7

Chapter 1

Introduction

The drug discovery process

Drug discovery is a time consuming and expensive process. Estimates of time of currently bringing a new drug to market is between 10 and 17 years and cost on average $1.8 billion.1 The drug discovery process is schematically presented in Figure 1. The first step is the investigation of the biochemical, cellular and pathophysiological mechanisms behind a certain disease. Understanding the cellular pathway is critical for identification of a molecular drug target. As soon as a target has been validated to have a potential impact for a special disease, the search for compounds that interact with the target starts. The medicinal chemistry part, screening, hit to lead and lead optimization, aims to identify and optimize compounds towards a potential drug. This process is described in more detail in the next paragraph. In the preclinical phase the compound is tested on animals to determine dosing and drug safety before it goes to human testing (clinical phase I/II/III). If the drug shows a beneficial effect in relevant patient groups without major side effects the drug can get the final approval and marketing can start.

Figure 1. Drug discovery pipeline. Biological part (target identification and validation) aims to select an appropriate disease related target. The medicinal chemistry part (screening, hit to lead and lead optimization) aims to develop potential drug compounds that interact with the selected target and show good pharmacokinetic and safety properties. Computational medicinal chemistry provides useful tools to guide medicinal chemists during this drug discovery procedure. In the development part (preclinic and clinic I/II/II) drug safety and effectiveness is tested. Finally registration authorities approve the drug for marketing.

Introduction

8

Medicinal chemistry:

The initial screening process aims to identify hits that can be used as starting points for further optimization. The classical high throughput screening (HTS) is a very common approach for hit identification.2 In this process a big compound library is screened against a certain target. The screening library contains drug size compounds ideally including the drug candidate. As an alternative method for HTS fragment-based screening (FBS) has evolved as an attractive method for hit identification.3-12 The heavy atom count of compounds in a fragment library is usually below 22 heavy atoms. The smaller size of the screening compounds usually leads to higher hit rates.9 For this reason FBS is now often applied as an alternative method. The fragment hits are subsequently ‘grown’ to drug candidates during the optimization process.

As soon as affinities of hits have been validated certain compound classes are selected for further optimization towards a drug candidate (hit to lead). Selection criteria are of course good affinity but also other properties apart from that are important. Molecules have to show good ADMET parameters. ADMET stands for absorption, distribution, metabolism, excretion and toxicology. During the lead optimization process both affinity and ADMET properties are optimized in an iterative cycle of compound synthesis and testing, which can be guided by computational medicinal chemistry (Figure 2).

Figure 2. Lead optimization cycle

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Computational medicinal chemistry

The aim of computational medicinal chemistry is to support and guide the drug discovery process. Only 1 out of ~10,000 synthesized and tested compounds will reach the market. This represents an enormous investment in terms of time, and resources. Time and cost-effective decisions are highly appreciated before expensive synthesis and/or compound testing is started. For medicinal chemist simple guidelines are very useful for drug design. Additionally, computational methods for property predictions, modeling and virtual screening have been developed which can be included in different stages in the drug discovery process (Figure 1).

Guidelines for medicinal chemists

The awareness that drugs do not only need to show sufficient affinity but also need to

have good physicochemical properties has lead to the development of simple guidelines

like the Lipinski’s ‘Rule of 5’.13 This rule states that poor absorption or permeation of a

molecule is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors,

the molecular weight (MW) is greater than 500 Da and the calculated octanol-water

partition coefficient (ClogP) is greater than 5. It had been shown that MW and ClogP

influence a lot of ADMET related parameters that are tested during drug optimization

process.14 Too high MW for example is a reason for low absorption14 and low

bioavailability14 and too high lipophilicity is the major reason for low solubility14, off-target

binding15 and toxicity16. Out of these insights the ‘ADMET rule of thumbs’ was proposed,

stating that a lot of ADMET properties will be more desirable if MW < 400 and ClogP <

4.14 Drug optimization therefore aims to increase affinity while controlling MW and

lipophilicity to avoid so-called ‘molecular obesity’.17 In this sense several scores that

combine affinity with MW and ClogP, like ligand efficiency18 and ligand-lipophilicity

efficiency15,19, are proposed. Such scores aim to be a better decision maker for hit

selection and optimization than affinity on its own (see also Chapter 3).20-22

Property prediction

Prediction of properties is cheaper and faster than most of the experimental assay methods. Therefore, large databases of compounds are often tested in silico before they – or better, subsets of them – are submitted to in vitro testing. Properties of interest are affinity, ADMET parameters and physicochemical properties like ClogP, solubility, melting point and pKa. Several computational methods for prediction of properties of a given

Introduction

10

molecular structure are described and include linear models where molecular descriptors are directly related to the property on interest, or machine learning techniques like support vector machines or artificial neuronal networks. Machine learning techniques are normally more accurate but only provide a black-box model for the prediction on the property of interest.23 Having accurate property prediction in hand might be useful for decision making but it might be even more useful for medicinal chemist to understand the underlying design principles to achieve a certain property (like low Clog, low melting point, high solubility,…). In this sense a quite recently described method, matched molecular pairs analysis, is a very appropriate tool to directly relate a change in the structure to an effect on a certain property.24-31 A demonstration how matched molecular pairs analysis can be applied to estimate the effects of a change in descriptors on the melting point is presented in Chapter 4.

For the target of interest also affinity can be predicted. This can be done purely ligand-based by relating structural changes to a change in affinity (Free Wilson Approach).32 A more rational design can be enabled if also the 3D structure of the target is available which allows an estimation of important protein ligand interactions.

