Strategy proposal using QSAR models to approach mutagenicity assessment of non intentionally added substances in recycled plastic resins

CONTEXT

Transition to the use of recycled plastics raises an issue concerning safety assessment of Non Intentionally Added Substances (NIAS). To assess the mutagenic potential of the recycled polyethylene impurities and to evaluate the need to perform in vitro assays on recycled resins, this study lies in identifying existing NIAS associated with recycled Low/High Density Polyethylene and assessing the mutagenicity data-gaps by employing in silico tools.

METHODS

Quantitative Structure-Activity Relationship (QSAR) models predicting Ames mutagenicity were selected from literature, then NIAS were run to 1/evaluate performances of each model, 2/apply a QSAR strategy on the NIAS molecular space and address data-gaps.

RESULTS

Among the 165 NIAS identified, experimental Ames results were not found for 50 substances while the substances with experimental data were predominantly negatives. No individual model was able to predict all NIAS due to applicability domain limitations. Taking into account 1/calculated performances, 2/availability of applicability domain, 3/description of the Training Set, an Integrated Strategy was founded including Sarpy, Consensus and Protox to extend the applicability domain.

CONCLUSION & PERSPECTIVES

Existing data and predictions generated by this strategy suggest a low mutagenic potential of NIAS. Further investigation is needed to explore other genotoxicity mechanisms.

Unlocking The Mysteries of DNA Adducts with Artificial Intelligence

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

Enhanced Antibacterial Activity of Novel Fluorescent Glutathione-Capped Ag Nanoclusters

Ag nanomaterials are promising candidates for the discovery of next-generation antibiotics with a high antibacterial effect against multi-drug resistant strains. This paper reports a simple synthesis of novel water-soluble glutathione-capped silver nanoclusters (GSH-Ag NCs) with an enhanced antibacterial activity. According to thin layer chromatography (TLC), the synthesized GSH-Ag NCs are an individual fraction of the same composition without any impurities. According to matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) and energy dispersive X-ray (EDX) analyses, the silver core of the GSH-Ag NCs contains approximately 35 silver atoms, and the molecular weight of these nanoclusters is about 11 kDa. The fabricated silver nanoclusters have a reddish fluorescence (λex/λem = 509/645 nm), with a large Stokes shift (>130 nm), and ultra-small size (less than 2 nm) according to transmission electron microscopy (TEM) data and dynamic light scattering (DLS) analysis. The antibacterial activity and minimal inhibitory concentrations of the silver nanoclusters towards Escherichia coli, Staphylococcus aureus, Bacillus cereus and Enterobacter cloacae were evaluated using the agar well-diffusion method and resazurin metabolism assay. The antibacterial activity of chelated silver in the nanoclusters was found to be significantly higher compared to the activity of free silver ions. To explain the possible mechanisms underlying the antibacterial actions of the GSH-Ag nanoclusters, molecular docking was performed, and prospective bacterial targets were identified using AutoDock.

Combined inhibition of BADSer99 phosphorylation and PARP ablates models of recurrent ovarian carcinoma

Background

Poly (ADP-ribose) polymerase inhibitors (PARPis) have been approved for the treatment of recurrent epithelial ovarian cancer (EOC), regardless of BRCA status or homologous recombination repair deficiency. However, the low response of platinum-resistant EOC, the emergence of resistance in BRCA-deficient cancer, and therapy-associated toxicities in patients limit the clinical utility of PARPis in recurrent EOC.

Methods

The association of phosphorylated (p) BADS99 with clinicopathological parameters and survival outcomes in an EOC cohort was assessed by immunohistochemistry. The therapeutic synergy, and mechanisms thereof, between a pBADS99 inhibitor and PARPis in EOC was determined in vitro and in vivo using cell line and patient-derived models.

Results

A positive correlation between pBADS99 in EOC with higher disease stage and poorer survival is observed. Increased pBADS99 in EOC cells is significantly associated with BRCA-deficiency and decreased Cisplatin or Olaparib sensitivity. Pharmacological inhibition of pBADS99 synergizes with PARPis to enhance PARPi IC₅₀ and decreases survival, foci formation, and growth in ex vivo culture of EOC cells and patient-derived organoids (PDOs). Combined inhibition of pBADS99 and PARP in EOC cells or PDOs enhances DNA damage but impairs PARPi stimulated DNA repair with a consequent increase in apoptosis. Inhibition of BADS99 phosphorylation synergizes with Olaparib to suppress the xenograft growth of platinum-sensitive and resistant EOC. Combined pBADS99-PARP inhibition produces a complete response in a PDX derived from a patient with metastatic and chemoresistant EOC.

