Organophosphate toxicity patterns: A new approach for assessing organophosphate neurotoxicity

Organophosphorus compounds or organophosphates (OPs) are widely used as flame retardants, plasticizers, lubricants and pesticides. This contributes to their ubiquitous presence in the environment and to the risk of human exposure. The persistence of OPs and their bioaccumulative characteristics raise serious concerns regarding environmental and human health impacts. To address the need for safer OPs, this study uses a New Approach Method (NAM) to analyze the neurotoxicity pattern of 42 OPs. The NAM consists of a 4-step process that combines computational modeling with in vitro and in vivo experimental studies. Using spherical harmonic-based cluster analysis, the OPs were grouped into four main clusters. Experimental data and quantitative structure-activity relationships (QSARs) analysis were used in conjunction to provide information on the neurotoxicity profile of each group. Results showed that one of the identified clusters had a favorable safety profile, which may help identify safer OPs for industrial applications. In addition, the 3D-computational analysis of each cluster was used to identify meta-molecules with specific 3D features. Toxicity was found to correspond to the level of phosphate surface accessibility. Substances with conformations that minimize phosphate surface accessibility caused less neurotoxic effect. This multi-assay NAM could be used as a guide for the classification of OP toxicity, helping to minimize the health and environmental impacts of OPs, and providing rapid support to the chemical regulators, whilst reducing reliance on animal testing.

Computational Approaches for the Design of Mosquito Repellent Chemicals

BACKGROUND

In view of many current mosquito-borne diseases there is a need for the design of novel repellents.

OBJECTIVE

The objective of this article is to review the results of the researches carried out by the authors in the computer-assisted design of novel mosquito repellents.

METHODS

Two methods in the computational design of repellents have been discussed: a) Quantitative Structure Activity Relationship (QSAR) studies from a set of repellents structurally related to DEET using computed mathematical descriptors, and b) Pharmacophore based modeling for design and discovery of novel repellent compounds including virtual screening of compound databases and synthesis of novel analogues.

RESULTS

Effective QSARs could be developed using mathematical structural descriptors. The pharmacophore based method is an effective tool for the discovery of new repellent molecules.

CONCLUSION

Results reviewed in this article show that both QSAR and pharmacophore based methods can be used to design novel repellent molecules.

Ligand Shaping in Induced Fit Docking of MraY Inhibitors. Polynomial Discriminant and Laplacian Operator as Biological Activity Descriptors

Docking-i.e., interaction of a small molecule (ligand) with a proteic structure (receptor)-represents the ground of drug action mechanism of the vast majority of bioactive chemicals. Ligand and receptor accommodate their geometry and energy, within this interaction, in the benefit of receptor-ligand complex. In an induced fit docking, the structure of ligand is most susceptible to changes in topology and energy, comparative to the receptor. These changes can be described by manifold hypersurfaces, in terms of polynomial discriminant and Laplacian operator. Such topological surfaces were represented for each MraY (phospho-MurNAc-pentapeptide translocase) inhibitor, studied before and after docking with MraY. Binding affinities of all ligands were calculated by this procedure. For each ligand, Laplacian and polynomial discriminant were correlated with the ligand minimum inhibitory concentration (MIC) retrieved from literature. It was observed that MIC is correlated with Laplacian and polynomial discriminant.

Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.