Virtual screening

Virtual screening is the computational search for new drug candidates with desired biological activities in large computer databases of small molecules that do not even have to exist physically.33 It is a fast and cost effective process that has evolved as an integral part in the drug discovery process. It is often applied either to reduce the size of a screening library or to completely replace high throughput screening, which is frequently the case in academia. In case the target 3D structure is available docking and scoring can be applied to rank the compounds. Ligand-based techniques (see Chapter 5) provide alternative and complementary approaches particularly when the target structure is not available but active ligand molecules are at hand.34 They are based on the nearest neighbour principle, that similar compounds to known active compounds might be also active.35 Several success stories in structure- as well as ligand-based virtual screening are reported showing the effectiveness of these methods to identify novel hits.36-39

Molecular modeling:

Knowing the binding mode of active compounds is essential for rational drug design. In the case an experimental 3D structure of the target is not available the structure can be modeled utilizing the experimental structure of a closely related target. Molecular docking is the preferred method to investigate binding poses and protein ligand interactions.

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Scoring functions give a rough evaluation of the correctness of the binding mode. However, other approaches like interaction fingerprint (IFP) that determines the protein-ligand interaction similarity to an experimentally supported binding mode have also been shown to be successful in pose selection.37,38 To evaluate the validity of a binding pose, knowledge of ligand structure-activity relationship (SAR) and protein SAR is advantageous.40-42 Site directed mutagenesis studies are useful to reveal important interaction sites in the binding pocket. Consideration of the dynamic behaviour of essential protein-ligand interactions using MD-simulations can be used as a further validation of protein-ligand binding poses and to explain observed SAR (see Chapter 6).40,42 In case of flexible ligands, energy calculations of different ligand conformations might be useful to get insight in the actual binding conformation (see Chapter 7).

Pharmaceutical targets

Criteria for target selection

Choosing a target to elicit a therapeutic effect is among the thorniest problems in drug discovery. Molecular and cell biology is providing new insight into molecular pathways. Genetic approaches like gene expression profiling and gene knockout screening can link disease phenotypes to special genes and proteins. A potential target needs to combine ‘drugability’ with disease modifying properties. Other criteria like expression, purification, assay development, crystallization properties and structural information can be decisive factors for target selection.

G-protein coupled receptors (GPCRs) – most targeted drug target class

From a comprehensive analysis in 2006 of existing drugs and their targets a consensus number of 324 drug targets for all classes of approved therapeutic drugs was proposed.43 Rhodopsine-like (or class A) G-protein coupled receptors (GPCRs) are the target where most of the drugs act on (26.8% of approved drugs).

GPCRs are integral membrane proteins consisting of seven α-helices that resemble a

barrel within the membrane (Figure 3) The transmembrane (TM) α-helices are connected

by three extracellular loops (ELs) and three intracellular loops. Ligands enter the binding pocket from the extracelluar side. Upon agonist binding, GPCRs get activated by

conformational rearrangements of the TM α-helices. On the intracellular side G-proteins

and also ß-arrestins44 are the two primary signal transducers that trigger a signal cascade in the cytoplasm.45

Introduction

12

Figure 3. Schematic representation of a GPCR structure (left). Crystal structure of histamine H1 receptor (H1R) co-crystallized with doxepin (right).46 Helices are colored from dark-blue (helix 1) to orange-red (helix 7).

The success of drug discovery in the field of GPCR in the past 4 decades was significant although GPCRs, like other membrane proteins, are difficult to crystallize. In 2000, the first crystal structure of a mammalian GPCR, that of bovine rhodopsin was solved.47 Success stories of recently solved GPCR structures (see Table 1) are expected to enhance research and drug discovery in the GPCR field.48

Table 1. x-ray structures of GPCRs

receptor species year of first structure REF

rhodopsin bovine 2000 47 β2-adrenergic human 2007 49 β1-adrenergic turkey 2008 50 A2A adenonsine human 2008 51 chemokine CXCR4 human 2010 52 dopamine D3 human 2010 53 histamine H1 human 2011 46 muscarinic M2 human 2012 54 sphingosine 1-phosphate 1 human 2012 55 muscarinic M3 rat 2012 56 κ-opioid human 2012 57 µ-opioid mouse 2012 58 nociceptin/orphanin FQ human 2012 59 δ-opioid mouse 2012 60 Neurotensin 1 rat 2012 61 PAR1 human 2010 62 5HT1B human 2013 63

Chapter 1

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Table 1. continued

receptor Species year of first structure REF

5HT2B Human 2013 64 smoothened 7TM Human 2013 65 CXCR2 N/A 2013 66 Alpha9 Nicotinic Acetylcholine Human 2013 67

GPCRs recognize a huge variety of ligands, ranging from small particles, photons and ions, through small ligands, amine and nucleoside, to peptides and lipids and large proteins. Especially aminergic GPCRs are preferred drug targets because of the drug-likeness of their natural ligands,

Histamine H4 receptor (H4R) – a promising new drug target

The histamine H4 receptor (H4R) is the latest identified receptor within the histaminergic receptor family. H4R is mainly expressed in haematopoietic cells, in particular eosinophils, T-cells, dendritic cells, basophils and mast cells.68-70 Several studies show that H4R antagonists are able to modulate immune responses and inflammatory processes.71 Inflammation, allergy, colitis, asthma, pain, pruritus cancer, arthritis and haematopoiesis are proposed therapeutic fields for H4R targeting ligands.72

The number of publications about H4R steadily increased since its initial identification in 200073-80 (green bars in Figure 4). Due to the high sequence homology to H3R73-80 the first compounds that were identified to bear considerable affinities for H4R were imidazole containing H3R ligands,75,81-83 This fact and the higher propensity of imidazoles to also inhibit CYPs84,85 are the reason for other compound classes to be more attractive as potential drugs. In 2003 the indolecarboxamide compound JNJ 7777120 was published as the first selective and potent non-imidazole human H4R antagonist.86 This compound is used as a reference ligand for most studies on H4R. Meanwhile a lot of compounds from different structural classes that show affinity for H4R are reported (blue bars in Figure 4). Prominent examples are presented in Figure 5. More then 10 years histamine H4R research has meanwhile led to significant interest of the pharmaceutical industry and resulted in a number of patent applications (red bars in Figure 4). 87-89 Moreover very recently the compound UR 63325 (undisclosed structure) has entered clinical phase II for respiratory diseases.

