An in silico method for predicting Ames activities of primary aromatic amines by calculating the stabilities of nitrenium ions.

In this paper, we describe an in silico first principal approach to predict the mutagenic potential of primary aromatic amines. This approach is based on the so-called "nitrenium hypothesis", which was developed by Ford et al. in the early 1990s. This hypothesis asserts that the mutagenic effect for this class of molecules is mediated through the transient formation of a nitrenium ion and that the stability of this cation is correlated with the mutagenic potential. Here we use quantum mechanical calculations at different levels of theory (semiempirical AM1, ab initio HF/3-21G, HF/6-311G(d,p), and DFT/B3LYP/6-311G(d,p)) to compute the stability of nitrenium ions. When applied to a test set of 257 primary aromatic amines, we show that this method can correctly differentiate between Ames active and inactive compounds, and furthermore that it is able to rationalize and predict SAR trends within structurally related chemical series. For this test set, the AM1 nitrenium stability calculations are found to provide a good balance between speed and accuracy, resulting in an overall accuracy of 85%, and sensitivity and specificity of 91% and 72%, respectively. The nitrenium-based predictions are also compared to the commercial software packages DEREK, MULTICASE, and the MOE-Toxicophore descriptor. One advantage of the approach presented here is that the calculation of relative stabilities results in a continuous spectrum of activities and not a simple yes/no answer. This allows us to observe and rationalize subtle trends due to the different electrostatic properties of the organic molecules. Our results strongly indicate that nitrenium ion stability calculations should be used as a complementary approach to assist the medicinal chemist in prioritizing and selecting nonmutagenic primary aromatic amines during preclinical drug discovery programs.

Discovery of novel inhibitors of Trypanosoma cruzi trans-sialidase from in silico screening.

trans-Sialidase from Trypanosoma cruzi (TcTS) has emerged as a potential drug target for treatment of Chagas disease. Here, we report the results of virtual screening for the discovery of novel TcTS inhibitors, which targeted both the sialic acid and sialic acid acceptor sites of this enzyme. A library prepared from the Evotec database of commercially available compounds was screened using the molecular docking program GOLD, following the application of drug-likeness filters. Twenty-three compounds selected from the top-scoring ligands were purchased and assayed using a fluorimetric assay. Novel inhibitor scaffolds, with IC(50) values in the submillimolar range were discovered. The 3-benzothiazol-2-yl-4-phenyl-but-3-enoic acid scaffold was studied in more detail, and TcTS inhibition was confirmed by an alternative sialic acid transfer assay. Attempts to obtain crystal structures of these compounds with TcTS proved unsuccessful but provided evidence of ligand binding at the active site.

Arylsulfonamide CB2 receptor agonists: SAR and optimization of CB2 selectivity.

A high-throughput screening campaign resulted in the discovery of a highly potent dual cannabinoid receptor 1 (CB1) and 2 (CB2) agonist. Following a thorough SAR exploration, a series of selective CB2 full agonists were identified.

Development of a spectral clustering method for the analysis of molecular data sets.

A spectral clustering method is presented and applied to two-dimensional molecular structures, where it has been found particularly useful in the analysis of screening data. The method provides a means to quantify (1) the degree of intermolecular similarity within a cluster and (2) the contribution that the features of a molecule make to a cluster. In an application of the spectral clustering method to an example data set of 125 COX-2 inhibitors, these two criteria were used to place the molecules into clusters of chemically related two-dimensional structures.