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

The present document provides the summary of the activities undertaken during the fourth of the framework contract (OC/EFSA/SCER/2018/01) to maintain, update and further develop the OpenFoodTox database (“OpenFodTox 2.0”). OpenFoodTox has been developed to map hazards data published in EFSA documents (opinions, statements, and conclusions) on risk assessment of food and feed. The repository holds summary data on identification of chemicals, document descriptors, hazard identification, and hazard characterisation. Within OpenFoodTox 2.0, the collection and entry of all hazard data assessed by EFSA scientific panels was performed according to the existing data model. During the fourth year, 141 new substances and 304 new hazard assessments (collected from new 188 EFSA documents) were added to the database. The OpenFoodTox 2.0 now includes more than 10800 assessments for over 5890 chemicals (each assessment potentially including multiple (eco-) toxicity endpoints and/or hazard/risk characterisation data) extracted from the screening of about 2435 documents (opinions, statements, conclusions) published by EFSA from 2020 to August 2022. To maintain the database, its data model was further expanded to incorporate new data types, including physicochemical properties (OHT 1 to 23-5) and toxicokinetic data (OHT 58). Overall, OpenFoodTox was enriched with physicochemical properties and/or ADME/PK/TK data (including quantitative values for key toxicokinetics parameters, e.g., Cmax, AUC, T-half and T-max) for 1,263 substances from 664 EFSA outputs. In total, 16,611 database records with experimentally-derived physicochemical properties data were added to the database for 969 substances (collected from 605 EFSA outputs) as well as 7,699 ADME/PK/TK database records for 852 substances (from 577 documents) and are available for download under https://zenodo.org/records/8120114. Using VEGA predicted values of physiochemical properties and toxicity endpoints were added for 3980 compounds. The flexible summary wad tested on a data rich scenario using the data on conazoles. Finally, new QSAR models and a new read-across tools (VERA) are presented here.

Read-across (RAx) translates available information from well-characterized chemicals to a substance for which there is a toxicological data gap. The OECD is working on case studies to probe general applicability of RAx, and several regulations (e.g., EU-REACH) already allow this procedure to be used to waive new in vivo tests. The decision to prepare a review on the state of the art of RAx as a tool for risk assessment for regulatory purposes was taken during a workshop with international experts in Ranco, Italy in July 2018. Three major issues were identified that need optimization to allow a higher regulatory acceptance rate of the RAx procedure: (i) the definition of similarity of source and target, (ii) the translation of biological/toxicological activity of source to target in the RAx procedure, and (iii) how to deal with issues of ADME that may differ between source and target. The use of new approach methodologies (NAM) was discussed as one of the most important innovations to improve the acceptability of RAx. At present, NAM data may be used to confirm chemical and toxicological similarity. In the future, the use of NAM may be broadened to fully characterize the hazard and toxicokinetic properties of RAx compounds. Concerning available guidance, documents on Good Read-Across Practice (GRAP) and on best practices to perform and evaluate the RAx process were identified. Here, in particular, the RAx guidance, being worked out by the European Commission’s H2020 project EU-ToxRisk together with many external partners with regulatory experience, is given.

Read-across is a non-testing data gap filling technique which provides information for toxicological assessments by inferring from known toxicity data of compound(s) with a "similar" property or chemical profile. The increased usage of read-across was driven by monetary, timing and ethical costs associated with in vivo testing, as well as promoted by regulatory frameworks to minimize new animal testing (e.g., EU-REACH). Several guidance documents have been published by ECHA and OECD providing guidelines on how to perform, assess and document a read-across study. In parallel, much effort was invested by the scientific community to provide good read-across practices and structured frameworks to enhance validity of read-across justifications. Nevertheless, read-across is an evolving method with several open issues and opportunities. A brief review is here provided on key developments on the use of read-across, regulatory and scientific expectations, practical hurdles and open challenges.

