New in silico models to predict in vitro micronucleus induction as marker of genotoxicity

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New in silico models to predict in vitro micronucleus induction as marker of genotoxicity Diego Baderna*, Domenico Gadaleta, Eleonora Lostaglio, Gianluca Selvestrel, Giuseppa Raitano, Azadi Golbamaki, Anna Lombardo, Emilio Benfenati Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy

G R A P H I C A L A B S T R A C T

A R T I C LE I N FO

A B S T R A C T

Editor: R. Debora

The evaluation of genotoxicity is a fundamental part of the safety assessment of chemicals due to the relevance of the potential health effects of genotoxicants. Among the testing methods available, the in vitro micronucleus assay with mammalian cells is one of the most used and required by regulations targeting several industrial sectors such as the cosmetic industry and food-related sectors. As an alternative to the testing methods, in recent years, lots in silico methods were developed to predict the genotoxicity of chemicals, including models for the Ames mutagenicity test, the in vitro chromosomal aberrations and the in vivo micronucleus assay. We developed several in silico models for the prediction of genotoxicity as reflected by the in vitro micronucleus assay. The resulting models include both statistical and knowledge-based models. The most promising model is the one based on fragments extracted with the SARpy platform. More than 100 structural alerts were extracted, including also fragments associated with the non-genotoxic activity. The model is characterized by high accuracy and the lowest false negative rate, making this tool suitable for chemical screening according to the regulators' needs. The SARpy model will be implemented on the VEGA platform (https://www.vegahub.eu) and will be freely available.

Keywords: In vitro micronucleus assay In silico models SARpy VEGAHUB Structural alerts

⁎ Corresponding author at: Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy. E-mail address: [email protected] (D. Baderna).

https://doi.org/10.1016/j.jhazmat.2019.121638 Received 22 September 2019; Received in revised form 3 November 2019; Accepted 7 November 2019 0304-3894/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Diego Baderna, et al., Journal of Hazardous Materials, https://doi.org/10.1016/j.jhazmat.2019.121638

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1. Introduction

2. Materials and methods

Genotoxicity is the ability of an agent to cause DNA damage as an alteration in the structure or information content of genetic material in cells, including those that are permanently transmissible (EFSA, 2011; Fioravanzo et al., 2012; Seukep et al., 2014; Pellevoisin et al., 2018). The evaluation of genotoxic potential is a fundamental part of the risk and safety assessment process of chemicals used in the industry (such as cosmetic, personal care products, food, feed and human and veterinary medicines) or acting as environmental contaminants due to the relevance of the potential health effects of genotoxicants (EFSA, 2011; Bolognesi et al., 2017; Corvi and Madia, 2017). Indeed, DNA damages and genetic alterations could lead to serious adverse effects such as cardiovascular and neurological diseases, endocrine-related disorders, reproductive diseases and cancer (De Flora and Izzotti, 2007; Bonassi et al., 2011; Magnander and Elmroth, 2012; İpek et al., 2017; Makvandi et al., 2017; Basu, 2018; Usman and Volpi, 2018). Several genotoxicity assays are already available to investigate the DNA damaging potential of chemicals and physical agents both in vivo (OECD, 1997a; 2013; 2016a; 2016b) and in vitro (OECD, 1997b; 2016c; 2016d; 2016e). Among the in vitro methods, the micronucleus assay with mammalian cells (MNvit) is one of the most used (Corvi and Madia, 2017). Micronuclei are small cytoplasmic bodies originated from chromosome fragments or whole chromosomes that were unable to migrate to the poles during anaphase in cell division (Fenech 2000; Doherty, 2012; OECD, 2016e). The assay is typically performed with human or rodent cell lines or primary cell cultures and it can discriminate between clastogens and aneugens (Fenech, 2007; Corvi et al., 2008; EFSA, 2011). Moreover, the MNvit can be used as an alternative to the in vitro chromosome aberration test that is time-consuming and advanced skills and training are required to correctly perform the analysis (Miller et al., 1997; Corvi et al., 2008; Heddle et al., 2011). In addition to the testing methods, several in silico models have been developed to assess the genotoxic potential of chemicals. Most of the models focus on the bacterial reverse mutation assay (Ames test) and carcinogenicity, while only a limited number of tools are available for the other tests, including the in vivo or in vitro micronucleus test (Greene et al., 1999; Snyder et al., 2004; Hayashi et al., 2005; Benfenati et al., 2009; Benigni et al., 2010; Fjodorova et al., 2010; Bakhtyari et al., 2013; Golbamaki and Benfenati, 2016; Zhang et al., 2017; Fan et al., 2018; Jean-Quartier et al., 2018; Toropov et al., 2018; Morita et al., 2019). A major boost to the development of in silico methods for genotoxicity was provided by the M7 guideline on assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit the potential carcinogenic risk of the international conference on harmonization (ICH). In this guideline, the SAR analysis and the in silico models are accepted for the assessment of the mutagenic potential of impurities in the pharmaceutical sector (ICH, 2014). The guideline also suggests performing the assessment using both expert rule-based system and statistics-based tools because the two approaches are considered complementary and can lead to a more precise assessment (Sutter et al., 2013, ICH, 2014; Aiba née Kaneko et al., 2015). The former approaches are based on expert-based defined structural alerts (SAs) and chemical substructures flagging for toxicity, while the latter results from the application of mathematical methods (i.e. machine learning) on a training set of experimental data. In the present work, we developed six in silico models for the prediction of genotoxicity induced by organic compounds as reflected by the in vitro micronucleus assay. Both expert rule-based and statisticalbased models were derived as suggested by ICHM7 guidelines. All the developed models were evaluated for the predictively on an external set of data, then the best solution was proposed for real-life application. A discussion on the mechanistic link between fragments and the toxicological endpoint is also proposed.