Conclusions

A rational and efficacious combination strategy involving combined inhibition of pBADS99 and PARP for the treatment of recurrent EOC is presented.

Ovarian cancer is difficult to successfully treat because it often recurs as the cancer becomes resistant to drugs used to treat it. As such, new drugs or combinations of drugs are needed to treat patients with recurrent ovarian cancer. Here, a drug combination is reported that is effective in experimental models of ovarian cancer, including those derived from patients. The combination approach uses drugs that have previously been approved for use in patients, known as PARP inhibitors, and another drug to inhibit cancer cell survival by targeting activation of a specific protein involved in cancer cell survival. The net effect of this drug combination in ovarian cancer models is greater than the sum of the drugs used individually. With further testing, this combination may offer a potential strategy to treat patients with recurrent ovarian cancer.

Zhang et al. test the therapeutic potential of an inhibitor of BAD phosphorylation, NPB, in epithelial ovarian cancer. The authors show that the small molecule synergises with PARP inhibition to kill patient-derived ovarian cancer organoids and suppress the growth of xenograft tumours, including a cisplatin-resistant model.

A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods

Carcinogenicity is one of the most critical endpoints for the risk assessment of food contact chemicals (FCCs). However, the carcinogenicity of FCCs remains insufficiently investigated. To fill the data gap, the application of standard experimental methods for identifying chemicals of carcinogenic concerns from a large set of FCCs is impractical due to their resource-intensive nature. In contrast, computational methods provide an efficient way to quickly screen chemicals with carcinogenic potential for subsequent experimental validation. Since every model was developed based on a limited number of training samples, the use of single models for carcinogenicity assessment may not cover the complex mechanisms of carcinogenesis. This study proposed a novel machine learning-based weight-of-evidence (WoE) model for prioritizing chemical carcinogenesis. The WoE model can nonlinearly integrate complementary computational methods of structural alerts, quantitative structure-activity relationship models and in silico toxicogenomics models into a WoE-score. Compared to the best single method, the WoE model gained 8% and 19.7% improvement in the area under the receiver operating characteristic curve (AUC) value and chemical coverage, respectively. The prioritization of 1623 FCCs concludes 44 chemicals of high carcinogenic concern. The machine learning-based WoE approach provides a fast and comprehensive way for prioritizing chemicals of carcinogenic concern.

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.

Bacterial mutagenicity test data: collection by the task force of the Japan pharmaceutical manufacturers association

Background

Ames test is used worldwide for detecting the bacterial mutagenicity of chemicals. In silico analyses of bacterial mutagenicity have recently gained acceptance by regulatory agencies; however, current in silico models for prediction remain to be improved. The Japan Pharmaceutical Manufacturers Association (JPMA) organized a task force in 2017 in which eight Japanese pharmaceutical companies had participated. The purpose of this task force was to disclose a piece of pharmaceutical companies’ proprietary Ames test data.

Results

Ames test data for 99 chemicals of various chemical classes were collected for disclosure in this study. These chemicals are related to the manufacturing process of pharmaceutical drugs, including reagents, synthetic intermediates, and drug substances. The structure-activity (mutagenicity) relationships are discussed in relation to structural alerts for each chemical class. In addition, in silico analyses of these chemicals were conducted using a knowledge-based model of Derek Nexus (Derek) and a statistics-based model (GT1_BMUT module) of CASE Ultra. To calculate the effectiveness of these models, 89 chemicals for Derek and 54 chemicals for CASE Ultra were selected; major exclusions were the salt form of four chemicals that were tested both in the salt and free forms for both models, and 35 chemicals called “known” positives or negatives for CASE Ultra. For Derek, the sensitivity, specificity, and accuracy were 65% (15/23), 71% (47/66), and 70% (62/89), respectively. The sensitivity, specificity, and accuracy were 50% (6/12), 60% (25/42), and 57% (31/54) for CASE Ultra, respectively. The ratio of overall disagreement between the CASE Ultra “known” positives/negatives and the actual test results was 11% (4/35). In this study, 19 out of 28 mutagens (68%) were detected with TA100 and/or TA98, and 9 out of 28 mutagens (32%) were detected with either TA1535, TA1537, WP2uvrA, or their combination.

