Evaluation of QSAR models for the prediction of ames genotoxicity: a retrospective exercise on the chemical substances registered under the EU REACH regulation

Identification and simultaneous quantification of potential genotoxic impurities in first-line HIV drug dolutegravir sodium using fast ultrasonication-assisted extraction method coupled with GC-MS and in-silico toxicity assessment

Dolutegravir (DLG) has become a distinctive first-line antiretroviral therapy for the treatment of HIV in most countries due to its affordability, high efficacy, and low drug-drug interactions. However, the evaluation of genotoxic impurities (GTIs) in DLG and their toxicity assessment has not been explored thoroughly. Thus, in this study, a simple, fast, and selective analytical methodology was developed for the identification and determination of 7 GTIs in the comprehensive, explicit route of synthesis for the dolutegravir sodium (DLG-Na) drug. A facile, fast ultrasonication-assisted liquid-liquid extraction procedure was adapted to isolate the GTIs in DLG-Na and then analyzed using the gas chromatography (GC)-electron impact (EI)/mass spectrometer (MS) quantification (using selective ion monitoring mode) technique. This EI-GC/MS method was validated as per the current requirements of ICH Q2 (R1) guidelines. Under optimal method conditions, excellent linearities were achieved with R between 0.9959 and 0.9995, and high sensitivity was obtained in terms of detection limits (LOD) between 0.15 to 0.63 µg/g, and quantification limits (LOQ) between 0.45 to 1.66 µg/g for the seven GTIs in DLG. The obtained recoveries ranged from 98.2 to 104.3 % at LOQ, 15 µg/g, and 18 µg/g concentration levels (maximum daily dose of 100 mg). This developed and validated method is rapid, easy to adopt, specific, sensitive, and accurate in estimating the seven GTIs in a relatively complex sodium matrix of the DLG-Na drug moiety. As a method application, two different manufactured samples of DLG-Na drug substances were analyzed for the fate of the GTIs and drug safety for the intended dosage applications. Moreover, an in-silico QSAR toxicity prediction assessment was carried out to prove scientifically the potential GTI nature of each impurity from the alerting functional groups.

Identification of New Hepatic Metabolites of Miconazole by Biological and Electrochemical Methods Using Ultra-High-Performance Liquid Chromatography Combined with High-Resolution Mass Spectrometry

The main objective of this study was to investigate the metabolism of miconazole, an azole antifungal drug. Miconazole was subjected to incubation with human liver microsomes (HLM) to mimic phase I metabolism reactions for the first time. Employing a combination of an HLM assay and UHPLC-HRMS analysis enabled the identification of seven metabolites of miconazole, undescribed so far. Throughout the incubation with HLM, miconazole underwent biotransformation reactions including hydroxylation of the benzene ring and oxidation of the imidazole moiety, along with its subsequent degradation. Additionally, based on the obtained results, screen-printed electrodes (SPEs) were optimized to simulate the same biotransformation reactions, by the use of a simple, fast, and cheap electrochemical method. The potential toxicity of the identified metabolites was assessed using various in silico models.

Electrochemical Simulation of Phase I Hepatic Metabolism of Voriconazole Using a Screen-Printed Iron(II) Phthalocyanine Electrode

Understanding the metabolism of pharmaceutical compounds is a fundamental prerequisite for ensuring their safety and efficacy in clinical use. However, conventional methods for monitoring drug metabolism often come with the drawbacks of being time-consuming and costly. In an ongoing quest for innovative approaches, the application of electrochemistry in metabolism studies has gained prominence as a promising approach for the synthesis and analysis of drug transformation products. In this study, we investigated the hepatic metabolism of voriconazole, an antifungal medication, by utilizing human liver microsomes (HLM) assay coupled with LC-MS. Based on the obtained results, the electrochemical parameters were optimized to simulate the biotransformation reactions. Among the various electrodes tested, the chemometric analysis revealed that the iron(II) phthalocyanine electrode was the most effective in catalyzing the formation of all hepatic voriconazole metabolites. These findings exemplify the potential of phthalocyanine electrodes as an efficient and cost-effective tool for simulating the intricate metabolic processes involved in drug biotransformation, offering new possibilities in the field of pharmaceutical research. Additionally, in silico analysis showed that two detected metabolites may exhibit significantly higher acute toxicity and mutagenic potential than the parent compound.

DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application

The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the International Council for Harmonization (ICH) guidelines. Building on this in silico approach, here we describe DeepAmes, a high performance and robust model developed with a novel deep learning (DL) approach for potential utility in regulatory science. DeepAmes was developed with a large and consistent Ames dataset (>10,000 compounds) and was compared with other five standard Machine Learning (ML) methods. Using a test set of 1,543 compounds, DeepAmes was the best performer in predicting the outcome of Ames assay. In addition, DeepAmes yielded the best and most stable performance up to when compounds were >30% outside of the applicability domain (AD). Regarding the potential for regulatory application, a revised version of DeepAmes with a much-improved sensitivity of 0.87 from 0.47. In conclusion, DeepAmes provides a DL-powered Ames test predictive model for predicting the results of Ames tests; with its defined AD and clear context of use, DeepAmes has potential for utility in regulatory application.

