Predicting Cyclooxygenase Inhibition by Three-Dimensional Pharmacophoric Profiling. Part I: Model Generation, Validation and Applicability in Ethnopharmacology

Discovery of inhibitors targeting protein tyrosine phosphatase 1B using a combined virtual screening approach

Protein tyrosine phosphatase 1B (PTP1B) acts as a therapeutic target for type 2 diabetes. However, the major challenges of PTP1B drug discovery are the poor selectivity and the weak oral bioavailability. In this study, we performed a combined virtual screening approach including multicomplex pharmacophore, molecular docking-based screening, van der Waals energy normalization, pose scaling factor, ADMET evaluation, and molecular dynamics simulation to select PTP1B inhibitors from three databases (PubChem, ChEMBL, and ZINC). We identified three potential PTP1B inhibitors, compounds 1, 4, and 5, with favorable binding energy and good oral bioavailability. The energetic and geometrical analyses show that the three compounds are stably bound to PTP1B, via occupying both the catalytic site (site A) and the proximal noncatalytic site (site B or C). Such occupancy may improve the selectivity. This work not only provided a feasible virtual screening protocol, but also suggested three potential PTP1B inhibitors for the treatment of type 2 diabetes.

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation

Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

In Silico Workflow for the Discovery of Natural Products Activating the G Protein-Coupled Bile Acid Receptor 1

The G protein-coupled bile acid receptor (GPBAR1) has been recognized as a promising new target for the treatment of diverse diseases, including obesity, type 2 diabetes, fatty liver disease and atherosclerosis. The identification of novel and potent GPBAR1 agonists is highly relevant, as these diseases are on the rise and pharmacological unmet therapeutic needs are pervasive. Therefore, the aim of this study was to develop a proficient workflow for the in silico prediction of GPBAR1 activating compounds, primarily from natural sources. A protocol was set up, starting with a comprehensive collection of structural information of known ligands. This information was used to generate ligand-based pharmacophore models in LigandScout 4.08 Advanced. After theoretical validation, the two most promising models, namely BAMS22 and TTM8, were employed as queries for the virtual screening of natural product and synthetic small molecule databases. Virtual hits were progressed to shape matching experiments and physicochemical clustering. Out of 33 diverse virtual hits subjected to experimental testing using a reporter gene-based assay, two natural products, farnesiferol B (27) and microlobidene (28), were confirmed as GPBAR1 activators reaching more than 50% receptor activation at 20 μM with EC50s of 13.53 μM and 13.88 μM, respectively. This activity is comparable to that of the endogenous ligand lithocholic acid (1). Seven further virtual hits showed activity reaching at least 15% receptor activation either at 5 or 20 μM, including new scaffolds from natural and synthetic origin.

Pharmacophore Modeling and in Silico/in Vitro Screening for Human Cytochrome P450 11B1 and Cytochrome P450 11B2 Inhibitors

Cortisol synthase (CYP11B1) is the main enzyme for the endogenous synthesis of cortisol and its inhibition is a potential way for the treatment of diseases associated with increased cortisol levels, such as Cushing's syndrome, metabolic diseases, and delayed wound healing. Aldosterone synthase (CYP11B2) is the key enzyme for aldosterone biosynthesis and its inhibition is a promising approach for the treatment of congestive heart failure, cardiac fibrosis, and certain forms of hypertension. Both CYP11B1 and CYP11B2 are structurally very similar and expressed in the adrenal cortex. To facilitate the identification of novel inhibitors of these enzymes, ligand-based pharmacophore models of CYP11B1 and CYP11B2 inhibition were developed. A virtual screening of the SPECS database was performed with our pharmacophore queries. Biological evaluation of the selected hits lead to the discovery of three potent novel inhibitors of both CYP11B1 and CYP11B2 in the submicromolar range (compounds 8–10), one selective CYP11B1 inhibitor (Compound 11, IC₅₀ = 2.5 μM), and one selective CYP11B2 inhibitor (compound 12, IC₅₀ = 1.1 μM), respectively. The overall success rate of this prospective virtual screening experiment is 20.8% indicating good predictive power of the pharmacophore models.

