Henri Xhaard

Community-wide assessment of GPCR structure modelling and ligand docking

Recent breakthroughs in the determination of the crystal structures of G protein-coupled receptors (GPCRs) have provided new opportunities for structure-based drug design strategies targeting this protein family. With the aim of evaluating the current status of GPCR structure prediction and ligand docking, a community-wide, blind prediction assessment — GPCR Dock 2008 — was conducted in coordination with the publication of the crystal structure of the human adenosine A2A receptor bound to the ligand ZM241385. Twenty-nine groups submitted 206 structural models before the release of the experimental structure, which were evaluated for the accuracy of the ligand binding mode and the overall receptor model compared with the crystal structure. This analysis highlights important aspects for success and future development, such as accurate modelling of structurally divergent regions and use of additional biochemical insight such as disulphide bridges in the extracellular loops.

Screening of Various Hormone‐Sensitive Lipase Inhibitors as Endocannabinoid‐Hydrolyzing Enzyme Inhibitors

Three classes of chemically diverse hormone-sensitive lipase (HSL) inhibitors, including oxadiazolones, 2H-isoxazol-5-ones and carbamoyl triazoles, were evaluated for their ability to inhibit endocannabinoid-hydrolyzing enzymes, fatty acid amide hydrolase (FAAH) and monoglyceride lipase (MGL, also called monoacylglycerol lipase (MAGL)). All the compounds belonging to these compound classes inhibited both FAAH and MGL with IC50 values varying from the nanomolar to low micromolar range. The most potent FAAH inhibitor was 2H-isoxazol-5-one 10 c with an IC50 value of 0.45 nm, whereas the most promising MGL inhibitor, albeit not selective over FAAH, was 1,3,4-oxadiACHTUNGTRENNUNGazol-2(3H)-one 16 c (IC50 = 78 nm against 2-AG hydrolysis). These results suggest that HSL inhibitors investigated in this paper may provide useful leads for the development of novel FAAH and/or MGL inhibitors. N-arachidonoylethanolamine (anandamide, AEA) and 2arachidonoylglycerol (2-AG) are the two best known and most investigated endocannabinoids that activate cannabinoid CB1 [3] and CB2 [4] receptors and modulate several physiological processes, such as pain sensation and inflammation (for reviews, see Reference [5]). However, endogenous levels of AEA and 2-AG are normally low, as these endocannabinoids are rapidly degraded by the specific enzymes, FAAH (EC 3.5.1.4) and MGL (EC 3.1.1.23), respectively. Inactivation of FAAH and MGL by chemical inhibitors leads to elevated levels of AEA and 2-AG, which have been evidenced to be an attractive and valuable goal in the treatment of a variety of pathological conditions. 8] In recent years, a range of different classes of FAAH inhibitors have been developed, mainly derivatized from the other known serine hydrolase inhibitors (for a review, see Reference [9]). These include various substrate analogues, as well as nonlipid inhibitors such as a-keto heterocycles, carbamates, (thio)hydantoins, piperidine/piperazine ureas and most recently, benzothiazole-based sulfonyl derivatives and boronic acids. Within carbamate-based inhibitors, cyclohexylcarbamic acid biphenyl-3-yl ester (URB597) (1) has been shown to be effica-

OX1 and OX2 orexin/hypocretin receptor pharmacogenetics

Orexin/hypocretin peptide mutations are rare in humans. Even though human narcolepsy is associated with orexin deficiency, this is only extremely rarely due to mutations in the gene coding prepro-orexin, the precursor for both orexin peptides. In contrast, coding and non-coding variants of the OX1 and OX2 orexin receptors have been identified in many human populations; sometimes, these have been associated with disease phenotype, although most confer a relatively low risk. In most cases, these studies have been based on a candidate gene hypothesis that predicts the involvement of orexins in the relevant pathophysiological processes. In the current review, the known human OX1/HCRTR1 and OX2/HCRTR2 genetic variants/polymorphisms as well as studies concerning their involvement in disorders such as narcolepsy, excessive daytime sleepiness, cluster headache, polydipsia-hyponatremia in schizophrenia, and affective disorders are discussed. In most cases, the functional cellular or pharmacological correlates of orexin variants have not been investigated—with the exception of the possible impact of an amino acid 10 Pro/Ser variant of OX2 on orexin potency—leaving conclusions on the nature of the receptor variant effects speculative. Nevertheless, we present perspectives that could shape the basis for further studies. The pharmacology and other properties of the orexin receptor variants are discussed in the context of GPCR signaling. Since orexinergic therapeutics are emerging, the impact of receptor variants on the affinity or potency of ligands deserves consideration. This perspective (pharmacogenetics) is also discussed in the review.