Multitask Deep Neural Networks for Ames Mutagenicity Prediction

The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strains of Salmonella typhimurium, the vast majority of the published in silico models for predicting mutagenicity do not take into account the test results of the individual experiments conducted for each strain. Instead, such QSAR models are generally trained employing overall labels (i.e., mutagenic and nonmutagenic). Recently, neural-based models combined with multitask learning strategies have yielded interesting results in different domains, given their capabilities to model multitarget functions. In this scenario, we propose a novel neural-based QSAR model to predict mutagenicity that leverages experimental results from different strains involved in the Ames test by means of a multitask learning approach. To the best of our knowledge, the modeling strategy hereby proposed has not been applied to model Ames mutagenicity previously. The results yielded by our model surpass those obtained by single-task modeling strategies, such as models that predict the overall Ames label or ensemble models built from individual strains. For reproducibility and accessibility purposes, all source code and datasets used in our experiments are publicly available.

The VEGAHUB Platform: The Philosophy and the Tools

VEGAHUB (www.vegahub.eu) is a repository of freely available, downloadable tools based on computational toxicology methodologies. The main software tool available in VEGAHUB is VEGA QSAR software encoding more than 90 quantitative structure-activity relationship (QSAR) models for tens of endpoints for human toxicology, ecotoxicology, environmental, physico-chemical and toxicokinetic properties. However, beyond VEGA QSAR, VEGAHUB offers several other tools. Here, we present these resources, the possibilities to fully exploit them and the ways in which to integrate results provided by different VEGAHUB tools. Read-across and weight-of-evidence represent a major advantage of VEGAHUB. Integration between hazard and exposure is provided within innovative tools, which are specific for well-defined scenarios, such as those for cosmetic products. Prioritisation can be achieved by integrating results from 48 models. Finally, we highlight how some tools may not only fit predefined endpoints but also could be applied to general problems and research applications in the QSAR field. A couple of examples are provided, in which a critical assessment of the predictions and the documentation associated with the prediction are considered, in order to properly assess the quality of the results. These results may be associated with different levels of uncertainty or even be conflicting.

Comparative analysis of in vivo and in silico toxicity evaluation of the organoiodine compounds towards D.magna using multivariate chemometric approach: A study on the example of amiodarone phototransformation products

In the present study the photochemical fate of organoiodine compound - amiodarone was performed. The drug turned out to be highly susceptible to UV-Vis irradiation, especially in the presence of humic substances and organic matrix. Qualitative LC-MS analysis revealed formation of twelve - mainly previously unreported - transformation products (TPs). Four major TPs were submitted to the toxicity analysis with the use of D. magna. All of the tested TPs presented higher toxic potential than the parent compound. The phenolic TPs were approximately 100 times more toxic than amiodarone. Toxic properties of the major TPs resulted in steadily increasing toxic potential of the photo-generated mixture over the time of irradiation. Moreover, the experimental toxicity data, concerning the TPs, were compared with results estimated by 6 in silico models with the use of a multivariate chemometric analysis. The results showed that the applied computational methods were able neither to correctly predict toxic properties of the studied compounds, nor the trends in change of their toxic parameters. Additional validation of in silico models ability to predict toxicity of iodinated organic compounds showed that the studied computational methods do not present sufficient prediction ability. Therefore their estimations concerning organoiodines should be verified using experimental tests.

Use of (Q)SAR genotoxicity predictions and fuzzy multicriteria decision-making for priority ranking of ethoxyquin transformation products

Ethoxyquin (EQ; 6-ethoxy-2,2,4-trimethyl-1,2-dihydroquinoline) has been used as an antioxidant in feed for pets and food-producing animals, including farmed fish such as Atlantic salmon. In Europe, the authorization for use of EQ as a feed additive was suspended, due to knowledge gaps concerning the presence and toxicity of EQ transformation products (TPs). Recent analytical studies focusing on the detection of EQ TPs in farmed Atlantic salmon feed and fillets reported the detection of a total of 27 EQ TPs, comprising both known and previously not described EQ TPs. We devised and applied an in silico workflow to rank these EQ TPs according to their genotoxic potential and their occurrence data in Atlantic salmon feed and fillet. Ames genotoxicity predictions were obtained applying a suite of five (quantitative) structure-activity relationship ((Q)SAR) tools, namely VEGA, TEST, LAZAR, Derek Nexus and Sarah Nexus. (Q)SAR Ames genotoxicity predictions were aggregated using fuzzy analytic hierarchy process (fAHP) multicriteria decision-making (MCDM). A priority ranking of EQ TPs was performed based on combining both fAHP ranked (Q)SAR predictions and analytical occurrence data. The applied workflow prioritized four newly identified EQ TPs for further investigation of genotoxicity. The fAHP-based prioritization strategy described here, can easily be applied to other toxicity endpoints and groups of chemicals for priority ranking of compounds of most concern for subsequent experimental and mechanistic toxicology analyses.

Use of In Silico Methods for Regulatory Toxicological Assessment of Pharmaceutical Impurities

The use of novel non-testing methodologies to support the toxicological assessment of drug impurities is having a growing impact in the regulatory framework for pharmaceutical development and marketed products. For DNA reactive (mutagenic) impurities specific recommendations for the use of in silico structure-based approaches (namely (Q)SAR methodologies) are provided in the ICH M7 guideline. In 2018 a draft reflection paper has been published by EMA addressing open issues in the qualification approach of non-genotoxic impurities (NGI) according to the ICH Q3A/Q3B guidelines, and proposing the use of alternative testing strategies, including TTC, (Q)SAR, read-across, and in vitro approaches, to gather impurity-specific safety information.In the present chapter we describe a workflow to perform the safety assessment of drug impurities based on non-testing in silico methodologies. The proposed approach consists of a stepwise decision scheme including three key phases: PHASE 1: assessment of bacterial mutagenicity and consequent classification of impurities according to ICH M7; PHASE 2: risk characterization of mutagenic impurities (Classes 1, 2 or 3); PHASE 3: qualification of non-mutagenic impurities (Classes 4 or 5). The proposed decision scheme offers the possibility to acquire impurity-specific data, also if testing is not feasible, and to decide on further in vitro testing, besides meeting 3R's principle.

