for mutagenicity and carcinogenicity, and combine that information with the available literature to draw conclusions regarding the strength of the predictions observed. Neither CASE Ultra nor Toxtree identified any structural alerts that were associated with mutagenic activity. Data for 4-MeI from a single study were used in the development of the CASE Ultra mouse and rat carcinogenicity models, but no additional similar structures were identified in the carcinogenicity model training set. One metabolite, 5-methylhydantoin, was predicted to be positive in the CASE Ultra carcinogenicity male and female mouse models; positive predictivity percentages of 60.9% and 73.7%, respectively. However, low structural similarity between 5-methylhydantoin and the compounds identified in the training set (<25%) decreases confidence in the positive prediction. Three metabolites were predicted to be positive in the CASE Ultra mouse micronucleus model, but again suffered from low structural similarity. Both limited structural similarity and inconsistent responses among the other clastogenicity models suggest that additional structurally similar compounds are needed to assess the predictive capacity of these alerts for biological activity of these compounds.

Evaluation of non-commercial models for genotoxicity and carcinogenicity in the assessment of EFSA's databases

Over the past years, the European Food Safety Authority (EFSA) released to the public domain several databases, with the main objectives of collecting and storing hazard data on the substances considered in EFSA's risk assessment and secondly to serve as a basis for further development of in silico tools such as quantitative structure-activity relationship (QSAR) models. In this work, we evaluated the ability of freely available QSAR models to estimate genotoxicity and carcinogenicity properties and their possible use for screening purposes on three different EFSA's databases. With an accuracy close to 90%, the results showed good capabilities of QSAR models to predict genotoxicity in terms of bacterial reverse mutation test, while statistics for in vivo micronucleus test are not satisfactory (accuracy in the predictions close to 50%). Interestingly, results on the carcinogenicity assessment showed an accuracy in prediction close to 70% for the best models. In addition, an example of the potential application of in silico models is presented in order to provide a preliminary screening of genotoxicity properties of botanicals intended for use as food supplements.

Modulation of lymphocyte-mediated tissue repair by rational design of heterocyclic aryl hydrocarbon receptor agonists

Aryl hydrocarbon receptor (AHR) is an essential regulator of gut immunity and a promising therapeutic target for inflammatory bowel disease (IBD). Current AHR agonists are inadequate for clinical translation due to low activity, inadequate pharmacokinetics, or toxicity. We synthesized a structurally diverse library and used integrated computational and experimental studies to discover mechanisms governing ligand-receptor interaction and to design potent drug leads PY109 and PY108, which display physiochemical drug-likeness properties, desirable pharmacokinetic profiles, and low toxicity. In a murine model of dextran sulfate sodium-induced colitis, orally administered compounds increase interleukin-22 (IL-22) production and accelerate mucosal healing by modulating mucosal adaptive and innate lymphoid cells. AHR and IL-22 pathway induction was confirmed using RNA sequencing and characterization of the lymphocyte protein-protein interaction network. Significant induction of IL-22 was also observed using human T cells from patients with IBD. Our findings support rationally designed AHR agonists for IBD therapy.

Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships

The aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.

Bronchioloalveolar lung tumors induced in “mice only” by non-genotoxic chemicals are not useful for quantitative assessment of pulmonary adenocarcinoma risk in humans

Chemicals classified as known human carcinogens by International Agency for Research on Cancer (IARC) show a low level of concordance between rodents and humans for induction of pulmonary carcinoma. Rats and mice exposed via inhalation for 2 years show a low level of concordance in both tumor development and organ site location. In 2-year inhalation studies using rats and mice, when pulmonary tumors are seen in only male or female mice or both, but not in either sex of rat, there is a high probability that the murine pulmonary tumor has been produced via Clara cell or club cell (CC) metabolism of the inhaled chemical to a cytotoxic metabolite. Cytotoxicity-induced mitogenesis increases mutagenesis via amplification of the background mutation rate. If the chemical being tested is also negative in the Ames Salmonella mutagenicity assay, and only mouse pulmonary tumors are induced, the probability that this pulmonary tumor is not relevant to human lung cancer risk goes even higher. Mice have a larger percentage of CCs in their distal airways than rats, and a much larger percentage than in humans. The CCs of mice have a much higher concentration of metabolic enzymes capable of metabolizing xenobiotics than CCs in either rats or humans. A principal threat to validity of extrapolating from the murine model lies in the unique capacity of murine CCs to metabolize a significant spectrum of xenobiotics which in turn produces toxicants not seen in rat or human pulmonary pathophysiology.

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

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

Is current risk assessment of non-genotoxic carcinogens protective?

