QSAR/QSPR models based on quantum chemistry for risk assessment of pesticides according to current European legislation

Classification Model of Pesticide Toxicity in Americamysis bahia Based on Quantum Chemical Descriptors

A set of quantum chemical descriptors (molecular polarization, heat capacity, entropy, Mulliken net charge of the most positive hydrogen atom, APT charge of the most negative atom and APT charge of the most positive atom with hydrogen summed into heavy atoms) was successfully used to establish the classification models for the toxicity pLC50 of pesticides in Americamysis bahia. The optimal random forest model (Class Model A) yielded predictive accuracy of 100% (training set of 217 pesticides), 95.8% (test set of 72 pesticides) and 99.0% (total set of 289 pesticides), which were very satisfactory, compared with previous classification models reported for the toxicity of compounds in aquatic organisms. Therefore, it is reasonable to apply the quantum chemical descriptors associated with molecular structural information on molecular bulk, chemical reactivity and weak interactions, to develop classification models for the toxicity pLC50 of pesticides in A. bahia.

QSPR predicting the vapor pressure of pesticides into high/low volatility classes

In this work, the vapor pressure of pesticides is employed as an indicator of their volatility potential. Quantitative Structure-Property Relationship models are established to predict the classification of compounds according to their volatility, into the high and low binary classes separated by the 1-mPa limit. A large dataset of 1005 structurally diverse pesticides with known experimental vapor pressure data at 20 °C is compiled from the publicly available Pesticide Properties DataBase (PPDB) and used for model development. The freely available PaDEL-Descriptor and ISIDA/Fragmentor molecular descriptor programs provide a large number of 19,947 non-conformational molecular descriptors that are analyzed through multivariable linear regressions and the Replacement Method technique. Through the selection of appropriate molecular descriptors of the substructure fragment type and the use of different standard classification metrics of model's quality, the classification of the structure-property relationship achieves acceptable results for discerning between the high and low volatility classes. Finally, an application of the obtained QSPR model is performed to predict the classes for 504 pesticides not having experimentally measured vapor pressures.

Pesticide soil microbial toxicity: setting the scene for a new pesticide risk assessment for soil microorganisms (IUPAC Technical Report)

Pesticides constitute an integral part of modern agriculture. However, there are still concerns about their effects on non-target organisms. To address this the European Commission has imposed a stringent regulatory scheme for new pesticide compounds. Assessment of the aquatic toxicity of pesticides is based on a range of advanced tests. This does not apply to terrestrial ecosystems, where the toxicity of pesticides on soil microorganisms, is based on an outdated and crude test (N mineralization). This regulatory gap is reinforced by the recent methodological and standardization advances in soil microbial ecology. The inclusion of such standardized tools in a revised risk assessment scheme will enable the accurate estimation of the toxicity of pesticides on soil microorganisms and on associated ecosystem services. In this review we (i) summarize recent work in the assessment of the soil microbial toxicity of pesticides and point to ammonia-oxidizing microorganisms (AOM) and arbuscular mycorrhizal fungi (AMF) as most relevant bioindicator groups (ii) identify limitations in the experimental approaches used and propose mitigation solutions, (iii) identify scientific gaps and (iv) propose a new risk assessment procedure to assess the effects of pesticides on soil microorganisms.

Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory

Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.

A study using QSAR/QSPR models focused on the possible occurrence and risk of alloxydim residues from chlorinated drinking water, according to the EU Regulation

Any active substance with phytosanitary capacity intended to be marketed in Europe must pass exhaustive controls to assess its risk before being marketed and used in European agriculture. Since the implementation of Regulation (EC) No 1107/2009, agrochemical companies have been obliged to study the formation of pesticide transformation products (TPs) during the treatment of drinking water containing pesticide residues. However, there is no consensus on how to address this requirement. In this research work, the open literature collection on alloxydim was used to propose potential chlorination paths from alloxydim isomers. Furthermore, several QSAR/QSPR models have been used to fill the of knowledge gap relative to some key parameters in the physico-chemical, environmental and ecotoxicological areas of potential alloxydim TPs from chlorinated water for which little information exists. In this way, it has been possible to estimate the state of aggregation of these TPs (they exist mainly as liquids) as well as their ease of transit between the different phases, to predict their possible behaviour in the three environmental compartments (e.g., thermophysical properties point to a change in their evolution with respect to the parent alloxydim isomers) and to anticipate their potential risk to human and animal health (e.g., all of them cause developmental toxicity). These and other results highlight that the hazards of several TPs, i.e., both chlorinated and nonchlorinated from parent alloxydim or from those obtained after cleavage of the N - O bond and the subsequent reaction with chlorine, should be seriously considered. The obtained results reopen the debate on the implications of the use of QSAR/QSPR models for pesticide risk assessment in the legislative framework.

A critical review of effect modeling for ecological risk assessment of plant protection products

A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).