The current paradigm of toxicity testing is set within a framework of Mode-of-Action (MoA)/Adverse Outcome Pathway (AOP) investigations, where novel methodologies alternative to animal testing play a crucial role, and allow to consider causal links between molecular initiating events (MIEs), further key events and an adverse outcome. In silico (computational) models are developed to support toxicity assessment within the MoA/AOP framework. This paper focuses on the evaluation of potential binding to the Liver X Receptor (LXR), as this has been identified among the MIEs leading to liver steatosis within an AOP framework addressing repeated dose and target-organ toxicity.The objective of this study was the development of a priority setting strategy, by means of in silico approaches and chemometric tools, to allow for the screening and ranking of chemicals according to their toxicity potential. As a case study, the present paper outlines the methodologies and procedures that have been developed in the context of the COSMOS/cosmetics safety assessment project [4], which developed computational methods in view of supporting cosmetics safety assessment, to rank chemicals based on their potential binding to LXR. Chemicals are ranked based on molecular and QSAR modelling outcomes. The contribution in this paper is threefold: the QSAR model for LXR dataset, an application of molecular modelling approaches, which have been developed and optimized for drug discovery, in the context of toxicology, and finally ranking chemicals based on diverse modelling outcomes. The novelty in this paper consists of the employment of linear (logistic regression) and non-linear (Random Forest) models in the context of ranking chemicals. The results show that these methods can be successfully applied for prioritization of compounds of major concern for potential liver toxicity, and that they perform better than the ranking methods reported in the literature to date (such as total ordering or data fusion).

Whilst general policy objectives to reduce airborne particulate matter (PM) health effects are to reduce exposure to PM as a whole, emerging evidence suggests that more detailed metrics associating impacts with different aerosol components might be needed. Since it is impossible to conduct toxicological screening on all possible molecular species expected to occur in aerosol, in this study we perform a proof-of-concept evaluation on the information retrieved from in silico toxicological predictions, in which a subset (N = 104) of secondary organic aerosol (SOA) compounds were screened for their mutagenicity potential. An extensive database search showed that experimental data are available for 13% of the compounds, while reliable predictions were obtained for 82 %. A multivariate statistical analysis of the compounds based on their physico-chemical, structural, and mechanistic properties showed that 80% of the compounds predicted as mutagenic were grouped into six clusters, three of which (five-membered lactones from monoterpene oxidation, oxygenated multifunctional compounds from substituted benzene oxidation, and hydroperoxides from several precursors) represent new candidate groups of compounds for future toxicological screenings. These results demonstrate that coupling model-generated compositions to in silico toxicological screening might enable more comprehensive exploration of the mutagenic potential of specific SOA components.

