The use of a refined chemical library called the CeMM library of unique drugs (CLOUD) identified a synergistic interaction between flutamide and phenprocoumon in decreasing the growth of prostate cancer cells by regulating androgen receptor stability.

Abstract Libraries of approved drugs contain some of the best studied small molecules and represent invaluable resources for drug repurposing and chemical biology studies. Access to comprehensive libraries of clinical compounds is limited and these collections are otherwise difficult to arrange. Using cheminformatics approaches we have assembled a non-redundant set of representative FDA-approved small molecules that we call the CeMM Library of Unique Drugs (CLOUD). The CLOUD covers the chemical and biological space of approved drugs, contains active forms of prodrugs, and can be screened at clinically relevant concentrations. In a combinatorial viability screen of the CLOUD we uncovered a synergistic interaction between two approved drugs. We show that the concomitant administration of these two small molecules induces apoptosis in the androgen receptor (AR)-dependent prostate cancer cell line LNCaP. Mechanistically, the combination affects AR signaling and the stability of the receptor itself. We are currently dissecting further aspects of the mechanism of action and performing in vivo experiments. Altogether, our data suggest that the combination of these two drugs can be repurposed for the treatment of prostate cancer. Citation Format: Marco P. Licciardello, Patrick Markt, Freya Klepsch, Charles-Hugues Lardeau, Gerhard Dürnberger, Vladimir Ivanov, Jacques Colinge, Stefan Kubicek. A high-throughput screen of approved drugs uncovers a synergistic interaction targeting prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-099. doi:10.1158/1538-7445.AM2015-LB-099

Approved drugs serve as functional probes for analyzing disease-relevant biochemical pathways or are screened against proteins to identify novel medical applications resulting in a repurposing of known drugs. Despite these benefits of approved drugs, it is almost impossible to physically obtain a complete collection. Commercial vendors cover about 50-65% of drugs. Academic efforts such as the Johns Hopkins Clinical Compound Library (JHCCL) [1] and the NCGC Pharmaceutical collection [2] are only accessible via collaboration agreements. Thus, an affordable reference drug library obtainable for all screening centers is still missing. Here we report the generation of the CeMM Library of Unique Drugs (CLOUD), a focused collection of 314 systemically bioavailable small molecule drugs. The library was derived from FDA-approved drugs applying data mining and structural clustering techniques. Reduction of approved drugs to a set of systemically available small molecules and clustering based on their activity classes (e.g. dopamine receptor agonist) resulted in 244 compounds. In addition to this representative set of structural and biological space of drugs, CLOUD contains 35 drugs where the biological target is still unknown. Another 35 molecules representing the active forms of CLOUD prodrugs ensure the usability of the library for both biochemical and cell-based assays. Finally, all CLOUD drugs are delivered in a single 384-well plate in concentrations related to their therapeutic plasma levels to make high-throughput screening as comfortable as possible.

We report the first application of ligand-based virtual screening (VS) methods for discovering new compounds able to inhibit both human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT)-associated functions, DNA polymerase and ribonuclease H (RNase H) activities. The overall VS campaign consisted of two consecutive screening processes. In the first, the VS platform Rapid Overlay of Chemical Structures (ROCS) was used to perform in silico shape-based similarity screening on the NCI compounds database in which a hydrazone derivative, previously shown to inhibit the HIV-1 RT, was chosen. As a result, 34 hit molecules were selected and assayed on both RT-associated functions. In the second, the 4 most potent RT inhibitors identified were selected as queries for parallel VS performed by combining shape-based, 2D-fingerprint and 3D-pharmacophore VS methods. Overall, a set of molecules characterized by new different scaffolds were identified as novel inhibitors of both HIV-1 RT-associated activities in the low micromolar range.