Conclusion

The Ames test data presented here will help avoid duplicated Ames testing in some cases, support duplicate testing in other cases, improve in silico models, and enhance our understanding of the mechanisms of mutagenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41021-021-00206-1.

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

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

Structural optimization, synthesis and in vitro synergistic anticancer activities of combinations of new N3-substituted dihydropyrimidine calcium channel blockers with cisplatin and etoposide

T-type calcium channels are considered potential drug targets to combat cancer. Combining T-type calcium channel blockers with conventional chemotherapy drugs represents a promising strategy towards successful cancer treatment. From this perspective, we report in this study the design and synthesis of a novel series of N3-sustituted dihydropyrimidines (DHPMs) as anticancer adjuvants to cisplatin (Cis) and etoposide (Eto). Full spectral characterization of the new compounds was done using FT-IR, ¹H NMR, ¹³C NMR, and HRMS. Structure elucidation was confirmed by 2D NMR ¹H-H COSY, HSQC and NOESY experiments. Novel derivatives were tested for their Ca²⁺ channel blocking activity by employing the whole cell patch-clamp technique. Results demonstrated that most compounds were potential T-type calcium channel blockers with the triazole-based C12 and C13 being the most selective agents against Ca_V3.2 channel. Further electrophysiological studies demonstrated that C12 and C13 inhibited Ca_V3.2 currents with respective affinity of 2.26 and 1.27 µM, and induced 5 mV hyperpolarizing shifts in the half-inactivation potential. Subsequently, C12 and C13 were evaluated for their anticancer activities alone and in combination with Cis and Eto against A549 and MDA-MB 231 cancer cells. Interestingly, both compounds exhibited potential anticancer effects with IC₅₀ values < 5 µM. Combination studies revealed that both compounds had synergistic effects (combination index CI < 1) on Cis and Eto through induction of apoptosis (p53 activation and up-regulation of BAX and p21 gene expression). Importantly, in silico physicochemical and ADMET assessment of both compounds revealed their potential drug-like properties with decreased risk of cardiac toxicity. Hence, C12 and C13 are promising anticancer adjuvants through inhibition of Ca_V3.2 T-type calcium channels, thereby serving as eminent leads for further modification.

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

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

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.

Docking and antibacterial activity of novel nontoxic 5-arylidenepyrimidine-triones as inhibitors of NDM-1 and MetAP-1

Background: Antibiotic resistance, which occurs through the action of metallo-β-lactamases (NDM-1), is a serious problem in the treatment of infectious diseases. Therefore, the discovery of new NDM-1 inhibitors and promising antibacterial agents as inhibitors of alternative targets (MetAP-1) is important. Method & results: In this study, a virtual library of 5-arylidene barbituric acids was created and molecular docking was performed for identification of novel possible inhibitors of NDM-1 and MetAP-1. Antibacterial activity (agar well-diffusion assay) and cytotoxicity (alamarBlue assay) of perspective compounds were evaluated. Pharmacokinetic profiles and molecular properties were predicted. Conclusion: We have identified possible novel inhibitors of NDM-1 and MetAP-1 with bacteriostatic activity, most of which are not cytotoxic and have potential excellent drug-likeness properties.

(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?

The assessment of human health risks resulting from the presence of metabolites in groundwater and food residues has become an important element in pesticide authorisation. In this context, the evaluation of mutagenicity is of particular interest and a paradigm shift from exposure-triggered testing to in silico-based screening has been recommended in the European Food Safety Authority (EFSA) Guidance on the establishment of the residue definition for dietary risk assessment. In addition, it is proposed to apply in silico predictions when experimental mutagenicity testing is not possible due to a lack of sufficient quantities of the pesticide metabolite. This, combined with animal welfare and economic considerations, has led to a situation where an increasing number of in silico studies are submitted to regulatory authorities. Whilst there is extensive experience with in silico predictions for mutagenicity in the chemical and pharmaceutical industry, their suitability in pesticide regulation is still insufficiently considered. Therefore, we herein discuss critical issues that need to be resolved to successfully implement (Quantitative) Structure-Activity Relationship ((Q)SAR) as an accepted tool in pesticide regulation. For illustration purposes, the results of a pilot study are included. The presented study highlights a need for further improvement regarding the predictivity and applicability domain of (Q)SAR systems for pesticides and their metabolites, but also raises other questions such as model selection, establishment of acceptance criteria, harmonised approaches to the combination of model outputs into overall conclusions, adequate reporting and data sharing. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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.

