Patient and Health Care Professional Perceptions of the Experience and Impact of Symptoms of Moderate-to-Severe Crohn's Disease in US and Europe: Results from the Cross-Sectional CONFIDE Study.

BACKGROUND: Crohn's disease (CD) significantly affects patients' health-related quality of life and well-being. AIMS: Communicating Needs and Features of IBD Experiences (CONFIDE) survey explores the experience and impact of moderate-to-severe CD symptoms on patients' lives and identifies communication gaps between patients and health care professionals (HCPs). METHODS: Online, quantitative, cross-sectional surveys of patients, and HCPs were conducted in the United States (US), Europe (France, Germany, Italy, Spain, United Kingdom), and Japan. Criteria based on previous treatment, steroid use, and/or hospitalization defined moderate-to-severe CD. US and Europe data are presented as descriptive statistics. RESULTS: Surveys were completed by 215 US and 547 European patients and 200 US and 503 European HCPs. In both patient groups, top three symptoms currently (past month) experienced were diarrhea, bowel urgency, and increased stool frequency, with more than one-third patients wearing diaper/pad/protection at least once a week in past 3 months due to fear of bowel urgency-related accidents. HCPs ranked diarrhea, blood in stool, and increased stool frequency as the most common symptoms. Although 34.0% US and 27.2% European HCPs ranked bowel urgency among the top five symptoms affecting patient lives, only 12.0% US and 10.9% European HCPs ranked it among top three most impactful symptoms on treatment decisions. CONCLUSION: Bowel urgency is common and impactful among patients with CD in the US and Europe. Differences in patient and HCP perceptions of experiences and impacts of bowel urgency exist, with HCPs underestimating its burden. Proactive communication between HCPs and patients in clinical settings is crucial for improving health outcomes in patients with CD.

Deciphering Cryptic Binding Sites on Proteins by Mixed-Solvent Molecular Dynamics.

In recent years, molecular dynamics simulations of proteins in explicit mixed solvents have been applied to various problems in protein biophysics and drug discovery, including protein folding, protein surface characterization, fragment screening, allostery, and druggability assessment. In this study, we perform a systematic study on how mixtures of organic solvent probes in water can reveal cryptic ligand binding pockets that are not evident in crystal structures of apo proteins. We examine a diverse set of eight PDB proteins that show pocket opening induced by ligand binding and investigate whether solvent MD simulations on the apo structures can induce the binding site observed in the holo structures. The cosolvent simulations were found to induce conformational changes on the protein surface, which were characterized and compared with the holo structures. Analyses of the biological systems, choice of probes and concentrations, druggability of the resulting induced pockets, and application to drug discovery are discussed here.

The Molecular Basis for Dual Fatty Acid Amide Hydrolase (FAAH)/Cyclooxygenase (COX) Inhibition.

The design of multitarget-directed ligands is a promising strategy for discovering innovative drugs. Here, we report a mechanistic study that clarifies key aspects of the dual inhibition of the fatty acid amide hydrolase (FAAH) and the cyclooxygenase (COX) enzymes by a new multitarget-directed ligand named ARN2508 (2-[3-fluoro-4-[3-(hexylcarbamoyloxy)phenyl]phenyl]propanoic acid). This potent dual inhibitor combines, in a single scaffold, the pharmacophoric elements often needed to block FAAH and COX, that is, a carbamate moiety and the 2-arylpropionic acid functionality, respectively. Molecular modeling and molecular dynamics simulations suggest that ARN2508 uses a noncovalent mechanism of inhibition to block COXs, while inhibiting FAAH via the acetylation of the catalytic Ser241, in line with previous experimental evidence for covalent FAAH inhibition. This study proposes the molecular basis for the dual FAAH/COX inhibition by this novel hybrid scaffold, stimulating further experimental studies and offering new insights for the rational design of novel anti-inflammatory agents that simultaneously act on FAAH and COX.