Various inflammatory stimuli that activate the nuclear factor kappa B (NF-κB) signaling pathway converge on a serine/threonine kinase that displays a key role in the activation of NF-κB: the I kappa B kinase β (IKK-β). Therefore, IKK-β is considered an interesting target for combating inflammation and cancer. In our study, we developed a ligand-based pharmacophore model for IKK-β inhibitors. This model was employed to virtually screen commercial databases, giving a focused hit list of candidates. Subsequently, we scored by molecular shape to rank and further prioritized virtual hits by three-dimensional shape-based alignment. One out of ten acquired and biologically tested compounds showed inhibitory activity in the low micromolar range on IKK-β enzymatic activity in vitro and on NF-κB transactivation in intact cells. Compound 8 (2-(1-adamantyl)ethyl 4-[(2,5-dihydroxyphenyl)methylamino]benzoate) represents a novel chemical class of IKK-β inhibitors and shows that the presented model is a valid approach for identification and development of new IKK-β ligands.

Monoamine oxidase B (MAO-B) is a promising target for the treatment of neurodegenerative disorders. We report the synthesis and the biological evaluation of halogenated derivatives of 1-aryliden-2-(4-phenylthiazol-2-yl)hydrazines. The fluorinated series shows interesting activity and great selectivity toward the human recombinant MAO-B isoform expressed in baculovirus infected BTI insect cells. The multiple crystal structures alignment of the enzyme highlighted pronounced induced fit (IF) adaptations with respect to bound ligands. Therefore, IF docking (IFD) experiments and molecular dynamic (MD) simulations were carried out to reveal the putative binding mode and to explain the experimentally observed differences in the activity of 1-(aryliden-2-(4-(4-chlorophenyl)thiazol-2-yl)hydrazines. The importance of water molecules within the binding site was also investigated. These are known to play an important role in the binding site cavity and to mediate protein ligand interactions. Detailed analyses of the trajectories provide insights on the chemical features required for the activity of this scaffold. In particular it was highlighted the importance of fluorine atom interacting with the water close to the cofactor and the influence of steric bulkiness of substituents in the arylidene moiety. Free energy perturbation (FEP) analysis confirmed experimental data. The information we deduced will help to develop novel high-affinity MAO-B inhibitors. (C) 2011 Elsevier Masson SAS. All rights reserved.

Microsomal prostaglandin E(2) synthase-1 (mPGES-1) catalyzes prostaglandin E(2) formation and is considered as a potential anti-inflammatory pharmacological target. To identify novel chemical scaffolds active on this enzyme, two pharmacophore models for acidic mPGES-1 inhibitors were developed and theoretically validated using information on mPGES-1 inhibitors from literature. The models were used to screen chemical databases supplied from the National Cancer Institute (NCI) and the Specs. Out of 29 compounds selected for biological evaluation, nine chemically diverse compounds caused concentration-dependent inhibition of mPGES-1 activity in a cell-free assay with IC(50) values between 0.4 and 7.9 μM, respectively. Further pharmacological characterization revealed that also 5-lipoxygenase (5-LO) was inhibited by most of these active compounds in cell-free and cell-based assays with IC(50) values in the low micromolar range. Together, nine novel chemical scaffolds inhibiting mPGES-1 are presented that may possess anti-inflammatory properties based on the interference with eicosanoid biosynthesis.

HIV‐1 reverse transcriptase (RT) has two associated activities, DNA polymerase and RNase H, both essential for viral replication and validated drug targets. Although all RT inhibitors approved for therapy target DNA polymerase activity, the search for new RT inhibitors that target the RNase H function and are possibly active on RTs resistant to the known non‐nucleoside inhibitors (NNRTI) is a viable approach for anti‐HIV drug development. In this study, several alizarine derivatives were synthesized and tested for both HIV‐1 RT‐associated activities. Alizarine analogues K‐49 and KNA‐53 showed IC50 values for both RT‐associated functions of ∼ 10 μm. When tested on the K103N RT, both derivatives inhibited the RT‐associated functions equally, whereas when tested on the Y181C RT, KNA‐53 inhibited the RNase H function and was inactive on the polymerase function. Mechanism of action studies showed that these derivatives do not intercalate into DNA and do not chelate the divalent cofactor Mg2+. Kinetic studies demonstrated that they are noncompetitive inhibitors, they do not bind to the RNase H active site or to the classical NNRTI binding pocket, even though efavirenz binding negatively influenced K‐49/KNA‐53 binding and vice versa. This behavior suggested that the alizarine derivatives binding site might be close to the NNRTI binding pocket. Docking experiments and molecular dynamic simulation confirmed the experimental data and the ability of these compounds to occupy a binding pocket close to the NNRTI site.