Background: EFSA's remit and chemical risk assessment of regulated products and contaminants The European Food Safety Authority (EFSA) has the remit to provide scientific advice to risk managers and decision makers through risk assessment and risk communication on issues related to “food and feed safety, animal health and welfare, plant health, nutrition, and environmental issues”. Risk assessment has been defined as "a scientifically based process consisting of four steps: hazard identification, hazard characterisation, exposure assessment and risk characterisation" (EC, 2002). In the food and feed safety area, hazard identification and hazard characterisation aim to determine safe levels of exposure for regulated products or contaminants as “reference values1” to protect human health, animal health, environmental-relevant species or the whole ecosystem. Such reference values for a given species are most often derived by using a “reference point2" determined from the critical toxicological study on which an uncertainty factor3 is applied. Since its creation in 2002, the European Food safety Authority (EFSA) has produced risk assessments for more than 4,750 unique substances in over 1,800 Scientific Opinions, Statements and Conclusions through the work of its scientific Panels, Units and Scientific Committee. For regulated products, these risk assessments have been performed by five scientific panels and four supporting units. EFSA's chemical Hazards Database : OpenFoodTox OpenFoodTox is a structured database summarising the outcomes of hazard identification and characterisation for the human health (all regulated products and contaminants), the animal health (feed additives, pesticides and contaminants) and the environment (feed additives and pesticides). OpenFoodTox the substance characterisation, the links to EFSA’s related output, background European legislation, and a summary of the critical toxicological endpoints and reference values. For each individual substance, the data model of OpenFoodTox has been designed using OECD Harmonised Template (OHTs) as a basis to collect and structure the data in a harmonised manner. OpenFoodTox provides open source data for the substance characterisation, EFSA outputs, reference points, reference values and genotoxicity. OpenFoodTox and can be searched under the following link using a microstrategy tool: http://www.efsa.europa.eu/en/microstrategy/openfoodtox In order to disseminate OpenFoodTox to a wider community, two sets of data can be downloaded: 1. Five individual spreadsheets extracted from the EFSA microstrategy tool providing for all compounds: a. substance characterisation, b. EFSA outputs, c. reference points, d. reference values and, e. genotoxicity. 2. The full database. OpenFoodTox contributes actively to EFSA’s 2020 Science Strategy (EFSA, 2016) and to the aim of widening EFSA’s evidence base and optimising access to its data as a valuable open source toxicological database that can be shared with all scientific advisory bodies and stakeholders with an interest in chemical risk assessment. In addition, OpenFoodTox has been submitted to the OECD’s Global Portal to Information on Chemical Substances (eChemPortal) so that individual substances can be searched as part of the national and international databases. Further description and associated references are described in the EFSA journal editorial (Dorne et al., 2017). Using OpenFoodTox to develop innovative in silico models Recently, in silico models using OpenFoodTox have been developed for ecological risk assessment (bees and rainbow trout) and human risk assessment using rat toxicological data (Como et al., 2017; Benefenati et al., 2017; Toporov et al., 2017, Toporov et al., 2018). These in silico models provide alternative means to animal experiments for the hazard identification and characterisation of chemicals, and are becoming of increasing interest in the risk assessment community to deliver the 3Rs (replacement, reduction, refinement) (Hartung, 2004; OECD, 2005) particularly since the banning of animal testing for the approval of cosmetics as consumer products (Regulation (EC) No. 1223/2009, Art.18(2)). Data model based on the OECD harmonised templates (OHTs) for reporting toxicological data; http://www.oecd.org/ehs/templates/ http://www.efsa.europa.eu/en/corporate/pub/strategy2020; http://www.echemportal.org/echemportal/index?pageID=0&request_locale=en Definitions 1 Reference Value : The estimated maximum dose (on a body mass basis) or the concentration of an agent to which an individual may be exposed over a specified period without appreciable risk. Reference values are established by applying an uncertainty factor to the reference point. Examples of reference values in human health include acceptable daily intake (ADI) for food and feed additives, and pesticides, tolerable upper intake levels (UL) for vitamins and minerals, and tolerable daily intake (TDI) for contaminants and food contact materials. For acute effects and operators, the acute reference dose (ARfD) and the acceptable operator exposure level (AOEL). In animal health and the ecological area, these include safe feed concentrations and the Predicted no effect concentration (PNEC) respectively (EFSA Scientific Committee, 2018). 2 Reference point : Defined point on an experimental dose–response relationship for the critical effect. This term is synonymous to Point of departure (USA). Reference points include the lowest or no observed adverse effect level (LOAEL/NOAEL) or benchmark dose lower confidence limit (BDML), used to derive a reference value or Margin of Exposure in human and animal health risk assessment. In the ecological area, these include lethal dose (LD50), effect concentration (EC5/ECx), no (adverse) effect concentration/dose (NOEC/NOAEC/NOAED), no (adverse) effect level (NEL/NOAEL), hazard concentration (HC5/HCx) derived from a Species Sensitivity Distributions (SSD) for the ecosystem (EFSA Scientific Committee,2018). 3 Uncertainty factor: Reductive factor by which an observed or estimated no observed adverse effect level or other reference point, such as the benchmark dose or benchmark dose lower confidence limit, is divided to arrive at a reference dose or standard that is considered safe or without appreciable risk (WHO, 2009).

