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  1. Article ; Online: 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.

    Benigni, Romualdo

    Regulatory toxicology and pharmacology : RTP

    2021  Volume 126, Page(s) 105042

    Abstract: 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 ... ...

    Abstract 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).
    MeSH term(s) Animal Welfare ; Computational Chemistry/methods ; Computational Chemistry/standards ; Cost-Benefit Analysis ; Databases, Factual ; Drug-Related Side Effects and Adverse Reactions/diagnosis ; False Negative Reactions ; Mutagenicity Tests/standards ; Pharmaceutical Preparations/chemistry ; Quantitative Structure-Activity Relationship ; Time Factors
    Chemical Substances Pharmaceutical Preparations
    Language English
    Publishing date 2021-09-08
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 604672-1
    ISSN 1096-0295 ; 0273-2300
    ISSN (online) 1096-0295
    ISSN 0273-2300
    DOI 10.1016/j.yrtph.2021.105042
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  2. Article ; Online: Towards quantitative read across: Prediction of Ames mutagenicity in a large database.

    Benigni, Romualdo

    Regulatory toxicology and pharmacology : RTP

    2019  Volume 108, Page(s) 104434

    Abstract: 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, ... ...

    Abstract 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.
    MeSH term(s) Databases, Factual ; Mutagenicity Tests ; Mutagens/chemistry ; Mutagens/toxicity ; Quantitative Structure-Activity Relationship ; Risk Assessment/methods ; Salmonella/drug effects ; Salmonella/genetics
    Chemical Substances Mutagens
    Language English
    Publishing date 2019-07-30
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 604672-1
    ISSN 1096-0295 ; 0273-2300
    ISSN (online) 1096-0295
    ISSN 0273-2300
    DOI 10.1016/j.yrtph.2019.104434
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  3. Article ; Online: In silico approaches to genetic toxicology: progress and future.

    Benigni, Romualdo

    Mutagenesis

    2018  Volume 34, Issue 1, Page(s) 1–2

    Abstract: Computational toxicology, also called 'in silico toxicology', is based on scientific knowledge gained from different scientific fields and on the premise that the toxicity of a chemical, depending on its intrinsic nature, can be predicted from its ... ...

    Abstract Computational toxicology, also called 'in silico toxicology', is based on scientific knowledge gained from different scientific fields and on the premise that the toxicity of a chemical, depending on its intrinsic nature, can be predicted from its molecular structure and inferred from the properties of similar compounds whose activities are known. With the aim of providing faster, more economical, animal-free tools for predicting toxicity, the 'old' and well established science of Structure-Activity Relationships plays a crucial role, with increasing applications to the assessment of chemical genotoxicity and carcinogenicity. The development of the Structure-Activity Relationships algorithms is a continuous process, and new models, as well as newer versions of applications, are continuously becoming available. This Mutagenesis Special Issue presents a collection of papers on the recent advances in the field, and provides a precious snapshot in time with the most updated information available today.
    MeSH term(s) Algorithms ; Carcinogenesis/genetics ; Computer Simulation ; Humans ; Mutagenicity Tests/trends ; Mutagens/toxicity ; Quantitative Structure-Activity Relationship
    Chemical Substances Mutagens
    Language English
    Publishing date 2018-07-30
    Publishing country England
    Document type Introductory Journal Article
    ZDB-ID 632903-2
    ISSN 1464-3804 ; 0267-8357
    ISSN (online) 1464-3804
    ISSN 0267-8357
    DOI 10.1093/mutage/gey018
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  4. Article ; Online: Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity.

    Tcheremenskaia, Olga / Benigni, Romualdo

    Expert opinion on drug metabolism & toxicology

    2021  Volume 17, Issue 8, Page(s) 987–1005

    Abstract: ... ...