The farnesoid X receptor (FXR) is involved in glucose and lipid metabolism regulation, which makes it an attractive target for the metabolic syndrome, dyslipidemia, atherosclerosis, and type 2 diabetes. In order to find novel FXR agonists, a structure-based pharmacophore model collection was developed and theoretically evaluated against virtual databases including the ChEMBL database. The most suitable models were used to screen the National Cancer Institute (NCI) database. Biological evaluation of virtual hits led to the discovery of a novel FXR agonist with a piperazine scaffold (compound 19) that shows comparable activity as the endogenous FXR agonist chenodeoxycholic acid (CDCA, compound 2).

OBJECTIVE:Cholesteryl ester transfer protein (CETP) plays a central role in the metabolism of high-density lipoprotein particles. Therefore, we searched for new drugs that bind to CETP and modulate its activity.METHODS:A preliminary pharmacophore-based parallel screening approach indicated that leoligin, a major lignan of Edelweiss (Leontopodium alpinum Cass.), might bind to CETP. Therefore we incubated leoligin ex vivo at different concentrations with human (n=20) and rabbit plasma (n=3), and quantified the CETP activity by fluorimeter. Probucol served as positive control. Furthermore, we dosed CETP transgenic mice with leoligin and vehicle control by oral gavage for 7 days and measured subsequently the in vivo modulation of CETP activity (n=5 for each treatment group).RESULTS:In vitro, leoligin significantly activated CETP in human plasma at 100 pM (p=0.023) and 1 nM (p=0.042), respectively, whereas leoligin concentrations of 1 mM inhibited CETP activity (p=0.012). The observed CETP activation was not species specific, as it was similar in magnitude for rabbit CETP. In vivo, there was also a higher CETP activity after oral dosage of CETP transgenic mice with leoligin (p=0.015). There was no short-term toxicity apparent in mice treated with leoligin.CONCLUSION:CETP agonism by leoligin appears to be safe and effective, and may prove to be a useful modality to alter high-density lipoprotein metabolism.

17β-Hydroxysteroid dehydrogenase type 3 and 5 (17β-HSD3 and 17β-HSD5) catalyze testosterone biosynthesis and thereby constitute therapeutic targets for androgen-related diseases or endocrine-disrupting chemicals. As a fast and efficient tool to identify potential ligands for 17βHSD3/5, ligand- and structure-based pharmacophore models for both enzymes were developed. The models were evaluated first by in silico screening of commercial compound databases and further experimentally validated by enzymatic efficacy tests of selected virtual hits. Among the 35 tested compounds, 11 novel inhibitors with distinct chemical scaffolds, e.g. sulfonamides and triazoles, and with different selectivity properties were discovered. Thereby, we provide several potential starting points for further 17β-HSD3 and 17β-HSD5 inhibitor development. Article from the Special issue on Targeted Inhibitors.

