Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models.

Spraying repellents on clothing limits toxicity and allergy problems that can occur when the repellents are directly applied to skin. This also allows the use of higher doses to ensure longer lasting effects. As the number of repellents available on the market is limited, it is necessary to propose new ones, especially by using in silico methods that reduce costs and time. In this context SAR models were built from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. The interest of using either the ECFP or MACCS fingerprints as input neurons of a three-layer perceptron was evaluated. Transformation of MACCS bit strings into disjunctive tables led to interesting results. Models obtained with both types of fingerprints were compared to a model including physicochemical and topological descriptors.

In silico prediction of mosquito repellents for clothing application.

Use of protective clothing is a simple and efficient way to reduce the contacts with mosquitoes and consequently the probability of transmission of diseases spread by them. This mechanical barrier can be enhanced by the application of repellents. Unfortunately the number of available repellents is limited. As a result, there is a crucial need to find new active and safer molecules repelling mosquitoes. In this context, a structure-activity relationship (SAR) model was proposed for the design of repellents active on clothing. It was computed from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. Molecules were described by means of 20 molecular descriptors encoding physicochemical properties, topological information and structural features. A three-layer perceptron was used as statistical tool. An accuracy of 87% was obtained for both the training and test sets. Most of the wrong predictions can be explained. Avenues for increasing the performances of the model have been proposed.

Toxicity profiling and prioritization of plant-derived antimalarial agents.

Human malaria is the most widespread mosquito-borne life-threatening disease worldwide. In the absence of effective vaccines, prevention and treatment of malaria only depend on prophylaxis and drug-based therapy either in monotherapy or in combination. Unfortunately, the number of available antimalarial drugs presenting different mechanisms of action is rather limited. In addition, the appearance of drug-resistance in the parasite strains impacts the efficacy of the treatments. As a result, there is a crucial need to find new drugs to circumvent resistance problems. In the quest to identify new antimalarial agents a huge number of plant-derived compounds (PDCs) have been investigated. Surprisingly in the in silico PDC screening programs, toxicity filters are either never used or so simple that their interest is limited. In this context, the goal of this study was to show how to take advantage of validated toxicity QSAR models for refining the selection of PDCs. From an original data set of 507 PDCs collected from the literature, the use of toxicity filters for endocrine disruption, developmental toxicity, and hepatotoxicity in conjunction with classical pharmacokinetic filters allowed us to obtain a list of 31 compounds of potential interest. The pros and cons of such a strategy have been discussed.

2D and 3D structure-activity modelling of mosquito repellents: a review $.

Repellents disrupt the behaviour of blood-seeking mosquitoes protecting humans against their bites which can transmit serious diseases. Since the mid-1950s, N,N-diethyl-m-toluamide (DEET) is considered as the standard mosquito repellent worldwide. However, DEET presents numerous shortcomings. Faced with the heightening risk of mosquito expansion caused by global climate changes and increasing international exchanges, there is an urgent need for a better repellent than DEET and the very few other commercialised repelling molecules such as picaridin and IR3535. In silico approaches have been used to find new repellents and to provide better insights into their mechanism of action. In this context, the goal of our study was to retrieve from the literature all the papers dealing with qualitative and quantitative structure-activity relationships on mosquito repellents. A critical analysis of the SAR and QSAR models was made focusing on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed. The hypotheses to compute homology and pharmacophore models, their interest to find new repellents and to provide insights into the mechanisms of repellency in mosquitoes were analysed. The interest to consider the mosquito olfactory system as the target to compute new repellents was discussed. The potential environmental impact of these chemicals as well as new ways of research were addressed.

QSAR modelling of synergists to increase the efficacy of deltamethrin against pyrethroid-resistant Aedes aegypti mosquitoes$.

