Interpretation of honeybees contact toxicity associated to acetylcholinesterase inhibitors

Leveraging In Silico Structure-Activity Models to Predict Acute Honey Bee (Apis mellifera) Toxicity for Agrochemicals

In the realm of crop protection products, ensuring the safety of pollinators stands as a pivotal aspect of advancing sustainable solutions. Extensive research has been dedicated to this crucial topic as well as new approach methodologies in toxicity testing. Hence, within the agricultural and chemical industries, prioritizing pollinator safety remains a constant objective during the development of predictive tools. One of these tools includes computational models like quantitative structure-activity relationships (QSARs) that are valuable in predicting the toxicity of chemicals. This research uses bee toxicity data to develop artificial neural network classification models for predicting honey bee acute toxicity. Bee toxicity data from 1542 compounds were used to develop models; the sensitivity and specificity of the best model were 0.90 and 0.91, respectively. These in silico models can aid in the discovery of next-generation crop protection products. These tools can guide the screening and selection of next-generation crop protection molecules with high margins of safety to pollinators, and candidates with favorable sustainability profiles can be identified at the early discovery stage as precursors to in vivo data generation.

Assessment of pesticide induced inhibition of Apis mellifera (honeybee) acetylcholinesterase by means of N-doped carbon dots/BSA nanocomposite modified electrochemical biosensor

This work describes the development and optimization of an electrochemical method to evaluate pesticide induced inhibition of honey bee (Apis mellifera) acetylcholinesterase (AChE) by means of acetylcholinesterase biosensor. The inhibition assay was based on the detection of changes in electrochemical activity of the enzyme caused by pesticide. As transducer, nitrogen doped carbon dots BSA (N-CD/BSA) nanocomposite electrodeposited on pencil graphite electrode was used to covalently immobilize AChE. The as-synthesized nanocomposite and fabricated electrodes were characterized for the structural, functional and electrochemical properties. Nanocomposite promoted the electron transfer reaction to catalyze the electro-oxidation of thiocholine and a large current response was obtained by cyclic voltammetry at 0.77 V, indicating successful immobilization of AChE. The sensitivity of Diazinon, an OP insecticide, for honeybee AChE was tested under optimal conditions and a linear response ranging 10-250 nM was obtained with a detection limit of 8.9 nM, and sensitivity 9 uA/nM/cm2. The method showed a good operational reproducibility and selectivity of biosensor. Further, the molecular docking provided additional support to the experimental data suggesting irreversible nature and contact toxicity of the pesticide for honey bee AChE. The developed biosensor has proved useful for the diazinon detection in wheat samples with 99% recovery rate.

Chemometric modeling of plant protection products (PPPs) for the prediction of acute contact toxicity against honey bees (A. mellifera): A 2D-QSAR approach

Honey bees (Apis mellifera) are vital for economic, viable agriculture and for food safety. Although Plant Protection Products (PPPs) are of undeniable importance in the global agricultural system, these have become potential threats for non-target organisms like pollinators (e.g., honey bees etc.), resulting in the disruption of the ecological balance. In the current work, we have used the 113 PPP analogs to develop a 2D-QSAR model and explored the structural features modulating the toxic effects on honey bees, following the Organization for Economic Co-operation and Development (OECD) guidelines. The extensive validation of the developed model has been performed using internal and external validation metrics to make sure that the model is statistically sound and interpretable enough to be acceptable. The obtained results (R² = 0.666, Q² = 0.594, Q²_F1 = 0.647 and Q²_F2 = 0.646) determine the predictability and reliability of the developed model. This model should be useful for the predictions (acute contact toxicity (LD₅₀)) of the new and untested compounds located inside the applicability domain of the developed model. Moreover, we have performed the in-silico prediction of toxicity against honey bees of a total of 709 compounds obtained from the pesticide properties database (PPDB) using the developed model.

A new method for predicting the acute toxicity of carbamate pesticides based on the perspective of binding information with carrier protein

Toxicity is one of the most important factors limiting the success of new drug development. In this paper, we built a fast and convenient new method (Carrier protein binding information-toxicity relationship, CPBITR) for predicting drug acute toxicity based on the perspective of binding information with carrier protein. First, we studied the binding information between carbamate pesticides and human serum albumin (HSA) through various spectroscopic methods and molecular docking. Then a total of 16 models were established to clarify the relationship between binding information with HSA and drug toxicity. The results showed that the binding information was related to toxicity. Finally we obtained the effective toxicity prediction model for carbamate pesticides. And the "Platform for Predicting Drug Toxicity Based on the Information of Binding with Carrier Protein" was established with the Back-propagation neural network model. We proposed and proved that it was feasible to predict drug toxicity from this new perspective: binding with carrier protein. According to this new perspective, toxicity prediction model of other drugs can also be established. This new method has the advantages of convenience and fast, and can be used to screen out low-toxic drugs quickly in the early stage. It is helpful for drug research and development.

Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation

Honey bees (Apis mellifera) provide key ecosystem services as pollinators bridging agriculture, the food chain and ecological communities, thereby ensuring food production and security. Ecological risk assessment of single Plant Protection Products (PPPs) requires an understanding of the exposure and toxicity. In silico tools such as QSAR models can play a major role for the prediction of structural, physico-chemical and pharmacokinetic properties of chemicals as well as toxicity of single and multiple chemicals. Here, the first integrative honey bee QSAR model has been developed for PPPs using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD₅₀) and ii) to profile the Mode of Action (MoA) of pesticides active substances. Three different classification-based and four regression-based models were developed and tested for their performance, thus identifying two models providing the most reliable predictions based on k-NN algorithm. The two-category QSAR model (toxic/non-toxic; n = 411) was validated using sensitivity (=0.93), specificity (=0.85), balanced accuracy (=0.90), and Matthews correlation coefficient (MCC = 0.78) as statistical parameters. The regression-based model (n = 113) was validated for its reliability and robustness (R² = 0.74; MAE = 0.52). Current study proposes the MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD_50s data points from three different databases. The classification allows to further define MoAs and the target site of PPPs active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals. Relevant future perspectives are briefly addressed to integrate MoA, adverse outcome pathways (AOPs) and toxicokinetic information for the refinement of single-chemical/combined toxicity predictions and risk estimates at different levels of biological organization in the bee health context.

Persistence of aerially applied mosquito-pesticide, Naled, in fresh and marine waters

Naled, an organophosphate pesticide, received considerable attention during 2016 as it was applied aerially to control the first mosquito-borne Zika virus outbreak in the continental United States. Stakeholders living in affected areas raised concerns about its environmental impacts. One factor influencing environmental impacts is the persistence of the chemical applied. The objective of this study was to evaluate the persistence of naled - and its degradation bi-product, dichlorvos - in natural waters. Initial naled concentrations were measured at ground level after full-scale aerial spray activities. Laboratory experiments were designed to evaluate factors (fresh versus marine water chemistry, temperature, and sunlight) that may promote the degradation of naled and dichlorvos in the environment. Results show that natural fresh and marine water chemistry promoted naled degradation as experiments with de-ionized water resulted in half-lives greater than 6 days. The half-life in natural waters without light ranged from 5 to 20 h with lower half lives at higher temperatures. Under light exposure, degradation was accelerated and yielded more dichlorvos. Detectable levels (0.05 μM for naled and 0.10 μM for dichlorvos) were measured in water samples collected from the field during aerial spray events. Results can be used in risk assessments that consider both naled and dichlorvos to better understand ecological impacts and to develop improved public health recommendations.

Hierarchical Virtual Screening of Potential Insectides Inhibitors of Acetylcholinesterase and Juvenile Hormone from Temephos

Aedes aegypti (Linnaeus, 1762; Diptera: Culicidae) is the main vector transmitting viral diseases such as dengue fever, dengue haemorrhagic fever, urban yellow fever, zika and chikungunya. Worldwide, especially in the Americas and Brazil, many cases of dengue have been reported in recent years, which have shown significant growth. The main control strategy is the elimination of the vector, carried out through various education programs, to change human habits, but the most usual is biological control, together with environmental management and chemical control. The most commonly insecticide used is temephos (an organophosphorus compound), but Aedes aegypti populations have shown resistance and the product is highly toxic, so we chose it as a template molecule to perform a ligand-based virtual screening in the ChemBrigde (DIVERSet-CL subcollection) database, searching for derivatives with similarity in shape (ROCS) and electrostatic potential (EON). Thus, fourty-five molecules were filtered based on their pharmacokinetic and toxicological properties and 11 molecules were selected by a molecular docking study, including binding affinity and mode of interaction. The L46, L66 and L68 molecules show potential inhibitory activity for both the insect (−9.28, −10.08 and −6.78 Kcal/mol, respectively) and human (−6.05, 6.25 and 7.2 Kcal/mol respectively) enzymes, as well as the juvenile hormone protein (−9.2; −10.96 and −8.16 kcal/mol, respectively), showing a significant difference in comparison to the template molecule temephos. Molecules L46, L66 and L68 interacted with important amino acids at each catalytic site of the enzyme reported in the literature. Thus, the molecules here investigated are potential inhibitors for both the acetylcholinesterase enzymes and juvenile hormone protein–from insect and humans, characterizing them as a potential insecticide against the Aedes aegypti mosquito.

