Combinatorial Chemistry & High Throughput Screening - Volume 17, Issue 5, 2014

Both a development of resistance to artemisinin monotherapy and lack of effective vaccine against malaria have created the urgent need for the development of new and efficient antimalarial agents. In this background, we have developed here a linear discriminant analysis (LDA) model and a few 3D-pharmacophore models for the classification of diverse quinolone compounds based on their antimalarial potency against Plasmodium falciparum. The discriminant model shows 70% correct classification for the test set compounds into higher active and lower active analogues. The best pharmacophore model (Hypo-1) with a correlation coefficient of 0.83 shows one hydrogen bond acceptor (HBA) and two ring aromatic (RA) features as the essential structural requirements for antimalarial activity against P falciparum. Both the models may act as in silico filters for a virtual screening and could be utilized for the selection of higher active molecules falling within the applicability of the models.

The identification of endocrine-disrupting chemicals (EDCs) is one of the important goals of environmental chemical hazard screening. The adverse health effects of EDCs in humans have been demonstrated to involve the developmental, reproductive, neurological, cardiovascular, metabolic, and immune systems. The present study reports QSAR classification studies on a large database comprising 8,212 compounds collected from the Estrogenic Activity Database and National Center for Biotechnology Information Database. In this study, four classification models (Bayesian Categorization Model with molecular fingerprints or molecular descriptors as an input and Neural Classification Models with and without Bayesian regularization) were used. Evaluation of these binomial classification methods indicated that the Bayesian method (Bayesian QSAR) works as an excellent method for prediction with fingerprints used as input. In the case of the multilayer perceptron with molecular descriptors as inputs, changing the training mode by introducing a Bayesian regularization algorithm significantly improved ANNs' predictive power. Our goal was to test two popular classification methods suitable for processing large data sets. Such datasets were required to ensure the prediction performances and applicability of the models as a virtual screening tool for an extensive database.

Liquid-phase combinatorial library synthesis is commonly developed into the viable alternatives or adjunct across the broad spectrum of polymer-supported organic chemistry. It includes the use of soluble polymer supports in the combinatorial synthesis of peptides and small-molecular library compounds which act as catalyst and reagent supports. It also includes high throughput biological screening with generation and evaluation of chemical leads for drug discovery development. In this review, liquid-phase combinatorial library synthesis is shown as the most efficient method of choice for the synthesis of most of the combinatorial library compounds with specific approaches from different groups that state potentials of solution-phase combinatorial synthesis.

In this paper, commercially available ZnO was found to be a highly effective catalyst for three-component coupling reactions of alkynes, aldehydes/or ketone and amines (A3-coupling) via C-H activation. The reactions could be applied to both aromatic and aliphatic aldehydes. Nearly quantitative yields of the desired products were obtained in most cases. No co-catalyst or activator is required, and water is the only by-product in the reactions. Heterogeneous nature of the catalyst made it reusable for further chemical reactions.

Marine organisms have been increasingly regarded as an excellent source of bioactive molecules for human health. As part of an on-going study to elucidate the chemical composition and biological activity of Marthastherias glacialis, we report here the lipidomic profile of this organism and screening for anticancer activity. Two classes of biologically active metabolites were identified and quantified by a GC-MS optimized method for screening several classes of metabolites in a single run. Free fatty acids and sterols were found, including ergosterol, β-sitosterol and cholesterol derivatives, some of which are reported in this species for the first time. Distinct assays were performed to screen extract's effect on viability, membrane integrity and density of three human cancer cell lines: human oestrogen receptor-positive (ER+) breast cancer (MCF-7), human neuroblastoma (SH-SY5Y) and human colon cancer (Caco-2). Differential activity towards the three cancer cell lines was found, SH-SY5Y cell line being the most susceptible. No activity was found for Caco-2 cell line. Non-cancer cell lines (human dermal fibroblasts and human foreskin fibroblasts) were also tested and revealed to be less susceptible. This work establishes M. glacialis as a potential source of bioactive molecules for further studies.

Direct renin inhibitors (DRIs) have increasingly shown a significant advantage in the treatment of hypertension and protection of target organs. In this paper, a series of azaindole class renin inhibitors were subjected to 3D-QSAR study using Topomer CoMFA. Five kinds of splitting mode for different fragment cutting and 6 different training and test sets grouping were attempted to build consensus models. The results indicated that 6 consensus models had similar predictability (q2 and r2pred) and stability (r2). The best model showed good stability and predictability (q2 of 0.616, r2 of 0.908). The r2pred value of the external test set was 0.70, which means that the model had an excellent external predictive ability, and the robustness of the developed model was assessed by the Y-randomization test. This study also adopted the methodology of fragment-based drug design (FBDD) to virtual screen new renin inhibitors by using Topomer Search technology. The R1-group of the compound No. 13 with the highest activity was chosen as the basic scaffold, and its remaining R2-group acted as a query to screen 142,025 molecules of ZINC database for similar fragments. The obtained 30 fragments with the highest R2-group contribution values were added to the basic scaffold respectively. Finally 30 new azaindole compounds with potent high activities were obtained. Further the binding modes were studied by using Surflex- Dock. The docking results showed good binding interactions of the designed compounds with the renin protein, thus the rationality of this design was further verified from the perspective of the renin receptor.

In the current study, identification of new potent small inhibitors of human lysosomal acid lipase using structure-based methods has been reported. Virtual Screening (VS), compounds from literature and molecular docking studies were employed to find the suitable inhibitors against lysosomal acid lipase (LAL). Specifically for this study a homology model of LipA enzyme was generated based on the structure of dog gastric lipase. As a result of structurebased virtual screening 28 inhibitors were identified from ZINC database. Rest of the inhibitors were selected from literature. Among the studied 65 inhibitors, compound having zinc ID ZINC15707335 exhibiting minimum binding affinity and hydrogen bond and hydrophobic interactions with specific amino acid residues was selected as lead compound.