Introduction

14

Figure 4. Number of H4R relevant publications (status: September 11th, 2012). Number of publications were extracted from pubmed (http://www.pubmed.com) by entering ‘histamine H4’ as search term (green bars). Publications were new affinity data for H4R were reported are additionally shown separately (blue bars). The number of patent applications (red bars) was extracted from esp@cenet (http://www.espacenet.com) by entering the search term ‘histamine H4’.

Figure 5. Structures and affinities of prominent H4R ligands sorted by the date of their publications (left to right).83,86,90-104

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The increasing number of GPCR X-ray structures,105,106 including the recently solved histamine H1 receptor (H1R) crystal structure,46 offers new opportunities for histamine receptor homology modeling and drug discovery.40,42 Key residues for ligand binding have been identified by site-directed mutagenesis studies which are useful for binding mode investigations40,41,107-109 (see Chapter 6). These insights in protein-ligand interactions are valuable contributions for structure-based discovery and design of improved and new H4R ligands.37,110-112

References

1. Paul, S. M.; Mytelka, D. S.; Dunwiddie, C. T.; Persinger, C. C.; Munos, B. H.; Lindborg, S. R.; Schacht, A. L. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature reviews. Drug discovery, 2010, 9, 203-214.

2. Goodnow Jr, R. A. Hit and lead identification: Integrated technology-based approaches. Drug Discovery Today: Technologies, 2006, 3, 367-375.

3. Congreve, M.; Chessari, G.; Tisi, D.; Woodhead, A. J. Recent developments in fragment-based drug discovery. Journal of medicinal chemistry, 2008, 51, 3661-3680.

4. de Kloe, G. E.; Bailey, D.; Leurs, R.; de Esch, I. J. Transforming fragments into candidates: small becomes big in medicinal chemistry. Drug discovery today, 2009, 14, 630-646.

5. Erlanson, D. A. Fragment-based lead discovery: a chemical update. Current opinion in biotechnology, 2006, 17, 643-652.

6. Erlanson, D. A. Introduction to fragment-based drug discovery. Topics in current chemistry, 2012, 317, 1-32.

7. Erlanson, D. A.; McDowell, R. S.; O'Brien, T. Fragment-based drug discovery. Journal of medicinal chemistry, 2004, 47, 3463-3482.

8. Hajduk, P. J. SAR by NMR: putting the pieces together. Molecular interventions, 2006, 6, 266-272.

9. Hajduk, P. J.; Greer, J. A decade of fragment-based drug design: strategic advances and lessons learned. Nature reviews. Drug discovery, 2007, 6, 211-219.

10. Murray, C. W.; Verdonk, M. L.; Rees, D. C. Experiences in fragment-based drug discovery. Trends in pharmacological sciences, 2012, 33, 224-232.

11. Orita, M.; Warizaya, M.; Amano, Y.; Ohno, K.; Niimi, T. Advances in fragment-based drug discovery platforms. Expert Opinion on Drug Discovery, 2009, 4, 1125-1144.

12. Rees, D. C.; Congreve, M.; Murray, C. W.; Carr, R. Fragment-based lead discovery. Nature reviews. Drug discovery, 2004, 3, 660-672.

13. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 2001, 46, 3-26.

14. Gleeson, M. P. Generation of a set of simple, interpretable ADMET rules of thumb. Journal of medicinal chemistry, 2008, 51, 817-834.

15. Leeson, P. D.; Springthorpe, B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nature reviews. Drug discovery, 2007, 6, 881-890.

16. Hughes, J. D.; Blagg, J.; Price, D. A.; Bailey, S.; Decrescenzo, G. A.; Devraj, R. V.; Ellsworth, E.; Fobian, Y. M.; Gibbs, M. E.; Gilles, R. W.; Greene, N.; Huang, E.; Krieger-Burke, T.; Loesel, J.; Wager, T.; Whiteley, L.; Zhang, Y. Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorganic & medicinal chemistry letters, 2008, 18, 4872-4875.

Introduction

16

17. Hann, M. M. Molecular obesity, potency and other addictions in drug discovery. MedChemComm, 2011, 2, 349-355.

18. Hopkins, A. L.; Groom, C. R.; Alex, A. Ligand efficiency: a useful metric for lead selection. Drug discovery today, 2004, 9, 430-431.

19. Keseru, G. M.; Makara, G. M. The influence of lead discovery strategies on the properties of drug candidates. Nature reviews. Drug discovery, 2009, 8, 203-212.

20. Hann, M. M.; Keseru, G. M. Finding the sweet spot: the role of nature and nurture in medicinal chemistry. Nature reviews. Drug discovery, 2012, 11, 355-365.

21. Schultes, S.; de Graaf, C.; Haaksma, E. E. J.; de Esch, I. J. P.; Leurs, R.; Krämer, O. Ligand efficiency as a guide in fragment hit selection and optimization. Drug Discovery Today: Technologies, 2010, 7, e157-e162.

22. Tarcsay, A.; Nyiri, K.; Keseru, G. M. Impact of lipophilic efficiency on compound quality. Journal of medicinal chemistry, 2012, 55, 1252-1260.

