Investigation of Binding Characteristics of Phosphoinositide-dependent Kinase-1 (PDK1) Co-crystallized Ligands Through Virtual Pharmacophore Modeling Leading to Novel Anti-PDK1 Hits.

BACKGROUND: 3-Phosphoinositide Dependent Protein Kinase-1 (PDK1) is being lately considered as an attractive and forthcoming anticancer target. A Protein Data Bank (PDB) cocrystallized crystal provides not only rigid theoretical data but also a realistic molecular recognition data that can be explored and used to discover new hits. OBJECTIVE: This incited us to investigate the co-crystallized ligands' contacts inside the PDK1 binding pocket via a structure-based receptor-ligand pharmacophore generation technique in Discovery Studio 4.5 (DS 4.5). METHODS: Accordingly, 35 crystals for PDK1 were collected and studied. Every single receptorligand interaction was validated and the significant ones were converted into their corresponding pharmacophoric features. The generated pharmacophores were scored by the Receiver Operating Characteristic (ROC) curve analysis. RESULTS: Consequently, 169 pharmacophores were generated and sorted, 11 pharmacophores acquired good ROC-AUC results of 0.8 and a selectivity value above 8. Pharmacophore 1UU3_2_01 was used in particular as a searching filter to screen NCI database because of its acceptable validity criteria and its distinctive positive ionizable feature. Several low micromolar PDK1 inhibitors were revealed. The most potent hit illustrated anti-PDK1 IC50 values of 200 nM with 70% inhibition against SW480 cell lines. CONCLUSION: Eventually, the active hits were docked inside the PDK1 binding pocket and the recognition points between the active hits and the receptor were analyzed that led to the discovery of new scaffolds as potential PDK1 inhibitors.

New leads for DPP IV inhibition: structure-based pharmacophore mapping and virtual screening study.

Dipeptidyl peptidase IV (DPP IV) is an attractive target for the development of new antidiabetic drugs. DPP IV inhibitors improve glycemic control by preventing the rapid inactivation of the incretin hormones; glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic peptide. In the current study, virtual screening, using 2D and 3D filters implemented in a hierarchical cascade, was employed to identify new DPP IV inhibitors. Co-crystallized ligands, with potent DPP IV-inhibitory activities, were utilized to generate structure-based pharmacophore models using DS Visualizer software. The derived pharmacophore maps were validated using in-house built database containing active and inactive DPP IV inhibitors. Subsequently, the optimum validated pharmacophore model was used as a search query against two 3D-databases (NCI and in-house built drug databases). Further hit filtration was carried out employing 2D virtual filters based on Lipinski's rule of 5; number of rotatable bonds and other physicochemical filters. 3D filter using high-throughput molecular docking was also applied. As a result, 5 novel DPP IV inhibitors were discovered as potential lead compounds and later confirmed via in vitro bioassay.

ß-Caryophyllene causes regression of endometrial implants in a rat model of endometriosis without affecting fertility.

Many studies have shown that anti-inflammatory agents are effective in the treatment of endometriosis. ß-Caryophyllene exerted a potent anti-inflammatory effect in vivo. However, its effect on endometriosis has not been investigated. This study aims at investigating the effect of ß-caryophyllene on endometriosis and on fertility and reproduction in adult female rats. Autologous fragments of the endometrium were implantated in the peritoneal cavity in adult female rats. The growth of the endometriotic implants that developed after four weeks was recorded. Treatment started then with ß-caryophyllene (10 mg/kg or 30 mg/kg) or vehicle (control) for 21 days and the growth of the endometriotic implants was measured again. In fertility studies, female rats that received ß-caryophyllene or vehicle were mated and reproductive functions were observed including number and viability of implants, number of corpora lutea, length of pregnancy and outcome of litter. ß-Caryophyllene (10 mg/kg) suppressed the growth of endometriotic implants by 52.5% compared with controls. Also ß-caryophyllene produced apoptosis in luminal epithelim of the cyst as well as in endothelial cells of blood vessels. Ultrstructural studies revealed the presence of active mast cells and eosinophils in both control and ß-caryophyllene-treated rat cysts. No statistically significant difference was observed in any studied parameter between control and ß-caryophyllene-treated groups in fertility study. Therapy with ß-caryophyllene may present a promising novel, non-toxic therapeutic option for patients with endometriosis.

Ligand-based modelling followed by synthetic exploration unveil novel glycogen phosphorylase inhibitory leads.