Development and Validation of a UPLC-MS/MS Method for Ultra-Trace Level Determination of Acyl Chloride Potential Genotoxic Impurity in Mezlocillin

3-Chlorocarbonyl-1-methanesulfonyl-2-imidazolidinone (CMI) is a critical intermediate used in the synthesis of mezlocillin drug substance and also a potential genotoxic impurity with acyl chloride moiety. The content of CMI in mezlocillin should be <0.16 ppm to avoid the carcinogenicity and mutagenicity threats to patients. Therefore, a workable determination of CMI was critically crucial for ensuring the safety of mezlocillin drug products. However, the conventional HPLC method is insufficient for detection limits at ppm or lower levels. Besides, the high activity of acyl chloride also raises a challenge to the direct measurement of CMI. Thus, we explored a simple esterification approach, which converts CMI into methyl 3-(methylonyl)-2-oxoimidazolidine-1-carboxylate completely by optimizing the reaction temperature and time. Furthermore, the selected reaction monitoring model of triple quadrupole mass spectrometer optimized by the Box-Behnken design significantly enhanced the sensitivity of ultra-trace level determination. The limit of detection and limit of quantification of the method were reached 0.014 and 0.02 ppm, respectively, in the following validation study. A sensitive and specific ultra-performance liquid chromatography tandem mass spectrometry method for ultra-trace level determination of acyl chloride potential genotoxic impurity in mezlocillin drug substance has been successfully established in this study, which will provide a practical quality control tool of mezlocillin.

Photochemical transformation of fentanyl under the simulated solar radiation - Enhancement of the process by heterogeneous photocatalysis and in silico analysis of toxicity

In this study the photochemical transformation of fentanyl-a very potent opioid drug-under simulated solar radiation was investigated for the first time. This pharmaceutical is frequently detected in various environment samples at concentrations that should be regarded as potentially harmful. Nevertheless, to date, no drug phototransformation products (TPs) have been reported. Considering fentanyl's exceptionally high toxicity, knowledge of the properties of these potential TPs is essential in order to properly assess its pollution impact. In this study, all photolytic experiments were performed using a xenon lamp (D65 filter) and RP-UHPLC coupled with the ESI-high-resolution tandem mass spectrometry. The phototransformation of fentanyl in natural river water and the application of heterogeneous photocatalysis as a possible way of decontaminating water were also investigated. Fentanyl turned out to be photostable, but twenty-six previously unreported TPs (formed mainly as a consequence of hydroxylation and oxidation) were found and characterized. The applied catalysts-TiO₂ and ZnO-showed very high effectiveness, and the presence of the natural water matrix further increased the photodecomposition rate (up to 600 times) relative to direct photolysis. Importantly, the almost complete degradation of the parent compound as well as its TPs after 16 min of irradiation indicated that heterogeneous photocatalysis can be considered an efficient way of treatment of fentanyl-contaminated water. The computational analysis of toxicity showed that fentanyl may be more harmful to rodents and aquatic species than its TPs. However, some of these products are probably more mutagenic and developmentally toxic. Additionally, one product in particular may be a strong estrogenic compound, proving the importance of assessing TPs' toxic properties. The evaluation of bioaccumulation, bioconcentration and biodegradability revealed that fentanyl possesses unfavorable properties compared to TPs.

The Green toxicology approach: Insight towards the eco-toxicologically safe development of benign catalysts

Green toxicology is a novel approach increasingly applied for the development of materials and chemicals that are more benign to the environment and human health than their conventional counterparts. It includes predictive eco-toxicological assessments of chemicals during the early developmental process to exclude adverse effects. In the present study, two guanidine zinc catalysts for the ring-opening polymerization of lactide were investigated using eco-toxicological tools. Namely, the fish embryo toxicity assay for teratogenic effects, the ER (α) CALUX assay for endocrine activity and the Ames fluctuation assay for mutagenic potential were applied. Both complexes showed no endocrine activity, mutagenicity or acute aquatic toxicity, however a delayed hatch could be observed, therefore suggesting potential effects on a molecular level. This proof-of-concept study aims to assess the toxicity of guanidine zinc catalysts and is a first step towards the incorporation of toxicological assessments into chemical developmental processes to achieve a sustainable and safe production of catalysts.

Characterization of Phase I Hepatic Metabolites of Anti-Premature Ejaculation Drug Dapoxetine by UHPLC-ESI-Q-TOF

Determination of the metabolism pathway of xenobiotics undergoing the hepatic pass is a crucial aspect in drug development since the presence of toxic biotransformation products may result in significant side effects during the therapy. In this study, the complete hepatic metabolism pathway of dapoxetine established according to the human liver microsome assay with the use of a high-resolution LC-MS system was described. Eleven biotransformation products of dapoxetine, including eight metabolites not reported in the literature so far, were detected and identified. N-dealkylation, hydroxylation, N-oxidation and dearylation were found to be the main metabolic reactions for the investigated xenobiotic. In silico analysis of toxicity revealed that the reaction of didesmethylation may contribute to the increased carcinogenic potential of dapoxetine metabolites. On the other hand, N-oxidation and aromatic hydroxylation biotransformation reactions possibly lead to the formation of mutagenic compounds.

Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset

Regulatory agencies world-wide face the challenge of performing risk-based prioritization of thousands of substances in commerce. In this study, a major effort was undertaken to compile a large genotoxicity dataset (54,805 records for 9299 substances) from several public sources (e.g., TOXNET, COSMOS, eChemPortal). The names and outcomes of the different assays were harmonized, and assays were annotated by type: gene mutation in Salmonella bacteria (Ames assay) and chromosome mutation (clastogenicity) in vitro or in vivo (chromosome aberration, micronucleus, and mouse lymphoma Tk ^+/- assays). This dataset was then evaluated to assess genotoxic potential using a categorization scheme, whereby a substance was considered genotoxic if it was positive in at least one Ames or clastogen study. The categorization dataset comprised 8442 chemicals, of which 2728 chemicals were genotoxic, 5585 were not and 129 were inconclusive. QSAR models (TEST and VEGA) and the OECD Toolbox structural alerts/profilers (e.g., OASIS DNA alerts for Ames and chromosomal aberrations) were used to make in silico predictions of genotoxicity potential. The performance of the individual QSAR tools and structural alerts resulted in balanced accuracies of 57-73%. A Naïve Bayes consensus model was developed using combinations of QSAR models and structural alert predictions. The 'best' consensus model selected had a balanced accuracy of 81.2%, a sensitivity of 87.24% and a specificity of 75.20%. This in silico scheme offers promise as a first step in ranking thousands of substances as part of a prioritization approach for genotoxicity.

Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow

Regulatory frameworks require information on acute fish toxicity to ensure environmental protection. The experimental assessment of this property relies on a substantial number of fish to be tested and it is in conflict with the current drive to replace in vivo testing. For this reason, alternatives to in vivo testing have been proposed during the past years. Among these alternatives, there are Quantitative Structure-Activity Relationships (QSAR) that require the sole knowledge of chemical structure to yield predictions of toxicities. In this context, the OECD QSAR Toolbox is one of the leading QSAR tools for regulatory purposes that enables the prediction of fish toxicities. The aim of this work is to provide evidence about the predictive reliability of the automated workflow for predicting acute toxicity in fish which is embedded within this toolbox. The results herein presented show that the logic underpinning this automated workflow can predict with a reliability that, in the majority of cases, is comparable to inter-laboratory variability and, in a significant number of cases, is also comparable with intra-laboratory variability. Moreover, considerations on the toxic mode of action provided by the OECD tool proved to be helpful in refining predictions and reducing the number of prediction outliers.

Transforming early pharmaceutical assessment of genotoxicity: applying statistical learning to a high throughput, multi end point in vitro micronucleus assay

To provide a comprehensive analysis of small molecule genotoxic potential we have developed and validated an automated, high-content, high throughput, image-based in vitro Micronucleus (IVM) assay. This assay simultaneously assesses micronuclei and multiple additional cellular markers associated with genotoxicity. Acoustic dosing (≤ 2 mg) of compound is followed by a 24-h treatment and a 24-h recovery period. Confocal images are captured [Cell Voyager CV7000 (Yokogawa, Japan)] and analysed using Columbus software (PerkinElmer). As standard the assay detects micronuclei (MN), cytotoxicity and cell-cycle profiles from Hoechst phenotypes. Mode of action information is primarily determined by kinetochore labelling in MN (aneugencity) and γH2AX foci analysis (a marker of DNA damage). Applying computational approaches and implementing machine learning models alongside Bayesian classifiers allows the identification of, with 95% accuracy, the aneugenic, clastogenic and negative compounds within the data set (Matthews correlation coefficient: 0.9), reducing analysis time by 80% whilst concurrently minimising human bias. Combining high throughput screening, multiparametric image analysis and machine learning approaches has provided the opportunity to revolutionise early Genetic Toxicology assessment within AstraZeneca. By multiplexing assay endpoints and minimising data generation and analysis time this assay enables complex genotoxicity safety assessments to be made sooner aiding the development of safer drug candidates.

Influence of the UV-Vis irradiation on the acute toxicity to zebrafish and mutagenicity of the selected psychotropic drugs

The influence of the UV-Vis radiation on the toxicity of agomelatine, loxapine, clozapine and tiapride was studied. The phototransformation procedure was conducted with the use of simulated solar radiation. In the case of each compound irradiation time necessary to decompose half of the initial concentration was chosen. The embryotoxicity and acute toxicity were evaluated using zebrafish (Danio rerio) embryos and larvae. The mutagenicity assay was done with the use of a micro-plate Ames test. Generally, the embryotoxicity decreased after the irradiation procedure. The obtained results showed that tiapride is the least toxic compound to zebrafish, however, its toxicity toward larvae increases after the irradiation. Similarly, the mutagenic potential of the mixture of tiapride photoproducts is higher than in the case of parent compound. The phototransformation of loxapine resulted in the change of the acute toxicity profile and increased the rate of reverse mutations in the Ames test. Oppositely, the irradiation of agomelatine caused the decrease of mutagenic potential and acute toxicity was also lower in the postirradiated mixture. The phototransformation of clozapine did not alter the mutagenicity and decreased the acute toxicity to the zebrafish larvae. In silico calculations showed a satisfactory prediction ability in some instances, especially in the case of mutagenic potential of the tiapride phototransformation products.

Predicting PBT and CMR properties of substances of very high concern (SVHCs) using QSAR models, and application for K-REACH

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BIOWIN is effective for predicting persistence and bioaccumulation.

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Toxtree is effective for predicting carcinogenicity and mutagenicity.

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WoE approach enhances the sensitivity.

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It is recommended to set a conservative criteria of log Kow more than 4.5 in K-REACH.