Space spraying of deltamethrin allows the control of adult Aedes (Stegomyia) aegypti mosquitoes. Unfortunately, many vector control programs are threatened by the development of resistances that decrease the efficacy of this adulticide. Faced with this situation, we can either try to use another insecticide presenting a different mechanism of action or find a strategy that brings back the efficacy of the insecticide at a satisfying level to pursue its use in vector control. Restoration of the efficacy of an insecticide can be obtained by means of a synergist. In this context, QSAR modelling was used to find synergists to combine with deltamethrin for increasing its efficacy against resistant strains of Ae. aegypti. Seventy-four structurally diverse chemicals with their 24-hour LD50 values, obtained under the same experimental conditions on Ae. aegypti females, were used. Molecules were described by means of autocorrelation vectors encoding lipophilicity, molar refractivity, H-bonding acceptor and donor ability. A three-layer perceptron (TLP) was employed as statistical tool. The performances of the models were evaluated through the analysis of the prediction results obtained on the different training and test sets (80%/20%) as well as from an out-sample test set. A 6/4/1 TLP computed with the Broyden-Fletcher-Goldfarb-Shanno second-order training algorithm led to the best prediction results. The convergence was obtained in 132 cycles. The sum of squares was used as error function. The hidden and output activation functions were tanh and exponential, respectively. Various chemical structures were identified as potential synergists and searched for their commercial availability. Molecules of interest were tested in vivo on Ae. aegypti by using the susceptible reference Bora Bora strain and two resistant strains from Martinique island. This led to the identification of the PSM-05 molecule that shows interesting synergistic activity.

Repurposing drugs for use against Zika virus infection.

Zika virus (ZIKV) is a mosquito-borne flavivirus for which there are no vaccines or specific therapeutics. To find drugs active on the virus is a complex, expensive and time-consuming process. The prospect of drug repurposing, which consists of finding new indications for existing drugs, is an interesting alternative to expedite drug development for specific diseases. In theory, drug repurposing is also able to respond much more rapidly to a crisis than a classical drug discovery process. Consequently, the methodology is attractive for vector-borne diseases that can emerge or re-emerge worldwide with the risk to become pandemic quickly. Different drugs, showing various structures, have been repurposed to be used against ZIKV infection. They are reviewed in this study and the conditions for their potential use in practice are discussed.

Expert judgment based multicriteria decision models to assess the risk of pesticides on reproduction failures of grey partridge.

A suite of models is proposed for estimating the risk of pesticides against the grey partridge (Perdix perdix) and their clutches. Radio-tracked data of females, description and location of the clutches, and data on the pesticide treatments during the laying periods of the partridges were used as basic information. Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modelling allowed us to characterize the pesticides by their 1-octanol/water partition coefficient (log P), vapour pressure, primary and ultimate biodegradation potential, acute toxicity (LD50) on P. perdix, and endocrine disruption potential. From these physicochemical and toxicological data, the system of integration of risk with interaction of scores (SIRIS) method was used to design scores of risk for pesticides, alone or in mixture. A program, written in R (version 3.1.1), called Simulation of Toxicity in Perdix perdix (SimToxPP), was designed for estimating the risk of substances, considered alone or in mixture, against the grey partridge during breeding. The software tool is flexible enough to simulate realistic in situ scenarios. Different examples of applications are shown. The advantages and limitations of the approach are briefly discussed.

QSAR models for predicting the toxicity of piperidine derivatives against Aedes aegypti.

QSAR models are proposed for predicting the toxicity of 33 piperidine derivatives against Aedes aegypti. From 2D topological descriptors, calculated with the PaDEL software, ordinary least squares multilinear regression (OLS-MLR) treatment from the QSARINS software and machine learning and related approaches including linear and radial support vector machine (SVM), projection pursuit regression (PPR), radial basis function neural network (RBFNN), general regression neural network (GRNN) and k-nearest neighbours (k-NN), led to four-variable models. Their robustness and predictive ability were evaluated through both internal and external validation. Determination coefficients (r2) greater than 0.85 on the training sets and 0.8 on the test sets were obtained with OLS-MLR and linear SVM. They slightly outperform PPR, radial SVM and RBFNN, whereas GRNN and k-NN showed lower performance. The easy availability of the involved structural descriptors and the simplicity of the MLR model make the corresponding model attractive at an exploratory level for proposing, from this limited dataset, guidelines in the design of new potentially active molecules.