23. Hughes, L. D.; Palmer, D. S.; Nigsch, F.; Mitchell, J. B. Why are some properties more difficult to predict than others? A study of QSPR models of solubility, melting point, and Log P. Journal of chemical information and modeling, 2008, 48, 220-232.

24. Birch, A. M.; Kenny, P. W.; Simpson, I.; Whittamore, P. R. Matched molecular pair analysis of activity and properties of glycogen phosphorylase inhibitors. Bioorganic & medicinal chemistry letters, 2009, 19, 850-853.

25. Gleeson, M. P.; Hersey, A.; Montanari, D.; Overington, J. Probing the links between in vitro potency, ADMET and physicochemical parameters. Nature reviews. Drug discovery, 2011, 10, 197-208.

26. Hajduk, P. J.; Sauer, D. R. Statistical analysis of the effects of common chemical substituents on ligand potency. Journal of medicinal chemistry, 2008, 51, 553-564.

27. Leach, A. G.; Jones, H. D.; Cosgrove, D. A.; Kenny, P. W.; Ruston, L.; MacFaul, P.; Wood, J. M.; Colclough, N.; Law, B. Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure. Journal of medicinal chemistry, 2006, 49, 6672-6682.

28. Lewis, M. L.; Cucurull-Sanchez, L. Structural pairwise comparisons of HLM stability of phenyl derivatives: Introduction of the Pfizer metabolism index (PMI) and metabolism-lipophilicity efficiency (MLE). Journal of computer-aided molecular design, 2009, 23, 97-103.

29. Papadatos, G.; Alkarouri, M.; Gillet, V. J.; Willett, P.; Kadirkamanathan, V.; Luscombe, C. N.; Bravi, G.; Richmond, N. J.; Pickett, S. D.; Hussain, J.; Pritchard, J. M.; Cooper, A. W.; Macdonald, S. J. Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of HERG inhibition, solubility, and lipophilicity. Journal of chemical information and modeling, 2010, 50, 1872-1886.

30. Schultes, S.; de Graaf, C.; Berger, H.; Mayer, M.; Steffen, A.; Haaksma, E. E. J.; de Esch, I. J. P.; Leurs, R.; Kramer, O. A medicinal chemistry perspective on melting point: matched molecular pair analysis of the effects of simple descriptors on the melting point of drug-like compounds. Medchemcomm, 2012, 3, 584-591.

31. Zhang, L.; Zhu, H.; Mathiowetz, A.; Gao, H. Deep understanding of structure-solubility relationship for a diverse set of organic compounds using matched molecular pairs. Bioorganic & medicinal chemistry, 2011, 19, 5763-5770.

32. Engelhardt, H.; de Esch, I. J.; Kuhn, D.; Smits, R. A.; Zuiderveld, O. P.; Dobler, J.; Mayer, M.; Lips, S.; Arnhof, H.; Scharn, D.; Haaksma, E. E.; Leurs, R. Detailed structure-activity relationship of indolecarboxamides as H4 receptor ligands. European journal of medicinal chemistry, 2012, 54, 660-668.

33. Ripphausen, P.; Nisius, B.; Peltason, L.; Bajorath, J. Quo vadis, virtual screening? A comprehensive survey of prospective applications. Journal of medicinal chemistry, 2010, 53, 8461-8467.

Chapter 1

17

34. Koeppen, H.; Kriegl, J.; Lessel, U.; Tautermann, C. S.; Wellenzohn, B. in Virtual Screening 61-85 (Wiley-VCH Verlag GmbH & Co. KGaA, 2011).

35. Eckert, H.; Bajorath, J. Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug discovery today, 2007, 12, 225-233.

36. Chen, Y.; Shoichet, B. K. Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nature chemical biology, 2009, 5, 358-364.

37. de Graaf, C.; Kooistra, A. J.; Vischer, H. F.; Katritch, V.; Kuijer, M.; Shiroishi, M.; Iwata, S.; Shimamura, T.; Stevens, R. C.; de Esch, I. J.; Leurs, R. Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor. Journal of medicinal chemistry, 2011, 54, 8195-8206.

38. Marcou, G.; Rognan, D. Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. Journal of chemical information and modeling, 2007, 47, 195-207.

39. Svensson, F.; Karlen, A.; Skold, C. Virtual screening data fusion using both structure- and ligand-based methods. Journal of chemical information and modeling, 2012, 52, 225-232.

40. Istyastono, E. P.; Nijmeijer, S.; Lim, H. D.; van de Stolpe, A.; Roumen, L.; Kooistra, A. J.; Vischer, H. F.; de Esch, I. J.; Leurs, R.; de Graaf, C. Molecular determinants of ligand binding modes in the histamine H(4) receptor: linking ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR) models to in silico guided receptor mutagenesis studies. Journal of medicinal chemistry, 2011, 54, 8136-8147.

41. Lim, H. D.; de Graaf, C.; Jiang, W.; Sadek, P.; McGovern, P. M.; Istyastono, E. P.; Bakker, R. A.; de Esch, I. J.; Thurmond, R. L.; Leurs, R. Molecular determinants of ligand binding to H4R species variants. Mol Pharmacol, 2010, 77, 734-743.

42. Schultes, S.; Nijmeijer, S.; Engelhardt, H.; Kooistra, A. J.; Vischer, H. F.; de Esch, I. J. P.; Haaksma, E. E. J.; Leurs, R.; de Graaf, C. Mapping histamine H4 receptor-ligand binding modes. Medchemcomm, 2013, 4, 193-204.

43. Overington, J. P.; Al-Lazikani, B.; Hopkins, A. L. How many drug targets are there? Nature reviews. Drug discovery, 2006, 5, 993-996.

44. Rosethorne, E. M.; Charlton, S. J. Agonist-biased signaling at the histamine H4 receptor: JNJ7777120 recruits beta-arrestin without activating G proteins. Mol Pharmacol, 2011, 79, 749-757.

