Determination of genotoxic impurities of aromatic aldehydes in pharmaceutical preparations by high performance liquid chromatography after derivatization with N-Cyclohexyl-4-hydrazino-1,8-naphthalenediimide.

The detection of aromatic aldehydes, considered potential genotoxic impurities, holds significant importance during drug development and production. Current analytical methods necessitate complex pre-treatment processes and exhibit insufficient specificity and sensitivity. This study presents the utilization of naphthalenediimide as a pre-column derivatisation reagent to detect aromatic aldehyde impurities in pharmaceuticals via high-performance liquid chromatography (HPLC). We screened a series of derivatisation reagents through density functional theory (DFT) and investigated the phenomenon of photoinduced electron transfer (PET) for both the derivatisation reagents and the resulting products. Optimal experimental conditions for derivatisation were achieved at 40 °C for 60 min. This approach has been successfully applied to detect residual aromatic aldehyde genotoxic impurities in various pharmaceutical preparations, including 4-Nitrobenzaldehyde, 2-Nitrobenzaldehyde, 1,4-Benzodioxane-6-aldehyde, and 5-Hydroxymethylfurfural. The pre-column derivatisation method significantly enhanced detection sensitivity and reduced the limit of detection (LOD), which ranged from 0.002 to 0.008 µg/ml for the analytes, with relative standard deviations < 3 %. The correlation coefficient (R2) >0.998 demonstrated high quality. In chloramphenicol eye drops, the concentration of 4-Nitrobenzaldehyde was measured to be 8.6 µg/mL below the specified concentration, with recoveries ranging from 90.0 % to 119.2 %. In comparison to existing methods, our work simplifies the pretreatment process, enhances the sensitivity and specificity of the analysis, and offers comprehensive insights into impurity detection in pharmaceutical preparations.

Mutagenicity assessment of two potential impurities in preparations of 5-amino-2,4,6 triiodoisophthalic acid, a key intermediate in the synthesis of the iodinated contrast agent iopamidol.

5-Aminoisophthalic acid and 5-nitroisophthalic acid (5-NIPA) are potential impurities in preparations of 5-amino-2,4,6-triiodoisophthalic acid, which is a key intermediate in the synthesis of the iodinated contrast agent iopamidol. We have studied their mutagenicity in silico (quantitative structure-activity relationships, QSAR) and by the bacterial reverse mutation assay (Ames test). First, the compounds were screened with the tools Derek Nexus™ and Leadscope®. Both compounds were flagged as potentially mutagenic (class 3 under ICH M7). However, contrary to the in silico prediction, neither chemical was mutagenic in the Ames test (plate incorporation method) with or without S9 metabolic activation.

Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split.

Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY database and 384 external test chemicals from published papers. The training accuracy was above 0.90 and the evaluation metrics (precision, recall and F1-score) all reached 0.78 or above on both internal and external test chemicals. In this model, the molecular-specific fragment importance of chemicals was first quantified independently. Then, the SA identification method based on the importance of these fragments was statistically analyzed and verified with the ISSSTY test and external test chemicals containing one of 28 typical SAs, and most of the performances were better than that of expert rules. Furthermore, a mutagenicity mechanism prediction method was developed using 237 chemicals with four known mutagenic mechanisms based on molecular similarity calibrated by the SSDL method and fragment importance, which significantly improved accuracy in three mechanisms and had comparable accuracy in the other one compared to traditional methods. Overall, the SSDL model quantifying fragment toxicity within molecules would be a novel potentially powerful tool in the determination and visualization of molecular-specific SAs and the prediction of mutagenicity mechanisms for environmental or industrial compounds and drugs.

Ames test study designs for nitrosamine mutagenicity testing: qualitative and quantitative analysis of key assay parameters.

