Structural alerts for estimating the carcinogenicity of pesticides and biocides

Devillers, James; Mombelli, Enrico; Samsera, R.

doi:10.1080/1062936x.2010.548349

Cited by 26 publications

(13 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The carcinogenicity data involved in this study has been used in QSAR studies mostly as a source of further data retrieval. It has been used, for example, as a part of considered data in [87]. A thorough analysis of the mutagenicity data set including the applicability domain estimation has been performed in [40].…”

Section: Resultsmentioning

confidence: 99%

Supervised extensions of chemography approaches: case studies of chemical liabilities assessment

et al. 2014

View full text Add to dashboard Cite

Chemical liabilities, such as adverse effects and toxicity, play a significant role in modern drug discovery process. In silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Herein, we propose an approach combining several classification and chemography methods to be able to predict chemical liabilities and to interpret obtained results in the context of impact of structural changes of compounds on their pharmacological profile. To our knowledge for the first time, the supervised extension of Generative Topographic Mapping is proposed as an effective new chemography method. New approach for mapping new data using supervised Isomap without re-building models from the scratch has been proposed. Two approaches for estimation of model’s applicability domain are used in our study to our knowledge for the first time in chemoinformatics. The structural alerts responsible for the negative characteristics of pharmacological profile of chemical compounds has been found as a result of model interpretation.

show abstract

Section: Resultsmentioning

confidence: 99%

Supervised extensions of chemography approaches: case studies of chemical liabilities assessment

et al. 2014

View full text Add to dashboard Cite

show abstract

“…For this purpose, we plan in further steps to 1. Refine the criteria to assess the toxicity of ASs by using (quantitative) structure-activity relationship ((Q)SAR) and quantitative structure-property relationship (QSPR) modeling (Devillers, 2004;Devillers and Devillers, 2009;Devillers et al, 2006Devillers et al, , 2011Saxena et al, 2014), 2. Relate these predicted activity and property data to several endpoints (clutch size, fertility and hatchability rates, embryo anomalies, eggshell thickness) by means of in silico approaches, 3.…”

Section: Exposure Statistics and Risk Of Reproductive Toxicitymentioning

confidence: 99%

Quantification of potential exposure of gray partridge (Perdix perdix) to pesticide active substances in farmlands

Bro¹,

Millot²,

Decors³

et al. 2015

Science of The Total Environment

View full text Add to dashboard Cite

Estimating exposure of wild birds to plant protection products is of key importance in the risk assessment process evaluating their harmful potential. In this paper, we propose an ecologically-relevant methodology to estimate potential exposure to active substances (ASs) of a farmland focal bird, the gray partridge Perdix perdix. It is based on bird habitat use of fields at the time of pesticide applications. It accounts for spatio-temporal heterogeneity at population and landscape scales. We identify and quantify the potential exposure to 179 ASs of 140 clutches during pre-laying, laying, and incubation phases, and of 75 coveys. The data come from a large scale field study combining radiotelemetry and a farmer survey. They were collected in 12 different representative sites. The proportion of clutches potentially exposed to a given chemical was ≥5% for 32 ASs; prothioconazole and epoxiconazole ranking first. 71% of clutches were potentially exposed to ≥1 AS and 67% to ≥2 ASs. Mixtures involved 2 to 22 ASs. They emerged from commercial formulations, tank mixtures, bird habitat use, and combinations. ASs were fungicides (53%), herbicides (25%), and insecticides (16%) used on a variety of crops in April-June, when ground-nesting birds are breeding. The European Food Safety Authority conclusions report a long-term first-tier toxicity-to-exposure ratio (TERlt) <5 for 11 out of 19 documented ASs, and higher-tier TERlt <5 for 5 out of 10 ASs. This suggests a potential risk for bird reproduction in farmlands. Globally 13% of coveys were potentially exposed to 18 ASs during the first month (1-4 coveys per AS). The use of our field data in future research and risk assessment is discussed.

show abstract

“…General Reviews [29][30][31][32][33][34][35][36][37][38][39][40] Genotoxicity/Carcinogenicity [25,26,[41][42][43][44][45][46] Skin sensitization [24,[47][48][49][50] Cardiotoxicity [51][52][53][54] P450 inhibition/Drug-drug interactions [21,[55][56][57][58][59][60][61] Metabolite prediction [61][62][63][64][65][66][67][68] Reproductive/Developmental toxicity [69][70][71] It should be noted that the in silico toxicology models discussed here are hazard identification methods, and in the majority of cases they do not take consider the dose and exposure into account. Therefore, these methods will not be capable of p...…”

Section: A List Of Comprehensive Reviews or Notable Papers In The Imentioning

confidence: 99%

“…The socalled 2D molecular descriptors are computed from a representation of a molecule which takes into account the precise connectivity. Examples include the presence of particular functional groups [26], or path-based descriptors/indices [113] and fingerprints [113][114][115]. Finally, 3D molecular descriptors capture structural information based on the three-dimensional structure of a molecule (or set of conformations, sometimes referred to as 4D descriptors [116]).…”

Section: Encoding Chemical Information In In Silico Modelsmentioning

confidence: 99%

“…In an effort to conform with the OECD QSAR regulatory guidelines [18], in silico methods to predict skin sensitization, which is believed to be a toxic response due to covalent modification of unknown proteins in the epidermis, rely on mechanistically interpretable models, at least in terms of their encoding of chemical reactivity. This means that models based on relatively simple chemical descriptors are generally preferred (such as the presence or absence of certain fragment indicator variables) [24][25][26].…”

mentioning

confidence: 99%

See 1 more Smart Citation

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

Gleeson¹,

Modi²,

Bender³

et al. 2012

CPD

View full text Add to dashboard Cite

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. In this review we discuss the challenges involved in both the building of in silico models on toxicology endpoints and their practical use in decision making. In particular, we will reflect upon the predictive strength of a number of different in silico models for a range of different endpoints, different approaches used to generate the models or rules, and limitations of the methods and the data used in model generation. Given that there exists no unique definition of a 'good' model, we will furthermore highlight the need to balance model complexity/interpretability with predictability, particularly in light of OECD/REACH guidelines. Special emphasis is put on the data and methods used to generate the in silico toxicology models, and their strengths and weaknesses are discussed. Switching to the applied side, we next review a number of toxicity endpoints, discussing the methods available to predict them and their general level of predictability (which very much depends on the endpoint considered). We conclude that, while in silico toxicology is a valuable tool to drug discovery scientists, much still needs to be done to, firstly, understand more completely the biological mechanisms for toxicity and, secondly, to generate more rapid in vitro models to screen compounds. With this biological understanding, and additional data available, our ability to generate more predictive in silico models should significantly improve in the future.

show abstract

Structural alerts for estimating the carcinogenicity of pesticides and biocides

Cited by 26 publications

References 45 publications

Supervised extensions of chemography approaches: case studies of chemical liabilities assessment

Supervised extensions of chemography approaches: case studies of chemical liabilities assessment

Quantification of potential exposure of gray partridge (Perdix perdix) to pesticide active substances in farmlands

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

Contact Info

Product

Resources

About