45. Nijmeijer, S.; de Graaf, C.; Leurs, R.; Vischer, H. F. Molecular pharmacology of histamine H4 receptors. Frontiers in bioscience : a journal and virtual library, 2012, 17, 2089-2106.

46. Shimamura, T.; Shiroishi, M.; Weyand, S.; Tsujimoto, H.; Winter, G.; Katritch, V.; Abagyan, R.; Cherezov, V.; Liu, W.; Han, G. W.; Kobayashi, T.; Stevens, R. C.; Iwata, S. Structure of the human histamine H1 receptor complex with doxepin. Nature, 2011, 475, 65-70.

47. Palczewski, K.; Kumasaka, T.; Hori, T.; Behnke, C. A.; Motoshima, H.; Fox, B. A.; Trong, I. L.; Teller, D. C.; Okada, T.; Stenkamp, R. E.; Yamamoto, M.; Miyano, M. Crystal structure of rhodopsin: a G protein-coupled receptor. Science, 2000, 289, 739-745.

48. Katritch, V.; Cherezov, V.; Stevens, R. C. Structure-function of the G protein–coupled receptor superfamily. Annu. Rev. Pharmacol. Toxicol., 2013, 53, 531-556.

49. Rosenbaum, D. M.; Cherezov, V.; Hanson, M. A.; Rasmussen, S. G.; Thian, F. S.; Kobilka, T. S.; Choi, H. J.; Yao, X. J.; Weis, W. I.; Stevens, R. C.; Kobilka, B. K. GPCR engineering yields high-resolution structural insights into beta2-adrenergic receptor function. Science, 2007, 318, 1266-1273.

50. Warne, T.; Serrano-Vega, M. J.; Baker, J. G.; Moukhametzianov, R.; Edwards, P. C.; Henderson, R.; Leslie, A. G.; Tate, C. G.; Schertler, G. F. Structure of a beta1-adrenergic G-protein-coupled receptor. Nature, 2008, 454, 486-491.

51. Xu, F.; Wu, H.; Katritch, V.; Han, G. W.; Jacobson, K. A.; Gao, Z. G.; Cherezov, V.; Stevens, R. C. Structure of an agonist-bound human A2A adenosine receptor. Science, 2011, 332, 322-327.

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18

52. Wu, B.; Chien, E. Y.; Mol, C. D.; Fenalti, G.; Liu, W.; Katritch, V.; Abagyan, R.; Brooun, A.; Wells, P.; Bi, F. C.; Hamel, D. J.; Kuhn, P.; Handel, T. M.; Cherezov, V.; Stevens, R. C. Structures of the CXCR4 chemokine GPCR with small-molecule and cyclic peptide antagonists. Science, 2010, 330, 1066-1071.

53. Chien, E. Y. T.; Liu, W.; Zhao, Q.; Katritch, V.; Won Han, G.; Hanson, M. A.; Shi, L.; Newman, A. H.; Javitch, J. A.; Cherezov, V.; Stevens, R. C. Structure of the human dopamine D3 receptor in complex with a D2/D3 selective antagonist. Science, 2010, 330, 1091-1095.

54. Haga, K.; Kruse, A. C.; Asada, H.; Yurugi-Kobayashi, T.; Shiroishi, M.; Zhang, C.; Weis, W. I.; Okada, T.; Kobilka, B. K.; Haga, T.; Kobayashi, T. Structure of the human M2 muscarinic acetylcholine receptor bound to an antagonist. Nature, 2012, 482, 547-551.

55. Hanson, M. A.; Roth, C. B.; Jo, E.; Griffith, M. T.; Scott, F. L.; Reinhart, G.; Desale, H.; Clemons, B.; Cahalan, S. M.; Schuerer, S. C.; Sanna, M. G.; Han, G. W.; Kuhn, P.; Rosen, H.; Stevens, R. C. Crystal structure of a lipid G protein-coupled receptor. Science, 2012, 335, 851-855.

56. Kruse, A. C.; Hu, J.; Pan, A. C.; Arlow, D. H.; Rosenbaum, D. M.; Rosemond, E.; Green, H. F.; Liu, T.; Chae, P. S.; Dror, R. O.; Shaw, D. E.; Weis, W. I.; Wess, J.; Kobilka, B. K. Structure and dynamics of the M3 muscarinic acetylcholine receptor. Nature, 2012, 482, 552-556.

57. Wu, H.; Wacker, D.; Mileni, M.; Katritch, V.; Han, G. W.; Vardy, E.; Liu, W.; Thompson, A. A.; Huang, X. P.; Carroll, F. I.; Mascarella, S. W.; Westkaemper, R. B.; Mosier, P. D.; Roth, B. L.; Cherezov, V.; Stevens, R. C. Structure of the human kappa-opioid receptor in complex with JDTic. Nature, 2012, 485, 327-332.

58. Manglik, A.; Kruse, A. C.; Kobilka, T. S.; Thian, F. S.; Mathiesen, J. M.; Sunahara, R. K.; Pardo, L.; Weis, W. I.; Kobilka, B. K.; Granier, S. Crystal structure of the micro-opioid receptor bound to a morphinan antagonist. Nature, 2012, 485, 321-326.

59. Thompson, A. A.; Liu, W.; Chun, E.; Katritch, V.; Wu, H.; Vardy, E.; Huang, X. P.; Trapella, C.; Guerrini, R.; Calo, G.; Roth, B. L.; Cherezov, V.; Stevens, R. C. Structure of the nociceptin/orphanin FQ receptor in complex with a peptide mimetic. Nature, 2012, 485, 395-399.