Modelling inhibition of avian aromatase by azole pesticides.

The potential effects of pesticides and their metabolites on the endocrine system are of major concern to wildlife and human health. In this context, the azole pesticides have earned special attention due to their cytochrome P450 aromatase inhibition potential. Cytochrome P450 aromatase (CYP19) catalyses the conversion of androstenedione and testosterone into oestrone and oestradiol, respectively. Thus, aromatase modulates the oestrogenic balance essential not only for females, but also for male physiology, including gonadal function. Its inhibition affects reproductive organs, fertility and sexual behaviour in humans and wildlife species. Several studies have shown that azole pesticides are able to inhibit human and fish aromatases but the information on birds is lacking. Consequently, it appeared to be of interest to estimate the aromatase inhibition of azoles in three different avian species, namely Gallus gallus, Coturnix coturnix japonica and Taeniopygia guttata. In the absence of the crystal structure of the aromatase enzyme in these bird species, homology models for the individual avian species were constructed using the crystal structure of human aromatase (hAr) (pdb: 3EQM) that showed high sequence similarity for G. gallus (82.0%), T. guttata (81.9%) and C. japonica (81.2%). A homology model with Oncorhynchus mykiss (81.9%) was also designed for comparison purpose. The homology-modelled aromatase for each avian and fish species and crystal structure of human aromatase were selected for docking 46 structurally diverse azoles and related compounds. We showed that the docking behaviour of the chemicals on the different aromatases was broadly the same. We also demonstrated that there was an acceptable level of correlation between the binding score values and the available aromatase inhibition data. This means that the homology models derived on bird and fish species can be used to approximate the potential inhibitory effects of azoles on their aromatase.

Prediction of the endocrine disruption profile of pesticides.

Numerous manmade chemicals released into the environment can interfere with normal, hormonally regulated biological processes to adversely affect the development and reproductive functions of living species. Various in vivo and in vitro tests have been designed for detecting endocrine disruptors, but the number of chemicals to test is so high that to save time and money, (quantitative) structure-activity relationship ((Q)SAR) models are increasingly used as a surrogate for these laboratory assays. However, most of them focus only on a specific target (e.g. estrogenic or androgenic receptor) while, to be more efficient, endocrine disruption modelling should preferentially consider profiles of activities to better gauge this complex phenomenon. In this context, an attempt was made to evaluate the endocrine disruption profile of 220 structurally diverse pesticides using the Endocrine Disruptome simulation (EDS) tool, which simultaneously predicts the probability of binding of chemicals on 12 nuclear receptors. In a first step, the EDS web-based system was successfully applied to 16 pharmaceutical compounds known to target at least one of the studied receptors. About 13% of the studied pesticides were estimated to be potential disruptors of the endocrine system due to their high predicted affinity for at least one receptor. In contrast, about 55% of them were unlikely to be endocrine disruptors. The simulation results are discussed and some comments on the use of the EDS tool are made.

Structure-activity relationship (SAR) modelling of mosquito larvicides.

An attempt was made to derive structure-activity models allowing the prediction of the larvicidal activity of structurally diverse chemicals against mosquitoes. A database of 188 chemicals with their activity on Aedes aegypti larvae was constituted from analysis of original publications. The activity values were expressed in log 1/IC50 (concentration required to produce 50% inhibition of larval development, mmol). All the chemicals were encoded by means of CODESSA and autocorrelation descriptors. Partial least squares analysis, classification and regression tree, random forest and boosting regression tree analyses, Kohonen self-organizing maps, linear artificial neural networks, three-layer perceptrons, radial basis function artificial neural networks and support vector machines with linear, polynomial, radial basis function and sigmoid kernels were tested as statistical tools. Because quantitative models did not give good results, a two-class model was designed. The three-layer perceptron significantly outperformed the other statistical approaches regardless of the threshold value used to split the data into active and inactive compounds. The most interesting configuration included eight autocorrelation descriptors as input neurons and four neurons in the hidden layer. This led to more than 96% of good predictions on both the training set and external test set of 88 and 100 chemicals, respectively. From the overall simulation results, new candidate molecules were proposed which will be shortly synthesized and tested.