The robust control of genotoxic N-nitrosamine (NA) impurities is an important safety consideration for the pharmaceutical industry, especially considering recent drug product withdrawals. NAs belong to the 'cohort of concern' list of genotoxic impurities (ICH M7) because of the mutagenic and carcinogenic potency of this chemical class. In addition, regulatory concerns exist regarding the capacity of the Ames test to predict the carcinogenic potential of NAs because of historically discordant results. The reasons postulated to explain these discordant data generally point to aspects of Ames test study design. These include vehicle solvent choice, liver S9 species, bacterial strain, compound concentration, and use of pre-incubation versus plate incorporation methods. Many of these concerns have their roots in historical data generated prior to the harmonization of Ames test guidelines. Therefore, we investigated various Ames test assay parameters and used qualitative analysis and quantitative benchmark dose modelling to identify which combinations provided the most sensitive conditions in terms of mutagenic potency. Two alkyl-nitrosamines, N-nitrosodimethylamine (NDMA) and N-nitrosodiethylamine (NDEA) were studied. NDMA and NDEA mutagenicity was readily detected in the Ames test and key assay parameters were identified that contributed to assay sensitivity rankings. The pre-incubation method (30-min incubation), appropriate vehicle (water or methanol), and hamster-induced liver S9, alongside Salmonella typhimurium strains TA100 and TA1535 and Escherichia coli strain WP2uvrA(pKM101) provide the most sensitive combination of assay parameters in terms of NDMA and NDEA mutagenic potency in the Ames test. Using these parameters and further quantitative benchmark dose modelling, we show that N-nitrosomethylethylamine (NMEA) is positive in Ames test and therefore should no longer be considered a historically discordant NA. The results presented herein define a sensitive Ames test design that can be deployed for the assessment of NAs to support robust impurity qualifications.

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project.

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Inoculum cell count influences separation efficiency and variance in Ames plate incorporation and Ames RAMOS test.

The Ames test is one of the most applied tools in mutagenicity testing of chemicals ever since its introduction by Ames et al. in the 1970s. Its principle is based on histidine auxotrophic bacteria that regain prototrophy through reverse mutations. In the presence of a mutagen, more reverse mutations occur that become visible as increased bacterial growth on medium without histidine. Many miniaturized formats of the Ames test have emerged to enable the testing of environmental water samples, increase experimental throughput, and lower the required amounts of test substances. However, most of these formats still rely on endpoint determinations. In contrast, the recently introduced Ames RAMOS test determines mutagenicity through online monitoring of the oxygen transfer rate. In this study, the oxygen transfer rate of Salmonella typhimurium TA100 during the Ames plate incorporation test was monitored and compared to the Ames RAMOS test to prove its validity further. Furthermore, the Ames RAMOS test in 96-well scale is newly introduced. For both the Ames plate incorporation and the Ames RAMOS test, the influence of the inoculum cell count on the negative control was highlighted: A lower inoculum cell count led to a higher coefficient of variation. However, a lower inoculum cell count also led to a higher separation efficiency in the Ames RAMOS test and, thus, to better detection of a mutagenic substance at lower concentrations.

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

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

Multiple Instance Learning Improves Ames Mutagenicity Prediction for Problematic Molecular Species.

The prediction of Ames mutagenicity continues to be a concern in both regulatory and pharmacological toxicology. Traditional quantitative structure-activity relationship (QSAR) models of mutagenicity make predictions based on molecular descriptors calculated on a chemical data set used in their training. However, it is known that molecules such as aromatic amines can be non-mutagenic themselves but metabolically activated by S9 rodent liver enzyme in Ames tests forming molecules such as iminoquinones or amine substituents that better stabilize mutagenic nitrenium ions in known pathways of mutagenicity. Modern in silico modeling methods can implicitly model these metabolites through consideration of the structural elements relevant to their formation but do not include explicit modeling of these metabolites' potential activity. These metabolites do not have a known individual mutagenicity label and, in their current state, cannot be fitted into a traditional QSAR model. Multiple instance learning (MIL) however can be applied to a group of metabolites and their parent under a single mutagenicity label. Here we trained MIL models on Ames data, first with an aromatic amines data set (n = 457), a class known to require metabolic activation, and subsequently on a larger data set (n = 6505) incorporating multiple molecular species. MIL was shown to be able to predict Ames mutagenicity with performance in line with previously established models (balanced accuracy = 0.778), suggesting its potential utility in Ames prediction applications. Furthermore, the MIL model predicted well on identified hard-to-predict molecule groups relative to the models in which these molecule groups were identified. These results are presumably due to the increased consideration of the metabolic contribution to the mutagenic outcome. Further exploration of MIL as a supplement to existing models could aid in the prediction of chemicals where implicit modeling of metabolites cannot fully grasp their characteristics. This paper demonstrates the potential of an MIL approach to modeling Ames tests with S9 and is particularly relevant to metabolically activated xenobiotic mutagens.