Computational chemistry has always played a key role in anti-viral drug development. The challenges and the quickly rising public interest when a virus is becoming a threat has significantly influenced computational drug discovery. The most obvious example is anti-AIDS research, where HIV protease and reverse transcriptase have triggered enormous efforts in developing and improving computational methods. Methods applied to anti-viral research include (i) ligand-based approaches that rely on known active compounds to extrapolate biological activity, such as machine learning techniques or classical QSAR, (ii) structure-based methods that rely on an experimentally determined 3D structure of the targets, such as molecular docking or molecular dynamics, and (iii) universal approaches that can be applied in a structure- or ligand-based way, such as 3D QSAR or 3D pharmacophore elucidation. In this review we summarize these molecular modeling approaches as they were applied to fight anti-viral diseases and highlight their importance for anti-viral research. We discuss the role of computational chemistry in the development of small molecules as agents against HIV integrase, HIV-1 protease, HIV-1 reverse transcriptase, the influenza virus M2 channel protein, influenza virus neuraminidase, the SARS coronavirus main proteinase and spike protein, thymidine kinases of herpes viruses, hepatitis c virus proteins and other flaviviruses as well as human rhinovirus coat protein and proteases, and other picornaviridae. We highlight how computational approaches have helped in discovering anti-viral activities of natural products and give an overview on polypharmacology approaches that help to optimize drugs against several viruses or help to optimize the metabolic profile of and anti-viral drug.

The nuclear receptor peroxisome proliferator-activated receptor gamma (PPAR gamma) is essential for adipogenesis. Although several fatty acids and their derivatives are known to bind and activate PPAR gamma, the nature of the endogenous ligand(s) promoting the early stages of adipocyte differentiation has remained enigmatic. Previously, we showed that lipoxygenase (LOX) activity is involved in activation of PPAR gamma during the early stages of adipocyte differentiation. Of the seven known murine LOXs, only the unconventional LOX epidermis-type lipoxygenase 3 (eLOX3) is expressed in 3T3-L1 preadipocytes. Here, we show that forced expression of eLOX3 or addition of eLOX3 products stimulated adipogenesis under conditions that normally require an exogenous PPAR gamma ligand for differentiation. Hepoxilins, a group of oxidized arachidonic acid derivatives produced by eLOX3, bound to and activated PPAR gamma. Production of hepoxilins was increased transiently during the initial stages of adipogenesis. Furthermore, small interfering RNA-mediated or retroviral short hairpin RNA-mediated knockdown of eLOX3 expression abolished differentiation of 3T3-L1 preadipocytes. Finally, we demonstrate that xanthine oxidoreductase (XOR) and eLOX3 synergistically enhanced PPAR gamma-mediated transactivation. Collectively, our results indicate that hepoxilins produced by the concerted action of XOR and eLOX3 may function as PPAR gamma activators capable of promoting the early PPAR gamma-dependent steps in the conversion of preadipocytes into adipocytes.

In this and previous studies we investigated the importance of partial structures of Telmisartan on PPAR gamma activation. The biphenyl-4-ylmethyl moiety at N1 and residues at C2 of the central benzimidazole were identified to be essential for receptor activation and potency of receptor binding. Now we focused our attention on positions 5 and 6 of the central benzimidazole and introduced bromine (3b-5/6, 3c), phenylcarbonyl (3d-5/6), hydroxy(phenyl) methyl (3g-5/6), hydroxymethyl (3h-5/6) and formyl (3i) groups. The selection of these moieties was inspired by the structure of Losartan and its metabolite EXP3179. In order to increase the hydrophobicity of the central part of the molecule, the benzimidazole was exchanged by a naphtho[2,3-d] imidazole (5). The compounds 3a-3i and 5 were tested in a differentiation assay using 3T3-L1 preadipocytes and a luciferase assay using COS-7 cells, transiently transfected with pGal4-hPPAR gamma DEF, pGal5-TK-pGL3 and pRL-CMV, as established models for the assessment of cellular PPAR gamma activation. An enhanced effect on PPAR gamma activation could be observed if lipophilic moieties are introduced in these positions. 4 '-[(2-Propyl-1H-naphtho[2,3-d] imidazol-1-yl) methyl] biphenyl-2-carboxylic acid (5) was identified as the most potent compound with an EC50 of 0.26 mu M and the profile of a full agonist.Together with compounds of the former structure-activity relationship study (position 2-substituted benzimidazole derivatives 4a-4j), the binding mode of Telmisartan and its derivatives have been analyzed in 3D pharmacophore-driven docking experiments. (C) 2010 Elsevier Ltd. All rights reserved.