In silico models for predicting vector control chemicals targeting Aedes aegypti.

Human arboviral diseases have emerged or re-emerged in numerous countries worldwide due to a number of factors including the lack of progress in vaccine development, lack of drugs, insecticide resistance in mosquitoes, climate changes, societal behaviours, and economical constraints. Thus, Aedes aegypti is the main vector of the yellow fever and dengue fever flaviviruses and is also responsible for several recent outbreaks of the chikungunya alphavirus. As for the other mosquito species, the A. aegypti control relies heavily on the use of insecticides. However, because of increasing resistance to the different families of insecticides, reduction of Aedes populations is becoming increasingly difficult. Despite the unquestionable utility of insecticides in fighting mosquito populations, there are very few new insecticides developed and commercialized for vector control. This is because the high cost of the discovery of an insecticide is not counterbalanced by the 'low profitability' of the vector control market. Fortunately, the use of quantitative structure-activity relationship (QSAR) modelling allows the reduction of time and cost in the discovery of new chemical structures potentially active against mosquitoes. In this context, the goal of the present study was to review all the existing QSAR models on A. aegypti. The homology and pharmacophore models were also reviewed. Specific attention was paid to show the variety of targets investigated in Aedes in relation to the physiology and ecology of the mosquito as well as the diversity of the chemical structures which have been proposed, encompassing man-made and natural substances.

Modelling the binding affinity of steroids to zebrafish sex hormone-binding globulin.

The circulating endogenous steroids are transported in the bloodstream. These are bound to a highly specific sex hormone-binding globulin (SHBG) and in lower affinity to proteins such as the corticosteroid-binding protein and albumin in vertebrates, including fish. It is generally believed that the glycoprotein SHBG protects these steroids from rapid metabolic degradation and thus intervenes in its availability at the target tissues. Endocrine disrupters binding to SHBG affect the normal activity of natural steroids. Since xenobiotics are primarily released in the aquatic environment, there is a need to evaluate the binding affinity of xenosteroid mimics on fish SHBG, especially in zebrafish (Danio rerio), a small freshwater fish originating in India and widely employed in ecotoxicology, toxicology, and genetics. In this context, a zebrafish SHBG (zfSHBG) homology model was developed using the human SHBG (hSHBG) receptor structure as template. It was shown that interactions with amino acids Ser-36, Asp-59 and Thr-54 were important for binding affinity. A ligand-based pharmacophore model was also developed for both zfSHBG and hSHBG inhibitors that differentiated binders from non-binders, but also demonstrated structural requirements for zfSHBG and hSHBG ligands. The study provides insights into the mechanism of action of endocrine disruptors in zebrafish as well as providing a useful tool for identifying anthropogenic compounds inhibiting zfSHBG.

External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics.

Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.

Structure-activity relationship study of trifluoromethylketones: inhibitors of insect juvenile hormone esterase.

The juvenile hormone esterase (JHE) regulates juvenile hormone titre in insect hemolymph during its larval development. It has been suggested that JHE could be targeted for use in insect control. This enzyme can also be considered as involved in the phenomenon of endocrine disruption by xenobiotics in beneficial insects. Consequently, there is a need to know the characteristics of the molecules able to act on the JHE. Trifluoromethylketones (TFKs) are the most potent JHE inhibitors found to date and different quantitative structure-activity relationships (QSARs) have been derived for this group of chemicals. In this context, a set of 181 TFKs (118 active and 63 inactive compounds), tested on Trichoplusia ni for their JHE inhibition activity and described by physico-chemical descriptors, was split into different training and test sets to derive structure-activity relationship (SAR) models from support vector classification (SVC). A SVC model including 88 descriptors and derived from a Gaussian kernel was selected for its predictive performances. Another model computed only with 13 descriptors was also selected due to its mechanistic interpretability. This study clearly illustrates the difficulty in capturing the essential structural characteristics of the TFKs explaining their JHE inhibitory activity.