Mechanistic Task Groupings Enhance Multitask Deep Learning of Strain-Specific Ames Mutagenicity.

The Ames test is a gold standard mutagenicity assay that utilizes various Salmonella typhimurium strains with and without S9 fraction to provide insights into the mechanisms by which a chemical can mutate DNA. Multitask deep learning is an ideal framework for developing QSAR models with multiple end points, such as the Ames test, as the joint training of multiple predictive tasks may synergistically improve the prediction accuracy of each task. This work investigated how toxicology domain knowledge can be used to handcraft task groupings that better guide the training of multitask neural networks compared to a naïve ungrouped multitask neural network developed on a complete set of tasks. Sixteen S. typhimurium ± S9 strain tasks were used to generate groupings based on mutagenic and metabolic mechanisms that were reflected in correlation data analyses. Both grouped and ungrouped multitask neural networks predicted the 16 strain tasks with a higher balanced accuracy compared with single task controls, with grouped multitask neural networks consistently featuring incremental increases in predictivity over the ungrouped approach. We conclude that the main variable driving these performance improvements is the general multitask effect with mechanistic task groupings acting as an enhancement step to further concentrate synergistic training signals united by a common biological mechanism. This approach enables incorporation of toxicology domain knowledge into multitask QSAR model development allowing for more transparent and accurate Ames mutagenicity prediction.

Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments.

Structural alerts are molecular substructures assumed to be associated with molecular initiating events in various toxic effects and an integral part of in silico toxicology. However, alerts derived using the knowledge of human experts often suffer from a lack of predictivity, specificity, and satisfactory coverage. In this work, we present a method to build hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments. Our objective was to find out if the combination is better than the individual systems. Lasso regularization-based variable selection was applied on combined sets of knowledge-based alerts and molecular fragments, but the variable elimination was only allowed to happen on the molecular fragments. We tested the concept on three toxicity end points, i.e., skin sensitization, acute Daphnia toxicity, and Ames mutagenicity, which covered both classification and regression problems. Results showed the predictive performance of such hybrid models is, indeed, better than the models based solely on expert alerts or statistically mined fragments alone. The method also enables the discovery of activating and mitigating/deactivating features for toxicity alerts and the identification of new alerts, thereby reducing false positive and false negative outcomes commonly associated with generic alerts and alerts with poor coverage, respectively.

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

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

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

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

From Structural Alerts to Signature Fragment Alerts: A Case Study on Pyrrolizidine Alkaloids.

Even though modeling is considered a valid alternative to mutagenicity testing for substances with known structures, it can be applied for mixtures only if all of the single chemical structures are identified. Within the present work, we investigate a new avenue to exploit computational toxicology for mixtures, such as plant-based food ingredients. Indeed, considering that in the absence of toxicological information, an important early consideration is whether any substance may be genotoxic through the mutagenic mechanism of action, we tried to establish a correspondence between genotoxic structural alerts (SAs) and so-called signature fragment alerts (SFAs). Once this correspondence is established, chromatograms could be screened for chemical features associated with genotoxic alerts. Pyrrolizidine alkaloids (PAs), a large group of natural toxins (several of them known as genotoxic) were used as a case study because their early identification would bring significant benefits. The method was built using 56 PA pure standards, resulting in the characterization of signature fragment alerts. Finally, the approach was verified in real plant-based samples such as herbal tea and alfalfa, where the screening of signature fragment alerts allowed highlighting quickly the presence of genotoxic PAs in plant-based mixtures. Therefore, the SFA analysis can be used for risk prioritization of newly identified PAs and for their identification in mixtures, contributing to the unnecessary use of animal experimentation for genotoxicity testing.

Automated detection of toxicophores and prediction of mutagenicity using PMCSFG algorithm.

Maximum common substructures (MCS) have received a lot of attention in the chemoinformatics community. They are typically used as a similarity measure between molecules, showing high predictive performance when used in classification tasks, while being easily explainable substructures. In the present work, we applied the Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm to automatically detect toxicophores (structural alerts) and to compute fingerprints based on MCS. We present a comparison between our MCS-based fingerprints and 12 well-known chemical fingerprints when used as features in machine learning models. We provide an experimental evaluation and discuss the usefulness of the different methods on mutagenicity data. The features generated by the MCS method have a state-of-the-art performance when predicting mutagenicity, while they are more interpretable than the traditional chemical fingerprints.