The present document is a summary of the update and maintenance of the EFSA's Chemical Hazards Database that has been established few years ago to map the hazard data as collected from the EFSA opinions, statements and conclusions; more specifically the repository holds summary data on chemical identification, document descriptors, hazard identification, and hazard characterisation/ risk characterisation. The repository includes data extracted from opinions and statements adopted by a number of EFSA panels including NDA (vitamins and minerals, novel foods, dietetic products), CONTAM (contaminants in the food chain, contaminants in the feed chain), FEEDAP (feed additives-application linked to 1381/2003, feed additives-application under to 1381/2003, feed additives-other), AFC (food additives, food contact materials, nutrient sources, processing aids, flavourings), CEF (food contact materials, food manufacturing processes, processing aids, flavourings), ANS (food additives, nutrient sources) and PPR (pesticides). Substances which do not fall within the category of chemicals (e.g., microorganisms and enzymes) are excluded from the EFSA's Chemical Hazards Database.

EFSA Supporting PublicationsVolume 14, Issue 4 1192E External scientific reportOpen Access Update and maintenance of EFSA's Chemical Hazards Database S-IN Soluzioni Informatiche, S-IN Soluzioni InformaticheSearch for more papers by this authorRossella Baldin, Rossella BaldinSearch for more papers by this authorSimona Kovarich, Simona KovarichSearch for more papers by this authorManuela Pavan, Manuela PavanSearch for more papers by this authorElena Fioravanzo, Elena FioravanzoSearch for more papers by this authorArianna Bassan, Arianna BassanSearch for more papers by this author S-IN Soluzioni Informatiche, S-IN Soluzioni InformaticheSearch for more papers by this authorRossella Baldin, Rossella BaldinSearch for more papers by this authorSimona Kovarich, Simona KovarichSearch for more papers by this authorManuela Pavan, Manuela PavanSearch for more papers by this authorElena Fioravanzo, Elena FioravanzoSearch for more papers by this authorArianna Bassan, Arianna BassanSearch for more papers by this author First published: 07 April 2017 https://doi.org/10.2903/sp.efsa.2017.EN-1192Citations: 1 Question number: EFSA-Q-2015-00173 Disclaimer: The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL References Bolton E, Wang Y, Thiessen PA and Bryant SH, 2008. PubChem: Integrated Platform of Small Molecules and Biological Activities. Chapter 12 IN Annual Reports in Computational Chemistry, Volume 4, American Chemical Society, Washington, DC, 2008. EC (European Commission), Scientific Committee on Food, 1999. Opinion on a programme for the evaluation of flavouring substances (expressed on 2 December 1999). Scientific Committee on Food. SCF/CS/FLAV/TASK/11 Final 6/12/1999. Annex I the minutes of the 119th Plenary meeting. European Commission, Health & Consumer Protection Directorate-General. EC (European Commission), 2000. Commission Regulation No 1565/2000 of 18 July 2000 laying down the measures necessary for the adoption of an evaluation programme in application of Regulation (EC) No 2232/96. Official Journal of the European Communities 19.7.2000, L 180, 8-16. EFSA (European Food Safety Authority), 2005a. Opinion of the Scientific Committee on a request from EFSA related to a harmonised approach for risk assessment of substances which are both genotoxic and carcinogenic. EFSA Journal (2005) 282, 1– 31. EFSA (European Food Safety Authority), 2006a. Tolerable upper intake levels for vitamins and minerals. Scientific Committee on Food Scientific Panel on Dietetic Products, Nutrition and Allergies. February 2006. www.efsa.europa.eu/en/ndatopics/docs/ndatolerableuil.pdf EFSA (European Food Safety Authority), 2008a. Polycyclic Aromatic Hydrocarbons in Food, EFSA Journal (2008) 724, 1– 114. EFSA (European Food Safety Authority), 2009a. Guidance Document on Risk Assessment for Birds & Mammals on request from EFSA. EFSA Journal 2009; 7(12):1438. doi:10.2903/j.efsa.2009.1438. EFSA (European Food Safety Authority), 2009b. Marine biotoxins in shellfish – Saxitoxin group. EFSA Journal (2009) 1019, 1– 76 EFSA (European Food Safety Authority), 2012a. Completion of data entry of pesticide ecotoxicology Tier 1 study endpoints in a XML schema – database. Pierobon E., Neri M.C., Marroncelli S., Croce V.; Supporting Publications 2012:EN-326. [96 pp.]