    Abstract Introduction
    MeSH term(s) Animal Testing Alternatives/methods ; Animals ; Computer Simulation ; Databases, Factual ; Humans ; Mutagenicity Tests/methods ; Mutagens/toxicity ; Quantitative Structure-Activity Relationship ; Reproducibility of Results
    Chemical Substances Mutagens
    Language English
    Publishing date 2021-06-21
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2214462-6
    ISSN 1744-7607 ; 1742-5255
    ISSN (online) 1744-7607
    ISSN 1742-5255
    DOI 10.1080/17425255.2021.1938540
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  5. Article ; Online: Predictive toxicology today: the transition from biological knowledge to practicable models.

    Benigni, Romualdo

    Expert opinion on drug metabolism & toxicology

    2016  Volume 12, Issue 9, Page(s) 989–992

    MeSH term(s) Animal Testing Alternatives/methods ; Animals ; Drug Design ; Humans ; Models, Theoretical ; Risk Assessment/methods ; Toxicity Tests/methods ; Toxicology/methods
    Language English
    Publishing date 2016-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 2214462-6
    ISSN 1744-7607 ; 1742-5255
    ISSN (online) 1744-7607
    ISSN 1742-5255
    DOI 10.1080/17425255.2016.1206889
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  6. Article ; Online: Predicting the carcinogenicity of chemicals with alternative approaches: recent advances.

    Benigni, Romualdo

    Expert opinion on drug metabolism & toxicology

    2014  Volume 10, Issue 9, Page(s) 1199–1208

    Abstract: Introduction: Alternative approaches to the rodent bioassay are necessary for early identification of problematic drugs and biocides during the development process, and are the only practicable tool for assessing environmental chemicals with no or ... ...

    Abstract Introduction: Alternative approaches to the rodent bioassay are necessary for early identification of problematic drugs and biocides during the development process, and are the only practicable tool for assessing environmental chemicals with no or adequate safety documentation.
    Areas covered: This review informs on: i) the traditional prescreening through genotoxicity testing; ii) an integrative approach that assesses DNA-reactivity and ability to disorganize tissues; iii) new applications of omics technologies (ToxCast/Tox21 project); iv) a pragmatic approach aimed at filling data gaps by intrapolating/extrapolating from similar chemicals (read-across, category formation). The review also approaches the issue of the concerns about false-positive and false-negative results that prevents a wider acceptance and use of alternatives.
    Expert opinion: The review addresses strengths and limitations of various proposals, and concludes on the need of differential approaches to the issue of false negatives and false positives. False negatives can be eliminated or reduced below the variability of the animal assay with conservative quantitative structure-activity relationships or in vitro tests; false positives can be cleared with ad hoc mechanistically based follow-ups. This framework can permit a reduction of animal testing and a better protection of human health.
    MeSH term(s) Animal Testing Alternatives ; Animals ; Biological Assay ; Carcinogenicity Tests/methods ; Carcinogens/chemistry ; Carcinogens/toxicity ; Disinfectants/toxicity ; False Negative Reactions ; False Positive Reactions ; Humans ; Mutagenicity Tests/methods ; Quantitative Structure-Activity Relationship
    Chemical Substances Carcinogens ; Disinfectants
    Language English
    Publishing date 2014-09
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2214462-6
    ISSN 1744-7607 ; 1742-5255
    ISSN (online) 1744-7607
    ISSN 1742-5255
    DOI 10.1517/17425255.2014.934670
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  7. Article ; Online: Evaluation of the toxicity forecasting capability of EPA's ToxCast Phase I data: can ToxCast in vitro assays predict carcinogenicity?

    Benigni, Romualdo

    Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews

    2013  Volume 31, Issue 3, Page(s) 201–212

    Abstract: Long-term rodent bioassays have played a central role in protecting human health from carcinogens; for ethical and practical reasons their use is decreasing whereas genotoxicity testing has taken a pivotal role. However, this strategy--as presently ... ...