Development and Application of a Virtual Screening Protocol for the Identification of Multitarget Fragments.

In this study, we report on a virtual ligand screening protocol optimized to identify fragments endowed with activity at multiple targets. Thanks to this protocol, we were able to identify a fragment that displays activity in the low-micromolar range at both ß-secretase 1 (BACE-1) and glycogen synthase kinase 3ß (GSK-3ß). These two structurally and physiologically unrelated enzymes likely contribute, through different pathways, to the onset of Alzheimer's disease (AD). Therefore, their simultaneous inhibition holds great potential in exerting a profound effect on AD. In perspective, the strategy outlined herein can be adapted to other target combinations.

Multitarget drug discovery for Alzheimer's disease: triazinones as BACE-1 and GSK-3ß inhibitors.

Cumulative evidence strongly supports that the amyloid and tau hypotheses are not mutually exclusive, but concomitantly contribute to neurodegeneration in Alzheimer's disease (AD). Thus, the development of multitarget drugs which are involved in both pathways might represent a promising therapeutic strategy. Accordingly, reported here in is the discovery of 6-amino-4-phenyl-3,4-dihydro-1,3,5-triazin-2(1H)-ones as the first class of molecules able to simultaneously modulate BACE-1 and GSK-3ß. Notably, one triazinone showed well-balanced in vitro potencies against the two enzymes (IC50 of (18.03±0.01) µM and (14.67±0.78) µM for BACE-1 and GSK-3ß, respectively). In cell-based assays, it displayed effective neuroprotective and neurogenic activities and no neurotoxicity. It also showed good brain permeability in a preliminary pharmacokinetic assessment in mice. Overall, triazinones might represent a promising starting point towards high quality lead compounds with an AD-modifying potential.

In silico deconstruction of ATP-competitive inhibitors of glycogen synthase kinase-3ß.

Fragment-based methods have emerged in the last two decades as alternatives to traditional high throughput screenings for the identification of chemical starting points in drug discovery. One arguable yet popular assumption about fragment-based design is that the fragment binding mode remains conserved upon chemical expansion. For instance, the question of the binding conservation upon fragmentation of a molecule is still unclear. A number of papers have challenged this hypothesis by means of experimental techniques, with controversial results, "underlining" the idea that a simple generalization, maybe, is not possible. From a computational standpoint, the issue has been rarely addressed and mostly to test novel protocols on limited data sets. To fill this gap, we here report on a computational retrospective study concerned with the in silico deconstruction of leadlike compounds, active on the pharmaceutically relevant enzyme glycogen synthase kinase-3ß.

Identification and characterization of carprofen as a multitarget fatty acid amide hydrolase/cyclooxygenase inhibitor.

Pain and inflammation are major therapeutic areas for drug discovery. Current drugs for these pathologies have limited efficacy, however, and often cause a number of unwanted side effects. In the present study, we identify the nonsteroidal anti-inflammatory drug carprofen as a multitarget-directed ligand that simultaneously inhibits cyclooxygenase-1 (COX-1), COX-2, and fatty acid amide hydrolase (FAAH). Additionally, we synthesized and tested several derivatives of carprofen, sharing this multitarget activity. This may result in improved analgesic efficacy and reduced side effects (Naidu et al. J. Pharmacol. Exp. Ther.2009, 329, 48-56; Fowler, C. J.; et al. J. Enzyme Inhib. Med. Chem.2012, in press; Sasso et al. Pharmacol. Res.2012, 65, 553). The new compounds are among the most potent multitarget FAAH/COX inhibitors reported so far in the literature and thus may represent promising starting points for the discovery of new analgesic and anti-inflammatory drugs.

The role of fragment-based and computational methods in polypharmacology.

