Oliver Rau

Carnosic acid and carnosol potently inhibit human 5-lipoxygenase and suppress pro-inflammatory responses of stimulated human polymorphonuclear leukocytes.

Carnosic acid (CA) and carnosol (CS) are phenolic diterpenes present in several labiate herbs like Rosmarinus officinalis (Rosemary) and Salvia officinalis (Sage). Extracts of these plants exhibit anti-inflammatory properties, but the underlying mechanisms are largely undefined. Recently, we found that CA and CS activate the peroxisome proliferator-activated receptor gamma, implying an anti-inflammatory potential on the level of gene regulation. Here we address short-term effects of CA and CS on typical functions of human polymorphonuclear leukocytes (PMNL). We found that (I), CA and CS inhibit the formation of pro-inflammatory leukotrienes in intact PMNL (IC(50)=15-20 microM [CA] and 7 microM [CS], respectively) as well as purified recombinant 5-lipoxygenase (EC number 1.13.11.34, IC(50)=1 microM [CA] and 0.1 microM [CS], respectively), (II) both CA and CS potently antagonise intracellular Ca(2+) mobilisation induced by a chemotactic stimulus, and (III) CA and CS attenuate formation of reactive oxygen species and the secretion of human leukocyte elastase (EC number 3.4.21.37). Together, our findings provide a pharmacological basis for the anti-inflammatory properties reported for CS- and CA-containing extracts.

Peroxisome Proliferator–Activated Receptor γ as a Molecular Target of Resveratrol-Induced Modulation of Polyamine Metabolism

Previous results indicate that the polyphenol resveratrol inhibits cell growth of colon carcinoma cells via modulation of polyamine metabolic key enzymes. The aim of this work was to specify the underlying molecular mechanisms and to identify a possible role of transcription factor peroxisome proliferator-activated receptor gamma (PPARgamma). Cell growth was determined by bromodeoxyuridine incorporation and crystal violet staining. Protein levels were examined by Western blot analysis. Spermine/spermidine acetyltransferase (SSAT) activity was determined by a radiochemical assay. PPARgamma ligand-dependent transcriptional activity was measured by a luciferase assay. A dominant-negative PPARgamma mutant was transfected in Caco-2 cells to suppress PPARgamma-mediated functions. Resveratrol inhibits cell growth of both Caco-2 and HCT-116 cells in a dose- and time-dependent manner (P < 0.001). In contrast to Caco-2-wild type cells (P < 0.05), resveratrol failed to increase SSAT activity in dominant-negative PPARgamma cells. PPARgamma involvement was further confirmed via ligand-dependent activation (P < 0.01) as well as by induction of cytokeratin 20 (P < 0.001) after resveratrol treatment. Coincubation with SB203580 abolished SSAT activation significantly in Caco-2 (P < 0.05) and HCT-116 (P < 0.01) cells. The involvement of p38 mitogen-activated protein kinase (MAPK) was further confirmed by a resveratrol-mediated phosphorylation of p38 protein in both cell lines. Resveratrol further increased the expression of PPARgamma coactivator PGC-1alpha (P < 0.05) as well as SIRT1 (P < 0.01) in a dose-dependent manner after 24 hours of incubation. Based on our findings, p38 MAPK and transcription factor PPARgamma can be considered as molecular targets of resveratrol in the regulation of cell proliferation and SSAT activity, respectively, in a cell culture model of colon cancer.

From Machine Learning to Natural Product Derivatives that Selectively Activate Transcription Factor PPARγ