Impact of probe compound in MRP2 vesicular transport assays

MRP2 is an efflux transporter that is expressed mainly in the canalicular membrane of hepatocytes, where it expels polar and ionic compounds into the bile. MRP2 is also present in the apical membrane of enterocytes and epithelial cells of proximal tubules of the kidney. Inhibition of MRP2 transport can lead to the accumulation of metabolites and other MRP2 substrates up to toxic levels in these cells. The transport properties of MRP2 are frequently studied with the vesicular transport assay. The assay identifies compounds that interact with MRP2 by measuring the effect of a compound on the transport of a radioactively labeled or fluorescent probe. We have compared the effect of eight selected test compounds (quercetin, disopyramide, paracetamol, indomethacin, diclofenac, estrone-3-sulfate, budesonide, and thioridazine) on the MRP2-mediated transport of three commonly used probes: 5(6)-carboxy-2,7-dichlorofluorescein, leukotriene C4 and estradiol-17-β-d-glucuronide (E217βG). Five of the test compounds had different probe-dependent effects on the MRP2-mediated transport, suggesting differences in the transport mechanism of the probes. Our results underline the complexity of substrate recognition by these efflux transporters and the difficulties in directly comparing results obtained with different assays, especially when different probes are used.

Visually interpretable models of kinase selectivity related features derived from field-based proteochemometrics.

Achieving selectivity for small organic molecules toward biological targets is a main focus of drug discovery but has been proven difficult, for example, for kinases because of the high similarity of their ATP binding pockets. To support the design of more selective inhibitors with fewer side effects or with altered target profiles for improved efficacy, we developed a method combining ligand- and receptor-based information. Conventional QSAR models enable one to study the interactions of multiple ligands toward a single protein target, but in order to understand the interactions between multiple ligands and multiple proteins, we have used proteochemometrics, a multivariate statistics method that aims to combine and correlate both ligand and protein descriptions with affinity to receptors. The superimposed binding sites of 50 unique kinases were described by molecular interaction fields derived from knowledge-based potentials and Schrödingers WaterMap software. Eighty ligands were described by Mold(2), Open Babel, and Volsurf descriptors. Partial least-squares regression including cross-terms, which describe the selectivity, was used for model building. This combination of methods allows interpretation and easy visualization of the models within the context of ligand binding pockets, which can be translated readily into the design of novel inhibitors.

Applying linear and non-linear methods for parallel prediction of volume of distribution and fraction of unbound drug.

Volume of distribution and fraction unbound are two key parameters in pharmacokinetics. The fraction unbound describes the portion of free drug in plasma that may extravasate, while volume of distribution describes the tissue access and binding of a drug. Reliable in silico predictions of these pharmacokinetic parameters would benefit the early stages of drug discovery, as experimental measuring is not feasible for screening purposes. We have applied linear and nonlinear multivariate approaches to predict these parameters: linear partial least square regression and non-linear recursive partitioning classification. The volume of distribution and fraction of unbound drug in plasma are predicted in parallel within the model, since the two are expected to be affected by similar physicochemical drug properties. Predictive models for both parameters were built and the performance of the linear models compared to models included in the commercial software Volsurf+. Our models performed better in predicting the unbound fraction (Q2 0.54 for test set compared to 0.38 with Volsurf+ model), but prediction accuracy of the volume of distribution was comparable to the Volsurf+ model (Q2 of 0.70 for test set compared to 0.71 with Volsurf+ model). The nonlinear classification models were able to identify compounds with a high or low volume of distribution (sensitivity 0.81 and 0.71, respectively, for test set), while classification of fraction unbound was less successful. The interrelationship between the volume of distribution and fraction unbound is investigated and described in terms of physicochemical descriptors. Lipophilicity and solubility descriptors were found to have a high influence on both volume of distribution and fraction unbound, but with an inverse relationship.

Predicting G-protein-coupled receptors families using different physiochemical properties and pseudo amino acid composition.