Quantitative structure-activity relationship (QSAR) models have been applied to predict a variety of toxicity endpoints. Their performance needs to be validated, in a variety of cases, to increase their applicability to chemical regulation. Using the data set of substances of very high concern (SVHCs), the performance of QSAR models were evaluated to predict the persistence and bioaccumulation of PBT, and the carcinogenicity and mutagenicity of CMR. BIOWIN and Toxtree showed higher sensitivity than other QSAR models – the former for persistence and bioaccumulation, the latter for carcinogenicity. In terms of mutagenicity, the sensitivities of QSAR models were underestimated, Toxtree was more accurate and specific than lazy structure–activity relationships (LAZARs) and Computer Assisted Evaluation of industrial chemical Substances According to Regulations (CAESAR). Using the weight of evidence (WoE) approach, which integrates results of individual QSAR models, enhanced the sensitivity of each toxicity endpoint. On the basis of obtained results, in particular the prediction of persistence and bioaccumulation by KOWWIN, a conservative criterion is recommended of log Kow greater than 4.5 in K-REACH, without an upper limit. This study suggests that reliable production of toxicity data by QSAR models is facilitated by a better understanding of the performance of these models.

A comparison of the persistence, toxicity, and exposure to high-volume natural plant-derived and synthetic pesticides

The immobility of plants exerted evolutionary selection pressures resulting in the production of thousands of chemical substances thought to function as pesticides against predation by insects and animals. More than 10,000 plant-derived compounds have been isolated with the existence of about 100,000 such compounds postulated. In 1990, Ames et al. reported that 99.99% by weight of the pesticides ingested in a normal human diet are derived from natural plant-based sources. This surprising result raised the question as to whether these natural plant pesticides were toxic to humans. These authors examined a relatively small subset of natural pesticides and determined that their tumorigenicity in rodent cancer bioassays was similar to synthetic pesticides. In this analysis, we used standard United States Environmental Protection Agency programs to estimate the toxicity (T.E.S.T. 4.2) and persistence (EPI Suite 4.1) of a series of high-volume synthetic and natural pesticides. On average, synthetic pesticides were more persistent in the environment than were natural pesticides. This result is consistent with cost, time, and logistical constraints under which farmers apply a limited number of applications of pesticides during a crop cycle. Synthetic and natural pesticides are predicted to possess toxicities including mutagenicity and developmental toxicity. Synthetic pesticides are less often mutagenic.

New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test

A large number of computer-based prediction methods to determine the potential of chemicals to induce mutations at the gene level has been developed over the last decades. Conversely, only few such methods are currently available to predict potential structural and numerical chromosome aberrations. Even fewer of these are based on the preferred testing method for this endpoint, i.e. the micronucleus test. For the present work, in vivo micronucleus test results of 718 structurally diverse compounds were collected and applied for the construction of new models by means of the freely available SARpy in silico model building software. Multiple QSAR models were created using parameter variation and manual verification of (non-) alerting structures. To this extent, the original set of 718 compounds was split into a training (80 %) and a test (20 %) set. SARpy was applied on the training set to automatically extract sets of rules by generating and selecting substructures based on their prediction performance whereas the test set was used to evaluate model performance. Five different splits were made randomly, each of which had a similar balance between positive and negative substances compared to the full dataset. All generated models were characterised by an overall better performance than existing free and commercial models for the same endpoint, while demonstrating high coverage.

Quantitative weight of evidence method for combining predictions of quantitative structure-activity relationship models

A method for combining statistical-based QSAR predictions of two or more binary classification models is presented. It was assumed that all models were independent. This facilitated the combination of positive and negative predictions using a quantitative weight of evidence (qWoE) procedure based on Bayesian statistics and the additivity of the logarithms of the likelihood ratios. Previous studies combined more than one prediction but used arbitrary strengths for positive and negative predictions. In our approach, the combined models were validated by determining the sensitivity and specificity values, which are performance metrics that are a point of departure for obtaining values that measure the weight of evidence of positive and negative predictions. The developed method was experimentally applied in the prediction of Ames mutagenicity. The method achieved a similar accuracy to that of the experimental Ames test for this endpoint when the overall prediction was determined using a combination of the individual predictions of more than one model. Calculating the qWoE value would reduce the requirement for expert knowledge and decrease the subjectivity of the prediction. This method could be applied to other endpoints such as developmental toxicity and skin sensitisation with binary classification models.

Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy

In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.

Rapid degradation of clozapine by heterogeneous photocatalysis. Comparison with direct photolysis, kinetics, identification of transformation products and scavenger study

In this study TiO₂-mediated photocatalytic degradation of the persistent drug clozapine under the simulated solar radiation was studied for the first time. The experiments were conducted both in the ultrapure and river water, which enabled the assessment of the organic matrix impact. The direct and indirect photolysis experiments were conducted for a comparison. Influence of the catalyst loading on the efficiency of the process was also assessed, and the highest catalyst loading (300 mg L^-1) was found to be the most effective. The TiO₂ photocatalysis was extremely effective for clozapine degradation - the decomposition was almost 300 times faster in comparison to the direct photolysis (t_1/2 = 1.7 min, neither clozapine, nor the intermediates were detected after 20 min of irradiation), and presence of the organic matrix did not negatively affect the process. Nevertheless the photocatalytic process turned out to be highly sensitive to act of the ROS scavengers. Thirteen transformation products (TPs) were found and their structures were elucidated by the means of high resolution mass spectrometry. Properties - toxicity, biodegradability, BCF and BAF - of TPs and the parent molecule were estimated with the use of computational methods. Identified TPs were found as generally less toxic and more biodegradable than clozapine.