Target site model: Application of the polyparameter target lipid model to predict aquatic organism acute toxicity for various modes of action

A database of 2049 chemicals with 47 associated modes of action (MoA) was compiled from the literature. The database includes alkanes, polycyclic aromatic hydrocarbons, pesticides, inorganic, and polar compounds. Brief descriptions of some critical MoA classification groups are provided. The MoA from the 14 sources were assigned using a variety of reliable experimental and modeling techniques. Toxicity information, chemical parameters, and solubility limits were combined with the MoA label information to create the data set used for model development. The model database was used to generate linear free energy relationships for each specific MoA using multilinear regression analysis. The model uses chemical-specific Abraham solute parameters estimated from AbSolv to determine MoA-specific solvent parameters. With this procedure, critical target site concentrations are determined for each genus. Statistical analysis showed a wide range in values of the solvent parameters for the significant MoA. Environ Toxicol Chem 2019;38:222-239. © 2018 SETAC.

Morphogenetic Alterations in Melipona quadrifasciata anthidioides (Hymenoptera: Apidae) Associated with Pesticides

Bees are major pollinators of both native flora and cultured crops. Nonetheless, despite their key functional role in ecosystems and agriculture, bee populations have been affected worldwide by deforestation and contamination by insecticides. Conversely, little is known about the effects of pesticides on morphogenetic development of neotropical stingless bees. We compared the fluctuating asymmetry (FA) in newly emerged bees and foragers of Melipona quadrifasciata anthidioides exposed to pesticides (experimental greenhouse and cultivated field). In addition, visitation behavior of foragers was inferred from pollen analyses and direct observation. A significant increase of FA (P < 0.001) was detected in bees from the greenhouse. Even though pesticides might affect their development, foragers seem to avoid contaminated plants whenever possible, as confirmed by pollen and visitation analyses. Consequently, the conservation of natural forests in agricultural landscapes is essential to ensure the health of colonies in stingless bees.

QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.)

Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (Apis mellifera L.). Thus, a particular attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD₅₀) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (Q ² and [Formula: see text] are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD₅₀ of 20 other pesticides. A good correlation between predicted and experimental values was found (R ² = 0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.

Neonicotinoids transference from the field to the hive by honey bees: Towards a pesticide residues biomonitor

The beehive as a quantitative monitor of pesticide residues applied over a soybean crop was studied through a semi field experiment of controlled exposure of honey bees to pesticides in macro tunnels. The distribution within exposed beehives of pesticides commonly used in soybean plantation, was assessed. Residue levels of insecticides in soybean leaves, honey bees, wax, honey and pollen were analyzed. The transference from pesticides present in the environment into the beehive was evidenced. The obtained results allow relating pesticide concentrations present in the environment with traces found in foraging bees. Therefore, pesticide transference ratios could be calculated for each detected compound (acetamiprid, imidacloprid and thiamethoxam) which showed a linear inverse trend with their 1-octanol/water partition coefficient (Kow). The least transferred pesticide to the hive (acetamiprid) has the highest vapor pressure (Vp). This study gives new insights on the usefulness of monitoring the environment through beehives aiming to evaluate if agroecosystems remain sustainable. It also contributes to generate valuable information for model building aiming to predict environmental quality through beehive's analysis.

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.

Protecting honey bees: identification of a new varroacide by in silico, in vitro, and in vivo studies

Varroa destructor is the main concern related to the gradual decline of honeybees. Nowadays, among the various acaricides used in the control of V. destructor, most presents increasing resistance. An interesting alternative could be the identification of existent molecules as new acaricides with no effect on honeybee health. We have previously constructed the first 3D model of AChE for honeybee. By analyzing data concerning amino acid mutations implicated in the resistance associated to pesticides, it appears that pirimicarb should be a good candidate for varroacide. To check this hypothesis, we characterized the AChE gene of V. destructor. In the same way, we proposed a 3D model for the AChE of V. destructor. Starting from the definition of these two 3D models of AChE in honeybee and varroa, a comparison between the gorges of the active site highlighted some major differences and particularly different shapes. Following this result, docking studies have shown that pirimicarb adopts two distinct positions with the strongest intermolecular interactions with VdAChE. This result was confirmed with in vitro and in vivo data for which a clear inhibition of VdAChE by pirimicarb at 10 μM (contrary to HbAChE) and a 100% mortality of varroa (dose corresponding to the LD50 (contact) for honeybee divided by a factor 100) were observed. These results demonstrate that primicarb could be a new varroacide candidate and reinforce the high relationships between in silico, in vitro, and in vivo data for the design of new selective pesticides.