2.1. Data sets and data curation Experimental data were retrieved from literature (See S1 in supplementary materials), SCCS and EFSA opinions, ECVAM guidelines and eChemPortal inventory (OECD, 2019). Data were selected according to their adherence to the OECD 487 guideline considering only data related to experiments conducted with human peripheral blood lymphocytes, CHO, V79, CHL/IU, L5178Y, TK6, HT29, Caco-2, HepaRG, HepG2, A549 and primary Syrian Hamster Embryo cells as cell lines listed in the guideline. Regarding the use of post-mitochondrial liver fraction (S9), we included data from experiments with S9 and positive data from studies without S9 metabolic activation. We carefully revised the sources in order to ensure their quality and reliability and, to our knowledge, most of the selected data can be classified with a Klimisch score of 1 due to the facts that the studies were done with test procedure in accordance with validated standard methods (Klimisch et al., 1997). A KNIME workflow for chemical data retrieval and quality checking (Gadaleta et al., 2018) was used to automatically retrieve SMILES of the chemicals. For each chemical, the structural data are retrieved automatically from four sound web-based chemo-informatic sources (National Cancer Institute (NCI) Chemical Identifier Resolver (CIR) < – > (NCI/CADD, 2019), the U.S. Environmental Protection Agency (EPA) CompTox Chemistry Dashboard (Williams et al., 2017), PubChem (NCBI, 2019) and ChemIDPlus (NIH, 2019)) using chemical name and CAS as input. Structural data that are consistent among all the web databases are further cleaned to remove inorganic and organometallic compounds, isomeric mixtures, polymers and data related to mixtures of chemicals. In the end, the neutralized SMILES are converted into a standardized QSAR-ready format using the OpenBabel KNIME implementation to generate Canonical SMILES. The data collection and curation resulted in a final dataset of 380 organic chemicals with binary (i.e., genotoxicant/non-genotoxicant) MNvit experimental data, including 153 inactive (Σ 42%) and 227 (Σ 58%) active chemicals. The curated structures were then split into training and test sets according to their structural similarity. Briefly, fingerprints of the structures in the dataset were calculated using the RDKit Fingerprint node and Morgan fingerprints as implemented in KNIME (version 4.0.2). The structural similarity was estimated with the Distance Matrix KNIME node using the Tanimoto function. A k-Medoids clustering algorithm was applied on the distance matrix followed by a partitioning node to split the dataset in training and test sets, with an analogue chemical distribution, in an 80:20 ratio. The training set includes 293 chemicals while 87 chemicals are listed in the test set. The “Organic functional groups, Norbert Haider (checkmol)” profiler from the OECD QSAR Toolbox (v. 4.3.1, https://qsartoolbox.org/) was used to classify chemicals based on the presence of 204 functional groups as recognized by the Checkmol program (Haider, 2010). 2.2. Development of the fragment-based model The structural alerts (SAs) related to MNvit were extracted using the software SARpy (SAR in python, version 1.0), a freely available software in the VEGA HUB platform (www.vegahub.eu). SARpy automatically extracts sets of rules by generating and selecting substructures based on their prediction performance on a training set used as input (Ferrari et al., 2013). The selection is made with a 3-steps process: - the fragmentation of chemicals to extract all the substructures within a user-customizable size range, - a validation step to derive the predictive power of each fragment through the analysis of the correlation between the occurrence of each molecular substructure and the experimental activity of the compounds containing the fragment, 2

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Table 1 SARpy settings for fragments extraction. Extraction ID

Structural Alerts Options

Target Activity Class

Single Alert Precision

1 2 3 4 5 6

Atom number: 3 to 20 Minimal occurrence: 3

Positive + Negative

High Accuracy High Coverage Maximal Accuracy Balanced Performance Maximal Accuracy Balanced Performance

Only Positive Only Negative

and the third step (prediction step) identifies a restricted set of important descriptors leading to a good prediction of the response variables (Genuer et al., 2015). This method reduced the initial pool of descriptors to an optimal pool of 23 descriptors (see S2 in supplementary materials).

- a selection step in which the most predictive fragments are listed in the form of rules “IF contains SA THEN apply activity label’’ (Ferrari et al., 2013; Lombardo et al., 2014). SARpy was applied to extract SAs for both positive (genotoxicant) and negative (non-genotoxicant) activity. Various runs with different settings for alert precision were done to obtain fragments with high accuracy or with high coverage (Table 1). Likelihood ratio (LR) and accuracy were used as statistical parameters to define the precision of each fragment. LR is the likelihood that a given test result would be expected in a compound with the target activity (true matching) compared to the likelihood that same result would be expected in a compound without the target activity (false matching):

LRpositive =

2.3.2. Machine-learning methods Five machine-learning methods were used in this study to build statistical classification models: decision trees (DTs), Random Forest (RF), Support Vector Machine (SVM), Multilayer perceptron artificial neural network (MLP-ANN), and K-nearest neighbor (k-NN). DTs, RF, SVM and MLP-ANN were developed within the KNIME platform applied on both the full list of descriptors and on the restricted pool of VSURF descriptors. k-NN was built up with the istKNN software (version 0.9.3, Kode, 2018), a commercial Java tool.

negatives positives TP TP and LRnegative = × × FP FP negatives positives

2.3.2.1. Decision tree. Decision trees (DTs) (Quinlan, 1986, 1987) are flowchart-like structures formed by a series of nodes hierarchically connected so that several child nodes branch from a common parent node. Starting from the root node, a query compound encounters a series of nodes that represent a “test” on a particular attribute. Based on the outcome of the test, the algorithm continues to one of the child nodes, where a new test is performed, until a leaf node is reached that represents a class to which the query is assigned. The “Decision Tree Learner” node implemented in KNIME was used for model derivation. “Minimal Description Length” (MDL) pruning method was used to reduces tree size and avoids overfitting and increase prediction quality.

where TP are experimentally positive compounds correctly predicted as positive (true positives), FP are experimentally negative compounds but wrongly predicted as positive (false positives), TN are experimentally negative compounds correctly predicted as negative (true negative) and FN are experimentally positive compounds wrongly predicted as negative (false negative). The LR ranges from 1 to infinity and higher value highlighted the more predictive accuracy for a specific fragment (Ferrari et al., 2013; Manganelli et al., 2019). Accuracy was calculated as the ratio between the number of correct matching of a fragment in the training set and the total occurrence:

Accuracypositive =

TP TN and Accuracynegative = occurrences occurrences

2.3.2.2. Random forest. Random Forests (RF) (Breiman, 2001) operate by combining a number of DTs in an ensemble model. In training the models, the whole training set is used. However, to enforce diversity, only randomly generated subsets of descriptors are used for each DT. The output of the ensemble model is represented by the class that is the mode of the classes. The “Ensample Learner” node implemented in KNIME was used for derived RF models. The class distribution in each tree was artificially altered so that classes are represented equally in each tree < – > (Chen et al., 2004). The number of attributes randomly selected in each tree was equal to the square root of the initial number of variables, while the number of trees was equal to 50.