Non-genotoxic carcinogens (NGTXCs) do not cause direct DNA damage but induce cancer via other mechanisms. In risk assessment of chemicals and pharmaceuticals, carcinogenic risks are determined using carcinogenicity studies in rodents. With the aim to reduce animal testing, REACH legislation states that carcinogenicity studies are only allowed when specific concerns are present; risk assessment of compounds that are potentially carcinogenic by a non-genotoxic mode of action is usually based on subchronic toxicity studies. Health-based guidance values (HBGVs) of NGTXCs may therefore be based on data from carcinogenicity or subchronic toxicity studies depending on the legal framework that applies. HBGVs are usually derived from No-Observed-Adverse-Effect-Levels (NOAELs). Here, we investigate whether current risk assessment of NGTXCs based on NOAELs is protective against cancer. To answer this question, we estimated Benchmark doses (BMDs) for carcinogenicity data of 44 known NGTXCs. These BMDs were compared to the NOAELs derived from the same carcinogenicity studies, as well as to the NOAELs derived from the associated subchronic studies. The results lead to two main conclusions. First, a NOAEL derived from a subchronic study is similar to a NOAEL based on cancer effects from a carcinogenicity study, supporting the current practice in REACH. Second, both the subchronic and cancer NOAELs are, on average, associated with a cancer risk of around 1% in rodents. This implies that for those chemicals that are potentially carcinogenic in humans, current risk assessment of NGTXCs may not be completely protective against cancer. Our results call for a broader discussion within the scientific community, followed by discussions among risk assessors, policy makers, and other stakeholders as to whether or not the potential cancer risk levels that appear to be associated with currently derived HBGVs of NGXTCs are acceptable.

Ozonation of Para-Substituted Phenolic Compounds Yields p-Benzoquinones, Other Cyclic α,β-Unsaturated Ketones, and Substituted Catechols

Phenolic moieties are common functional groups in organic micropollutants and in dissolved organic matter, and are exposed to ozone during drinking water and wastewater ozonation. Although unsubstituted phenol is known to yield potentially genotoxic p-benzoquinone during ozonation, little is known about the effects of substitution of the phenol ring on transformation product formation. With batch experiments employing differing ozone/target compound ratios, it is shown that para-substituted phenols ( p-alkyl, p-halo, p-cyano, p-methoxy, p-formyl, p-carboxy) yield p-benzoquinones, p-substituted catechols, and 4-hydroxy-4-alkyl-cyclohexadien-1-ones as common ozonation products. Only in a few cases did para-substitution prevent the formation of these potentially harmful products. Quantum chemical calculations showed that different reaction mechanisms lead to p-benzoquinone, and that cyclohexadienone can be expected to form if no such pathway is possible. These products can thus be expected from most phenolic moieties. Kinetic considerations showed that substitution of the phenolic ring results in rather small changes of the apparent second order rate constants for phenol-ozone reactions at pH 7. Thus, in mixtures, most phenolic structures can be expected to react with ozone. However, redox cross-reactions between different transformation products, as well as hydrolysis, can be expected to further alter product distributions under realistic treatment scenarios.

In silico genotoxicity and carcinogenicity prediction for food-relevant secondary plant metabolites

Humans are exposed to thousands of different secondary plant metabolites which may have beneficial health effects, but numerous compounds may also have toxic potential. In the present study we have examined the genotoxic and carcinogenic potential of 609 food-relevant phytochemicals by using computer models for toxicity prediction. We developed a scoring method and combined the results of different models to increase the predictive power. A combination of the VEGA models SARpy, KNN, ISS, and CAESAR, and of the LAZAR model "Salmonella typhimurium" for genotoxicity prediction performed better than the single models regarding specificity and accuracy. Statistical evaluation of the combined model for carcinogenicity prediction was not possible due to the low number of substances suitable for model validation. The in silico results of the present exercise will be useful for priority setting purposes regarding future risk assessment of secondary plant metabolites. Based on our analysis, (-)-asimilobine, aloin, annoretine, chrysothrone, coptisine, elymoclavine, and thalicminine were predicted to be genotoxic with high probability and may therefore be selected for subsequent experimental genotoxicity testing. Moreover, the class of pyrrolizidine alkaloids is suggested to be a high priority subject for further studies as these substances have been predicted to be carcinogenic with high probability.