60. Granier, S.; Manglik, A.; Kruse, A. C.; Kobilka, T. S.; Thian, F. S.; Weis, W. I.; Kobilka, B. K. Structure of the delta-opioid receptor bound to naltrindole. Nature, 2012, 485, 400-404.

61. White, J. F.; Noinaj, N.; Shibata, Y.; Love, J.; Kloss, B.; Xu, F.; Gvozdenovic-Jeremic, J.; Shah, P.; Shiloach, J.; Tate, C. G.; Grisshammer, R. Structure of the agonist-bound neurotensin receptor. Nature, 2012, 490, 508-513.

62. Zhang, C.; Srinivasan, Y.; Arlow, D. H.; Fung, J. J.; Palmer, D.; Zheng, Y.; Green, H. F.; Pandey, A.; Dror, R. O.; Shaw, D. E.; Weis, W. I.; Coughlin, S. R.; Kobilka, B. K. High-resolution crystal structure of human protease-activated receptor 1. Nature, 2012, 492, 387-392.

63. Wang, C.; Jiang, Y.; Ma, J.; Wu, H.; Wacker, D.; Katritch, V.; Han, G. W.; Liu, W.; Huang, X. P.; Vardy, E.; McCorvy, J. D.; Gao, X.; Zhou, E. X.; Melcher, K.; Zhang, C.; Bai, F.; Yang, H.; Yang, L.; Jiang, H.; Roth, B. L.; Cherezov, V.; Stevens, R. C.; Xu, H. E. Structural Basis for Molecular Recognition at Serotonin Receptors. Science, 2013.

64. Wacker, D.; Wang, C.; Katritch, V.; Han, G. W.; Huang, X. P.; Vardy, E.; McCorvy, J. D.; Jiang, Y.; Chu, M.; Siu, F. Y.; Liu, W.; Xu, H. E.; Cherezov, V.; Roth, B. L.; Stevens, R. C. Structural Features for Functional Selectivity at Serotonin Receptors. Science, 2013.

65. Wang, C.; Wu, H.; Katritch, V.; Han, G. W.; Huang, X.; Liu, W.; Siu, F. Y.; Roth, B. L.; Cherezov, V.; Stevens, R. C.; (GPCR), G. N. Structure of the human smoothened 7TM receptor in complex with an antitumor agent (GPCR Dock Target). 2013.

66. Lu, G.; Wu, Y.; Jiang, Y.; Brunzelle, J.; Sirinupong, N.; Li, C.; Yang, Z. Crystal structure of the chemokine receptor CXCR2 in complex with the first PDZ domain of NHERF1. 2013.

67. Giastas, P.; Zouridakis, M.; Zarkadas, E.; Tzartos, S. J. Crystal structure of the extracellular domain of the human alpha9 nicotinic acetylcholine receptor. 2013.

Chapter 1

19

68. Gantner, F.; Sakai, K.; Tusche, M. W.; Cruikshank, W. W.; Center, D. M.; Bacon, K. B. Histamine h(4) and h(2) receptors control histamine-induced interleukin-16 release from human CD8(+) T cells. J Pharmacol Exp Ther, 2002, 303, 300-307.

69. Hofstra, C. L.; Desai, P. J.; Thurmond, R. L.; Fung-Leung, W. P. Histamine H4 receptor mediates chemotaxis and calcium mobilization of mast cells. J Pharmacol Exp Ther, 2003, 305, 1212-1221.

70. O'Reilly, M.; Alpert, R.; Jenkinson, S.; Gladue, R. P.; Foo, S.; Trim, S.; Peter, B.; Trevethick, M.; Fidock, M. Identification of a histamine H4 receptor on human eosinophils--role in eosinophil chemotaxis. J Recept Signal Transduct Res, 2002, 22, 431-448.

71. Walter, M.; Kottke, T.; Stark, H. The histamine H(4) receptor: targeting inflammatory disorders. European journal of pharmacology, 2011, 668, 1-5.

72. Marson, C. M. Targeting the histamine H4 receptor. Chemical reviews, 2011, 111, 7121-7156. 73. Coge, F.; Guenin, S. P.; Rique, H.; Boutin, J. A.; Galizzi, J. P. Structure and expression of the

human histamine H4-receptor gene. Biochemical and biophysical research communications, 2001, 284, 301-309.

74. Hough, L. B. Genomics meets histamine receptors: new subtypes, new receptors. Mol Pharmacol, 2001, 59, 415-419.

75. Liu, C.; Ma, X.; Jiang, X.; Wilson, S. J.; Hofstra, C. L.; Blevitt, J.; Pyati, J.; Li, X.; Chai, W.; Carruthers, N.; Lovenberg, T. W. Cloning and pharmacological characterization of a fourth histamine receptor (H(4)) expressed in bone marrow. Mol Pharmacol, 2001, 59, 420-426.

76. Morse, K. L.; Behan, J.; Laz, T. M.; West, R. E., Jr.; Greenfeder, S. A.; Anthes, J. C.; Umland, S.; Wan, Y.; Hipkin, R. W.; Gonsiorek, W.; Shin, N.; Gustafson, E. L.; Qiao, X.; Wang, S.; Hedrick, J. A.; Greene, J.; Bayne, M.; Monsma, F. J., Jr. Cloning and characterization of a novel human histamine receptor. J Pharmacol Exp Ther, 2001, 296, 1058-1066.

77. Nakamura, T.; Itadani, H.; Hidaka, Y.; Ohta, M.; Tanaka, K. Molecular cloning and characterization of a new human histamine receptor, HH4R. Biochemical and biophysical research communications, 2000, 279, 615-620.