Peroxisome proliferator-activated receptors (PPARs) are nuclear proteins that act as transcription factors. They represent a validated drug target class involved in lipid and glucose metabolism as well as inflammatory response regulation. We combined state-of-the-art machine learning methods including Gaussian process (GP) regression, multiple kernel learning, the ISOAK molecular graph kernel, and a novel loss function to virtually screen a large compound collection for potential PPAR activators; 15 compounds were tested in a cellular reporter gene assay. The most potent PPARg-selective hit (EC50 = 10 0.2 mm) is a derivative of the natural product truxillic acid. Truxillic acid derivatives are known to be anti-inflammatory agents, potentially due to PPARg activation. Our study underscores the usefulness of modern machine learning algorithms for finding potent bioactive compounds and presents an example of scaffold-hopping from synthetic compounds to natural products. We thus motivate virtual screening of natural product collections as a source of novel lead compounds. The results of our study suggest that pharmacophoric patterns of synthetic bioactive compounds can be traced back to natural products, and this will be useful for “de-orphanizing” the natural bioactive agent. PPARs are present in three known isoforms: PPARa, PPARb (d), and PPARg, with different expression patterns according to their function. PPAR activation leads to an increased expression of key enzymes and proteins involved in the uptake and metabolism of lipids and glucose. Unsaturated fatty acids and eicosanoids such as linoleic acid and arachidonic acid are physiological PPAR activators. Owing to their central role in glucose and lipid homeostasis, PPARs represent attractive drug targets for the treatment of diabetes and dyslipidemia. Glitazones (thiazolidinediones) such as pioglitazone and rosiglitazone act as selective activators of PPARg and are used as therapeutics for diabetes mellitus type 2. In addition to synthetic activators, herbs are traditionally used for treatment of metabolic disorders, and some herbal ingredients have been identified as PPARg activators, for example, carnosol and carnosic acid, as well as several terpenoids and flavonoids. 12] We used several machine learning methods, with synthetic PPAR agonists as input, to find common pharmacophoric patterns for virtual screening in both synthetic and natural product derived substances. We focused on GP models, which originate from Bayesian statistics. Their original applications in cheminformatics were aimed at predicting aqueous solubility, blood–brain barrier penetration, hERG (human ethergo-go-related gene) inhibition, 15] and metabolic stability. A particular advantage of GPs is that they provide error estimates with their predictions. In GP modeling of molecular properties, one defines a positive definite kernel function to model molecular similarity. Compound information enters GP models only via this function, so relevant (context-dependent) physicochemical properties must be captured. This is done by computing molecular descriptors (physicochemical property vectors), or by graph kernels that are defined directly on the molecular graph. From a family of functions that are potentially able to model the underlying structure–activity relationship (“prior”), only functions that agree with the data are retained (Figure 1). The weighted average of the retained functions (“posterior”) acts as predictor, and its variance as an estimate of the confidence in the predic-

α‐Alkyl Substituted Pirinixic Acid Derivatives as Potent Dual Agonists of the Peroxisome Proliferator Activated Receptor Alpha and Gamma

Peroxisome proliferator‐activated receptors (PPAR) are nuclear receptors, playing a pivotal role in energy homeostasis. Activators of the PPARα subtype are in widespread use for the treatment of hyperlipidemia, while activators of the PPARγ subtype are in clinical use for the treatment of type‐2 diabetes. Since both of these diseases are frequently associated, the combined treatment with one drug simultaneously activating PPARα and PPARγ seems worthwhile. Starting with pirinixic acid, which is a moderately active dual PPARα/γ agonist, we improved potency at the human PPARα and PPARγ by substituting the α‐position with an aliphatic chain. The maximal effect was achieved at a chain length of four and six carbons, respectively, leading to an activity induction by a factor of 36 for PPARα and 18 for PPARγ, respectively.

From Molecular Shape to Potent Bioactive Agents II: Fragment‐Based de novo Design

Agonists of peroxisome proliferator-activated receptors (PPARs) are important drugs for dyslipidemia and type-II-diabetes. Agonists of PPARa and PPARg, like Bezafibrate, Fenofibrate, and Rosiglitazone, are already on the market, but novel PPAR agonists with improved pharmacological profiles are still required. In this study, we present the successful de novo design of a PPAR agonist using a novel fragment-based compound assembly strategy. Previously, we have demonstrated the superiority of methods that combine shape and pharmacophoric information for fully automated virtual screening of PPAR agonists. For the present work, we built on this knowledge and developed a software tool that generates suggestions for bioisosteric ACHTUNGTRENNUNGreplacements to be exploited by medicinal chemists for the ACHTUNGTRENNUNGgeneration of novel lead structures. Our software, SQUIRREL (Sophisticated QUantification of InteRaction RELationships), uses decomposition of the molecular surface into regions with equal curvature (“Shapelets”) for shape-based molecular alignment of two molecules by an established subgraph matching routine implemented using a Bron–Kerbosch algorithm. For the de novo design of a compound, we used this procedure to match molecular building-blocks, rather than complete molecules, which were obtained from pseudo-retrosynthetic decomposition of druglike bioactive agents. The computer program then suggests a ranked list of best-fitting buildingblocks, and expert visual inspection was then used to identify the best fragments and gain ideas for bioisosteric replacement of molecular moieties.

