Derivation of pharmacophore and CoMFA models for leukotriene D(4) receptor antagonists of the quinolinyl(bridged)aryl series

Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms

Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 receptor antagonists which were divided into two groups: highly active molecules (IC₅₀ < 1000 nM) and weakly active molecules (IC₅₀ ≥ 1000 nM). The molecules were characterized by several descriptors including CORINA descriptors, MACCS fingerprints, Morgan fingerprint and molecular SMILES. For CORINA descriptors and two types of fingerprints, we used the random forests (RF) and deep neural networks (DNN) to build models. For molecular SMILES, we used recurrent neural networks (RNN) with the self-attention to build models. The accuracies of test sets for all models reached 85%, and the accuracy of the best model (Model 2C) was 93%. In addition, we made structure-activity relationship (SAR) analyses on CysLT1 receptor antagonists, which were based on the output from the random forest models and RNN model. It was found that highly active antagonists usually contained the common substructures such as tetrazoles, indoles and quinolines. These substructures may improve the bioactivity of the CysLT1 receptor antagonists.

X-ray crystallographic structures as a source of ligand alignment in 3D-QSAR

Three-dimensional quantitative structure-activity relationships (3D-QSAR) analyses are methods correlating a pharmacological property with a mathematical representation of a molecular property distribution around three-dimensional molecular models for a set of congeners. 3D-QSAR methods are known to be highly sensitive to ligand conformation and alignment method. The current study collects 32 unique positions of congeneric ligands co-crystallized with the binding domain of AMPA receptors and aligns them using protein coordinates. Thus, it allows for a unique opportunity to consider a ligands' orientation aligned by their mode of binding in a native molecular target. Comparative molecular field analysis (CoMFA) models were generated for this alignment and compared with the results of analogous modeling using standard structure-based alignment or obtained in docking simulations of the ligands' molecules. In comparison with classically derived models, the model based on X-ray crystallographic studies showed much better performance and statistical significance. Although the 3D-QSAR methods are mainly employed when crystallographic information is limited, the current study underscores the importance that the selection of inappropriate molecular conformations and alignment methods can lead to generation of erroneous models and false conclusions.

Pharmacophore identification, synthesis, and biological evaluation of carboxylated chalcone derivatives as CysLT1 antagonists

The pharmacophore model (Hypo1) with a well prediction capacity for CysLT(1) antagonists was developed using Catalyst/HypoGen program. Virtual screening against an in-house database consisted of carboxylated chalcones using Hypo1 was performed. Retrieved hits 26a, 26b, 27a, and 27b were synthesized and biological evaluated, the results of which demonstrated that these compounds showed moderate to good CysLT(1) antagonistic activities. This study indicated that the generated model (Hypo1) is a reliable and useful tool in lead optimization for novel CysLT(1) antagonists.

Volume learning algorithm significantly improved PLS model for predicting the estrogenic activity of xenoestrogens

Volume learning algorithm (VLA) artificial neural network and partial least squares (PLS) methods were compared using the leave-one-out cross-validation procedure for prediction of relative potency of xenoestrogenic compounds to the estrogen receptor. Using Wilcoxon signed rank test we showed that VLA outperformed PLS by producing models with statistically superior results for a structurally diverse set of compounds comprising eight chemical families. Thus, CoMFA/VLA models are successful in prediction of the endocrine disrupting potential of environmental pollutants and can be effectively applied for testing of prospective chemicals prior their exposure to the environment.