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.

Structural alerts for estimating the carcinogenicity of pesticides and biocides.

More than 20 years ago, Ashby and Tennant showed the interest of structural alerts for the prediction of the carcinogenicity of chemicals. These structural alerts are functional groups or structural features of various sizes that are linked to the level of carcinogenicity of chemicals. Since this pioneering work it has been possible to refine the alerts over time, as more experimental results have become available and additional mechanistic insights have been gained. To date, one of the most advanced lists of structural alerts for evaluating the carcinogenic potential of chemicals is the list proposed by Benigni and Bossa and that is implemented as a rule-based system in Toxtree and in the OECD QSAR Application Toolbox. In order to gain insight into the applicability of this system to the detection of potential carcinogens we screened about 200 pesticides and biocides showing a high structural diversity. Prediction results were compared with experimental data retrieved from an extensive bibliographical review. The prediction correctness was only equal to 60.14%. Attempts were made to analyse the sources of mispredictions.

Evaluation of the OECD (Q)SAR Application Toolbox and Toxtree for predicting and profiling the carcinogenic potential of chemicals.

The OECD (Q)SAR Application Toolbox and Toxtree are software tools used in regulatory toxicology to fill gaps in (eco)toxicity data. They include different SAR and QSAR models for estimating (eco)toxicological endpoints. Among them, the Benigni/Bossa rule-based system is proposed to characterize the carcinogenic potential of chemicals. Our study evaluates the predictive performance that can be expected from the OECD (Q)SAR Toolbox and Toxtree when analysing chemicals by means of the structural alerts coded within the Benigni/Bossa rule-based system for carcinogenicity and the associated QSAR model (QSAR8). These evaluations have been carried out thanks to a large collection of chemicals retrieved from original publications and public databases. Overall, our findings confirm the performance of the system of structural alerts while suggesting that the sensitivity of QSAR8, as implemented in the two tools, is lower than what was previously reported. They also indicate that attention has to be paid when interpreting the output of the two tools because of possible malfunctions involving the coding of two-dimensional structures. A set of possible modulating factors for the structural alert identifying polycyclic aromatic hydrocarbons is also proposed together with candidates for putative new structural alerts not included in the tested tools.

Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 1. Aromatic amines.

The Ames Salmonella typhimurium mutagenicity assay is a short-term bacterial reverse mutation test that was designed to detect mutagens. For several decades, it has been used in research laboratories and by regulatory agencies throughout the world for the detection and characterization of potential mutagens among natural products and man-made chemicals. Faced with the ever-growing number of chemicals available on the market, congeneric and non-congeneric (Q)SAR models have been designed from Ames test results obtained on specific S. typhimurium strains such as TA 100 or TA 98. Such models have great potential for a quick and cheap identification and classification of large numbers of potential chemical mutagens. The OECD QSAR Application Toolbox and Toxtree, which were developed for facilitating the practical use of (Q)SAR approaches in regulatory contexts, include two mechanistic SAR models for predicting the mutagenicity of aromatic amines and α-ß unsaturated aliphatic aldehydes. The aim of this study was to estimate the interest and limitations of the former model. The model was first re-computed to check its transparency and to verify its statistical validity. Then, it was tested on about 150 chemicals not previously used for the design of the model but belonging to its domain of application. A critical analysis of the results was performed and proposals were made for increasing the model performances.

Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 2. α-ß unsaturated aliphatic aldehydes.

The OECD QSAR Application Toolbox versions 1.1.01 and 1.1.02 and Toxtree version 1.60, which were developed for facilitating the practical use of (Q)SAR approaches by regulators, include a mechanistic SAR model for predicting the mutagenicity of α-ß unsaturated aliphatic aldehydes. The aim of this study was to estimate the interest and limitations of this model. First, the model was re-computed to check its transparency and to verify its statistical validity. Then, the model implemented in the two software tools was tested on 34 chemicals not previously used for its design and for which experimental mutagenic activity data were available in the literature. A critical analysis of the results was performed and the practical interest of the model was discussed.