Structure Identification and Anti-Cancer Pharmacological Prediction of Triterpenes from Ganoderma lucidum

Ganoderma triterpenes (GTs) are the major secondary metabolites of Ganoderma lucidum, which is a popularly used traditional Chinese medicine for complementary cancer therapy. In the present study, systematic isolation, and in silico pharmacological prediction are implemented to discover potential anti-cancer active GTs from G. lucidum. Nineteen GTs, three steroids, one cerebroside, and one thymidine were isolated from G. lucidum. Six GTs were first isolated from the fruiting bodies of G. lucidum, including 3β,7β,15β-trihydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid methyl ester (1), 3β,7β,15β-trihydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid (2), 3β,7β,15α,28-tetrahydroxy-11,23-dioxo-lanost-8,16-dien-26-oic acid (3), ganotropic acid (4), 26-nor-11,23-dioxo-5α-lanost-8-en-3β,7β,15α,25-tetrol (5) and (3β,7α)-dihydroxy-lanosta-8,24-dien- 11-one (6). (4E,8E)-N-d-2'-hydroxypalmitoyl-l-O-β-d-glucopyranosyl-9-methyl-4,8-spingodienine (7), and stigmasta-7,22-dien-3β,5α,6α-triol (8) were first reported from the genus Ganodema. By using reverse pharmacophoric profiling of the six GTs, thirty potential anti-cancer therapeutic targets were identified and utilized to construct their ingredient-target interaction network. Then nineteen high frequency targets of GTs were selected from thirty potential targets to construct a protein interaction network (PIN). In order to cluster the pharmacological activity of GTs, twelve function modules were identified by molecular complex detection (MCODE) and gene ontology (GO) enrichment analysis. The results indicated that anti-cancer effect of GTs might be related to histone acetylation and interphase of mitotic cell cycle by regulating general control non-derepressible 5 (GCN5) and cyclin-dependent kinase-2 (CDK2), respectively. This research mode of extraction, isolation, pharmacological prediction, and PIN analysis might be beneficial to rapidly predict and discover pharmacological activities of novel compounds.

Optimized Virtual Screening Workflow for the Identification of Novel G-Quadruplex Ligands

G-quadruplexes, alternative DNA secondary structures present in telomeres, emerge as promising targets for the treatment of cancer, because they prevent telomere elongation and accordingly cell proliferation. Within this study, theoretically validated pharmacophore- and shape-based models as well as a theoretically validated docking protocol were generated and applied in parallel for virtual screening and the identification of novel G-quadruplex ligands. Top-ranked hits retrieved with all methods independently and in addition in a consensus approach were selected for biological testing. Of the 32 tested virtual hits seven selectively stabilized G-quadruplexes over duplex DNA in the fluorescence melting assay. For the five most active compounds, chemically closely related analogues were collected and subjected to in vitro analysis. Thereby, seven further novel G-quadruplex ligands could be identified. These molecules do not only represent novel scaffolds, but some of them are in addition even more potent G-quadruplex stabilizers than the established reference compound berberine. This study proposes an optimized in silico workflow for the identification of novel G-quadruplex stabilizers, which can also be applied in future studies. In addition, structurally novel and promising lead candidates with strong and selective G-quadruplex stabilizing properties are reported.

Pharmacophore modeling for COX-1 and -2 inhibitors with LigandScout in comparison to Discovery Studio

BACKGROUND

Pharmacophore modeling has become an integrated tool in drug discovery. However, no prospective study compares the performance of the available software.

METHODS

The two widely used pharmacophore modeling and screening software programs Discovery Studio and LigandScout were used to generate, validate, and prospectively apply COX-1 and -2 models. Selected virtual hits were tested in cell-free enzymatic assays. The correct retrieval of active compounds was compared.

RESULTS

In the enzymatic testing, 10.5% of the tested hits for COX-2 and 6.6% of the predicted compounds for COX-1 were active. To directly compare the two models, both based on the same PDB entry, were selected for virtual screening. The two programs yielded vastly different hit lists, but both predicted active compounds.

CONCLUSION

To obtain a comprehensive selection of active compounds, more than one program should be used for modeling.

Experimentally validated HERG pharmacophore models as cardiotoxicity prediction tools

The goal of this study was to design, experimentally validate, and apply a virtual screening workflow to identify novel hERG channel blockers. The hERG channel is an important antitarget in drug development since cardiotoxic risks remain as a major cause of attrition. A ligand-based pharmacophore model collection was developed and theoretically validated. The seven most complementary and suitable models were used for virtual screening of in-house and commercially available compound libraries. From the hit lists, 50 compounds were selected for experimental validation through bioactivity assessment using patch clamp techniques. Twenty compounds inhibited hERG channels expressed in HEK 293 cells with IC50 values ranging from 0.13 to 2.77 μM, attesting to the suitability of the models as cardiotoxicity prediction tools in a preclinical stage.