Structure-Based Pharmacophore Screening for Natural-Product-Derived PPARγ Agonists

Peroxisome proliferator-activated receptors (PPARs) are transcription factors that play a critical role in lipid signalling and immunomodulation and functionally interact with other nuclear receptors, like PXR and NF-kB, in the regulation of lipid metabolism. Therefore, agonists of PPARa and PPARg serve as therapeutic agents for the treatment of dyslipidaemia, type II diabetes and artherosclerosis, while their effects on the regulation of cell proliferation are under investigation. Several natural compounds have been identified that activate PPARs, including the tetrahydrocannabinol (THC) metabolite THC-11-oic acid, carnosic acid and carnosol, and resveratol. These can provide a starting point for the combinatorial exploration of natural-product-derived compounds for lead discovery and development, with the aim of substituting existing PPAR agonists with potentially safer drugs containing novel scaffolds. Here, we present a virtual screening protocol that led to a PPARg agonist from a combinatorial compound library that was derived from the scaffold structure of a-santonin, a natural sesquiterpene lactone found in mugwort. We demonstrate that it is possible to find lead candidates in small combinatorial compound collections with minimal experimental effort by “fuzzy” pharmacophore screening. For the generation of a pharmacophore query, we superimposed four high-resolution X-ray structures of the PPARg ligand binding domain in complex with agonists (PDB ID: 1nyx with Ragaglitazar, 1knu with YPA, 1i7i with Tesaglitazar, 1zgy with Rosiglitazone, Figure 1 A). The resulting ligand alignment served as the basis for pharmacophoric point assignment by our software LIQUID, as described previously. Briefly, LIQUID represents potential pharmacophoric points (lipophilic, hydrogen-bonding) in a molecule by Gaussian densities. These densities are converted to probabilities for the pairwise matching of compounds. As a result of LIQUID matching and scoring, a screening library is sorted so that the best matching compounds appear at the top of the ranked list. From this list, the most promising screening candidates are picked. For the generation of the LIQUID descriptors from 3D molecular conformations, we used cluster radii of 1 for lipophilic centres and 2 for hydrogen-bonding centres (donors, acceptors, and donor + acceptor). No other pharmacophoric features were considered to obtain a coarse-grained model that allowed for scaffold hopping to occur. The resulting pharmacophore query is depicted in Figure 1 B. This procedure was performed in order to obtain a “receptor-relevant” pharmacophore model of PPARg agonists instead of using a ligandbased spatial alignment of artificially generated conformers. This structure-based alignment of multiple ligands allowed us to compute “fuzzy” pharmacophoric feature points, so that we obtained a probability-weighted consensus model. It is noteworthy that the explicit consideration of “voids” or “forbidden regions” is not required, as the probabilities for the presence of a pharmacophoric feature adopt values close to zero in the vicinity of the model. We then searched the AnalytiCon Discovery collection of natural-product-derived combinatorial compounds (version 01/ 2007, 15 590 entries) for hits matching the LIQUID pharmacophore query. A single 3D conformation was computed for each compound by using Corina v3.2 (Molecular Networks GmbH, Erlangen). This concept was shown to be sufficient for firstFigure 1. A) Structural superimposition of four PPARg-agonist complexes; B) The alignment-derived LIQUID pharmacophore query. In the pharmacophore model, lipophilic centres are shown in green, potential hydrogenbond donor sites are shown in blue, and potential hydrogen-bond acceptor sites are shown in red. Approximate locations of helix 3 and the AF2-helix are indicated. The trivariate Gaussians are shown with widths of one standard deviation in each direction.

An Innovative Method To Study Target Protein−Drug Interactions by Mass Spectrometry

We report the combination of chemical cross-linking and high-resolution mass spectrometry for analyzing conformational changes in target proteins that are induced by drug binding. With this approach conformational changes in the peroxisome proliferator-activated receptor alpha (PPARalpha) upon binding of low-molecular weight compounds were readily detected, proving that the strategy provides a basis to efficiently characterize target protein-drug interactions.

Acidic elements in histamine H(3) receptor antagonists.

Antagonists of the human histamine H(3) receptor (hH(3)R) often contain a second basic moiety, which is well known to boost affinity on this histamine receptor subtype. Here, we prepared compounds with acidic moieties of different pK(a) values to figure out that the hH(3)R tolerates these functionalities when added to a common pharmacophore blueprint. Depending on the acidic, electronic and steric features the designed ligands showed hH(3)R affinities in the nanomolar concentration range. Additionally, selected ligands were tested but failed as dual acting hH(3)R/hPPAR (human peroxisome proliferator-activated receptor) ligands.