Characterization of β3-adrenergic receptor: determination of pharmacophore and 3D QSAR model for β3 adrenergic receptor agonism

The beta3-adrenoreceptor (beta3-AR) has been shown to mediate various pharmacological and physiological effects such as lipolysis, thermogenesis, and intestinal smooth muscle relaxation. It also plays an important role in glucose homeostasis and energy balance. Molecular modeling studies were undertaken to develop predictive pharmacophoric hypothesis and 3D-QSAR model, which may explain variations in beta3-AR agonistic activity in terms of chemical features and physicochemical properties. The two softwares, CATALYST for pharmacophoric alignment and APEX-3D for 3D-QSAR modeling were used to establish the structure activity relationships for beta3-AR agonistic activity. Among the several statistically significant models, the selection of the best pharmacophore and 3D-QSAR model was based on its ability to estimate the activity of external test sets of similar and different structural types along with the reasonable consistency of the model with the limited information of the active site of beta3-AR. The final 3D-QSAR model was derived using the pharmacophoric alignments from the hypothesis which consisted of four chemical features: basic or positive ionizable feature on the nitrogen of the aryloxypropylamino group, two ring aromatic features corresponding to the phenyl ring of the phenoxide and the benzenesulphonamido groups and a hydrogen-bond donor (HBD) in the vicinity of the nitrogen atom of the benzenesulphonamido group with the most active molecule mapping in an energetically favorable extended conformation. This hypothesis was in agreement with the site directed mutagenesis studies on human beta3-AR and correlated well the observed and estimated activity both in, training and both the external test sets. It also mapped reasonably well to six beta3-AR agonists of different structural classes under clinical development and thus this hypothesis may have a universal applicability in providing a powerful template for virtual screening and also for designing new chemical entities (NCEs) as beta3-AR agonists.

GRIND/ALMOND investigations on CysLT1 receptor antagonists of the quinolinyl(bridged)aryl type

One of the current routes in developing antiasthmatics is CysLT(1) receptor antagonism. For a training set of 54 CysLT(1) receptor antagonists of the quinolinyl(bridged)aryl type we developed chemometric QSAR models applying GRID independent descriptors (=GRIND). PLS analysis resulted in a two-component model explaining 67% of the variance for CysLT(1) receptor binding (r2=0.67, SDEC = 0.47, q2=0.54). GRIND variables 11-50 and 22-55 are responsible for high-affinity binding; variable 11-62 is detrimental. The predictivity of the above chemometric model is tested with a set of 69 CysLT(1) receptor antagonists, exhibiting varying chemical similarity to the training set. Nearly 50% of the test set are quite well predicted. The quality of prediction coincides in part with chemical subclassification: phenylene bridged compounds are quite well predicted; for structures with bridging heterocycles predictions are rather poor. For explaining the outlier behavior, a PLS discriminant analysis including the training set and the strongest outliers of the test set was performed. The scores plot of discriminant PLS shows an almost complete separation between the two subsets. A PLS coefficients plot explains which GRIND variables are important for the discrimination between the training set and the outliers of the test set.

Derivatives of (Phenylsulfanyl)benzoic Acids with Multiple Antileukotrienic Activity

A series of derivatives of (phenylsulfanyl)benzoic acids bearing quinoline, 2,4-dihydroxy-3-propylacetophenone and 2,4-difluorobiphenyl moieties were prepared and their antileukotrienic activities evaluated. Some of the compounds were found to display multiple antileukotrienic effect in the inhibition of LTB4biosynthesis, binding to LTD4and LTB4receptors, superior to the standards (zileuton and zafirlukast) used. The compounds had an antiinflammatory effect, manifested with quinoline derivatives by a significant inhibition of bronchospasm induced by LTD4and/or albumin. The results of regression analysis correspond to the observation that the most active compounds belong to quinoline derivatives with the lowest lipophilicity. X-ray analysis of the quinoline compounds revealed that an intramolecular hydrophobic interaction of their aromatic rings does not occur in the solid state.

Novel phthalimide derivatives, designed as leukotriene D(4) receptor antagonists

A series of phthalimide acid derivatives was synthesized and evaluated as leukotriene D(4) receptor antagonists. The tetrazolephthalimide LASSBio 552 (7) was shown to be able to inhibit the contractile activity induced by 100 nM of LTD(4) in guinea-pig tracheal strips with an IC(50) = 31.2 microM. In addition, LASSBio 552 (7) has been showed to present a better efficacy than zafirlukast (1) used as standard.