Mutagenic potential and structural alerts of phytotoxins.

Toxic plant-produced chemicals, so-called phytotoxins, constitute a category of natural compounds belonging to a diversity of chemical classes. Some of them (e.g., alkaloids, terpenes, saponins) are associated with high toxic potency, while for many of others no toxicological data is available. In this study, the mutagenic potential of 1586 phytotoxins, as obtained from a publicly available database, was investigated applying different in silico approaches. (Q)SAR models (including statistical-based and rule-based systems) were used for the prediction of bacterial in vitro mutagenicity (Ames test) and the results from multiple tools were combined to assign consensus predicted values (i.e., positive, negative, inconclusive). The overall consensus outcome was then employed to investigate relationships between structural features of classes of phytotoxins and potential mutagenicity, allowing the identification of structural alerts raising a specific concern. The results highlighted that about 10% of the screened compounds were predicted to have mutagenic potential and the critical classes of concern, such as alkaloids, were further investigated in terms of subclasses (e.g., indole alkaloids, isoquinoline alkaloids), getting a deeper insight into the mutagenic potential of possible naturally occurring chemicals in plant materials and their structural alerts.

Implications of N7-hydrogen and C8-keto on the base pairing, mutagenic potential and repair of 8-oxo-2'-deoxy-adenosine: Investigation by nucleotide analogues.

Oxidative lesions, such as 8-oxo-dG and 8-oxo-dA, are continuously generated from exposure to reactive oxygen species. While 8-oxo-dG has been extensively studied, 8-oxo-dA has not received as much attention until recently. Herein, we report the synthesis of duplex DNAs incorporating dA, 8-oxo-dA, 7-deaza-dA, 8-Br-dA, and 8-Br-7-deaza-dA, which have different substitutions at 7- and 8-position, for the investigation into the implications of N7-hydrogen and C8-keto on the base pairing preference, mutagenic potential and repair of 8-oxo-dA. Base pairing study suggested that the polar N7-hydrogen and C8-keto of 8-oxo-dA, rather than the syn-preference, might be essential for 8-oxo-dA to form a stable base pair with dG. Insertion and extension studies using KF-exo- and human DNA polymerase ß indicated that the efficient dGTP insertion opposite 8-oxo-dA and extension past 8-oxo-dA:dG are contingent upon not only the stable base pair with dG, but also the flexibility of the active site in polymerase. The N7-hydrogen in 8-oxo-dA or C7-hydrogen in 7-deaza-dA and 8-Br-7-deaza-dA was suggested to be important for the recognition by hOGG1, although the excision efficiencies of 7-deaza-dA and 8-Br-7-deaza-dA were much lower than 8-oxo-dA. This study provides an insight into the structure-function relationship of 8-oxo-dA by nucleotide analogues.

Structural Dynamics of a Common Mutagenic Oxidative DNA Lesion in Duplex DNA and during DNA Replication.

N6-(2-Deoxy-α,ß-d-erythro-pentofuranosyl)-2,6-diamino-4-hydroxy-5-formamido pyrimidine (Fapy•dG) is a prevalent form of genomic DNA damage. Fapy•dG is formed in greater amounts under anoxic conditions than the well-studied, chemically related 7,8-dihydro-8-oxo-2'-deoxyguanosine (8-oxodGuo). Fapy•dG is more mutagenic in mammalian cells than 8-oxodGuo. A distinctive property of Fapy•dG is facile epimerization, but prior works with Fapy•dG analogues have precluded determining its effect on chemistry. We present crystallographic characterization of natural Fapy•dG in duplex DNA and as the template base for DNA polymerase ß (Pol ß). Fapy•dG adopts the ß-anomer when base paired with cytosine but exists as a mixture of α- and ß-anomers when promutagenically base paired with adenine. Rotation about the bond between the glycosidic nitrogen atom and the pyrimidine ring is also affected by the opposing nucleotide. Sodium cyanoborohydride soaking experiments trap the ring-opened Fapy•dG, demonstrating that ring opening and epimerization occur in the crystalline state. Ring opening and epimerization are facilitated by propitious water molecules that are observed in the structures. Determination of Fapy•dG mutagenicity in wild type and Pol ß knockdown HEK 293T cells indicates that Pol ß contributes to G → T transversions but also suppresses G → A transitions. Complementary kinetic studies have determined that Fapy•dG promotes mutagenesis by decreasing the catalytic efficiency of dCMP insertion opposite Fapy•dG, thus reducing polymerase fidelity. Kinetic studies have determined that dCMP incorporation opposite the ß-anomer is ∼90 times faster than the α-anomer. This research identifies the importance of anomer dynamics, a feature unique to formamidopyrimidines, when considering the incorporation of nucleotides opposite Fapy•dG and potentially the repair of this structurally unusual lesion.