Polypharmacology-based strategies are gaining increased attention as a novel approach to obtaining potentially innovative medicines for multifactorial diseases. However, some within the pharmaceutical community have resisted these strategies because they can be resource-hungry in the early stages of the drug discovery process. Here, we report on fragment-based and computational methods that might accelerate and optimize the discovery of multitarget drugs. In particular, we illustrate that fragment-based approaches can be particularly suited for polypharmacology, owing to the inherent promiscuous nature of fragments. In parallel, we explain how computer-assisted protocols can provide invaluable insights into how to unveil compounds theoretically able to bind to more than one protein. Furthermore, several pragmatic aspects related to the use of these approaches are covered, thus offering the reader practical insights on multitarget-oriented drug discovery projects.

Substrate binding process and mechanistic functioning of type 1 11ß-hydroxysteroid dehydrogenase from enhanced sampling methods.

In humans, type 1 11ß-hydroxysteroid dehydrogenase (11ß-HSD-1) plays a key role in the regulation of the glucocorticoids balance by converting the inactive hormone cortisone into cortisol. Numerous functional aspects of 11ß-HSD-1 have been understood thanks to the availability at the Worldwide Protein Data Bank of a number of X-ray structures of the enzyme either alone or in complex with inhibitors, and to several experimental data. However at present, a complete description of the dynamic behaviour of 11ß-HSD-1 upon substrate binding is missing. To this aim we firstly docked cortisone into the catalytic site of 11ß-HSD-1 (both wild type and Y177A mutant), and then we used steered molecular dynamics and metadynamics to simulate its undocking. This methodology helped shedding light at molecular level on the complex relationship between the enzyme and its natural substrate. In particular, the work highlights a) the reason behind the functional dimerisation of 11ß-HSD-1, b) the key role of Y177 in the cortisone binding event, c) the fine tuning of the active site degree of solvation, and d) the role of the S228-P237 loop in ligand recognition.

SERAPhiC: a benchmark for in silico fragment-based drug design.

Our main objective was to compile a data set of high-quality protein-fragment complexes and make it publicly available. Once assembled, the data set was challenged using docking procedures to address the following questions: (i) Can molecular docking correctly reproduce the experimentally solved structures? (ii) How thorough must the sampling be to replicate the experimental data? (iii) Can commonly used scoring functions discriminate between the native pose and other energy minima? The data set, named SERAPhiC (Selected Fragment Protein Complexes), is publicly available in a ready-to-dock format ( http://www.iit.it/en/drug-discovery-and-development/seraphic.html ). It offers computational medicinal chemists a reliable test set for both in silico protocol assessment and software development.

Design, synthesis, and biological evaluation of imidazolyl derivatives of 4,7-disubstituted coumarins as aromatase inhibitors selective over 17-α-hydroxylase/C17-20 lyase.

The design, synthesis, and biological evaluation of a series of new aromatase (AR, CYP19) inhibitors bearing an imidazole ring linked to a 7-substituted coumarin scaffold at position 4 (or 3) are reported. Many compounds exhibited an aromatase inhibitory potency in the nanomolar range along with a high selectivity over 17-α-hydroxylase/C17-20 lyase (CYP17). The most potent AR inhibitor was the 7-(3,4-difluorophenoxy)-4-imidazolylmethyl coumarin 24 endowed with an IC(50) = 47 nM. Docking simulations on a selected number of coumarin derivatives allowed the identification of the most important interactions driving the binding and clearly indicated the allowed and disallowed regions for appropriate structural modifications of coumarins and closely related heterocyclic molecular scaffolds.

Theoretical and computational approaches to ligand-based drug discovery.

The basic idea behind ligand-based approaches is that the analysis of sets of molecules with experimentally determined activities can highlight those chemical features responsible for the activity changes. Historically, such approaches have been devised before structure-based methods. Nowadays, despite the ever increasing availability of experimentally determined structures, ligand-based approaches still play a major role in drug design either alone or in conjunction with structure-based efforts. This manuscript aims to provide a general overview of the main computational approaches in ligand-based drug discovery, particularly 3D QSAR methods, along with relevant references to the literature.