78. Nguyen, T.; Shapiro, D. A.; George, S. R.; Setola, V.; Lee, D. K.; Cheng, R.; Rauser, L.; Lee, S. P.; Lynch, K. R.; Roth, B. L.; O'Dowd, B. F. Discovery of a novel member of the histamine receptor family. Mol Pharmacol, 2001, 59, 427-433.

79. Oda, T.; Morikawa, N.; Saito, Y.; Masuho, Y.; Matsumoto, S. Molecular cloning and characterization of a novel type of histamine receptor preferentially expressed in leukocytes. The Journal of biological chemistry, 2000, 275, 36781-36786.

80. Zhu, Y.; Michalovich, D.; Wu, H.; Tan, K. B.; Dytko, G. M.; Mannan, I. J.; Boyce, R.; Alston, J.; Tierney, L. A.; Li, X.; Herrity, N. C.; Vawter, L.; Sarau, H. M.; Ames, R. S.; Davenport, C. M.; Hieble, J. P.; Wilson, S.; Bergsma, D. J.; Fitzgerald, L. R. Cloning, expression, and pharmacological characterization of a novel human histamine receptor. Mol Pharmacol, 2001, 59, 434-441.

81. Govoni, M.; Lim, H. D.; El-Atmioui, D.; Menge, W. M.; Timmerman, H.; Bakker, R. A.; Leurs, R.; De Esch, I. J. A chemical switch for the modulation of the functional activity of higher homologues of histamine on the human histamine H3 receptor: effect of various substitutions at the primary amino function. Journal of medicinal chemistry, 2006, 49, 2549-2557.

82. Lazewska, D.; Wiecek, M.; Ligneau, X.; Kottke, T.; Weizel, L.; Seifert, R.; Schunack, W.; Stark, H.; Kiec-Kononowicz, K. Histamine H3 and H4 receptor affinity of branched 3-(1H-imidazol-4-yl)propyl N-alkylcarbamates. Bioorganic & medicinal chemistry letters, 2009, 19, 6682-6685.

83. Lim, H. D.; van Rijn, R. M.; Ling, P.; Bakker, R. A.; Thurmond, R. L.; Leurs, R. Evaluation of histamine H1-, H2-, and H3-receptor ligands at the human histamine H4 receptor: identification of 4-methylhistamine as the first potent and selective H4 receptor agonist. J Pharmacol Exp Ther, 2005, 314, 1310-1321.

84. Murray, M. Mechanisms of the inhibition of cytochrome P-450-mediated drug oxidation by therapeutic agents. Drug metabolism reviews, 1987, 18, 55-81.

Introduction

20

85. Yang, R.; Hey, J. A.; Aslanian, R.; Rizzo, C. A. Coordination of histamine H3 receptor antagonists with human adrenal cytochrome P450 enzymes. Pharmacology, 2002, 66, 128-135.

86. Jablonowski, J. A.; Grice, C. A.; Chai, W.; Dvorak, C. A.; Venable, J. D.; Kwok, A. K.; Ly, K. S.; Wei, J.; Baker, S. M.; Desai, P. J.; Jiang, W.; Wilson, S. J.; Thurmond, R. L.; Karlsson, L.; Edwards, J. P.; Lovenberg, T. W.; Carruthers, N. I. The first potent and selective non-imidazole human histamine H4 receptor antagonists. Journal of medicinal chemistry, 2003, 46, 3957-3960.

87. Leurs, R.; Vischer, H. F.; Wijtmans, M.; de Esch, I. J. En route to new blockbuster anti-histamines: surveying the offspring of the expanding histamine receptor family. Trends in pharmacological sciences, 2011, 32, 250-257.

88. Engelhardt, H.; Smits, R. A.; Leurs, R.; Haaksma, E.; de Esch, I. J. A new generation of anti-histamines: Histamine H4 receptor antagonists on their way to the clinic. Current opinion in drug discovery & development, 2009, 12, 628-643.

89. Smits, R. A.; Leurs, R.; de Esch, I. J. Major advances in the development of histamine H4 receptor ligands. Drug discovery today, 2009, 14, 745-753.

90. Hashimoto, T.; Harusawa, S.; Araki, L.; Zuiderveld, O. P.; Smit, M. J.; Imazu, T.; Takashima, S.; Yamamoto, Y.; Sakamoto, Y.; Kurihara, T.; Leurs, R.; Bakker, R. A.; Yamatodani, A. A selective human H4-receptor agonist:(-)-2-cyano-1-methyl-3-{(2R,5R)-5- [1H-imidazol-4(5)-yl]tetrahydrofuran-2-yl}methylguanidine. Journal of medicinal chemistry, 2003, 46, 3162-3165.

91. Smits, R. A.; Lim, H. D.; Stegink, B.; Bakker, R. A.; de Esch, I. J. P.; Leurs, R. Characterization of the histamine H4 receptor binding site. part 1. synthesis and pharmacological evaluation of dibenzodiazepine derivatives. Journal of medicinal chemistry, 2006, 49, 4512-4516.

92. Lee-Dutra, A.; Arienti, K. L.; Buzard, D. J.; Hack, M. D.; Khatuya, H.; Desai, P. J.; Nguyen, S.; Thurmond, R. L.; Karlsson, L.; Edwards, J. P.; Breitenbucher, J. G. Identification of 2-arylbenzimidazoles as potent human histamine H4 receptor ligands. Bioorganic & medicinal chemistry letters, 2006, 16, 6043-6048.

93. Lim, H. D.; Smits, R. A.; Bakker, R. A.; van Dam, C. M.; de Esch, I. J.; Leurs, R. Discovery of S-(2-guanidylethyl)-isothiourea (VUF 8430) as a potent nonimidazole histamine H4 receptor agonist. Journal of medicinal chemistry, 2006, 49, 6650-6651.

