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.

Decision trees versus support vector machine for classification of androgen receptor ligands.

With the current concern of limiting experimental assays, increased interest now focuses on in silico models able to predict toxicity of chemicals. Endocrine disruptors cover a large number of environmental and industrial chemicals which may affect the functions of natural hormones in humans and wildlife. Structure-activity models are now increasingly used for predicting the endocrine disruption potential of chemicals. In this study, a large set of about 200 chemicals covering a broad range of structural classes was considered in order to categorize their relative binding affinity (RBA) to the androgen receptor. Classification of chemicals into four activity groups, with respect to their log RBA value, was carried out in a cascade of recursive partitioning trees, with descriptors calculated from CODESSA software and encoding topological, geometrical and quantum chemical properties. The hydrophobicity parameter (log P), Balaban index, and descriptors relying on charge distribution (maximum partial charge, nucleophilic index on oxygen atoms, charged surface area, etc.) appear to play a major role in the chemical partitioning. Separation of strongly active compounds was rather straightforward. Similarly, about 90% of the inactive compounds were identified. More intricate was the separation of active compounds into subsets of moderate and weak binders, the task requiring a more complex tree. A comparison was made with support vector machine yielding similar results.

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.

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.

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.

Prediction of biological activity profiles of cyanobacterial secondary metabolites.

Over the past decade cyanobacteria have become an interesting source of new classes of pharmacologically active natural products. Some cyanobacterial secondary metabolites (CSMs) are also well known for their toxic effects on living species. The PASS (Prediction of Activity Spectra for Substances) computer program, which is able to simultaneously predict more than one thousand biological and toxicological activities from only the structural formulas of the chemicals, was used to predict the biological activity profile of 681 CSMs. Multivariate methods were employed to structure and analyse this wealth of biological and chemical information. PASS predictions were successfully compared to the available information on the pharmacological and toxicological activity of these compounds.

Endocrine disruption profile analysis of 11,416 chemicals from chemometrical tools.

A number of chemicals released into the environment have the potential to disturb the normal functioning of the endocrine system. These chemicals termed endocrine disruptors (EDs) act by mimicking or antagonizing the normal functions of natural hormones and may pose serious threats to the reproductive capability and development of living species. Batteries of laboratory bioassays exist for detecting these chemicals. However, due to time and cost limitations, they cannot be used for all the chemicals which can be found in the ecosystems. SAR and QSAR models are particularly suited to overcome this problem but they only deal with specific targets/endpoints. The interest to account for profiles of endocrine activities instead of unique endpoints to better gauge the complexity of endocrine disruption is discussed through a SAR study performed on 11,416 chemicals retrieved from the US-NCI database and for which 13 different PASS (Prediction of Activity Spectra for Substances) endocrine activities were available. Various multivariate analyses and graphical displays were used for deriving structure-activity relationships based on specific structural features.

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.

SAR and QSAR modeling of endocrine disruptors.

A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention 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.

Homology model of the rainbow trout estrogen receptor (rtERalpha) and docking of endocrine disrupting chemicals (EDCs).

A model for rainbow trout (Oncorhynchus mykiss) estrogen receptor (rtERa) was built by homology with the human estrogen receptor (hERa). A high level of sequence conservation between the two receptors was found with 64% and 80% of identity and similarity, respectively. Selected endocrine disrupting chemicals were docked into the ligand binding domain (LBD) of rtERa and the corresponding free binding energies Delta(DeltaG(bind)) values were calculated. A Quantitative Structure-Activity Relationship (QSAR) model between the relative binding affinity data and the Delta(DeltaG(bind)) values was derived in order to predict which further organic pollutants are likely to bind to rtERa.

Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study.

The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.

A new strategy for using supervised artificial neural networks in QSAR.

A new type of environmental QSAR model is presented for the common situation in which the biological activity of molecules mainly depends on their 1-octanol/water partition coefficient (log P). In a first step, a classical regression equation with log P is derived. The residuals obtained with this simple linear equation are then modeled from a supervised artificial neural network including different molecular descriptors as input neurons. Finally, results produced by the linear and nonlinear models are both considered for calculating the activity values, which are compared with the initial actual activity values. A heterogeneous database of 569 organic compounds with 96-h LC50s measured to the fathead minnow (Pimephales promelas), randomly divided into a training set of 484 chemicals and a testing set of 85 chemicals, was used as illustrative example to show the potentialities of this new modeling strategy Finally, practical suggestions are given for designing this type of hybrid QSAR model.

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.

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.

Characterization of a potent agonist for NPFF receptors: binding study on rat spinal cord membranes.

Specific receptors for the octapeptide FLFQPQRFamide (NPFF), a mammalian FMRFamide-like neuropeptide with anti-opiate properties have been identified in rat central nervous system. However, exploration of the biological role of this peptide requires a peptidase-resistant agonist. In this study, the stability and binding characteristics of [125I]DYLMeFQPQRFamide, a radioiodinated analogue of NPFF, on rat spinal cord tissue were determined and compared with those of [125I]YLFQPQRFamide, the reference ligand which previously permitted to characterize NPFF binding sites. In a binding assay, [125I]DYLMeFQPQRFamide remained intact in the presence of membranes without peptidase inhibitors, whereas [125I]YLFQPQRFamide was completely hydrolysed. The specific binding was time-dependent, dose-dependent, saturable and reversible. [125I]DYLMeFQPQRFamide shared the same binding characteristics as [125I]YLFQPQRFamide (Kd = 0.07 nM; Bmax = 14.7 fmol/mg protein). Binding was not affected by various spinal cord opioids or peptides. Autoradiographic studies indicated that binding sites were mainly located in the most external layers of dorsal horn where high densities of NPFF binding sites have previously been described. [125I]YLFQPQRFamide and [125I]DYLMeFQPQRFamide binding sites were both GTP-regulated. These findings indicate that DYLMeFQPQRFamide should be of value in studies on NPFF-mediated actions in vivo.

A nonlinear map of substituent constants for selecting test series and deriving structure-activity relationships. 1. Aromatic series.

A nonlinear mapping (NLM) analysis was performed on a set of 166 aromatic substituents described by six variables encoding hydrophobic (pi), steric (MR), and electronic effects (HBA, HBD, F, and R). NLM allowed to easily summarize the main information contained in the original data table. By means of collections of graphs, it was possible to relate the structure of the substituents to their pi, MR, HBA, HBD, F, and R values. The proposed approach provides a useful and easy tool for the selection of test series and for deriving structure-activity relationships.