Considerations for the Use of Mutation as a Regulatory Endpoint in Risk Assessment

Assessment of a chemical's potential to cause permanent changes in the genetic code has been a common practice in the industry and regulatory settings for decades. Furthermore, the genetic toxicity battery of tests has typically been employed during the earliest stages of the research and development programs of new product development. A positive outcome from such battery has a major impact on the chemical's utility, industrial hygiene, product stewardship practices, and product life cycle analysis, among many other decisions that need to be taken by the industry, even before the registration of a chemical is undertaken. Under the prevailing regulatory paradigm, the dichotomous (yes/no) evaluation of the chemical's genotoxic potential leads to a conservative, linear no-threshold (LNT) risk assessment, unless compelling and undeniable data to the contrary can be provided to satisfy regulators, typically in a number of different global jurisdictions. With the current advent of predictive methods, new testing paradigms, mode-of-action/adverse outcome pathways, and quantitative risk assessment approaches, various stakeholders are starting to employ these state-of-the-science methodologies to further the conversation on decision making and advance the regulatory paradigm beyond the dominant LNT status quo. This commentary describes these novel methodologies, relevant biological responses, and how these can affect internal and regulatory risk assessment approaches. Environ. Mol. Mutagen. 61:84-93, 2020. © 2019 Wiley Periodicals, Inc.

Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks

Current practice of building QSAR models usually involves computing a set of descriptors for the training set compounds, applying a descriptor selection algorithm and finally using a statistical fitting method to build the model. In this study, we explored the prospects of building good quality interpretable QSARs for big and diverse datasets, without using any pre-calculated descriptors. We have used different forms of Long Short-Term Memory (LSTM) neural networks to achieve this, trained directly using either traditional SMILES codes or a new linear molecular notation developed as part of this work. Three endpoints were modeled: Ames mutagenicity, inhibition of P. falciparum Dd2 and inhibition of Hepatitis C Virus, with training sets ranging from 7,866 to 31,919 compounds. To boost the interpretability of the prediction results, attention-based machine learning mechanism, jointly with a bidirectional LSTM was used to detect structural alerts for the mutagenicity data set. Traditional fragment descriptor-based models were used for comparison. As per the results of the external and cross-validation experiments, overall prediction accuracies of the LSTM models were close to the fragment-based models. However, LSTM models were superior in predicting test chemicals that are dissimilar to the training set compounds, a coveted quality of QSAR models in real world applications. In summary, it is possible to build QSAR models using LSTMs without using pre-computed traditional descriptors, and models are far from being “black box.” We wish that this study will be helpful in bringing large, descriptor-less QSARs to mainstream use.

Towards quantitative read across: Prediction of Ames mutagenicity in a large database

In silico chemical safety assessment can support the evaluation of hazard and risk following potential exposure to a substance, thus stimulating an increased interest for the use of Structure-Activity based approaches by regulatory authorities, particularly QSAR and Read Across. Whereas the longer history of QSAR led to recognize the crucial requirements for predictivity, there are still challenges faced by adopting Read Across to a larger extent in a regulatory setting, namely standardization and objective criteria. In previous research, suitable conditions for applying Read Across to the prediction of the Ames mutagenicity of metabolites and degradation products of pesticides were established: a standardized similarity criterion based simultaneously on basic molecular properties and Structural Similarity was successfully applied to a number of case studies. Here the investigation is extended to a large database of curated Ames mutagenicity results. For around 2,000 chemicals for which the similarity criterion was applicable, the predictivity of Read Across was high: specificity 0.72, sensitivity 0.90, accuracy 0.85. This compares favourably with the Ames test intra-assay variability, and with the predictivity of QSAR models. The need for standardization and rigorous validation of Read Across is emphasized.