CYP6 P450 enzymes and ACE-1 duplication produce extreme and multiple insecticide resistance in the malaria mosquito Anopheles gambiae

Malaria control relies heavily on pyrethroid insecticides, to which susceptibility is declining in Anopheles mosquitoes. To combat pyrethroid resistance, application of alternative insecticides is advocated for indoor residual spraying (IRS), and carbamates are increasingly important. Emergence of a very strong carbamate resistance phenotype in Anopheles gambiae from Tiassalé, Côte d'Ivoire, West Africa, is therefore a potentially major operational challenge, particularly because these malaria vectors now exhibit resistance to multiple insecticide classes. We investigated the genetic basis of resistance to the most commonly-applied carbamate, bendiocarb, in An. gambiae from Tiassalé. Geographically-replicated whole genome microarray experiments identified elevated P450 enzyme expression as associated with bendiocarb resistance, most notably genes from the CYP6 subfamily. P450s were further implicated in resistance phenotypes by induction of significantly elevated mortality to bendiocarb by the synergist piperonyl butoxide (PBO), which also enhanced the action of pyrethroids and an organophosphate. CYP6P3 and especially CYP6M2 produced bendiocarb resistance via transgenic expression in Drosophila in addition to pyrethroid resistance for both genes, and DDT resistance for CYP6M2 expression. CYP6M2 can thus cause resistance to three distinct classes of insecticide although the biochemical mechanism for carbamates is unclear because, in contrast to CYP6P3, recombinant CYP6M2 did not metabolise bendiocarb in vitro. Strongly bendiocarb resistant mosquitoes also displayed elevated expression of the acetylcholinesterase ACE-1 gene, arising at least in part from gene duplication, which confers a survival advantage to carriers of additional copies of resistant ACE-1 G119S alleles. Our results are alarming for vector-based malaria control. Extreme carbamate resistance in Tiassalé An. gambiae results from coupling of over-expressed target site allelic variants with heightened CYP6 P450 expression, which also provides resistance across contrasting insecticides. Mosquito populations displaying such a diverse basis of extreme and cross-resistance are likely to be unresponsive to standard insecticide resistance management practices.

Malaria control depends heavily on only four classes of insecticide to which Anopheles mosquitoes are increasingly resistant. It is important to manage insecticide application carefully to minimise increases in resistance, for example by using different compounds in combination or rotation. Recently, mosquitoes resistant to all available insecticides have been found in Tiassalé, West Africa, which could be problematic for resistance management, particularly if common genetic mechanisms are responsible (‘cross-resistance’). Tiassalé mosquitoes also exhibit extreme levels of resistance to the two most important classes, pyrethroids and carbamates. We investigated the genetic basis of extreme carbamate resistance and cross-resistance in Tiassalé, and the applicability of results in an additional population from Togo. We find that specific P450 enzymes are involved in both extreme and cross-resistance, including one, CYP6M2, which can cause resistance to three insecticide classes. However, amplification of a mutated version of the gene which codes for acetycholinesterase, the target site of both the carbamate and organophosphate insecticides, also plays an important role. Mechanisms involved in both extreme resistance and cross resistance are likely to be very resilient to insecticide management practices, and represent an alarming scenario for mosquito-targeted malaria control.

In silico study on hydroxylated polychlorinated biphenyls as androgen receptor antagonists

Hydroxylated polychlorinated biphenyls (HO-PCBs), major metabolites of PCBs, may have the potential to disrupt androgen hormone homeostasis. However, there is a lack of systematic investigation into the intermolecular interaction mechanism between HO-PCBs and the androgen receptor (AR). In this study, the combination of three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD) simulations was performed to elucidate structural characteristics that influence the anti-androgen activity of HO-PCBs, and to provide a better understanding of the binding modes between HO-PCBs and AR. A predictive comparative molecular field analysis (CoMFA) model was developed with good robustness and predictive ability. Graphical interpretation of the model provided some insights into the structural features that affect the anti-androgen activity of HO-PCBs. The hydrogen bond interaction with Gln711, and hydrophobic interactions with residues in the hydrophobic pocket played important roles in the binding of ligand with receptor. These results are expected to be beneficial to predict anti-androgen activities of other HO-PCBs and provided possible clues for further elucidation of the binding mechanism of HO-PCBs with AR.