Only structural alerts with LR higher than 2 were considered for the development of the model to obtain a more accurate model minimizing the occurrence of wrong predictions. Prediction of the genotoxic activity of chemical is based on selected structural alerts, applying the rule “IF contains SA THEN apply activity label’’ and considering their LR values. For the definition of the applicability domain of this, no activity is predicted if no structural alerts are present in the target chemical. 2.3. Development of statistical based models 2.3.1. Calculation and selection of descriptors Molecular descriptors were calculated for each compound using Dragon software < – > (v. 7.0.8, Kode srl, 2017) (Talete Srl, Milano, Italy). Constant and semi-constant descriptors and those having at least one missing value were removed. In case of highly correlated pairs of descriptors, only one was retained and the descriptor showing the highest pair correlation with all the other descriptors was removed. This led to a final list of 1911 descriptors. Optimal subsets of descriptors for modeling were obtained with the R package VSURF. The algorithm was applied to TrS chemicals and consists of a three-step variable selection based on the Random Forest (RF) algorithm. The first step eliminates irrelevant descriptors according to the RF score of importance and a user-defined threshold. The second step finds important descriptors closely related to the response variable (interpretation step),

2.3.2.3. Support Vector Machine. Support Vector Machine (SVM) < – > (Vapnik and Lerner, 1963) is a machine learning method that aims to define a decision hyperplane linearly separating two classes of compounds. Such hyperplane is defined by maximizing the margin between the hyperplane and the closest compounds (i.e., support vectors) included in each class. In case no linear separation is possible, to account for misclassification, a soft-margin classifier is used that penalizes compounds that cannot be correctly classified. SVM, as implemented in the “SVM Learner” KNIME node, was applied. We left standard parameters unmodified. 2.3.2.4. Multilayer perceptron - Artificial Neural Network. Artificial Neural Networks (ANNs) (Jain et al., 1996) are machine learning 3

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2.6. Evaluation of the models

methods based on an architecture of interconnected units, called neurons, organized in layers. In Multi-Layer Perceptrons (MLP), neurons in the input layer forward input descriptors to neurons in one or more hidden layers that compute a weighted linear combination of input values. The output layer generates a single classification response. Weights assigned to network connections are gradually adjusted during the training of the MLP-ANNs via is a feedback mechanism based on the backward propagation of prediction error. A standard architecture as implemented in the “MultiLayerPerceptron (MLP) Predictor” KNIME node was applied with one input layer, one hidden layer, and one output layer. The number of neurons in the input layer is equal to the number of descriptors used for the derivation of each model, while in the output layer there is one neuron.

The performance of the models can be arranged in a confusion matrix (Kohavi & Provost, 1998) as in Table 3 that identifies the number of substances classified correctly (TPs, TNs) or not (FNs, FPs). Cooper statistics (Cooper et al., 1979) based on the confusion matrix for classification models were calculated:

− Accuracy =

2.3.2.5. K-nearest neighbour. The k-NN approach is based on the identification of a certain (k) number of neighbours for the target compound that are used to provide a prediction of the endpoint. The selection accounts for structural similarity < – > (Manganaro et al., 2016). For the development of our model, we used the istKNN software < – > (Kode, 2019), a commercial Java tool based on the CDK (Steinbeck et al., 2003) and VEGA libraries (VEGA, 2019). This tool evaluates the predictive power by leave-one-out (LOO) approach and it was previously used to develop k-NN models closely related to the read-across approach done by the human expert (Manganaro et al., 2016; Como et al., 2017; Benfenati et al., 2018; Raitano et al., 2018). The final prediction is derived from a weighted consensus of the experimental values among the more similar molecules using the similarity value of each molecule as weight. The similarity index currently used in the VEGA platform was used (Floris et al., 2014; Floris and Olla, 2018). We used the istKNN software in the batch mode to automatically build several models with different settings to increase the predictive role of chemicals with the higher similarity values (Manganaro et al., 2016): the number of neighbors (k) ranged from 3 to 10, the minimal similarity thresholds for multiple or single comparisons were set to 0.5 and 0.7 respectively, and the enhance factor (EF) ranged from 1 to 5. This batch process resulted in 1800 models as output that were evaluated accounting for the best compromise in terms of predictive accuracy and the prediction coverage (i.e. the number of compounds predicted). Only few models showing the most interesting and promising statistics were selected for the external validation performed on the test set. Concerning the applicability domain, the genotoxic activity of the chemical is predicted if at least a molecule with a similarity index of 0.7 is present in the dataset.

(TP + TN ) (TP + TN + FP + FN )

− Sensitivity =

TP (TP + FN )

− Specificity =

TN (TN + FP )

− Balanced Accuracy (BACC ) =

(Sensitivity + Specificity ) 2

Matthews Correlation Coefficient (MCC) (Matthews, 1975) was also calculated to better estimate goodness of classification when the populations of the two classes are numerically unbalanced:

− Matthews Correlation Coefficient (MCC ) (TPxTN ) − (FPxFN ) = (TP + FP )(TP + FN )(TN + FP )(TN + FN ) Additional statistical parameters were calculated to evaluate the performances of CA-MNT profiler and SARpy model alone or in combination:

− Positive predictive value (PPV ) =

TP (TP + FP )

− Negative predictive value (NPV ) =

FN (TN + FP )

− False positive rate (FPR) =

− False omission rate (FOR) =

FN (TN + FP )

− False negative rate (FNR) =

FN (TP + FN )

− False discovery rate (FDR) =

FP (TP + FP )

− Unpredicted rate (UPR) =

2.4. Comparison with the OECD QSAR profiler for chromosomal aberrations and micronuclei

TN (TN + FN )

number of unpredicted compounds number of compounds in the dataset

− True positive discory rate (TPDR) =

The “DNA alerts for CA and MNT by OASIS (version 1.2) (CA-MNT)” profiler (LMC, 2017) of the OECD QSAR toolbox was used to analyse our dataset. This profiler investigates the presence of 85 structural alerts responsible for interaction with DNA resulting in chromosomal aberrations and micronuclei induction through eight mechanistic domains. The predictive performances were then compared to the ones obtained with the active (S+) or inactive (S-) SAs extracted with SARpy.

