Linear and non-linear QSAR modelling of juvenile hormone esterase inhibitors.

A tight control of juvenile hormone (JH) titre is crucial during the life cycle of a holometabolous insect. JH metabolism is made through the action of enzymes, particularly the juvenile hormone esterase (JHE). Trifluoromethylketones (TFKs) are able to inhibit this enzyme to disrupt the endocrine function of the targeted insect. In this context, a set of 96 TFKs, tested on Trichoplusia ni for their JHE inhibition, was split into a training set (n = 77) and a test set (n = 19) to derive a QSAR model. TFKs were initially described by 42 CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) descriptors, but a feature selection process allowed us to consider only five descriptors encoding the structural characteristics of the TFKs and their reactivity. A classical and spline regression analysis, a three-layer perceptron, a radial basis function network and a support vector regression were experienced as statistical tools. The best results were obtained with the support vector regression (r(2) and r(test)(2) = 0.91). The model provides information on the structural features and properties responsible for the high JHE inhibition activity of TFKs.

Internet resources for agent-based modelling.

The use of agent-based models (ABMs) is steadily increasing in all the disciplines including environmental chemistry and toxicology. This growth is mainly driven by their ability to address problems that conventional modelling techniques cannot, such as the change of scale or the emergence of unanticipated phenomena resulting from interactions between their constitutive goal-directed agents. After a brief introduction on the basic principles of agent-based modelling and the presentation of selected case studies, the main software resources available on the Internet are presented. An attempt is made to estimate the complexity of these tools versus their potentialities and flexibility.

Does Cry1Ab protein affect learning performances of the honey bee Apis mellifera L. (Hymenoptera, Apidae)?

Genetically modified Bt crops are increasingly used worldwide but side effects and especially sublethal effects on beneficial insects remain poorly studied. Honey bees are beneficial insects for natural and cultivated ecosystems through pollination. The goal of the present study was to assess potential effects of two concentrations of Cry1Ab protein (3 and 5000 ppb) on young adult honey bees. Following a complementary bioassay, our experiments evaluated effects of the Cry1Ab on three major life traits of young adult honey bees: (a) survival of honey bees during sub-chronic exposure to Cry1Ab, (b) feeding behaviour, and (c) learning performance at the time that honey bees become foragers. The latter effect was tested using the proboscis extension reflex (PER) procedure. The same effects were also tested using a chemical pesticide, imidacloprid, as positive reference. The tested concentrations of Cry1Ab protein did not cause lethal effects on honey bees. However, honey bee feeding behaviour was affected when exposed to the highest concentration of Cry1Ab protein, with honey bees taking longer to imbibe the contaminated syrup. Moreover, honey bees exposed to 5000 ppb of Cry1Ab had disturbed learning performances. Honey bees continued to respond to a conditioned odour even in the absence of a food reward. Our results show that transgenic crops expressing Cry1Ab protein at 5000 ppb may affect food consumption or learning processes and thereby may impact honey bee foraging efficiency. The implications of these results are discussed in terms of risks of transgenic Bt crops for honey bees.

Comparative sublethal toxicity of nine pesticides on olfactory learning performances of the honeybee Apis mellifera.

Using a conditioned proboscis extension response (PER) assay, honeybees (Apis mellifera L.) can be trained to associate an odor stimulus with a sucrose reward. Previous studies have shown that observations of conditioned PER were of interest for assessing the behavioral effects of pesticides on the honeybee. In the present study, the effects of sublethal concentrations of nine pesticides on learning performances of worker bees subjected to the PER assay were estimated and compared. Pesticides were tested at three concentrations. The highest concentration of each pesticide corresponded to the median lethal dose value (48-h oral LD50), received per bee and per day, divided by 20. Reduced learning performances were observed for bees surviving treatment with fipronil, deltamethrin, endosulfan, and prochloraz. A lack of behavioral effects after treatment with lambda-cyalothrin, cypermethrin, tau-fluvalinate, triazamate, and dimethoate was recorded. No-observed-effect concentrations (NOECs) for the conditioned PER were derived for the studied pesticides. Our study shows that the PER assay can be used for estimating sublethal effects of pesticides on bees. Furthermore, comparisons of sensitivity as well as the estimation of NOECs, useful for regulatory purposes, are possible.

Comparative toxicity and hazards of pesticides to Apis and non-Apis bees. A chemometrical study.

The adverse effects of 158 pesticides to the Honey bee (Apis mellifera), the alfalfa leafcutting bee (Megachile rotundata) and the alkali bee (Nomia melanderi) were compared by means of various linear and non-linear multivariate analyses. A comparison exercise including the bumble bee (Bombus spp.) was also performed from a more restricted set of 32 pesticides. While no difference of sensitivity was found between A. mellifera and Bombus spp., M. rotundata appeared the most susceptible to pesticides followed by N. melanderi.

Structure-toxicity modeling of pesticides to honey bees.

A quantitative structure-activity relationship (QSAR) model was derived for estimating the acute toxicity of pesticides on the honey bee. Chemicals were described by means of autocorrelation descriptors encoding lipophilicity (H), molar refractivity (MR) and the H-bonding acceptor ability (HBA) of the pesticides. A three-layer feedforward neural network trained by the back-propagation algorithm was used as statistical engine for deriving a powerful QSAR model. The root mean square residual (RMSR) values for the training and testing sets were 0.430 and 0.386, respectively. The practical interest of this original model was discussed.