Nathalie Marchand-Geneste

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.

Polyphosphorylated Triphenylenes: Synthesis, Crystal Structure, and Selective Catechol Recognition.

Designed as a multivalent hydrogen bond acceptor, new receptors, Discopus 1a,b, were built from a triphenylene core surrounded by six (diaryl)phosphinate groups. An efficient synthesis was developed to prepare these elaborated structures in a high overall yield. The X-ray structure of receptor 1b showed strong cooperative hydrogen bonds with two water molecules and intermolecular CH-pi contacts. In chloroform, Discopus 1a,b displayed recognition properties toward dihydroxybenzenes, selectively forming complexes with catechol derivatives 4a-c in a 1:2 (host:guest) stoichiometry. According to NMR and microcalorimetry titrations, association constants were found in the 30-2837 M(-1) range, which were larger than those reported for curvated catechol receptors (14-120 M(-1)). Interestingly, Discopus present two distinct catechol binding sites. Weak hydrogen bonding between host phosphinates and guest hydroxyl groups was shown by infrared spectroscopy and (31)P NMR. Molecular dynamics simulations and recognition experiments suggested that a stronger hydrogen bond assisted by a pi-interaction between the Discopus core and one catechol molecule could exist within the 1:2 complex.

Theoretical study of the thermal degradation pathways of abietane skeleton diterpenoids: aromatization to retene

The results of a theoretical study concerning the thermal degradation of abietane skeleton diterpenoids which results in the fully aromatized retene product are presented. The semi-empirical AM1/UHF method has been used to elaborate all reaction pathways that could be encountered during dehydrogenation, demethylation and decarboxylation processes. The topological study of these reaction pathways allows to propose the most probable reaction mechanisms involved in the aromatization of abietic acid to retene.

Homology modelling of the Apis mellifera nicotinic acetylcholine receptor (nAChR) and docking of imidacloprid and fipronil insecticides and their metabolites.

Five homology models for honeybee (Apis mellifera) nicotinic acetylcholine receptor (nAChR) α1/β1, α3/β2, α4/β2, α6/β2 and α9/α9 subtypes were built from the Torpedo marmorata nAChR X-ray structure. Then, imidacloprid, fipronil and their metabolites were docked into the ligand binding domain (LBD) of these receptors and the corresponding scoring functions were calculated. The binding modes of the docked compounds were carefully analysed. Finally, multivariate analyses were used for deriving structure-activity relationships based on hydrogen bond number and scoring functions between the insecticides and the nAChR models.

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. 1Presented at CMTPI 2007: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Moscow, Russia, September 1–5, 2007).

Ab initio calculations of tautomer equilibrium and protonation enthalpy of 2-amino-2-oxazoline in the gas phase: basis set and correlation effects

Abstract Ab initio calculations are performed to evaluate molecular properties of the two tautomeric forms of 2-amino-2-oxazoline and the protonated form with extended basis set at Hartree–Fock, Moller–Plesset perturbation (MP2) and density functional (BLYP, B3LYP) levels. Optimized geometries, atomic charge distributions, dipole moments, energies, tautomer equilibrium constant and protonation enthalpy are carefully analysed. The results obtained at different computational levels are compared to highlight the effects of basis set and correlation.

Docking and QSAR comparative studies of polycyclic aromatic hydrocarbons and other procarcinogen interactions with cytochromes P450 1A1 and 1B1

To obtain chemical clues on the process of bioactivation by cytochromes P450 1A1 and 1B1, some QSAR studies were carried out based on cellular experiments of the metabolic activation of polycyclic aromatic hydrocarbons and heterocyclic aromatic compounds by those enzymes. Firstly, the 3D structures of cytochromes 1A1 and 1B1 were built using homology modelling with a cytochrome 1A2 template. Using these structures, 32 ligands including heterocyclic aromatic compounds, polycyclic aromatic hydrocarbons and corresponding diols, were docked with LigandFit and CDOCKER algorithms. Binding mode analysis highlighted the importance of hydrophobic interactions and the hydrogen bonding network between cytochrome amino acids and docked molecules. Finally, for each enzyme, multilinear regression and artificial neural network QSAR models were developed and compared. These statistical models highlighted the importance of electronic, structural and energetic descriptors in metabolic activation process, and could be used for virtual screening of ligand databases. In the case of P450 1A1, the best model was obtained with artificial neural network analysis and gave an r 2 of 0.66 and an external prediction of 0.73. Concerning P450 1B1, artificial neural network analysis gave a much more robust model, associated with an r 2 value of 0.73 and an external prediction of 0.59.

Structural e-bioinformatics and drug design.

Nowadays the in silico scenario for drug design is totally dependent on structural biology and structural bioinformatics. A myriad of free bioinformatics applications and services have been posted on the web. This mini-review mentions web sites that are useful in structure-based drug design. The information is given in a logical manner, following the drug design process i.e. characterization of a protein target, modelling the protein using sequence homology, optimization of the protein structure and finally docking of small ligands into the active site. † Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).

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

A model for rainbow trout (Oncorhynchus mykiss) estrogen receptor (rtERα) was built by homology with the human estrogen receptor (hERα). 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 rtERα and the corresponding free binding energies Δ(ΔGbind) values were calculated. A Quantitative Structure-Activity Relationship (QSAR) model between the relative binding affinity data and the Δ(ΔGbind) values was derived in order to predict which further organic pollutants are likely to bind to rtERα. ‖Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).

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. ⊥Presented at the 12th International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology (QSAR2006), 8–12 May 2006, Lyon, France.