Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework

Villaverde, Juan José; Sevilla-Morán, Beatriz; López-Goti, Carmen; Alonso‐Prados, José Luis; Sandín‐España, Pilar

doi:10.1016/j.scitotenv.2018.04.033

Cited by 83 publications

(42 citation statements)

References 86 publications

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“…In this sense, Gramatica (2007) [ 30 ] and Tropsha (2010) [ 31 ] performed excellent reviews focused on the validation of the QSAR models. Recently, Villaverde et al (2018) [ 32 ] also treated this concern in a work focused on nanopesticide risk assessment within the European legislative framework. Work such as those performed by Zhang et al (2006) [ 33 ] and Melagraki et al (2017) [ 34 ] provide interesting modeling approaches to predict the activity of chemicals, fulfilling the criteria for model validation.…”

Section: Resultsmentioning

confidence: 99%

Assessing the Effects of Alloxydim Phototransformation Products by QSAR Models and a Phytotoxicity Study

Villaverde¹,

Santín-Montanyá²,

Sevilla-Morán³

et al. 2018

Molecules

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Once applied, an herbicide first makes contact with leaves and soil. It is known that photolysis can be one of the most important processes of dissipation of herbicides in the field. However, degradation does not guarantee detoxification and can give rise to byproducts that could be more toxic and/or persistent than the active substance. In this work, the photodegradation of alloxydim herbicide in soil and leaf cuticle surrogates was studied and a detailed study on the phytotoxicity of the main byproduct on sugar beet, tomato, and rotational crops was performed. Quantitative structure–activity relationship (QSAR) models were used to obtain a first approximation of the possible ecotoxicological and environmental implications of the alloxydim and its degradation product. The results show that alloxydim is rapidly degraded on carnauba and sandy loam soil surfaces, two difficult matrices to analyze and not previously studied with alloxydim. Two transformation products that formed in both matrices were identified: alloxydim Z-isomer and imine derivative (mixture of two tautomers). The phytotoxicity of alloxydim and the major byproduct shows that tomato possesses high sensitivity to the imine byproduct, while wheat crops are inhibited by the parent compound. This paper demonstrates the need to further investigate the behavior of herbicide degradation products on target and nontarget species to determine the adequate use of herbicidal products to maximize productivity in the context of sustainable agriculture.

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Section: Resultsmentioning

confidence: 99%

Assessing the Effects of Alloxydim Phototransformation Products by QSAR Models and a Phytotoxicity Study

Villaverde¹,

Santín-Montanyá²,

Sevilla-Morán³

et al. 2018

Molecules

Self Cite

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“…The quality of regression can be assessed by the squared correlation coefficient (R 2 ) [54] or the standard error of estimation (SEE) [57]. Only models with a higher R 2 than the thresholds defined in previous studies should be considered acceptable [8]. Furthermore, the adjusted R-squared (Radj 2 ) value can also be calculated in order to prevent over-fitting [38].…”

Section: Goodness-of-fitmentioning

confidence: 99%

“…Nanoforms toxicity databases are available at a developmental stage and data obtained from research studies originate from different experimental procedures. Furthermore, the development of reliable data sets from a computational perspective requires that data be sufficient to allow splitting after assessing its accuracy and suitability specifically for computational use [8]. Knowledge-based expert systems often refer to data-driven modeling.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Practices and Trends of Machine Learning Application in Nanotoxicology

et al. 2020

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Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

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“…Indeed, due to a growing need for toxicity assessment and the increasing variety and number of products, regulatory agencies and legislations, such as REACH (Registration, Evaluation and Authorisation of Chemical, the European legislation on chemicals) encourage the use of alternatives to animal testing. Computational approaches can accelerate advances in (eco)toxicological understanding as they support the experimental data with additional in silico studies and results (Mas et al 2010;Hamadache et al 2017;Villaverde et al 2017Villaverde et al , 2018. It was recently proposed to transpose QSAR models to nanomaterials to rapidly and cheaply screen and predict their toxicity (Pan et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…This adaptation is still at the development stage and is not without challenges (Gajewicz et al 2012). To continue with the example of pesticides, the environmental fate and behavior of these products formulated as nano are complex and vary from traditional formulations, arguing for the need of innovative and adapted risk assessment methodologies (Villaverde et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Towards an alternative to nano-QSAR for nanoparticle toxicity ranking in case of small datasets

et al. 2019

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Statistical analysis approaches have been developed to predict the biological response to nanoparticles, especially quantitative structure-activity relationship (QSAR) models. But one major limitation remains the quantitative lack of data to build accurate models. The aim of this study was to investigate if simple alternative mathematical models could rank nanoparticles in a very binary way (i.e. toxic or not) in case of small dataset. We synthesized and characterized 25 nanoparticles from 6 metal (hydr)oxide families with particle size and shape tuning. We assessed their toxicity on RAW 264.7 cells and investigated relationships with both physicochemical and dimensional descriptors. A simple partial least square (PLS) regression analysis allowed ranking nanoparticles with respect to their toxicity, without false-negative results. But this model was not predictive due to the specific response of each family to dimensional parameters variations. A classification tree extracted the same main bulk descriptor as PLS, but interestingly showed the relevance of dimensional descriptors for the second and third node. We thus recommend the development of family-specific models and propose the combination of these two simple methods as pre-screening tools, a compromise to bridge the gap between case-by-case studies (expensive and time-consuming) and sophisticated nano-QSAR models (not suitable for small datasets).

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Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework

Cited by 83 publications

References 86 publications

Assessing the Effects of Alloxydim Phototransformation Products by QSAR Models and a Phytotoxicity Study

Assessing the Effects of Alloxydim Phototransformation Products by QSAR Models and a Phytotoxicity Study

Practices and Trends of Machine Learning Application in Nanotoxicology

Towards an alternative to nano-QSAR for nanoparticle toxicity ranking in case of small datasets

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