On the diversity of physicochemical environments experienced by identical ligands in binding pockets of unrelated proteins.

Most function prediction methods that identify cognate ligands from binding site analyses work on the assumption of molecular complementarity. These approaches build on the conjectured complementarity of geometrical and physicochemical properties between ligands and binding sites so that similar binding sites will bind similar ligands. We found that this assumption does not generally hold for protein-ligand interactions and observed that it is not the chemical composition of ligand molecules that dictates the complementarity between protein and ligand molecules, but that the ligand's share within the functional mechanism of a protein determines the degree of complementarity. Here, we present for a set of cognate ligands a descriptive analysis and comparison of the physicochemical properties that each ligand experiences in various nonhomologous binding pockets. The comparisons in each ligand set reveal large variations in their experienced physicochemical properties, suggesting that the same ligand can bind to distinct physicochemical environments. In some protein ligand complexes, the variation was found to correlate with the electrochemical characteristic of ligand molecules, whereas in others it was disclosed as a prerequisite for the biochemical function of the protein. To achieve binding, proteins were observed to engage in subtle balancing acts between electrostatic and hydrophobic interactions to generate stabilizing free energies of binding. For the presented analysis, a new method for scoring hydrophobicity from molecular environments was developed showing high correlations with experimental determined desolvation energies. The presented results highlight the complexities of molecular recognition and underline the challenges of computational structural biology in developing methods to detect these important subtleties.

Protein promiscuity and its implications for biotechnology.

Molecular recognition between proteins and their interacting partners underlies the biochemistry of living organisms. Specificity in this recognition is thought to be essential, whereas promiscuity is often associated with unwanted side effects, poor catalytic properties and errors in biological function. Recent experimental evidence suggests that promiscuity, not only in interactions but also in the actual function of proteins, is not as rare as was previously thought. This has implications not only for our fundamental understanding of molecular recognition and how protein function has evolved over time but also in the realm of biotechnology. Understanding protein promiscuity is becoming increasingly important not only to optimize protein engineering applications in areas as diverse as synthetic biology and metagenomics but also to lower attrition rates in drug discovery programs, identify drug interaction surfaces less susceptible to escape mutations and potentiate the power of polypharmacology.

Analysis of the substrate-binding site of human carbonyl reductases CBR1 and CBR3 by site-directed mutagenesis.

Human carbonyl reductase is a member of the short-chain dehydrogenase/reductase (SDR) protein superfamily and is known to play an important role in the detoxification of xenobiotics bearing a carbonyl group. The two monomeric NADPH-dependent human isoforms of cytosolic carbonyl reductase CBR1 and CBR3 show a sequence similarity of 85% on the amino acid level, which is definitely high if compared to the low similarities usually observed among other members of the SDR superfamily (15-30%). Despite the sequence similarity and the similar features found in the available crystal structures of the two enzymes, CBR3 shows only low or no activity towards substrates that are metabolised by CBR1. This surprising substrate specificity is still not fully understood. In the present study, we introduced several point mutations and changed sequences of up to 17 amino acids of CBR3 to the corresponding amino acids of CBR1, to gather insight into the catalytic mechanism of both enzymes. Proteins were expressed in Escherichia coli and purified by Ni-affinity chromatography. Their catalytic properties were then compared using isatin and 9,10-phenanthrenequinone as model substrates. Towards isatin, wild-type CBR3 showed a catalytic efficiency of 0.018 microM(-1)min(-1), whereas wild-type CBR1 showed a catalytic efficiency of 13.5 microM(-1)min(-1). In particular, when nine residues (236-244) in the vicinity of the catalytic center and a proline (P230) in CBR3 were mutated to the corresponding residues of CBR1 a much higher k(cat)/K(m) value (5.7 microM(-1)min(-1)) towards isatin was observed. To gain further insight into the protein-ligand binding process, docking simulations were perfomed on this mutant and on both wild-type enzymes (CBR1 and CBR3). The theoretical model of the mutant was ad hoc built by means of standard comparative modelling.