G-protein-coupled receptors (GPCRs) initiate signaling pathways via trimetric guanine nucleotide-binding proteins. GPCRs are classified based on their ligand-binding properties and molecular phylogenetic analyses. Nonetheless, these later analyses are in most case dependent on multiple sequence alignments, themselves dependent on human intervention and expertise. Alignment-free classifications of GPCR sequences, in addition to being unbiased, present many applications uncovering hidden physicochemical parameters shared among specific groups of receptors, to being used in automated workflows for large-scale molecular modeling applications. Current alignment-free classification methods, however, do not reach a full accuracy. This chapter discusses how GPCRs amino acid sequences can be classified using pseudo amino acid composition and multiscale energy representation of different physiochemical properties of amino acids. A hybrid feature extraction strategy is shown to be suitable to represent GPCRs and to be able to exploit GPCR amino acid sequence discrimination capability in spatial as well as transform domain. Classification strategies such as support vector machine and probabilistic neural network are then discussed in regards to GPCRs classification. The work of GPCR-Hybrid web predictor is also discussed.

Pharmacophore Model To Discover OX1 and OX2 Orexin Receptor Ligands

Small molecule agonists and antagonists of the orexinergic system have key implications for research and therapeutic purposes. We report a pharmacophore model trained on ∼200 antagonists and prospectively validated by screening a collection of ∼137,000 compounds. The resulting hit list, 395 compounds, was tested for OX1 and OX2 receptor activity using calcium mobilization assay in recombinant cell lines. Validation was conducted using both calcium mobilization and [(125)I]-orexin-A competition binding. Compounds 4-7 have weak agonist activity and Kis in the 1-30 μM range; compounds 8-14 are antagonists with Kis in the 0.1-10 μM range for OX2 and 1-50 μM for the OX1 receptor. Docking simulations were used to devise a working hypothesis where two subpockets are important for activation, one between TM5 and TM6 lined by Phe5.42, Tyr5.47, and Tyr6.48 and another above the orthosteric pocket lined by Asp2.65 and Tyr7.32.

High-throughput screening with a miniaturized radioligand competition assay identifies new modulators of human α2-adrenoceptors.

Human α(2)-adrenoceptors (α(2)-ARs) are rhodopsin-like G-protein coupled receptors, and potential drug targets. The three different human α(2)-AR subtypes α(2A), α(2B) and α(2C) are widely distributed in tissues, but so far only a few subtype-selective ligands have been identified. In this project, we set off to conduct a large chemical screen for activity on the human α(2B)-AR and studied the selectivity of the active compounds towards the human α(2A)- and α(2C)-AR subtypes. We employed a radioligand competition binding assay that was optimized and miniaturized into a robotic environment. Membrane fractions containing recombinant human receptor subtypes were prepared from stably transfected Chinese hamster ovary (CHO) cell lines. Initially identified hits were followed up and characterized, and chemoinformatics tools were applied to gain better understanding of the relevance of the results. After a primary screen against α(2B)-AR, 176 compounds of the 17,952 included in the library were declared as active at 10 μM, of which 89 compounds were further selected for potency and affinity determinations using the three human α(2)-AR subtypes. One of the identified positive hits was 2″,2″″-Bisepigallocatechin digallate, which was found to have high affinity at all three human α(2)-AR subtypes. This represents the first non-protonable molecule identified as able to interact with these receptors. Additionally, results obtained with a functional assay (agonist-induced stimulation of [(35)S]GTPγS binding) supported the identification of another positive hit, lysergol, as a partial agonist of the human α(2)-AR subtypes. The dataset of confirmed active chemical species represents a readily available, high quality source for follow-up studies. Altogether, these results provide novel research approaches for drug discovery of modulators of the α(2)-AR subtypes.

Structural Isosteres of Phosphate Groups in the Protein Data Bank

We developed a computational workflow to mine the Protein Data Bank for isosteric replacements that exist in different binding site environments but have not necessarily been identified and exploited in compound design. Taking phosphate groups as examples, the workflow was used to construct 157 data sets, each composed of a reference protein complexed with AMP, ADP, ATP, or pyrophosphate as well other ligands. Phosphate binding sites appear to have a high hydration content and large size, resulting in U-shaped bioactive conformations recurrently found across unrelated protein families. A total of 16 413 replacements were extracted, filtered for a significant structural overlap on phosphate groups, and sorted according to their SMILES codes. In addition to the classical isosteres of phosphate, such as carboxylate, sulfone, or sulfonamide, unexpected replacements that do not conserve charge or polarity, such as aryl, aliphatic, or positively charged groups, were found.