Development of QSAR models for evaluating pesticide toxicity against Skeletonema costatum

Nowadays, the emergence of pesticides and its application in agriculture greatly improved the crop quality and food production. However, the resulted ecological problem caused by the widespread pesticide residues attracted more and more attention since the pesticides were harmful to most living organisms. Regulatory agencies such as Environmental Protection Agency (EPA) and European Chemicals Agency (ECHA) stipulated that a comprehensive pesticides risk assessment was essential and also underscored the application of computation method in evaluating pesticides. The present study aimed to use the Quantitative Structure-Activity Relationship (QSAR) method to establish models for quantitatively and qualitatively predicting the toxicity of pesticide against Skeletonema costatum. The regression model was developed using the Genetic Algorithm plus Multiple Linear Regression method and the classification model was established based on the Random Forest algorithm, respectively. Various internal and external validation metrics suggested that the obtained regression model was of good fitness (R²=0.722), robustness (Q_LOO²=0.653) and external predictive ability (Q_Fn²:0.719-0.776, CCC = 0.878). The classification could correctly predict 79.4% of pesticides in the training set and 69.7% in the validation set. The relatively high sensitivity value of the classification model indicated its good performance in identifying high-toxic pesticides. It could be concluded from the selected modelling descriptors that molecular weight and polarizability impacted the toxicity the most. The atom-type E-state descriptors generally contributed negatively to the pesticide toxicity which verified the negative influence of molecular hydrophilicity. Moreover, the lipophilic, carbon-type, charge related descriptors demonstrated the important influence of lipophilicity and polarity on pesticide toxicity. The models presented in this work could be used to pre-evaluate the toxicity of pesticides within the applicability domain, thus focusing resources on the high-toxic pesticides and assessing the environmental risk of pesticides quickly and economically.

Application of Molecular Docking, Homology Modeling, and Chemometric Approaches to Neonicotinoid Toxicity against Aphis craccivora

Neonicotinoids are known as effective pesticides against various insect species. They can harm useful insects including honeybees, with a relatively low threat to nontarget organisms and the environment. This paper presents combined methods to explore the insecticidal activity of neonicotinoids with diverse scaffolds, active against Aphis craccivora. Pharmacophore, molecular docking into the active site of nicotinic acetylcholine receptor homology model, and linear and non-linear regression approaches were used to find new insecticide candidates. The potential toxic effects against honeybees were evaluated using the molecular docking in the active site of the new Aphis mellifera homology model. Four new untested compounds were assigned as insecticide candidates, active against Aphis craccivora with less potential toxic effects for honeybees. This approach may be an effective strategy to design environmentally friendly insecticides against the cowpea aphid.

Molecular Modifications and Control of Processes to Facilitate the Synergistic Degradation of Polybrominated Diphenyl Ethers in Soil by Plants and Microorganisms Based on Queuing Scoring Method

In this paper, a combination of modification of the source and regulation of the process was used to control the degradation of PBDEs by plants and microorganisms. First, the key proteins that can degrade PBDEs in plants and microorganisms were searched in the PDB (Protein Data Bank), and a molecular docking method was used to characterize the binding ability of PBDEs to two key proteins. Next, the synergistic binding ability of PBDEs to the two key proteins was evaluated based on the queuing integral method. Based on this, three groups of three-dimensional quantitative structure-activity relationship (3D-QSAR) models of plant-microbial synergistic degradation were constructed. A total of 30 PBDE derivatives were designed using BDE-3 as the template molecule. Among them, the effect on the synergistic degradation of six PBDE derivatives, including BDE-3-4, was significantly improved (increased by more than 20%) and the environment-friendly and functional evaluation parameters were improved. Subsequently, studies on the synergistic degradation of PBDEs and their derivatives by plants and microorganisms, based on the molecular docking method, found that the addition of lipophilic groups by modification is beneficial to enhance the efficiency of synergistic degradation of PBDEs by plants and microorganisms. Further, while docking PBDEs, the number of amino acids was increased and the binding bond length was decreased compared to the template molecules, i.e., PBDE derivatives could be naturally degraded more efficiently. Finally, molecular dynamics simulation by the Taguchi orthogonal experiment and a full factorial experimental design were used to simulate the effects of various regulatory schemes on the synergistic degradation of PBDEs by plants and microorganisms. It was found that optimal regulation occurred when the appropriate amount of carbon dioxide was supplied to the plant and microbial systems. This paper aims to provide theoretical support for enhancing the synergistic degradation of PBDEs by plants and microorganisms in e-waste dismantling sites and their surrounding polluted areas, as well as, realize the research and development of green alternatives to PBDE flame retardants.

Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures

The adverse outcome pathway (AOP) was introduced as an alternative method to avoid unnecessary animal tests. Under the AOP framework, an in silico methods, molecular initiating event (MIE) modeling is used based on the ligand-receptor interaction. Recently, the intersecting AOPs (AOP 347), including two MIEs, namely peroxisome proliferator-activated receptor-gamma (PPAR-γ) and toll-like receptor 4 (TLR4), associated with pulmonary fibrosis was proposed. Based on the AOP 347, this study developed two novel quantitative structure-activity relationship (QSAR) models for the two MIEs. The prediction performances of different MIE modeling methods (e.g., molecular dynamics, pharmacophore model, and QSAR) were compared and validated with in vitro test data. Results showed that the QSAR method had high accuracy compared with other modeling methods, and the QSAR method is suitable for the MIE modeling in the AOP 347. Therefore, the two QSAR models based on the AOP 347 can be powerful models to screen biocidal mixture related to pulmonary fibrosis.