. EFSA (European Food Safety Authority), 2013a. Report on ‘Data collection and data entry for EFSA's chemical hazards database NP/EFSA/EMRISK/2011/01’, S-IN Soluzioni-Informatiche; Supporting Publications 2013: EN-458. [140 pp.]. Available online: http://www.efsa.europa.eu/en/supporting/pub/458e.htm EFSA (European Food Safety Authority), 2013b, Standard Sample Description ver. 2.0, EFSA Journal 2013; 11(10):3424 [114 pp.]. Available online: http://www.efsa.europa.eu/en/efsajournal/pub/3424.htm EFSA (European Food Safety Authority), 2014, Report on ‘Further development and update of EFSA's Chemical Hazards Database NP/EFSA/EMRISK/2012/01′, S-IN Soluzioni-Informatiche; Supporting Publications 2014: EN-654. [103 pp.]. Available online: http://www.efsa.europa.eu/it/supporting/pub/654e.htm EFSA (European Food Safety Authority), 2015, Further development and update of EFSA's Chemical Hazards Database. EFSA supporting publication 2015: EN-823. 84 pp. Available online: http://www.efsa.europa.eu/it/supporting/pub/823e. EFSA Panel on Food Additives and Nutrient Sources added to Food (ANS), 2012b. Guidance for submission for food additive evaluations. EFSA Journal 2012; 10(7):2760. EFSA Scientific Committee, 2012c. Scientific Opinion on Exploring options for providing advice about possible human health risks based on the concept of Threshold of Toxicological Concern (TTC). EFSA Journal 2012; 10(7):2750. EPA (US Environmental Protection Agency), 1993. Reference Dose (RfD): Description and Use in Health Risk Assessments Background Document 1A March 15, 1993. http://www.epa.gov/iris/rfd.htm EPA (US Environmental Protection Agency), 2011. Vocabulary Catalog List Detail - Integrated Risk Information System (IRIS) Glossary. http://ofmpub.epa.gov/sor_internet/registry/termreg/searchandretrieve/glossariesandkeywordlists/search.do?details=&glossaryName=IRIS%20Glossary, last updated: August 31, 2011. FDA (US Food and Drug Administration), 2005. Guidance for Industry Estimating the Maximum Safe Starting Dose in Initial Clinical Trials for Therapeutics in Adult Healthy Volunteers. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER), 2005. ILSI (International Life Sciences Institute), 2009. Application of the Margin of Exposure Approach to Compounds in Food which are both Genotoxic and Carcinogenic. Summary Report of a Workshop held in October 2008 Organised by the ILSI Europe Risk Assessment of Genotoxic Carcinogens in Food Task Force. Pushing date: October 2009. IPCS/OECD (International Programme on Chemical Safety/Organisation for Economic Cooperation and Development), 2004. Harmonization Project Document No. 1. IPCS Risk Assessment Terminology. World Health Organization Geneva, 2004 Kroes R, Renwick A G, Cheeseman M A, Kleiner J, Mangelsdorf I, Piersma A, Schilter B, Schlatter J, van Schothorst F, Vos J G and Wurtzen G, 2004. Structure-based thresholds of toxicological concern (TTC): guidance for application to substances present at low levels in the diet. Food and Chemical Toxicology 42: 65– 83. MeSH - Medical Subject Headings (MeSH®), 2014a. NLM controlled subject vocabulary used for indexing and cataloging: https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings (MeSH®), 2014b. NLM controlled subject vocabulary used for indexing and cataloging. 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This paper reviews in silico models currently available for the prediction of skin permeability. A comprehensive discussion on the developed methods is presented, focusing on quantitative structure-permeability relationships. In addition, the mechanistic models and comparative studies that analyse different models are discussed. Limitations and strengths of the different approaches are highlighted together with the emergent issues and perspectives.