    Abstract Long-term rodent bioassays have played a central role in protecting human health from carcinogens; for ethical and practical reasons their use is decreasing whereas genotoxicity testing has taken a pivotal role. However, this strategy--as presently implemented--is not sensitive enough to detect all genotoxic carcinogens, and cannot detect nongenotoxic carcinogens. Among the alternative approaches under study there is the ToxCast/Tox21 project. Following a previous study from our laboratory, here we present a new, more extensive analysis of ToxCast Phase I results, indicating that at the present state-of-art this approach is not able to predict the carcinogenicity of chemicals. Possible reasons for this mediocre performance are discussed, and opinions on ways to tune up the project in the next phases are presented.
    MeSH term(s) Animal Testing Alternatives/methods ; Animals ; Carcinogenicity Tests/methods ; Carcinogens/toxicity ; High-Throughput Screening Assays/methods ; Humans ; Mice ; Rats ; United States ; United States Environmental Protection Agency
    Chemical Substances Carcinogens
    Language English
    Publishing date 2013
    Publishing country United States
    Document type Evaluation Studies ; Journal Article
    ISSN 1532-4095
    ISSN (online) 1532-4095
    DOI 10.1080/10590501.2013.824188
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  8. Article ; Online: In silico

    Benigni, Romualdo / Bassan, Arianna / Pavan, Manuela

    Expert opinion on drug metabolism & toxicology

    2020  Volume 16, Issue 8, Page(s) 651–662

    Abstract: Introduction: Whereas in the past, (Q)SAR methods have been largely used to support the design of new drugs, in the last few decades, there has been a new interest in its applications for the assessment of drug safety. In particular, the ICH M7 ... ...

    Abstract Introduction: Whereas in the past, (Q)SAR methods have been largely used to support the design of new drugs, in the last few decades, there has been a new interest in its applications for the assessment of drug safety. In particular, the ICH M7 guideline has introduced the concept that (Q)SAR predictions for the Ames mutagenicity of drug impurities can be used for regulatory purposes.
    Areas covered: This review introduces the ICH M7 conceptual framework and illustrates the most updated evaluations of the
    Expert opinion: Given the growing recognition of (Q)SAR approaches, more investment will be devoted to its improvement. The major areas of research should be the expansion and curation of the experimental training sets, with particular attention to the portions of chemical space which are poorly represented. New modeling methodologies (e.g. machine-learning methods) may support this effort, particularly for treating proprietary data without disclosure. Research on new integrative approaches for regulatory decisions will also be important.
    MeSH term(s) Animals ; Computer Simulation ; Drug Contamination ; Drug Design ; Drug and Narcotic Control ; Humans ; Machine Learning ; Mutagenicity Tests/methods ; Quantitative Structure-Activity Relationship
    Language English
    Publishing date 2020-06-28
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2214462-6
    ISSN 1744-7607 ; 1742-5255
    ISSN (online) 1744-7607
    ISSN 1742-5255
    DOI 10.1080/17425255.2020.1785428
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  9. Article ; Online: Alternatives to the carcinogenicity bioassay for toxicity prediction: are we there yet?

    Benigni, Romualdo

    Expert opinion on drug metabolism & toxicology

    2012  Volume 8, Issue 4, Page(s) 407–417

    Abstract: Introduction: For decades, traditional toxicology has been the ultimate source of information on the carcinogenic potential of chemicals; however, with increasing demand on regulation of chemicals and decreasing resources for testing, opportunities to ... ...