Performance of In Silico Models for Mutagenicity Prediction of Food Contact Materials

In silico methodologies, such as (quantitative) structure-activity relationships ([Q]SARs), are available to predict a wide variety of toxicological properties and biological activities for structurally diverse substances. To obtain insights in the scientific value of these predictions, the capacity of the prediction models to generate (sufficiently) reliable results for a particular type of compounds needs to be evaluated. In the current study, performance parameters to predict the endpoint "bacterial mutagenicity" were calculated for a battery of common (Q)SAR tools, namely Toxtree, Derek Nexus, VEGA Consensus, and Sarah Nexus. Printed paper and board food contact material (FCM) constituents were chosen as study substances because many of these lack experimental data, making them an interesting group for in silico screening. Accuracy, sensitivity, specificity, positive predictivity, negative predictivity, and Matthews correlation coefficient for the individual models and for the combination of VEGA Consensus and Sarah Nexus were determined and compared. Our results demonstrate that performance varies among the four models, but can be increased by applying a combination strategy. Furthermore, the importance of the applicability domain is illustrated. Limited performance to predict the mutagenic potential of substances that are new to the model (ie, not included in the training set) is reported. In this context, the generally poor sensitivity for these new substances is also addressed.

Photolytic and photocatalytic transformation of an antipsychotic drug asenapine: Comparison of kinetics, identification of transformation products, and in silico estimation of their properties

The photolytic and photocatalytic transformation of an antipsychotic drug asenapine with the use of H₂O₂ and TiO₂ was studied. A method employing irradiation with a simulated full solar spectrum in the photostability chamber was applied, then the reverse-phase ultra high performance liquid chromatography with diode array detector, coupled with electrospray quadrupole time-of-flight mass spectrometer (RP-UHPLC-DAD - ESI-Q-TOF) was used for the quantitative and qualitative analysis of the processes. The developed quantitative method was fully validated, according to the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) guidelines, and the kinetic parameters of asenapine photodecomposition were compared. Nineteen phototransformation products were detected, and their probable structures - mainly hydroxylated and oxidized asenapine derivatives - were suggested. On the basis of the elucidated structures the computational prediction of their toxicity at the various endpoints, as well as bioconcentration factors and biodegradability was performed. The obtained results were then subjected to the principal component analysis (PCA). This chemometric technique facilitated comparison of the applied models, calculated properties of the TPs, and enabled visualization of relationships between them.

(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.

Marine Bacterial Compounds Evaluated by In Silico Studies as Antipsychotic Drugs Against Schizophrenia

Schizophrenia (SCZ) is one of the brain disorders which affects the thinking and behavioral skills of patients. This disorder comes along with an overproduction of kynurenic acid in the cerebrospinal fluid and the prefrontal cortex of SCZ patients. In this study, marine bacterial compounds were screened for their suitability as antagonists against human kynurenine aminotransferase (hKAT-1) which causes the synthesis of kynurenic acid downstream which ultimately causes the SCZ disorder according to the kynurenic hypothesis of SCZ. The marine actinobacterial compound bonactin shows more promising results than other tested marine compounds such as the histamine H2 blocker famotidine and indole-3-acetic acid (IAC) from docking and in silico toxicological studies carried out here. The obtained results of the Grid-based Ligand Docking with Energetics (Glide) scores of extra-precision (XP) Glide against the target protein hKAT-1 on IAC, famotidine, and bonactin were - 6.581, - 6.500 and - 7.730 kcal/mol where Glide energies were - 29.84, - 28.391, and - 47.565 kcal/mol, respectively. Bonactin is known as an antibacterial and antifungal compound being extracted from a marine Streptomyces sp. Comparing tested compounds against the drug target hKAT-1, bonactin alone showed the best Glide score and Glide energy on the target protein hKAT-1.

A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity$

Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds. The novelty of our study is that we collected a much larger set of about 18,000 compounds and used the new data to build a family of integrated models. These integrations used probabilistic approaches, decision theory, machine learning, and voting strategies in the integration scheme. Results are discussed considering balanced or conservative perspectives, regarding the possible uses for different purposes, including screening of large collection of substances for prioritization.

(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.

Integrating computational methods to predict mutagenicity of aromatic azo compounds

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

Photolytic and photocatalytic degradation of tandospirone: Determination of kinetics, identification of transformation products and in silico estimation of toxicity

The photolytic and photocatalytic transformation of tandospirone with the use of TiO₂ and H₂O₂ was investigated. A micro-scale method for simultaneous irradiation with simulated full solar spectrum of multiple samples in photostability chamber was proposed. RP-UHPLC-DAD coupled with ESI-Q-TOF mass spectrometer was used for the quantitative and qualitative analysis of the processes. The developed method was fully validated and the kinetic parameters of tandospirone photodegradation were compared. The structures of eighteen photoproducts as well as phototransformation pathways were proposed. Based on the elucidated structures, computational toxicity assessment with the use of various software was performed and most of the photoproducts were found as less or similarly toxic to the parent compound. Nevertheless, several products, including one of the drug main metabolites, were significantly more toxic than the parent drug. The multivariate chemometric method (principal component analysis) was used to compare the toxicity of phototransformation products as well as the toxicity of the assessment methods.