Peroxisome proliferator-activated receptor gamma (PPARγ) agonists are used for the treatment of type 2 diabetes and metabolic syndrome. However, the currently used PPARγ agonists display serious side effects, which has led to a great interest in the discovery of novel ligands with favorable properties. The aim of our study was to identify new PPARγ agonists by a PPARγ pharmacophore–based virtual screening of 3D natural product libraries. This in silico approach led to the identification of several neolignans predicted to bind the receptor ligand binding domain (LBD). To confirm this prediction, the neolignans dieugenol, tetrahydrodieugenol, and magnolol were isolated from the respective natural source or synthesized and subsequently tested for PPARγ receptor binding. The neolignans bound to the PPARγ LBD with EC50 values in the nanomolar range, exhibiting a binding pattern highly similar to the clinically used agonist pioglitazone. In intact cells, dieugenol and tetrahydrodieugenol selectively activated human PPARγ-mediated, but not human PPARα- or -β/δ-mediated luciferase reporter expression, with a pattern suggesting partial PPARγ agonism. The coactivator recruitment study also demonstrated partial agonism of the tested neolignans. Dieugenol, tetrahydrodieugenol, and magnolol but not the structurally related eugenol induced 3T3-L1 preadipocyte differentiation, confirming effectiveness in a cell model with endogenous PPARγ expression. In conclusion, we identified neolignans as novel ligands for PPARγ, which exhibited interesting activation profiles, recommending them as potential pharmaceutical leads or dietary supplements.

3D pharmacophore modeling has evolved as an established and state‐of‐the‐art method for performing in‐silico predictions of biological activity. Using one single model is limited to single binding modes, while the combination of several models bears a broader application scope. We demonstrate the generation of a complete and predictive 3D model set for cyclooxygenase 1 and 2 inhibitors, along with a selection and validation protocol optimized for parallel virtual screening. This model set was applied to explain the cyclooxygenase activity of an ethnopharmacologically known mixture of natural products, the Thai traditional medicine “Prasaplai”. Results show that rationalizing natural product activity by modern in‐silico approaches is promising and can be tremendously useful in the identification of the mechanisms of action for known biological effects of complex herbal remedies.

Shape-based molecular similarity approaches have been established as important and popular virtual screening techniques. Recent applications have shown successful screening campaigns using different parameters and query selection. It is common sense that pure volume overlap scoring (or "shape-based screening") under-represents chemical or pharmacophoric information of a molecule. Using the "Directory of Useful Decoys" (DUD) as a benchmark set, we systematically evaluate how (i) the choice of query conformations, (ii) the selection of the active compound to be used as a query structure, and (iii) the inclusion of chemical information (i.e., the pharmacophoric properties of the query molecule) affect screening performance. Varying these parameters bears remarkable potential for improvements and delivers the best screening performance reported using these tools so far. From these insights, guidelines on how to reach optimum performance during virtual screening are developed.

In the current work, we measure the performance of seven ligand-based virtual screening tools - five similarity search methods and two pharmacophore elucidators - against the MUV data set. For the similarity search tools, single active molecules as well as active compound sets clustered in terms of their chemical diversity were used as templates. Their score was calculated against all inactive and active compounds in their target class. Subsequently, the scores were used to calculate different performance metrics including enrichment factors and AUC values. We also studied the effect of data fusion on the results. To measure the performance of the pharmacophore tools, a set of active molecules was picked either random- or chemical diversity-based from each target class to build a pharmacophore model which was then used to screen the remaining compounds in the set. Our results indicate that template sets selected by their chemical diversity are the best choice for similarity search tools, whereas the optimal training sets for pharmacophore elucidators are based on random selection underscoring that pharmacophore modeling cannot be easily automated. We also suggest a number of improvements for future benchmark sets and discuss activity cliffs as a potential problem in ligand-based virtual screening.