Glycogen phosphorylase (GP) is a valid anti-diabetic target. Accordingly, we applied a drug discovery workflow to unveil novel inhibitory GP leads via combining pharmacophore modeling, QSAR analysis and in silico screening, followed by synthetic exploration of active hits. Virtual screening identified six low micromolar inhibitory leads from the National Cancer Institute (NCI) list of compounds. The most potent hits exhibited anti-GP IC(50) values of 3.2 and 4.1 µM. Synthetic exploration of hit 59 (IC(50)=4.1 µM) yielded 25 lead inhibitors with the best illustrating IC(50) of 3.0 µM. Interestingly, we prepared several novel mixed oxalyl amide anti-GP leads employing new chemical reaction involving succinic acid-based adducts.

Thujone corrects cholesterol and triglyceride profiles in diabetic rat model.

Thujone, which is the major constituent in Salvia sp. (Lamiaceae), was found to correct the lipid profile (cholesterol and triglycerides) in diabetic rats. Oral treatment with thujone (5 mg kg⁻¹ body weight dose) significantly adjusted cholesterol and triglyceride levels in diabetic rats (p ≤ 0.05) to normal levels compared to diabetic untreated rats. This provides a premise in the field of finding new agents to treat diabetic complications.

Pharmacophore modeling, quantitative structure-activity relationship analysis, and shape-complemented in silico screening allow access to novel influenza neuraminidase inhibitors.

Neuraminidase (NA) enzyme is one of the valid targets against influenza viruses. With this in mind, the pharmacophoric space of influenza NA was explored using three sets of diverse inhibitors. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select optimal combinations of pharmacophoric models and 2D descriptors capable of yielding self-consistent and predictive quantitative structure-activity relationships (QSARs) against 181 training compounds. The optimal QSAR equations were validated against 43 external test compounds with r(2)(PRESS) values ranging from 0.488 to 0.591. Interestingly, five orthogonal pharmacophores emerged in the optimal QSAR equations suggesting the existence of several distinct ligand/NA binding modes within the NA binding pocket. The resulting pharmacophores were complemented with tight shape constraints and employed as three-dimensional (3D) search queries against the National Cancer Institute (NCI) list of compounds. Several hits exhibited potent inhibitory activities against NA. The highest ranking hit demonstrated an in vitro IC(50) value of 1.8 muM. Docking studies supported the binding modes suggested by our pharmacophore/QSAR analysis.

Combining ligand-based pharmacophore modeling, quantitative structure-activity relationship analysis and in silico screening for the discovery of new potent hormone sensitive lipase inhibitors.

Hormone sensitive lipase (HSL) has been recently implicated in diabetes and obesity, prompting attempts to discover new HSL inhibitors. Toward this end, we explored the pharmacophoric space of HSL inhibitors using four diverse sets of compounds. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of yielding a self-consistent and predictive quantitative structure-activity relationship (QSAR) (r = 0.822, n = 99, F = 11.1, r LOO (2) = 0.521, r PRESS (2) against 23 external test inhibitors = 0.522). Interestingly, two pharmacophoric models emerged in the QSAR equation suggesting at least two binding modes. These pharmacophores were employed to screen the National Cancer Institute (NCI) list of compounds and our in-house built database of established drugs and agrochemicals. Active hits included the safe herbicidal agent bifenox (IC 50 = 0.43 microM) and the nonsteroidal anti-inflammatory naproxen (IC 50 = 1.20 microM). Our active hits undermined the traditional believe that HSL inhibitors should possess covalent bond-forming groups.

Discovery of new MurF inhibitors via pharmacophore modeling and QSAR analysis followed by in-silico screening.

The pharmacophoric space of streptococcal MurF was explored using a set of 39 known inhibitors. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that access self-consistent quantitative structure-activity relationship (QSAR) (r(2)=0.93,F=56.9,r(LOO)(2)=0.91,r(PRESS)(2) against eight external test inhibitors=0.75). Two orthogonal pharmacophores (of cross-correlation r(2)=0.26) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within MurF binding pocket. The validity of the QSAR equation and the associated pharmacophore models was experimentally established by the identification of three promising new MurF inhibitors retrieved from the NCI database. Docking studies conducted on active hits supported the binding modes suggested by the pharmacophore/QSAR analysis.

Discovery of potent inhibitors of pseudomonal quorum sensing via pharmacophore modeling and in silico screening.

HipHop-Refine was employed to derive a binding hypothesis for pseudomonal quorum sensing (QS) antagonists. The model was employed as 3D search query to screen the National Cancer Institute (NCI) database. One of the hits illustrated nanomolar QS inhibitory activity. The fact that this compound contained tetravalent lead (Pb) prompted us to evaluate the activities of phenyl mercuric nitrate and thimerosal, both fit the binding pharmacophore. The two mercurials illustrated nanomolar to low micromolar IC50 inhibitory values against pseudomonal QS. The three compounds represent a new class of QS inhibitors.