From Molecular Shape to Potent Bioactive Agents I: Bioisosteric Replacement of Molecular Fragments

Ligand binding to a macromolecular receptor is based on complementarity of both molecular shape and receptor–ligand interaction points. With surprisingly few exceptions, ligandbased virtual screening approaches consider only one of these two principles explicitly. To fill this gap, we have developed a method for comparison of both molecular shape and potential pharmacophore points (PPPs), termed SQUIRREL (Sophisticated QUantification of InteRaction RELationships). This ligand-based technique was applied in the design of a small, focused screening library with the aim to find novel agonists of peroxisome proliferator-activated receptors (PPARs). PPARs are pharmaceutically relevant members of the nuclear receptor superfamily. Although agonists of PPARa and PPARg have been approved for treatment of dyslipidemia and type-2 diabetes, novel leads with distinct selectivity profiles are required to improved safety and enhanced therapeutic efficacy. Among several new bioactive chemotypes, we identified a potent PPARaselective activator (EC50 = 44 5 nm) from a large compound collection with minimal experimental effort. This compound represents a scaffold-hop from known PPAR agonists and provides proof-of-concept for the potential utility of ligand-based virtual screening in early phases of drug discovery. A necessary prerequisite for SQUIRREL is one or more active reference molecules, that is, known PPARa agonists in this study (compounds 1–3, Scheme 1). The ligand binding site of all PPARs is large and deeply buried, and a great part of the ligand surface interacts with protein residues. In particular, a potent PPAR agonist should interact with residues stabilizing the AF2 “activation” helix (S280, Y314, H440, Y464 in PPARa ; S289, H323, Y473, H449 in PPARg). Therefore, we decided to follow a two-step virtual screening protocol for shape-based matching and subsequent pharmacophore-based scoring of candidate compounds. The first step was to obtain a multitude of possible shapebased alignments of the reference molecules and the screening compounds. For this task, we used our Shapelets approach. The basic idea of Shapelets is to locate similar local shapes on molecular surfaces, and compare two molecules based on the pair-wise similarity of local shape descriptions (Figure 1 a). The method starts from a smooth triangulated molecular surface, which is obtained by isosurface extraction. Isosurfaces are then decomposed into “knobs” and “planes” by fitting hyperbolic paraboloids. By representing two molecules as two sets of such hyperbolic paraboloids, pair-wise, shapebased alignments can be obtained by clique detection in association graphs. The second step was to assess the quality of the shapebased alignments in terms of a “fuzzy” pharmacophore function, which originates from the LIQUID concept. This scoring function matches Gaussian representations of PPPs and computes their overlap for two aligned molecules. The sum of all PPPs can be interpreted as a pharmacophoric density field (Figure 1 b). The overlap of two fields is computed as a similarity score indicating the match of a molecule to a given reference molecule. As an initial test of SQUIRREL, we performed retrospective virtual screening. The task was to retrieve known PPAR agonists from a large collection of druglike compounds. Virtual screening methods that use a combination of shape and pharmacophore information performed better on subtype-selective PPAR agonists than methods that exclusively used only shapeor pharmacophore-based matching (see Supporting Information). SQUIRREL was shown to be well-suited to the task of PPAR ligand retrieval, with high success rates for the top-ranking compounds. Scheme 1. Reference PPAR agonists for virtual screening. Compound 1 (GW590735): EC50 = 4 nm (PPARa),>10 mm (PPARg) ; [5] Compound 2 (Merck): IC50 = 140 nm (PPARa) IC50 = 1.7 mm (PPARg) [14] ; Compound 3 (Aventis): EC50 = 0.3 nm (PPARa), [15] activity on PPARg not reported.

The Treatment of Dyslipidemia—What's Left in the Pipeline?

Dyslipidemia is a pathological alteration of serum lipid levels. The most common forms are either elevations of triglycerides or low density lipoprotein cholesterol associated with a reduction of high density lipoprotein cholesterol. Most frequently both forms of lipid disorders are combined. Elevations of free fatty acid blood levels are commonly not subsumed under the term dyslipidemia. However, free fatty acids should also be considered, as they are frequently associated with dyslipidemia and represent a risk factor for cardiovascular diseases. Dyslipidemias are among the major etiologic factors for arterial occlusive diseases. Resulting in fatal implications such as stroke and coronary heart disease, dyslipidemias contribute to the most prevalent causes of death. Lowering of low density lipoprotein and raising of high density lipoprotein cholesterol levels have been shown in both epidemiologic and intervention studies to decrease mortality. Established treatments of dyslipidemias are statins and fibrates. However, recent research has established some new potential therapeutic targets which are currently investigated in clinical trials. New therapeutic approaches include subtype selective, dual, and pan‐agonists of the peroxisome proliferator activated receptor, inhibitors of the cholesterol ester transfer protein, Acyl‐CoA‐cholesterol‐acyltransferase, squalene synthase, microsomal triglycerid‐transfer‐protein, and cholesterol absorption. Clinical implications of new drugs under investigation are discussed in this review.