Determination of Neonicotinoids in Honey Samples Originated from Poland and Other World Countries

A method development for determination of neonicotinoid residues in honey samples was developed. The proposed methodology consisted in QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe). That was used for sample preparation and UHPLC/UV (ultra-performance liquid chromatography with ultraviolet detection) utilized for chromatographic analysis. The developed method proved to be sensitive, with LOD (Limit of detection) value in the range of 60.80 to 80.98 ng/g hence LOQ (Limit of quantification) value was in the range of 184.26 to 245.40 ng/g. The method has tested on Polish honey and applied to honey from various countries (Bulgaria, Czech Republic, France, Greece, Italy, Portugal, Romania, Australia, Brazil, Cameroon, Russia, USA and Turkey). Several honey types were tested, while physicochemical properties of all honeys and were investigated. The methodology for general characterization of pollen grains originated from selected plants, to confirm the type of honey was also presented. There was a total lack of the mentioned neonicotinoids in sunflower honey. Except of this, only two samples of rapeseed and two samples of acacia honey (from Poland and Romania) were neonicotinoids free. In 19 samples the targeted pesticides were detected above LOQ. In all other investigated samples, the neonicotinoids were found at least at the LOD or LOQ level.

QSAR modeling the toxicity of pesticides against Americamysis bahia

The widespread use of pesticides has received increasing attention in regulatory agencies because their extensive overuse and various adverse effects on all living organisms. Organizations such as EPA and ECHA have published laws that pesticides should be fully evaluated before bring them to market. In the present study, we evaluated the pesticides toxicity using the Quantitative Structural-Activity Relationship (QSAR) method. The models for the single class pesticides (herbicides, insecticides and fungicides) as well as the general class pesticides (the combined dataset plus some microbicides, molluscicides, etc.) were developed using the Genetic Algorithm and Multiple Linear Regression method. The internal and external validation results suggested that all the obtained models were stable and predictive. According to the modeling descriptors, the lipophilic descriptors contributed positively while all the electrotopological state descriptors showed a negative contribution, their presences in every model verified the conspicuous influence of molecular lipophilicity and hydrophilicity on the pesticides toxicity. However, the influence of topological structure descriptors was different and varies with the physiochemical information they encode. Finally, the models presented in this paper would help assess the pesticides toxicity against Americamysis bahia, shorten test time, and reduce the cost of pesticides risk assessment.

(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.

"Good Epidemiology Practice" Guidelines for Pesticide Exposure Assessment

Both toxicology and epidemiology are used to inform hazard and risk assessment in regulatory settings, particularly for pesticides. While toxicology studies involve controlled, quantifiable exposures that are often administered according to standardized protocols, estimating exposure in observational epidemiology studies is challenging, and there is no established guidance for doing so. However, there are several frameworks for evaluating the quality of published epidemiology studies. We previously developed a preliminary list of methodology and reporting standards for epidemiology studies, called Good Epidemiology Practice (GEP) guidelines, based on a critical review of standardized toxicology protocols and available frameworks for evaluating epidemiology study quality. We determined that exposure characterization is one of the most critical areas for which standards are needed. Here, we propose GEP guidelines for pesticide exposure assessment based on the source of exposure data (i.e., biomonitoring and environmental samples, questionnaire/interview/expert record review, and dietary exposures based on measurements of residues in food and food consumption). It is expected that these GEP guidelines will facilitate the conduct of higher-quality epidemiology studies that can be used as a basis for more scientifically sound regulatory risk assessment and policy making.

Application of chemometric methods and QSAR models to support pesticide risk assessment starting from ecotoxicological datasets

The EFSA 'Guidance on tiered risk assessment for edge-of-field surface waters' underscores the importance of in silico models to support the pesticide risk assessment. The aim of this work was to use in silico models starting from an available, structured and harmonized pesticide dataset that was developed for different purposes, in order to stimulate the use of QSAR models for risk assessment. The present work focuses on the development of a set of in silico models, developed to predict the aquatic toxicity of heterogeneous pesticides with incomplete/unknown toxic behavior in the water compartment. The generated models have good fitting performances (R²: 0.75-0.99), they are internally robust (Q²loo: 0.66-0.98) and can handle up to 30% of perturbation of the training set (Q² lmo: 0.64-0.98). The absence of chance correlation was guaranteed by low values of R² calculated on scrambled responses (R² Y_scr: 0.11-0.38). Different statistical parameters were used to quantify the external predictivity of the models (CCC_ext: 0.73-0.91, Q² ext-Fn: 0.53-0.96). The results indicate that all the best models are predictive when applied to chemicals not involved in the models development. In addition, all models have similar accuracy both in fitting and in prediction and this represents a good degree of generalization. These models may be useful to support the risk assessment procedure when experimental data for key species are missing or to create prioritization lists for the general a priori assessment of the potential toxicity of existing and new pesticides which fall in the applicability domain.