    Abstract Introduction: For decades, traditional toxicology has been the ultimate source of information on the carcinogenic potential of chemicals; however, with increasing demand on regulation of chemicals and decreasing resources for testing, opportunities to accept 'alternative' approaches have dramatically expanded. The need for tools able to identify carcinogens in shorter times and at a lower cost in terms of animal lives and money is still an open issue, and the present strategies and regulations for carcinogenicity prescreening do not adequately protect human health.
    Areas covered: This paper briefly summarizes the theories on the early steps of carcinogenesis and presents alternative detection methods for carcinogens based on genetic toxicology, structure-activity relationships and cell transformation assays.
    Expert opinion: There is evidence that the combination of Salmonella and structural alerts for the DNA-reactive carcinogens, and in vitro cell transformation assays for nongenotoxic carcinogens, permits the identification of a very large proportion of carcinogens. If implemented, this alternative strategy could improve considerably the protection of human health.
    MeSH term(s) Animal Testing Alternatives ; Animals ; Biological Assay ; Carcinogenicity Tests/economics ; Carcinogenicity Tests/methods ; Carcinogens/toxicity ; Cell Transformation, Neoplastic ; DNA/drug effects ; Drug Evaluation, Preclinical ; Drug-Related Side Effects and Adverse Reactions ; Humans ; Mice ; Mutagenicity Tests ; Mutagens/toxicity ; Predictive Value of Tests ; Rats ; Structure-Activity Relationship
    Chemical Substances Carcinogens ; Mutagens ; DNA (9007-49-2)
    Language English
    Publishing date 2012-04
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2214462-6
    ISSN 1744-7607 ; 1742-5255
    ISSN (online) 1744-7607
    ISSN 1742-5255
    DOI 10.1517/17425255.2012.666238
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  10. Article ; Online: Data-based review of QSARs for predicting genotoxicity: the state of the art.

    Benigni, Romualdo / Bossa, Cecilia

    Mutagenesis

    2018  Volume 34, Issue 1, Page(s) 17–23

    Abstract: With the aim of providing faster, more economical, animal-free tools to predict toxicity, quantitative structure-activity relationships (QSAR) approaches are increasingly applied to the chemical risk assessment-in particular genotoxicity and ... ...

    Abstract With the aim of providing faster, more economical, animal-free tools to predict toxicity, quantitative structure-activity relationships (QSAR) approaches are increasingly applied to the chemical risk assessment-in particular genotoxicity and carcinogenicity. The more recent period of time has witnessed refinements of the predictive systems, with the collection of larger training sets and continued fine-tuning, together with an increased interest for the use of QSAR by regulatory authorities. This literature review provides an updated snapshot of the present state of the art in the evaluation of QSAR methods as applied to genotoxicity. Overall, the abilities of software tools to predict Ames test mutagenicity were comparable with previously published evaluations, with sensitivity ranging 0.72-0.96, and specificity ranging 0.65-0.86 in applications to public data sets. These values compare quite fairly with the intrinsic variability of the Ames test. A preliminary analysis indicated a consistency with the results of the Japan Division of Genetics and Mutagenesis, National Institute of Health Sciences of Japan (DGM/NIHS) Ames/QSAR international collaborative project. Applications to a variety of external test sets pointed to the need of further improvements of the coverage/representation of the whole chemical space. Combinations of tools showed that sensitivity is usually increased at the expense of a decrease in specificity, whereas the supervision by expert judgement generated more equilibrated results. This points to the existence of a large area of context-dependent expert knowledge, which has not been formalised yet and has the potential to substantially improve the prediction systems. The overall evidence suggests that (Q)SARs for the Ames test have sufficient reliability for use in prioritisation processes as well as to support regulatory decisions in combination with other evidence. The use of highly sensitive genotoxicity QSARs in tiered integrations with other tools is suggested as a mean to shortlist chemicals for which no further testing is necessary.
    MeSH term(s) Carcinogens/toxicity ; DNA Damage/drug effects ; DNA Damage/genetics ; Databases, Factual ; Humans ; Mutagenicity Tests ; Mutagens/toxicity ; Quantitative Structure-Activity Relationship ; Risk Assessment ; Software
    Chemical Substances Carcinogens ; Mutagens
    Language English
    Publishing date 2018-11-19
    Publishing country England
    Document type Journal Article
    ZDB-ID 632903-2
    ISSN 1464-3804 ; 0267-8357
    ISSN (online) 1464-3804
    ISSN 0267-8357
    DOI 10.1093/mutage/gey028
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