Ligand-based pharmacophore modeling and virtual screening for the discovery of novel 17β-hydroxysteroid dehydrogenase 2 inhibitors

17β-Hydroxysteroid dehydrogenase 2 (17β-HSD2) catalyzes the inactivation of estradiol into estrone. This enzyme is expressed only in a few tissues, and therefore its inhibition is considered as a treatment option for osteoporosis to ameliorate estrogen deficiency. In this study, ligand-based pharmacophore models for 17β-HSD2 inhibitors were constructed and employed for virtual screening. From the virtual screening hits, 29 substances were evaluated in vitro for 17β-HSD2 inhibition. Seven compounds inhibited 17β-HSD2 with low micromolar IC50 values. To investigate structure-activity relationships (SAR), 30 more derivatives of the original hits were tested. The three most potent hits, 12, 22, and 15, had IC50 values of 240 nM, 1 μM, and 1.5 μM, respectively. All but 1 of the 13 identified inhibitors were selective over 17β-HSD1, the enzyme catalyzing conversion of estrone into estradiol. Three of the new, small, synthetic 17β-HSD2 inhibitors showed acceptable selectivity over other related HSDs, and six of them did not affect other HSDs.

Pharmacophore modeling, virtual screening, and in vitro testing reveal haloperidol, eprazinone, and fenbutrazate as neurokinin receptors ligands

Neurokinin receptors (NKRs) have been shown to be involved in many physiological processes, rendering them promising novel drug targets, but also making them the possible cause for side effects of several drugs. Aiming to answer the question whether the binding to NKRs could have a share in the side effects or even the desired effects of already licensed drugs, we generated a set of ligand-based common feature pharmacophore models based on the structural information about subtype-selective and nonselective NKR antagonists and screened an in-house database mainly composed of licensed drugs. The prospective pharmacological investigations of the virtual hits haloperidol, eprazinone, and fenbutrazate confirmed them to be NKR ligands in vitro. By the identification of licensed drugs as so far unknown NKR ligands, this study contributes to establishing an activity profile of the investigated compounds and confirms the presented pharmacophore models as useful tools for this purpose.

Identification of novel liver X receptor activators by structure-based modeling

Liver X receptors (LXRs) are members of the nuclear receptor family. Activators of LXRs are of high pharmacological interest as LXRs regulate cholesterol, fatty acid, and carbohydrate metabolism as well as inflammatory processes. On the basis of different X-ray crystal structures, we established a virtual screening workflow for the identification of novel LXR modulators. A two-step screening concept to identify active compounds included 3D-pharmacophore filters and rescoring by shape alignment. Eighteen virtual hits were tested in vitro applying a reporter gene assay, where concentration-dependent activity was proven for four novel lead structures. The most active compound 10, a 1,4-naphthochinone, has an estimated EC₅₀ of around 5 μM.

Predicting cyclooxygenase inhibition by three-dimensional pharmacophoric profiling. Part II: Identification of enzyme inhibitors from Prasaplai, a Thai traditional medicine

Prasaplai is a medicinal plant mixture that is used in Thailand to treat primary dysmenorrhea, which is characterized by painful uterine contractility caused by a significant increase of prostaglandin release. Cyclooxygenase (COX) represents a key enzyme in the formation of prostaglandins. Former studies revealed that extracts of Prasaplai inhibit COX-1 and COX-2. In this study, a comprehensive literature survey for known constituents of Prasaplai was performed. A multiconformational 3D database was created comprising 683 molecules. Virtual parallel screening using six validated pharmacophore models for COX inhibitors was performed resulting in a hit list of 166 compounds. 46 Prasaplai components with already determined COX activity were used for the external validation of this set of COX pharmacophore models. 57% of these components were classified correctly by the pharmacophore models. These findings confirm that the virtual approach provides a helpful tool (i) to unravel which molecular compounds might be responsible for the COX-inhibitory activity of Prasaplai and (ii) for the fast identification of novel COX inhibitors.