The SDR (short-chain dehydrogenase/reductase and related enzymes) nomenclature initiative.

Short-chain dehydrogenases/reductases (SDR) constitute one of the largest enzyme superfamilies with presently over 46,000 members. In phylogenetic comparisons, members of this superfamily show early divergence where the majority have only low pairwise sequence identity, although sharing common structural properties. The SDR enzymes are present in virtually all genomes investigated, and in humans over 70 SDR genes have been identified. In humans, these enzymes are involved in the metabolism of a large variety of compounds, including steroid hormones, prostaglandins, retinoids, lipids and xenobiotics. It is now clear that SDRs represent one of the oldest protein families and contribute to essential functions and interactions of all forms of life. As this field continues to grow rapidly, a systematic nomenclature is essential for future annotation and reference purposes. A functional subdivision of the SDR superfamily into at least 200 SDR families based upon hidden Markov models forms a suitable foundation for such a nomenclature system, which we present in this paper using human SDRs as examples.

11beta-Hydroxysteroid dehydrogenase type 1 inhibitors with oleanan and ursan scaffolds.

The enzyme 11beta-hydroxysteroid dehydrogenase type 1 (11beta-HSD1) converts cortisone to the active glucocorticoid cortisol, thereby acting as a cellular switch to mediate glucocorticoid action in many tissues. Several studies have indicated that 11beta-HSD1 plays a crucial role in the onset of type 2 diabetes and central obesity. As a consequence, selective inhibition of 11beta-HSD1 in humans might become a new and promising approach for lowering blood glucose concentrations and for counteracting the accumulation of visceral fat and its related metabolic abnormalities in type 2 diabetes. In this study, we present the synthesis and the biological evaluation of ursan or oleanan type triterpenoids which may act as selective 11beta-HSD1 inhibitors in liver as well as in peripheral tissues, like adipocytes and muscle cells. In order to rationalise the outcomes of the inhibition data, docking simulations of the ligands were performed on the experimentally determined structure of 11beta-HSD1. Furthermore, we discuss the structural determinants that confer enzymatic specificity. From our investigation, valuable information has been obtained to design selective 11beta-HSD1 blockers based on the oleanan and ursan scaffold.

Molecular docking for substrate identification: the short-chain dehydrogenases/reductases.

Protein ligand docking has recently been investigated as a tool for protein function identification, with some success in identifying both known and unknown substrates of proteins. However, identifying a protein's substrate when cross-docking a large number of enzymes and their cognate ligands remains a challenge. To explore a more limited yet practically important and timely problem in more detail, we have used docking for identifying the substrates of a single protein family with remarkable substrate diversity, the short-chain dehydrogenases/reductases. We examine different protocols for identifying candidate substrates for 27 short-chain dehydrogenase/reductase proteins of known catalytic function. We present the results of docking >900 metabolites from the human metabolome to each of these proteins together with their known cognate substrates and products, and we investigate the ability of docking to (a) reproduce a viable binding mode for the substrate and (b) to rank the substrate highly amongst the dataset of other metabolites. In addition, we examine whether our docking results provide information about the nature of the substrate, based on the best-scoring metabolites in the dataset. We compare two different docking methods and two alternative scoring functions for one of the docking methods, and we attempt to rationalise both successes and failures. Finally, we introduce a new protocol, whereby we dock only a set of representative structures (medoids) to each of the proteins, in the hope of characterising each binding site in terms of its ligand preferences, with a reduced computational cost. We compare the results from this protocol with our original docking experiments, and we find that although the rank of the representatives correlates well with the mean rank of the clusters to which they belong, a simple structure-based clustering is too naive for the purpose of substrate identification. Many clusters comprise ligands with widely varying affinities for the same protein; hence important candidates can be missed if a single representative is used.