− True negative discory rate (TNDR) =

TP positive compounds in the dataset

TN negative compounds in the dataset

2.7. Analysis of the structural alerts The active SAs obtained with SARpy were analysed with several profilers for genotoxicity available on the OECD QSAR Toolbox to derive information on their potential mechanisms of action (MOAs). The following profilers were used on the list of active SAs: CA-MNT (LMC, 2017), Protein binding alerts for Chromosomal aberration by OASIS (LMC, 2018a), in vitro mutagenicity (Ames test) alerts by ISS (Benigni and Bossa, 2011), in vivo mutagenicity (Micronucleus) alerts by ISS (Benigni et al., 2012), DNA binding by OASIS (Mekenyan et al., 2004; Serafimova et al., 2007), DNA binding by OECD (OECD, 2016f) and DNA alerts for AMES by OASIS (LMC, 2018b).

2.5. Combination of profilers Several combinations of SARpy model and the CA-MNT profiler were defined to evaluate if an integration strategy could result in enhanced performance. Table 2 includes the combinations tested on the entire dataset (n = 380). 4

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Table 2 Combination of profilers used on the entire MNvit dataset. Acronyms

First method

Second method on unpredicted compounds from first method

Third method on unpredicted compounds from second method

CA-MNT/S+ CA-MNT/SCA-MNT/S+/SCA-MNT/S-/S+ CA-MNT/SARpy

CA-MNT

SARpy SARpy SARpy SARpy SARpy

SARpy non-genotoxic SAs SARpy genotoxic SAs -

genotoxic SAs non-genotoxic SAs genotoxic SAs non-genotoxic SAs genotoxic and non-genotoxic SAs

3.2. Identification of structural alerts

Table 3 The scheme of the confusion matrix.

Predicted Positive Predicted Negative

Positive

Negative

True Positive (TP) False Negative (FN)

False Positive (FP) True Negative (TN)

A total of 164 fragments were extracted with SARpy, of which 138 were selected as components of the fragment-based models, keeping the alerts with likelihood ratio values higher than 2. The selection includes 82 genotoxic SAs (Table S3) and 56 non-genotoxic SAs (Table S4). The excluded fragments are collected in Table S5 of the Supplementary Materials.

3. Results 3.1. Dataset description and chemical domain

3.3. Models results

A retrospective analysis was done to understand the chemical coverage of the dataset used in this work. The dataset is available in the Supplementary materials. The final dataset includes active substances (drugs, biocides; Σ 36%), industrial agents (reagents, solvents; Σ 27%), cosmetic and PCPs ingredients (Σ 16%), laboratory use products (Σ 7%), dyes (Σ 6%) and other products (food contact materials and additives, food contaminants, natural products Σ 8% in total) (Fig. 1A). Data were results of in vitro studies done with 7 of the 12 cell lines listed in the OECD guidelines and the human peripheral blood lymphocytes and V79 cells were the most used cell models (Fig. 1B). 99 of the 204 classes included in the Checkmol profiler were found in our dataset, highlighting its heterogenicity from a chemical point of view (Fig. 1C). Considering that a compound can be present in more than one chemical class, the most frequent classes were aromatic compounds, heterocyclic compounds, amines, carboxylic acid derivatives, and hydroxyl compounds.

In this study, one molecular fingerprint and five machine‐learning methods were applied to develop binary classification models for the in vitro micronucleus induction as a marker of genotoxicity. The models were validated using the external set validation and the prediction statistics are shown in Table 4. In building the models, the accuracy values were higher than 70% in all the models. As expected, sensitivity and specificity were higher in RF respect to the others, but all the models gave good statistical performance. Considering the overall performances on the training set, RF was the best classifier, followed by MLP and SARpy. Also the performances on the test set were good, especially for SARpy and RF models as highlighted by their MCC values greater than 0.6 which indicate that classifiers have good performances on the identification of both genotoxic and non-genotoxic compounds. Accounting of the overall model performance, SARPY was selected as the best candidate to develop the classification model for the prediction of genotoxicity based on micronuclei induction in vitro.

Fig. 1. Dataset description. A) Uses of the selected chemicals, B) cell lines used in the experiments, C) occurrence of functional groups. 5

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Table 4 Performance of the developed models (training set = 293; test set = 87). SARpy

k-NN

SVM

RF

MLP

DT

PARAMETER

TRAINING

TEST

TRAINING

TEST

TRAINING

TEST

TRAINING

TEST

TRAINING

TEST

TRAINING

TEST

TN TP FN FP UNP ACC SEN SPE BACC MCC MISSING (%)

68 140 3 28 54 0,870 0,979 0,708 0,844 0,737 18,43

16 47 1 10 13 0,851 0,979 0,615 0,797 0,675 14,94

73 135 35 48 2 0,715 0,794 0,603 0,699 0,405 0,68

19 44 12 12 0 0,724 0,786 0,613 0,699 0,399 0,00

78 154 17 44 0 0,792 0,901 0,639 0,770 0,569 0

16 49 7 15 0 0,747 0,875 0,516 0,696 0,425 0

122 171 0 0 0 1 1 1 1 1 0

23 41 15 8 0 0,736 0,732 0,742 0,737 0,781 0

86 161 10 15 21 0,908 0,942 0,851 0,897 0,802 7,17

16 46 10 15 0 0,713 0,821 0,516 0,669 0,353 0

88 151 20 34 0 0,816 0,883 0,721 0,802 0,618 0

21 41 15 10 0 0,713 0,732 0,677 0,705 0,398 0

TN = true negative, TP = true positive, FN = false negative, FP = false positive, UNP = unpredicted, ACC = accuracy, SEN = sensitivity; SPE = specificity, BACC = balanced accuracy, MCC = Mattews correlation coefficient

3.6. Analysis of the active SAs with the OECD profilers

3.4. Comparison with the OECD QSAR profiler for DNA alerts for CA and MNT by OASIS

The genotoxicity profilers included in the OECD QSAR Toolbox (version 4.3.1.) were applied to the active structural alerts extracted by SARpy to elucidate potential MOAs of our fragments (Fig. 2). The highlighted functional groups and the relative MOAs are listed in Table 7.

