Combinatorial Chemistry & High Throughput Screening - Volume 16, Issue 4, 2013

The article describes the development of a robust pharmacophore model and the investigation of structure activity relationship analysis of 46 xanthine derivatives reported for DPP-IV inhibition using PHASE module of Schrodinger software. The present works also encompasses molecular interaction of 46 xanthine ligand through maestro 8.5 software. The QSAR study comprises AHHR.7 pharmacophore hypothesis, which elaborates the three points, e.g. one hydrogen bond acceptor (A), two hydrophobic rings (H) and one aromatic ring (R). The discrete geometries as pharmacophoric feature were developed and the generated pharmacophore model was used to derive a predictive atombased 3D QSAR model for the studied data set. The obtained 3D QSAR model has an excellent correlation coefficient value (r2= 0.9995) along with good statistical significance which is indicated by high Fisher ratio (F= 8537.4). The model also exhibits good predictive power confirmed by the high value of cross validated correlation coefficient (q2 = 0.6919). The QSAR model suggests that hydrophobic character is crucial for the DPP-IV inhibitory activity exhibited by these compounds and inclusion of hydrophobic substituents will enhance the DPP-IV inhibition. In addition to the hydrophobic character, electron withdrawing groups positively contribute to the DPP-IV inhibition potency. The findings of the QSAR study provide a set of guidelines for designing compounds with better DPP-IV inhibitory potency.

Fragmental topology-activity landscapes (FRAGTAL), a new concept for encoding molecular descriptors for fragonomics into the framework of the molecular database records is presented in this paper. Thus, a structural repository containing biological activity data was searched in a substructure mode by a series of molecular fragments constructed in an incremental or decremental manner. The resulted series of database hits annotated with their activities construct FRAGTAL descriptors encoding a frequency of the certain fragments among active compounds and/or their activities. Actually, this method might be interpreted as a simplified adaptation of the frequent subgraph mining (FSM) method. The FRAGTAL method reconstructs the way in which medicinal chemists are used to designing a prospective drug structure intuitively. A representative example of the practical application of FRAGTAL within the ChemDB Anti-HIV/OI/TB database for disclosing new fragments for HIV-1 integrase inhibition is discussed. In particular, FRAGTAL method identifies ethyl malonate amide (EMA) as the diketo acid (DKA) related arrangement. Since new molecular constructs based on the EMA fragment are still a matter of future investigations we referred to this as anthe DKA offspring.

In the spirit of reporting valid and reliable Quantitative Structure-Activity Relationship (QSAR) models, the aim of our research was to assess how the leverage (analysis with Hat matrix, hi) and the influential (analysis with Cook's distance, Di) of QSAR models may reflect the models reliability and their characteristics. The datasets included in this research were collected from previously published papers. Seven datasets which accomplished the imposed inclusion criteria were analyzed. Three models were obtained for each dataset (full-model, hi-model and Di-model) and several statistical validation criteria were applied to the models. In 5 out of 7 sets the correlation coefficient increased when compounds with either hi or Di higher than the threshold were removed. Withdrawn compounds varied from 2 to 4 for himodels and from 1 to 13 for Di-models. Validation statistics showed that Di-models possess systematically better agreement than both full-models and hi-models. Removal of influential compounds from training set significantly improves the model and is recommended to be conducted in the process of quantitative structure-activity relationships developing. Cook's distance approach should be combined with hat matrix analysis in order to identify the compounds candidates for removal.

The group of imidazoline-1 receptors (I1-IR) agonists encompasses drugs are currently used in treatment of high blood pressure and hyperglycemia. The I1-IR protein structures have not been determined yet, but Nischarin protein that binds numerous imidazoline ligands inducing initiation of various cell-signaling cascades, including apoptosis, is identified as strong I1-IR candidate. In this study we examined apoptotic activity of rilmenidine (potent I1-IR agonist), moxonidine (moderate I1-IR agonist), and efaroxan (I1-IR partial agonist) on cancer cell line (K562) expressing Nischarin. The Nischarine domains mapping was performed by use of the Informational Spectrum Method (ISM). The 3DQuantitative Structure-Activity Relationship (3D-QSAR) and virtual docking studies of 29 I1-IR ligands (agonists, partial agonists, and antagonists) were carried out on I1-IR receptors binding affinities. The 3D-QSAR study defined 3Dpharmacophore models for I1-IR agonistic and I1-IR antagonistic activity and created regression model for prediction of I1-IR activity of novel compounds. The 3D-QSAR models were applied for design and evaluation of novel I1-IR agonists and I1-IR antagonists. The most promising I1-IR ligands with enhanced activities than parent compounds were proposed for synthesis. The results of 3D-QSAR, ISM, and virtual docking studies were in perfect agreement and allowed precise definition of binding mode of I1-IR agonists (Arg 758, Arg 866, Val 981, and Glu 1057) and significantly different binding modes of I1-IR antagonists or partial I1-IR agonists. The performed theoretical study provides reliable system for evaluation of I1-IR agonistic and I1-IR antagonistic activity of novel I1-IR ligands, as drug candidates with anticancer activities.

Receptor-mediated cellular uptake of physiological regulators such as nutritional and hormonal factors represents a transport event with important consequences for cell differentiation, death, or proliferation. Although internalization pathways are important points of regulation, they have not been extensively explored as pharmacological targets in most cell types. An experimental strategy based on ligand-enzyme conjugates is presented in this report that may facilitate high-throughput screening for potent chemical modulators of the transport events. The method was tested on a human epidermoid cell line using a streptavidin-peroxidase conjugate, and human holo-transferrin as the model ligand, in a biotin-ligand conjugate. The proposed screening method is rapid, can be performed using multi-well plates, and involves small assay volumes. The modular nature of the ligand complex makes this method adaptable to the use of other biotinylated ligands, and the use of avidins conjugated to other enzymes. As is discussed, the method may also be applicable to other in vitro and in vivo transport assays.

A new high-expression endothelial growth factor receptor (EGFR) cell membrane chromatography (CMC) method was applied to recognize the ligands acting on EGFR specifically, and investigate the affinity of gefitinib/HMQ1611 to EGFR. In the self and direct competitive assay, gefitinib/HMQ1611 was used as a competitor in the mobile phase to evaluate the effect of the competitor's concentrations on the retention of the ligands, respectively, and the competition between gefitinib and HMQ1611 binding to EGFR was also been examined. The retention behavior indicated that gefitinib had one type of binding sites on the EGFR, and the equilibrium dissociation constant (KD) was (9.11 ± 1.89) x 10-6 M; HMQ1611 had two major binding regions on the EGFR, and the KD values obtained from the model were (2.39 ± 0.33) x 10-7 and (3.87 ± 0.93) x 10-5 M for HMQ1611 at the high- and low-affinity sites, respectively. The competition between gefitinib and HMQ1611 occurred at the low-affinity sites on the EGFR. The low-affinity sites were of higher concentrations and contributed to a much larger part of retention of HMQ1611. The results suggested that gefitinib and HMQ1611 competed for the common binding sites on the EGFR, no matter the ligand was used as an analyte or a competitor.