Background: Combining computational toxicology with ExpoCast exposure estimates and ToxCast assay data gives us access to predictions of human health risks stemming from exposures to chemical mixtures. Objectives: To explore, through mathematical modeling and simulations, the size of potential effects of random mixtures of aromatase inhibitors on the dynamics of women's menstrual cycles. Methods: We simulated random exposures to millions of potential mixtures of 86 aromatase inhibitors. A pharmacokinetic model of intake and disposition of the chemicals predicted their internal concentration as a function of time (up to two years). A ToxCast aromatase assay provided concentration-inhibition relationships for each chemical. The resulting total aromatase inhibition was input to a mathematical model of the hormonal hypothalamus-pituitary-ovarian control of ovulation in women. Results: Above 10% inhibition of estradiol synthesis by aromatase inhibitors, noticeable (eventually reversible) effects on ovulation were predicted. Exposures to individual chemicals never led to such effects. In our best estimate, about 10% of the combined exposures simulated had mild to catastrophic impacts on ovulation. A lower bound on that figure, obtained using an optimistic exposure scenario, was 0.3%. Conclusions: These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach, suitable for high-throughput ranking and risk assessment. The size of the effects predicted is consistent with an increased risk of infertility in women from everyday exposures to our chemical environment.

This paper reviews in silico models currently available for the prediction of skin permeability. A comprehensive discussion on the developed methods is presented, focusing on quantitative structure-permeability relationships. In addition, the mechanistic models and comparative studies that analyse different models are discussed. Limitations and strengths of the different approaches are highlighted together with the emergent issues and perspectives.

The European Union’s ban on animal testing for cosmetic ingredients and products has generated a strong momentum for the development of in silico and in vitro alternative methods. One of the focus of the COSMOS project was ab initio prediction of kinetics and toxic effects through multiscale pharmacokinetic modeling and in vitro data integration. In our experience, mathematical or computer modeling and in vitro experiments are complementary. We present here a summary of the main models and results obtained within the framework of the project on these topics. A first section presents our work at the organelle and cellular level. We then go toward modeling cell levels effects (monitored continuously), multiscale physiologically based pharmacokinetic and effect models, and route to route extrapolation. We follow with a short presentation of the automated KNIME workflows developed for dissemination and easy use of the models. We end with a discussion of two challenges to the field: our limited ability to deal with massive data and complex computations.

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q2cv=0.610, Nopt=7, SEPcv=0.505, r2pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development.