The whole MNvit data set was analysed with the CA-MNT profiler to predict the activity of the chemicals included in our dataset. Within target molecules, the profiler highlights only the presence of alerts responsible for chromosomal aberrations and micronucleus formations. Unlike SAR tools, the CA-MNT alerts are not recommended to be used directly for prediction purposes, but it can be used as support to readacross (Mekenyan et al., 2007; LMC, 2017). The predictive performances were then compared with the ones obtained using only active or inactive SAs from SARpy (Table 5). The CA-MNT gained the best performances in terms of positive predictive value (PPV) and false discovery rate (FDR) but the profiler had limited coverage of the data as highlighted by the highest number of unpredicted compounds. On the other hand, the use of the active SAs from SARpy led to increased coverage with a good PPV.

4. Discussion The evaluation of genotoxicity plays a key role in the safety assessment of chemicals due to the relevance of the potential health effects of genotoxicants: indeed, these chemicals can induce genetic damages in cells leading to mutations that may progress to teratogenic or carcinogenic effects. For this reason, genotoxicity testing is fundamental in the drug development process (FDA, 2012; Galloway, 2017) and is required in different regulatory frameworks including REACH (Annexes VII to X), the Cosmetic regulation (EC, 2009; SCCS, 2018), agrochemicals (Booth et al., 2017) and food contact materials < – > (EFSA, 2008). Different in vitro and in vivo methodologies have been set up to assess genotoxicity such as the Ames test, the chromosomal aberration, cell gene mutation tests, and the micronucleus assay. More recently, several in silico tools for the genotoxicity assessment were developed and applied worldwide, especially in the pharmaceutical sectors after the release, in 2014, of the ICH M7 guideline, in which the SAR analysis and the in silico models have been cleared and approved for the assessment of the mutagenic potential of impurities (ICH, 2014). The genotoxicity assessment in silico is generally done by using expert rule-based system with structural alerts or statistics-based tools. Combining both the approaches, it is possible to obtain a more precise assessment because the two strategies are complementary (Sutter et al., 2013, ICH, 2014; Aiba née Kaneko et al., 2015). In the present work, we developed 5 statistical and 1 fragmentbased models for the prediction of genotoxicity as reflected by the in vitro micronucleus assay.

3.5. Performances of combined profilers Table 6 shows the performances of combined profilers on the whole MNvit dataset to enhance the predictive power of the approach. Using CA-MNT profiler with inactive SAs from SARpy resulted in the best value for PPV, FPR, FDR, and specificity with a lower number of unpredicted compounds compared to the use of CA-MNT only. The best values for FNR, TPDR, and sensitivity were reached with the combination of CA-MNT with all the SAs from SARpy or with the SARpy model, contemporarily leading to higher coverage. Table 5 Comparison of the performances of the genotoxicity profiler based on MNvit: CA-MNT, positive (S+) and negative (S-) SAs from SARpy and SARpy model. Best values are bold. PARAMETER

CA-MNT

S+

S-

SARpy

TP FP TN FN UNP PPV NPV FOR FDR UPR TPDR TNDR

88 14 0 0 278 0,863 N.A. N.A. 0,137 0,732 0,388 N.A.

187 65 0 0 128 0,742 N.A. N.A. 0,258 0,337 0,824 N.A.

0 0 101 52 227 N.A. 0,660 0,340 N.A. 0,597 N.A. 0,660

187 38 84 4 67 0,831 0,948 0,045 0,169 0,176 0,824 0,549

4.1. Chemical diversity analysis of data set In model building, the chemical heterogenicity of a data set is a crucial feature: indeed, an excessively homogeneous dataset could lead to a model that cannot be easily applied to new, different molecules, resulting in poor prediction performances (Jiang et al., 2019). In our study, 380 organic chemicals with binary MNvit experimental data were collected from the literature according to the criteria described in the OECD 487 guideline for the in vitro micronucleus. The collection, slightly imbalanced toward genotoxicants (Σ 60% of the set), includes chemicals from all the commercial and production sectors for

*N.A. = not applicable. 6

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Table 6 Performances of the combination of profilers. Best values are bold. PARAMETER

CA-MNT

CA-MNT/S+

CA-MNT/S-

SARpy

CA-MNT/S+/S-

CA-MNT/S-/S+

CA-MNT/ SARpy

TP FP TN FN UNP PPV NPV FPR FOR FNR FDR UPR TPDR TNDR ACC SENS SPEC BACC MCC

88 14 0 0 278 0,863 N.A. N.A. N.A. N.A. 0,137 0,732 0,388 N.A. N.A. N.A. N.A. N.A. N.A.

197 69 0 0 114 0,741 N.A. N.A. N.A. N.A. 0,259 0,300 0,868 N.A. N.A. N.A. N.A. N.A. N.A.

88 14 94 37 147 0,863 0,718 0,130 0,282 0,296 0,137 0,387 0,388 0,614 0,781 0,704 0,870 0,787 0,577

187 38 84 4 67 0,831 0,955 0,311 0,045 0,021 0,169 0,176 0,824 0,549 0,866 0,979 0,689 0,834 0,724

197 69 55 3 56 0,741 0,948 0,556 0,052 0,015 0,259 0,147 0,868 0,359 0,778 0,985 0,444 0,714 0,543

163 30 94 37 56 0,845 0,718 0,242 0,282 0,185 0,155 0,147 0,718 0,614 0,793 0,815 0,758 0,787 0,568

197 44 80 3 56 0,817 0,964 0,355 0,036 0,015 0,183 0,147 0,868 0,523 0,855 0,985 0,645 0,815 0,702

*N.A. = not applicable.

the validation set by the SARpy model is significantly higher than the one obtained with the RF, correctly identifying a greater number of genotoxic chemicals and considerably limiting the number of false negatives (genotoxicants predicted as non-genotoxicants). This last feature is particularly relevant for the models predicting genotoxicity as a lower sensitivity leads to a greater number of unidentified genotoxic compounds and, consequently, a greater risk for the population, especially considering the potential hazardousness of the induce adverse health effects, such as cancer.

which genotoxicity testing is required including drugs, plant protection products, industrial reagents, and PCPs ingredients. We investigated also the chemical domain using the Checkmol profiler of the OECD QSAR Toolbox (Haider, 2010), revealing that our dataset contains aromatic compounds, heterocyclic compounds, amines, carboxylic acid derivatives, and hydroxyl compounds and other 90 functional groups. The combination of these two pieces of information on dataset features testifies the heterogeneity of the data collected. On the other hand, inorganic and organometallic compounds and mixtures were removed from the dataset, so our models cannot provide predictions about the genotoxicity of these compounds, limiting the applicability domain of the proposed tools.