Optimizing machine-learning models for mutagenicity prediction through better feature selection.

Assessing a compound's mutagenicity using machine learning is an important activity in the drug discovery and development process. Traditional methods of mutagenicity detection, such as Ames test, are expensive and time and labor intensive. In this context, in silico methods that predict a compound mutagenicity with high accuracy are important. Recently, machine-learning (ML) models are increasingly being proposed to improve the accuracy of mutagenicity prediction. While these models are used in practice, there is further scope to improve the accuracy of these models. We hypothesize that choosing the right features to train the model can further lead to better accuracy. We systematically consider and evaluate a combination of novel structural and molecular features which have the maximal impact on the accuracy of models. We rigorously evaluate these features against multiple classification models (from classical ML models to deep neural network models). The performance of the models was assessed using 5- and 10-fold cross-validation and we show that our approach using the molecule structure, molecular properties, and structural alerts as feature sets successfully outperform the state-of-the-art methods for mutagenicity prediction for the Hansen et al. benchmark dataset with an area under the receiver operating characteristic curve of 0.93. More importantly, our framework shows how combining features could benefit model accuracy improvements.

In silico toxicity evaluation for transformation products of antimicrobials, from aqueous photolysis degradation.

This study investigates degradation processes of three antimicrobials in water (norfloxacin, ciprofloxacin, and sulfamethoxazole) by photolysis, focusing on the prediction of toxicity endpoints via in silico quantitative structure-activity relationship (QSAR) of their transformation products (TPs). Photolysis experiments were conducted in distilled water with individual solutions at 10 mg L-1 for each compound. Identification of TPs was performed by means of LC-TOF-MS, employing a method based on retention time, exact mass fragmentation pattern, and peak intensity. Ten main compounds were identified for sulfamethoxazole, fifteen for ciprofloxacin, and fifteen for norfloxacin. Out of 40 identified TPs, 6 have not been reported in the literature. Based on new data found in this work, and TPs already reported in the literature, we have proposed degradation pathways for all three antimicrobials, providing reasoning for the identified TPs. QSAR risk assessment was carried out for 74 structures of possible isomers. QSAR predictions showed that all 19 possible structures of sulfamethoxazole TPs are non-mutagenic, whereas 16 are toxicant, 18 carcinogenic, and 14 non-readily biodegradable. For ciprofloxacin, 28 out of the 30 possible structures for the TPs are mutagenic and non-readily biodegradable, and all structures are toxicant and carcinogenic. All 25 possible norfloxacin TPs were predicted mutagenic, toxicant, carcinogenic, and non-readily biodegradable. Results obtained from in silico QSAR models evince the need of performing risk assessment for TPs as well as for the parent antimicrobial. An expert analysis of QSAR predictions using different models and degradation pathways is imperative, for a large variety of structures was found for the TPs.

In Silico Methods for Chromosome Damage.

Due to the link with serious adverse health effects, genotoxicity is an important toxicological endpoint in each regulatory setting with respect to human health, including for pharmaceuticals. To this extent, a compound potential to induce gene mutations as well as chromosome damage needs to be addressed. For chromosome damage, i.e., the induction of structural or numerical chromosome aberrations, several in vitro and in vivo test methods are available. In order to rapidly collect toxicological data without the need for test material, several in silico tools for chromosome damage have been developed over the last years. In this chapter, a battery of freely available in silico chromosome damage prediction tools for chromosome damage is applied on a dataset of pharmaceuticals. Examples of the different outcomes obtained with the in silico battery are provided and briefly discussed. Furthermore, results for coumarin are presented in more detail as a case study. Overall, it can be concluded that although they are in general less developed than those for mutagenicity, in silico tools for chromosome damage can provide valuable information, especially when combined in a battery.