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

EFSA Supporting PublicationsVolume 12, Issue 7 823E External scientific reportOpen Access Further development and update of EFSA's Chemical Hazards Database Beatrice Barbaro, Beatrice Barbaro S-IN Soluzioni InformaticheSearch for more papers by this authorRossella Baldin, Rossella Baldin S-IN Soluzioni InformaticheSearch for more papers by this authorSimona Kovarich, Simona Kovarich S-IN Soluzioni InformaticheSearch for more papers by this authorManuela Pavan, Manuela Pavan S-IN Soluzioni InformaticheSearch for more papers by this authorElena Fioravanzo, Elena Fioravanzo S-IN Soluzioni InformaticheSearch for more papers by this authorArianna Bassan, Arianna Bassan S-IN Soluzioni InformaticheSearch for more papers by this author Beatrice Barbaro, Beatrice Barbaro S-IN Soluzioni InformaticheSearch for more papers by this authorRossella Baldin, Rossella Baldin S-IN Soluzioni InformaticheSearch for more papers by this authorSimona Kovarich, Simona Kovarich S-IN Soluzioni InformaticheSearch for more papers by this authorManuela Pavan, Manuela Pavan S-IN Soluzioni InformaticheSearch for more papers by this authorElena Fioravanzo, Elena Fioravanzo S-IN Soluzioni InformaticheSearch for more papers by this authorArianna Bassan, Arianna Bassan S-IN Soluzioni InformaticheSearch for more papers by this author First published: 10 July 2015 https://doi.org/10.2903/sp.efsa.2015.EN-823Citations: 1 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Published date: 10 July 2015 Question number: EFSA-Q-2014-00312 AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL References Bolton E, Wang Y, Thiessen PA and Bryant SH, 2008. PubChem: Integrated Platform of Small Molecules and Biological Activities. Chapter 12, Annual Reports in Computational Chemistry, Volume 4, American Chemical Society, Washington, DC, 2008. EC (European Commission), 2000. Commission Regulation No 1565/2000 of 18 July 2000 laying down the measures necessary for the adoption of an evaluation programme in application of Regulation (EC) No 2232/96. Official Journal of the European Communities 19.7.2000, L 180, 8– 16. EFSA (European Food Safety Authority), 2005. Opinion of the Scientific Committee on a request from EFSA related to a harmonised approach for risk assessment of substances which are both genotoxic and carcinogenic. The EFSA Journal 2005, 282, 1– 31. EFSA, (European Food Safety Authority), 2006. Tolerable upper intake levels for vitamins and minerals. Scientific Committee on Food Scientific Panel on Dietetic Products, Nutrition and Allergies. February 2006. www.efsa.europa.eu/en/ndatopics/docs/ndatolerableuil.pdf EFSA, (European Food Safety Authority), 2008. Polycyclic Aromatic Hydrocarbons in Food, The EFSA Journal (2008) 724, 1– 114. EFSA, (European Food Safety Authority), 2009a. Guidance Document on Risk Assessment for Birds & Mammals on request from EFSA. EFSA Journal 2009; 7(12): 1438. doi:10.2903/j.efsa.2009.1438. EFSA, (European Food Safety Authority), 2009b. Marine biotoxins in shellfish – Saxitoxin group. EFSA Journal 2009; 1019, 1– 76 EFSA, (European Food Safety Authority), 2012a. Completion of data entry of pesticide ecotoxicology Tier 1 study endpoints in a XML schema – database. Pierobon E., Neri M.C., Marroncelli S., Croce V.; Supporting Publications 2012:EN-326. 96 pp. EFSA, (European Food Safety Authority), 2012b. Guidance for submission for food additive evaluations. EFSA Journal 2012; 10(7): 2760. EFSA, (European Food Safety Authority), 2012c. Opinion of the Scientific Committee on Exploring options for providing advice about possible human health risks based on the concept of Threshold of Toxicological Concern (TTC). EFSA Journal 2012; 10(7): 2750. EFSA, (European Food Safety Authority), 2013a. Report on ‘Data collection and data entry for EFSA's chemical hazards database NP/EFSA/EMRISK/2011/01’, S-IN Soluzioni-Informatiche; Supporting Publications 2013:EN-458.[ 140pp.]. 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In the present study, quantitative structure activity relationships were developed for predicting ready biodegradability of approximately 200 heterogeneous fragrance materials. Two classification methods, classification and regression tree (CART) and k-nearest neighbors (kNN), were applied to perform the modeling. The models were validated with multiple external prediction sets, and the structural applicability domain was verified by the leverage approach. The best models had good sensitivity (internal 80%; external 68%), specificity (internal 80%; external 73%), and overall accuracy (75%). Results from the comparison with BIOWIN global models, based on group contribution method, show that specific models developed in the present study perform better in prediction than BIOWIN6, in particular for the correct classification of not readily biodegradable fragrance materials. Environ Toxicol Chem 2015;34:1224-1231. (c) 2015 SETAC