Tumor site concordance and genetic toxicology test correlations in NTP 2-year gavage, drinking water, dermal, and intraperitoneal injection studies

The National Toxicology Program has conducted 594, 2-year studies exposing various strains of rats and mice via different routes of exposure. In the current study, we analyze the results from 108 chemicals tested in 106, 2-year studies conducted by exposing F334/N rats and B6C3F1 mice via gavage. An additional 18, 2-year gavage studies have been conducted in Osborne–Mendel rats and B6C3F1 mice on 19 different chemicals. We analyze the results from 23 chemicals tested in 21, 2-year studies conducted by exposing F334/N rats and B6C3F1 mice via drinking water; 18 chemicals tested in 18, 2-year studies conducted by exposing F334/N rats and B6C3F1 mice via dermal application; and 11 chemicals tested in 11, 2-year studies conducted by exposing F334/N rats and B6C3F1 mice via intraperitoneal injection. The results from these 174 studies are analyzed and discussed separately. The neoplasticity of each chemical was analyzed for tumor incidence by species–sex category, tumor site concordance across species, and tumor site concordance across sex within species. When available the Ames Salmonella mutagenicity assay results, and any results from a test for genotoxicity other than the Ames test, were correlated with the neoplasticity results. Tumor site concordance across sex within species is generally higher than tumor site concordance across species. In addition, the high degree of variability of Ames test results suggests that historical Ames test data are less reliable than recent results conducted under good laboratory practices and employing Organization for Economic Cooperation and Development protocols relevant to the physicochemical characteristics of the test chemical.

Ames mutagenicity, structural alerts of carcinogenicity, Hansch QSAR parameters (ClogP, CMR, MgVol), tumor site concordance/multiplicity, and tumorigenicity rank in NTP 2-year rodent studies

Since its inception in 1976, the National Toxicology Program (NTP) has conducted 594 2-year studies on rats and mice by a number of routes of administration including inhalation, feed, drinking water, dermal, and intraperitoneal injection. Of these studies, the results on 470 chemicals were of adequate technical quality to be incorporated into final technical reports. In this study, the 470 chemicals were categorized from 1 to 48 by the level of “clear” neoplastic evidence in male and female rats, and in male and female mice, and given an ordinal rank from 1 to 135 following additional considerations regarding tumor site concordance and tumor multiplicity. The resultant tumorigenicity category score and ordinal rank score were examined for associations with results in the Ames Salmonella mutagenicity assay; presence or absence of structural alerts of carcinogenicity; and three Hansch Quantitative Structure-Activity Relationship (QSAR) parameters, namely, calculated base 10 logarithm of the octanol–water partition coefficient (ClogP), calculated molar refractivity (CMR), and McGowan molecular volume (MgVol). Smaller molecular volumes were found to be associated with higher levels of tumorigenicity. Whereas lower rather than higher levels of lipophilicity were found to be associated with higher levels of tumorigenicity. Positive Ames test results were positively correlated with overall tumorigenicity and with possession of structural alerts. Since larger organic molecules have more chemical reaction centers, it was not surprising that higher ClogP values were positively correlated with the number of structural alerts. The results from this study demonstrate the ability to devise rational rules for relative tumorigenicity that correlate, in biologically plausible ways, with known parameters of toxicity.

(Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks

Knowledge of the genotoxicity and carcinogenicity potential of chemical substances is one of the key scientific elements able to better protect human health. Genotoxicity assessment is also considered as prescreening of carcinogenicity. The assessment of both endpoints is a fundamental component of national and international legislations, for all types of substances, and has stimulated the development of alternative, nontesting methods. Over the recent decades, much attention has been given to the use and further development of structure-activity relationships-based approaches, to be used in isolation or in combination with in vitro assays for predictive purposes. In this chapter, we briefly introduce the rationale for the main (Q)SAR approaches, and detail the most important regulatory initiatives and frameworks. It appears that the existence and needs of regulatory frameworks stimulate the development of better predictive tools; in turn, this allows the regulators to fine-tune their requirements for an improved defense of human health.

Glyphosate toxicity and carcinogenicity: a review of the scientific basis of the European Union assessment and its differences with IARC

Glyphosate is the most widely used herbicide worldwide. It is a broad spectrum herbicide and its agricultural uses increased considerably after the development of glyphosate-resistant genetically modified (GM) varieties. Since glyphosate was introduced in 1974, all regulatory assessments have established that glyphosate has low hazard potential to mammals, however, the International Agency for Research on Cancer (IARC) concluded in March 2015 that it is probably carcinogenic. The IARC conclusion was not confirmed by the EU assessment or the recent joint WHO/FAO evaluation, both using additional evidence. Glyphosate is not the first topic of disagreement between IARC and regulatory evaluations, but has received greater attention. This review presents the scientific basis of the glyphosate health assessment conducted within the European Union (EU) renewal process, and explains the differences in the carcinogenicity assessment with IARC. Use of different data sets, particularly on long-term toxicity/carcinogenicity in rodents, could partially explain the divergent views; but methodological differences in the evaluation of the available evidence have been identified. The EU assessment did not identify a carcinogenicity hazard, revised the toxicological profile proposing new toxicological reference values, and conducted a risk assessment for some representatives uses. Two complementary exposure assessments, human-biomonitoring and food-residues-monitoring, suggests that actual exposure levels are below these reference values and do not represent a public concern.