Origin of the TTC values for compounds that are genotoxic and/or carcinogenic and an approach for their re-evaluation

The threshold of toxicological concern (TTC) approach is a resource-effective de minimis method for the safety assessment of chemicals, based on distributional analysis of the results of a large number of toxicological studies. It is being increasingly used to screen and prioritize substances with low exposure for which there is little or no toxicological information. The first step in the approach is the identification of substances that may be DNA-reactive mutagens, to which the lowest TTC value is applied. This TTC value was based on the analysis of the cancer potency database and involved a number of assumptions that no longer reflect the state-of-the-science and some of which were not as transparent as they could have been. Hence, review and updating of the database is proposed, using inclusion and exclusion criteria reflecting current knowledge. A strategy for the selection of appropriate substances for TTC determination, based on consideration of weight of evidence for genotoxicity and carcinogenicity is outlined. Identification of substances that are carcinogenic by a DNA-reactive mutagenic mode of action and those that clearly act by a non-genotoxic mode of action will enable the protectiveness to be determined of both the TTC for DNA-reactive mutagenicity and that applied by default to substances that may be carcinogenic but are unlikely to be DNA-reactive mutagens (i.e. for Cramer class I-III compounds). Critical to the application of the TTC approach to substances that are likely to be DNA-reactive mutagens is the reliability of the software tools used to identify such compounds. Current methods for this task are reviewed and recommendations made for their application.

Photolytic and photocatalytic degradation of the antipsychotic agent tiapride: Kinetics, transformation pathways and computational toxicity assessment

The photolytic and photocatalytic transformation of tiapride with the use of TiO₂ and H₂O₂ was investigated. A novel micro-scale method for simultaneous irradiation with simulated full solar spectrum of multiple samples in photostability chamber was proposed. RP-UHPLC-DAD coupled with ESI-Q-TOF mass spectrometer was used for the quantitative and qualitative analysis of the processes. Quantitative method was fully validated, and kinetic parameters of tiapride photodegradation were compared. Structures of twenty-one photoproducts as well as phototransformation pathways were proposed. Based on the elucidated structures, computational toxicity assessment with the use of various software was performed and some of transformation products were found as a potentially highly mutagenic and carcinogenic compounds. The multivariate statistical method (principal component analysis) was used to compare toxicity of phototransformation products as well as toxicity assessment.

Fragment Prioritization on a Large Mutagenicity Dataset

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

In silico prediction of genotoxicity

The in silico prediction of genotoxicity has made considerable progress during the last years. The main driver for the pharmaceutical industry is the ICH M7 guideline about the assessment of DNA reactive impurities. An important component of this guideline is the use of in silico models as an alternative approach to experimental testing. The in silico prediction of genotoxicity provides an established and accepted method that defines the first step in the assessment of DNA reactive impurities. This was made possible by the growing amount of reliable Ames screening data, the attempts to understand the activity pathways and the subsequent development of computer-based prediction systems. This paper gives an overview of how the in silico prediction of genotoxicity is performed under the ICH M7 guideline.

Improving confidence in (Q)SAR predictions under Canada's Chemicals Management Plan - a chemical space approach

One of the key challenges of Canada's Chemicals Management Plan (CMP) is assessing chemicals with limited/no empirical hazard data for their risk to human health. In some instances, these chemicals have not been tested broadly for their toxicological potency; as such, limited information exists on their potential to induce human health effects following exposure. Although (quantitative) structure activity relationship ((Q)SAR) models are able to generate predictions to address data gaps for certain toxicological endpoints, the confidence in predictions also needs to be addressed. One way to address this issue is to apply a chemical space approach. This approach uses international toxicological databases, for example, those available in the Organisation for Economic Co-operation and Development (OECD) QSAR Toolbox. The approach,assesses a model's ability to predict the potential hazards of chemicals that have limited hazard data that require assessment under the CMP when compared to a larger, data-rich chemical space that is structurally similar to chemicals of interest. This evaluation of a model's predictive ability makes (Q)SAR analysis more transparent and increases confidence in the application of these predictions in a risk-assessment context. Using this approach, predictions for such chemicals obtained from four (Q)SAR models were successfully classified into high, medium and low confidence levels to better inform their use in decision-making.

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties

Interest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g., the European Union's Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine structural similarity with shared mechanisms of action. Substances with similar chemical structure and toxicological profile form candidate categories suitable for read-across. We combined two databases on repeated dose toxicity, RepDose database, and ELINCS database to form a common database for the identification of categories. The resulting database contained physicochemical, structural, and toxicological data, which were refined and curated for cluster analyses. We applied the Predictive Clustering Tree (PCT) approach for clustering chemicals based on structural and on toxicological information to detect groups of chemicals with similar toxic profiles and pathways/mechanisms of toxicity. As many of the experimental toxicity values were not available, this data was imputed by predicting them with a multi-label classification method, prior to clustering. The clustering results were evaluated by assessing chemical and toxicological similarities with the aim of identifying clusters with a concordance between structural information and toxicity profiles/mechanisms. From these chosen clusters, seven were selected for a quantitative read-across, based on a small ratio of NOAEL of the members with the highest and the lowest NOAEL in the cluster (< 5). We discuss the limitations of the approach. Based on this analysis we propose improvements for a follow-up approach, such as incorporation of metabolic information and more detailed mechanistic information. The software enables the user to allocate a substance in a cluster and to use this information for a possible read- across. The clustering tool is provided as a free web service, accessible at http://mlc-reach.informatik.uni-mainz.de.