4.3. Comparison with existing models Limited tools are available to predict the genotoxicity based on the induction of micronuclei and most of them target the in vivo micronucleus test (Benigni et al., 2010; Mekenyan et al., 2012; Kamath et al., 2015; Fan et al., 2018). Other commercial suites predict the in vitro genotoxicity based on Ames test or chromosomal aberrations on mammalian cells (Vuorinen et al., 2017). This last assay is intended to measure clastogenic events in cells while the in vitro micronucleus test detects the aneuploidy” as stated in the OECD 487 guideline. To our actual knowledge, only the “DNA alerts for CA and MNT by OASIS” profiler (CA-MNT) included in the OECD QSAR toolbox was developed with data derived from in vitro chromosomal aberration test, including MNvit (Mekenyan et al., 2007, LMC, 2017). This profiler analyses the structures of chemical compounds in search of 85 SAs associated with genotoxicity as chromosomal aberrations. This tool was chosen as a comparison because, in our opinion, it represents the best comparison currently available given the substantial compatibility of the target endpoint and the methodology used (fragments). The CA-MNT profiler highlights only the presence of alerts responsible for chromosomal aberrations and micronucleus formations, while our proposed approach considers alerts for both genotoxicity and non-genotoxicity. This is an important difference because the inclusion of more than 50 fragments inherent to non-genotoxicity is useful to detect non-hazardous compounds and to increase the overall coverage of the model. Although the CA-MNT profiler has the best performances in terms of PPV and FDR, the ability to identify a genotoxicant, as the ratio between the predictive active compounds and the active compound in the dataset, is very low: the profiler is precise but identifies only the 38% of the genotoxicants in our dataset. Moreover, providing information only for 27% of the dataset, the coverage of the approach is also low. The unpredicted values include both inactive compounds but also

4.2. Comparison of the developed models Different fingerprints and machine learning approaches were used to classify genotoxicants according to the outcomes of the in vitro micronucleus assays. Even if the methods had different predictive power, good performances were achieved with all the models as shown in Table 4. The best performances in model building were obtained by the RF, MLP and SARpy methods: the balanced accuracy (BACC), calculated to better describe the accuracy of imbalanced dataset, was 1, 0.897 and 0.844 respectively while the Matthews Correlation Coefficient (MCC), as measure of quality of binary classifications, was 1, 0.802 and 0.737. The value of both the parameters suggests that the selected models have good performances on the identification of both genotoxic and non-genotoxic compounds in the training set. BACC and MCC were considered to select the most promising tools in the model validation: SARpy and RF models were the most powerful tools, respectively with a BACC value of 0.797 and 0.737 and a MCC value of 0.675 and 0.781. The MLP reached good performance in terms of BACC (0.669) but, due to low specificity, the MCC was low (0.353). According to the overall prediction performance, RF and SARpy models were the top classification models. The robustness and predictive capability of RF were higher and gave the best prediction also on the validation set. The performances of the fragment-based model were slightly worse in terms of MCC due to a lower sensitivity compared to the RF but the BACC and the sensitivity were higher. The aim of our work was to create a classifier able to efficiently discriminate among genotoxic and non-genotoxic compounds but, contemporary, paying attention to sensitivity as a measure of the ability to correctly identify DNA damaging agents. The sensitivity achieved on 7

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Fig. 2. Matching of the structural alerts from the “DNA alerts for CA and MNT” and “Protein binding alerts for Chromosomal aberration” profilers by OASIS in the active structural alerts of the SARpy model. The blue shading shows the total or partial overlap of the OASIS SAs in the structures obtained with SARpy.

4.4. Enhancing the predictive power by the combination of CA-MNT and SAs

genotoxicants that are unidentified. Both these features constitute a strong limitation of this approach because, in our conditions, the profiler is precise but can be applied to a limited number of compounds. The use of only active fragments extracted from SARpy, in analogy to what performed by CA-MNT, leads to a considerable increase in coverage and the PPV remains however high (0.742), highlighting a good capacity of the approach. Profiling the dataset with the SARpy model, which considers both active and inactive SAs, allows us the prediction of a higher number of compounds (313 chemicals, 82%) still maintaining good performance in the identification of genotoxicants.

Several combinations between the CA-MNT profiler and SAs extracted with SARpy were tested to increase the performance of the singular model. In the combined strategy, the CA-MNT was used as the first step and the unpredicted compounds were then subjected to 1 or 2 further steps of analysis with the SAs extracted with SARpy or with the entire SARpy model. The main purpose of the combination was to increase the low coverage of the CA-MNT profiler, which we highlighted as the main limitation of the approach, trying to preserve its precision.

8

9

HALOALKANES Vicinal Dihaloalkanes/Halogenated Vicinal Hydrocarbons/1,2-dihaloalkanes

Polycyclic (PAHs) and heterocyclic (HACs) aromatic hydrocarbon

POLYCYCLIC (PAHs) AND HETEROCYCLIC (HACs) AROMATIC HYDROCARBONS Polycyclic Aromatic Hydrocarbons and Naphthalenediimide Derivatives

α, β - Unsaturated Carboxylic Acids and Esters

α, β - UNSATURATED COMPOUNDS α, β - Unsaturated Carbonyls, Aldehydes and Related Compounds

Formamides

Ethylenediamines (including piperazine)

15, 55 15, 55

SN2 > > DNA alkylation SN2 > > Internal SN2 reaction with aziridinium and/or cyclic sulfonium ion formation (enzymatic) SN2 > > Nucleophilic type substitution together with ring-opening of an episulfonium ion intermediate SN2 > > Episulfonium Ion Formation