New Quantitative Structure-Activity Relationship Models Improve Predictability of Ames Mutagenicity for Aromatic Azo Compounds

Existing Quantitative Structure-Activity Relationship (QSAR) models have limited predictive capabilities for aromatic azo compounds. In this study, 2 new models were built to predict Ames mutagenicity of this class of compounds. The first one made use of descriptors based on simplified molecular input-line entry system (SMILES), calculated with the CORAL software. The second model was based on the k-nearest neighbors algorithm. The statistical quality of the predictions from single models was satisfactory. The performance further improved when the predictions from these models were combined. The prediction results from other QSAR models for mutagenicity were also evaluated. Most of the existing models were found to be good at finding toxic compounds but resulted in many false positive predictions. The 2 new models specific for this class of compounds avoid this problem thanks to a larger set of related compounds as training set and improved algorithms.

Results of a round-robin exercise on read-across

A round-robin exercise was conducted within the CALEIDOS LIFE project. The participants were invited to assess the hazard posed by a substance, applying in silico methods and read-across approaches. The exercise was based on three endpoints: mutagenicity, bioconcentration factor and fish acute toxicity. Nine chemicals were assigned for each endpoint and the participants were invited to complete a specific questionnaire communicating their conclusions. The interesting aspect of this exercise is the justification behind the answers more than the final prediction in itself. Which tools were used? How did the approach selected affect the final answer?

New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds

In this study, new molecular fragments associated with genotoxic and nongenotoxic carcinogens are introduced to estimate the carcinogenic potential of compounds. Two rule-based carcinogenesis models were developed with the aid of SARpy: model R (from rodents' experimental data) and model E (from human carcinogenicity data). Structural alert extraction method of SARpy uses a completely automated and unbiased manner with statistical significance. The carcinogenicity models developed in this study are collections of carcinogenic potential fragments that were extracted from two carcinogenicity databases: the ANTARES carcinogenicity dataset with information from bioassay on rats and the combination of ISSCAN and CGX datasets, which take into accounts human-based assessment. The performance of these two models was evaluated in terms of cross-validation and external validation using a 258 compound case study dataset. Combining R and H predictions and scoring a positive or negative result when both models are concordant on a prediction, increased accuracy to 72% and specificity to 79% on the external test set. The carcinogenic fragments present in the two models were compared and analyzed from the point of view of chemical class. The results of this study show that the developed rule sets will be a useful tool to identify some new structural alerts of carcinogenicity and provide effective information on the molecular structures of carcinogenic chemicals.

The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities

The toxicological assessment of DNA-reactive/mutagenic or clastogenic impurities plays an important role in the regulatory process for pharmaceuticals; in this context, in silico structure-based approaches are applied as primary tools for the evaluation of the mutagenic potential of the drug impurities. The general recommendations regarding such use of in silico methods are provided in the recent ICH M7 guideline stating that computational (in silico) toxicology assessment should be performed using two (Q)SAR prediction methodologies complementing each other: a statistical-based method and an expert rule-based method.Based on our consultant experience, we describe here a framework for in silico assessment of mutagenic potential of drug impurities. Two main applications of in silico methods are presented: (1) support and optimization of drug synthesis processes by providing early indication of potential genotoxic impurities and (2) regulatory evaluation of genotoxic potential of impurities in compliance with the ICH M7 guideline. Some critical case studies are also discussed.

Comparison between bioconcentration factor (BCF) data provided by industry to the European Chemicals Agency (ECHA) and data derived from QSAR models

The bioconcentration factor (BCF) is the ratio of the concentration of a chemical in an organism to the concentration in the surrounding environment at steady state. It is a valuable indicator of the bioaccumulation potential of a substance. BCF is an essential environmental property required for regulatory purposes within the Registration, Evaluation, Authorization and restriction of Chemicals (REACH) and Globally Harmonized System (GHS) regulations. In silico models for predicting BCF can facilitate the risk assessment for aquatic toxicology and reduce the cost and number of animals used. The aim of the present study was to examine the correlation of BCF data derived from the dossiers of registered chemicals submitted to the European Chemical Agency (ECHA) with the results of a battery of Quantitative Structure-Activity Relationship (QSAR). After data pruning, statistical analysis was performed using the predictions of the selected models. Results in terms of R(2) had low rating around 0.5 for the pruned dataset. The use of the model applicability domain index (ADI) led to an improvement of the performance for compounds falling within it. The variability of the experimental data and the use of different parameters to define the applicability domain can influence the performance of each model. All available information should be adapted to the requirements of the regulation to obtain a safe decision.

Hierarchical Rules for Read-Across and In Silico Models of Mutagenicity

A broad set of rules has been implemented within the ToxRead software for read-across of chemicals for bacterial mutagenicity. These rules were obtained by manually analyzing more than 6000 chemicals and the associated chemical classes. A hierarchy of rules was established to identify those most specifically relating to the target compounds, linked in sequence to the other, more generic ones, which may match with the target compound. Rules related to both mutagenicity and lack of mutagenicity were found. Some of the latter are exceptions to the mutagenicity rules, while others are modulators of activity. These rules can also be used to predict mutagenicity, offering good performance.

ToxRead: a tool to assist in read across and its use to assess mutagenicity of chemicals

Life sciences, and toxicology in particular, are heavily impacted by the development of methods for data collection and data analysis; they are moving from an analytical approach to a modelling approach. The scarce availability of experimental data is a known bottleneck in assessing the properties of new chemicals. Even when a model is available, the resulting predictions have to be assessed by close scrutiny of the chemicals and the biological properties of the compounds concerned. To avoid unnecessary testing, a read across strategy is often suggested and used. In this paper we discuss how to improve and standardize read across activity using ad hoc visualization and data search methods which use similarity measures and fragment search to organize in a chart a picture of all the relevant information that the expert needs to make an assessment. We show in particular how to apply our system to the case of mutagenicity.