8

SN1 > > Alkylation after metabolically formed carbenium ion species SN2 > > Alkylation, direct acting epoxides and related after P450-mediated metabolic activation Michael addition > > P450 Mediated Activation to Quinones and Quinone-type Chemicals SN1 > > Carbenium Ion Formation

15, 55

15, 55

8

8

15, 55

80

41

41

6, 13, 38, 44, 49, 66 6, 13,38, 44, 49, 66

2, 36 2, 36 2, 36

OASIS

41

41

52 2, 38, 66

PB-CA

Alerts for DNA binding

41

38, 44, 49, 66

38, 44, 49, 66

2, 36 2, 36 2, 36

Alerts from CA and MNT by OASIS CA-MNT

Non-covalent interaction > > DNA intercalation

AN2 > > Nucleophilic addition to alpha, beta-unsaturated carbonyl compounds AN2 > > Schiff base formation; AN2 > > Michael addition to activated double bonds Michael addition > > Polarised Alkenes-Michael addition AN2 > > Michael addition to alpha, beta-unsaturated acids and esters Michael addition > > Polarised Alkenes-Michael addition

SN1 > > Nitrenium Ion formation; SN1 > > Nitrenium Ion formation; SN1 > > Iminium Ion Formation AN2 > > Michael addition to the quinoid type structures AN2 > > Michael addition to the quinoid type structure Schiff base formers > > Chemicals Activated by P450 to Glyoxal Schiff base formers > > Chemicals Activated by P450 to Glyoxal Acylation > > P450 Mediated Activation to Isocyanates or Isothiocyanates

Primary aromatic amines

Secondary aromatic amines Tertiary aromatic amines Aliphatic tertiary amines N-Subsituted Aromatic Amines Substituted Anilines Ethanolamines (including morpholine)

SN1 > > Nucleophilic attack after nitrenium ion formation SN1 > > Nitrenium Ion formation;

Single-Ring Substituted Primary Aromatic Amines

MOA

Non-covalent interaction > > DNA intercalation Radical mechanism via ROS formation (indirect) SN1 > > Nucleophilic attack after metabolic nitrenium ion formation Radical mechanism via ROS formation (indirect)

AMINES Fused-Ring Primary Aromatic Amines

Functional groups

Table 7 Functional groups and mechanisms of Action of active SARpy SAs according to the OECD QSAR Toolbox profilers.

15, 55

8, 22

8, 22

80

41

1, 57

72

70

2, 6, 13, 20, 36, 66, 74 3, 44, 49, 52 14, 19, 38, 46 78

OECD

15, 55

15, 55

41

41

6

6

2, 36 2, 36 2, 36

DNA alerts for AMES by OASIS

(continued on next page)

in vivo MN (ISS)

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Epoxides and Aziridines Quinones and Trihydroxybenzenes

Mono aldehydes Sulfonates and Sulfates

H-acceptor-path3-H-acceptor

Aromatic nitro and nitroso OTHER FUNCTIONAL GROUPS Arenes

N-Nitroso Compounds

N-methylol derivatives

Nitrophenols, Nitrophenyl Ethers and Nitrobenzoic Acids

NITROCOMPOUNDS Nitroaniline Derivatives

Alkyl phenols

Michael addition > > P450 Mediated Activation to Quinones and Quinone-type Chemical non-covalent binding to DNA or proteins as a result of the presence of two bonded atoms connecting two hydrogen bond acceptors Schiff base formers > > Direct Acting Schiff Base Formers SN2 > > Alkylation, nucleophilic substitution at sp3carbon atom SN2 > > SN2 at an sp3 Carbon atom SN2 > > Alkylation, direct acting epoxides and related AN2 > > Michael-type addition, quinoid structures Non-covalent interaction > > DNA intercalation Radical mechanism via ROS formation (indirect)

SN1 > > Nucleophilic attack after reduction and nitrenium ion formation AN2 > > Schiff base formation by aldehyde formed after metabolic activation Schiff base formers > > Chemicals Activated by P450 to Mono-aldehydes SN1 > > Nucleophilic attack after carbenium ion formation SN1 > > Nucleophilic attack after nitrosonium cation formation SN1 > > Carbenium Ion Formation SN2 > > Nitrosation SN1 > > Nitrenium Ion formation

Radical mechanism via ROS formation (indirect) SN1 > > Nucleophilic attack after reduction and nitrenium ion formation Radical mechanism via ROS formation (indirect)

Michael addition > > P450 Mediated Activation to Quinones and Quinone-type Chemicals

AN2 > > Michael addition to the quinoid type structures

AN2 > > Schiff base formation by aldehyde formed after metabolic activation Radical mechanism via ROS formation (indirect) SN2 > > Acylation involving a leaving group after metabolic activation SN2 > > Nucleophilic substitution at sp3 carbon atom after thiol (glutathione) conjugation

Geminal Polyhaloalkane Derivative

PHENOLS Substituted Phenols

MOA

Functional groups

Table 7 (continued)

34 37 37 37

60

17

17

3, 74 3, 74

Alerts from CA and MNT by OASIS CA-MNT

7, 40, 42, 53, 54, 68

PB-CA

34 37 37 37

21

27, 48

48

60

17

17

21 34

11, 33, 79

82

27, 48 27, 48 12, 17, 75

60

34 37 37 37

21

60

17

17

52 52

64

64

3, 52, 74 3, 52, 74

64 64

64 64

7, 40, 53, 68

64

OECD

DNA alerts for AMES by OASIS

64

OASIS

Alerts for DNA binding

1, 3, 9, 18, 23, 29, 33, 34, 38, 39, 47, 49, 56, 57, 70, 72, 74, 79, 80

in vivo MN (ISS)

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Fig. 3. New active structural alerts for genotoxicity extracted with SARpy.

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genotoxicity assessment in silico. We also evaluated different combination strategies of our model with the OECD profiler for the in vitro chromosomal aberrations to obtain a better genotoxic prediction of the compounds we collected. In fact, the profiler, also based on structural alerts, provided precise predictions but only for a very limited number of compounds. The combination of CA-MNT profiler with the SARpy model was identified as the best solution for the achieved performance in terms of sensitivity. The SARpy model will be implemented on the VEGA platform (https://www.vegahub.eu), an online repository of QSAR models for regulatory purposes, and will be freely available. The other models will be provided on request. The recognition and validation of MNvit as an alternative to the chromosomal aberrations test (Corvi et al., 2008) and the availability of more performing technological solutions for the High Throughput Screening of compounds (Shibai-Ogata et al., 2011; Bryce et al., 2013; Rodrigues, 2018) will boost the availability of genotoxicity data based on MNvit, greatly encouraging the development of new models in the coming years. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Each combination led to a significant increase in coverage and the best results (in terms of UPR) were achieved with the combinations that contained both types of SAs. The CA-MNT/SARpy combination was the best considering, above all, the sensitivity and ability to detect a genotoxicant (TPDR and FNR) and it was also very efficient in the recognition of non-genotoxic compounds as shown by the NPV value. Comparable performances were also recorded for the combos in which the SAs extracted by us are used in series with the profiler but with a greater number of false predictions. In particular, in the combo CAMNT/S-/S+ the number of false negatives is the highest recorded, with a consequent worsening of sensitivity and MCC values. 4.5. Insight the mechanism of action of structural alerts The structural alerts are high chemical reactivity molecular fragments associated with a toxicological response (Enoch and Roberts, 2013; Alves et al., 2016) and they are the core of the SARpy model we are proposing. SAs are widely used in toxicology to flag potential chemical hazards (Enoch and Roberts, 2013; Alves et al., 2016). Using different extraction settings in SARpy, we obtained more than 130 SAs for genotoxicity and non-genotoxicity that were carefully and manually revised to improve their predictive potential increasing, in parallel, the identification of genotoxic and minimizing the possibility of incorrect classifications, especially for false negatives. To obtain information on potential mechanisms of action with which the positive SAs can induce genotoxicity, we analysed the 82 fragments with different genotoxic profilers included in the OECD QSAR Toolbox. The profilers related to in vitro chromosomal aberrations (which includes MNs) founds genotoxicity alerts in 24 of our fragments (Fig. 2) and, in most of the cases, the alerts (i.e. amines and substituted phenols) cover the whole fragments we extracted. The MOAs highlighted by the two profilers were the DNA intercalation, ROS formation, SN1 nucleophilic attack, AN2 nucleophilic and Michael addictions, Schiff base formation, SN2 Nucleophilic type substitution, and DNA alkylation. Additional genotoxicity alerts were found in our fragments by the profilers related to the Ames test, DNA binding and in vivo mutagenicity highlighting the presence of amines, α, β – unsaturated compounds, PAHs and heterocyclic aromatic hydrocarbons, haloalkenes, phenols, nitrocompounds and other functionals groups known to induce genotoxicity through DNA intercalation or alkylation, SN1 and SN2 nucleophilic substitutions, ROS formation or AN2 type Michael additions and Schiff base formation. 24 SAs extracted with SARpy in our conditions (Fig. 3) can be considered new potential alerts for in vitro genotoxicity because no alerts previously associated with genotoxicity were found by the profilers we used.

Acknowledgments This work was developed under the framework of the LIFE+ Projects VERMEER [LIFE16/ENV/IT/000167] and CONCERT REACH [LIFE17/GIE/IT/000461]. Authors would like to thank Chemaxon (http://www.chemaxon.com) for the academic license of Marvin suite used to drawing, displaying and characterizing chemical structures. A special thanks to Matteo Sironi from the graphical office of Istituto di Ricerche Farmacologiche Mario Negri IRCCS for the precious help with the graphical editing. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jhazmat.2019.121638. References Aiba née Kaneko, M., Hirota, M., Kouzuki, H., Mori, M., 2015. Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based system. J. Toxicol. Sci. 40, 77–98. Alves, V.M., Muratov, E.N., Capuzzi, S.J., Politi, R., Low, Y., Braga, R.C., Zakharov, A.V., Sedykh, A., Mokshyna, E., Farag, S., Andrade, C.H., Kuz’min, V.E., Fourches, D., Tropsha, A., 2016. Alarms about structural alerts. Green Chem. 18, 4348–4360. https://doi.org/10.1039/C6GC01492E. Basu, A.K., 2018. DNA Damage, Mutagenesis and Cancer. Int J Mol Sci 19. Bakhtyari, N.G., Raitano, G., Benfenati, E., Martin, T., Young, D., 2013. Comparison of in silico models for prediction of mutagenicity. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 31, 45–66. Benfenati, E., Benigni, R., Demarini, D.M., Helma, C., Kirkland, D., Martin, T.M., Mazzatorta, P., Ouédraogo-Arras, G., Richard, A.M., Schilter, B., Schoonen, W.G.E.J., Snyder, R.D., Yang, C., 2009. Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 27, 57–90. Benfenati, E., Golbamaki, A., Raitano, G., Roncaglioni, A., Manganelli, S., Lemke, F., Norinder, U., Piparo, E.L., Honma, M., Manganaro, A., Gini, G., 2018. A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity$. SAR and QSAR in Environmental Research 29, 591–611. Benigni, R., Bossa, C., Worth, A., 2010. Structural analysis and predictive value of the rodent in vivo micronucleus assay results. Mutagenesis 25, 335–341. Benigni, R., Bossa, C., 2011. Mechanisms of chemical carcinogenicity and mutagenicity: a review with implications for predictive toxicology. Chem. Rev. 111, 2507–2536. Benigni, R., Bossa, C., Tcheremenskaia, O., Battistelli, C.L., Crettaz, P., 2012. The new ISSMIC database on in vivo micronucleus and its role in assessing genotoxicity testing strategies. Mutagenesis 27, 87–92.

5. Conclusions In this study, we proposed several binary classification models for predicting the induction of micronuclei in vitro by organic compounds. To our knowledge no freely available or commercial models specific for MNvit were previously developed. Considering the overall performances, the SARpy model was selected as the best model: the combination of structural alerts related to genotoxic and non-genotoxic activities resulted in reliable and robust predictions of chemicals, with a very low number of false negative compounds. Moreover, this model has better performance than the existing tools modelling the same endpoint in terms of prediction coverage and precision. More than 130 structural alerts for the in vitro genotoxicity were identified, including both active and inactive fragments. 24 active SAs were newly discovered fragments in which no previously known alerts were found by existing profilers. In addition, the SAs for non-genotoxicity can be considered an added value of the proposed approach compared to the existing freely available and fee-base suites for the 12

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