94. Lim, H. D.; Adami, M.; Guaita, E.; Werfel, T.; Smits, R. A.; De Esch, I. J. P.; Bakker, R. A.; Gutzmer, R.; Coruzzi, G.; Leurs, R. Pharmacological characterization of the new histamine H4 receptor agonist VUF 8430. British Journal of Pharmacology, 2009, 157, 34-43.

95. Smits, R. A.; Lim, H. D.; Hanzer, A.; Zuiderveld, O. P.; Guaita, E.; Adami, M.; Coruzzi, G.; Leurs, R.; de Esch, I. J. Fragment based design of new H4 receptor-ligands with anti-inflammatory properties in vivo. Journal of medicinal chemistry, 2008, 51, 2457-2467.

96. Cowart, M. D.; Altenbach, R. J.; Liu, H.; Hsieh, G. C.; Drizin, I.; Milicic, I.; Miller, T. R.; Witte, D. G.; Wishart, N.; Fix-Stenzel, S. R.; McPherson, M. J.; Adair, R. M.; Wetter, J. M.; Bettencourt, B. M.; Marsh, K. C.; Sullivan, J. P.; Honore, P.; Esbenshade, T. A.; Brioni, J. D. Rotationally constrained 2,4-diamino-5,6-disubstituted pyrimidines: a new class of histamine H4 receptor antagonists with improved druglikeness and in vivo efficacy in pain and inflammation models. Journal of medicinal chemistry, 2008, 51, 6547-6557.

97. Strakhova, M. I.; Cuff, C. A.; Manelli, A. M.; Carr, T. L.; Witte, D. G.; Baranowski, J. L.; Vortherms, T. A.; Miller, T. R.; Rundell, L.; McPherson, M. J.; Adair, R. M.; Brito, A. A.; Bettencourt, B. M.; Yao, B. B.; Wetter, J. M.; Marsh, K. C.; Liu, H.; Cowart, M. D.; Brioni, J. D.; Esbenshade, T. A. In vitro and in vivo characterization of A-940894: a potent histamine H4 receptor antagonist with anti-inflammatory properties. Br J Pharmacol, 2009, 157, 44-54.

98. Liu, H.; Altenbach, R. J.; Carr, T. L.; Chandran, P.; Hsieh, G. C.; Lewis, L. G.; Manelli, A. M.; Milicic, I.; Marsh, K. C.; Miller, T. R.; Strakhova, M. I.; Vortherms, T. A.; Wakefield, B. D.; Wetter, J. M.; Witte, D. G.; Honore, P.; Esbenshade, T. A.; Brioni, J. D.; Cowart, M. D. cis-4-(Piperazin-1-yl)-5,6,7a,8,9,10,11,11a-octahydrobenzofuro[2,3-h]quinazolin-2 -amine (A-

Chapter 1

21

987306), a new histamine H4R antagonist that blocks pain responses against carrageenan-induced hyperalgesia. Journal of medicinal chemistry, 2008, 51, 7094-7098.

99. Lim, H. D.; Istyastono, E. P.; van de Stolpe, A.; Romeo, G.; Gobbi, S.; Schepers, M.; Lahaye, R.; Menge, W. M.; Zuiderveld, O. P.; Jongejan, A.; Smits, R. A.; Bakker, R. A.; Haaksma, E. E.; Leurs, R.; de Esch, I. J. Clobenpropit analogs as dual activity ligands for the histamine H3 and H4 receptors: synthesis, pharmacological evaluation, and cross-target QSAR studies. Bioorganic & medicinal chemistry, 2009, 17, 3987-3994.

100. Igel, P.; Geyer, R.; Strasser, A.; Dove, S.; Seifert, R.; Buschauer, A. Synthesis and structure-activity relationships of cyanoguanidine-type and structurally related histamine H4 receptor agonists. Journal of medicinal chemistry, 2009, 52, 6297-6313.

101. Smits, R. A.; Adami, M.; Istyastono, E. P.; Zuiderveld, O. P.; van Dam, C. M.; de Kanter, F. J.; Jongejan, A.; Coruzzi, G.; Leurs, R.; de Esch, I. J. Synthesis and QSAR of quinazoline sulfonamides as highly potent human histamine H4 receptor inverse agonists. Journal of medicinal chemistry, 2010, 53, 2390-2400.

102. Wijtmans, M.; de Graaf, C.; de Kloe, G.; Istyastono, E. P.; Smit, J.; Lim, H.; Boonnak, R.; Nijmeijer, S.; Smits, R. A.; Jongejan, A.; Zuiderveld, O.; de Esch, I. J.; Leurs, R. Triazole ligands reveal distinct molecular features that induce histamine H4 receptor affinity and subtly govern H4/H3 subtype selectivity. Journal of medicinal chemistry, 2011, 54, 1693-1703.

103. Mowbray, C. E.; Bell, A. S.; Clarke, N. P.; Collins, M.; Jones, R. M.; Lane, C. A. L.; Liu, W. L.; Newman, S. D.; Paradowski, M.; Schenck, E. J.; Selby, M. D.; Swain, N. A.; Williams, D. H. Challenges of drug discovery in novel target space. The discovery and evaluation of PF-3893787: A novel histamine H4 receptor antagonist. Bioorganic &amp; Medicinal Chemistry Letters, 2011, 21, 6596-6602.

104. Lane, C. A.; Hay, D.; Mowbray, C. E.; Paradowski, M.; Selby, M. D.; Swain, N. A.; Williams, D. H. Synthesis of novel histamine H4 receptor antagonists. Bioorganic & medicinal chemistry letters, 2012, 22, 1156-1159.

105. Katritch, V.; Cherezov, V.; Stevens, R. C. Diversity and modularity of G protein-coupled receptor structures. Trends in pharmacological sciences, 2012, 33, 17-27.

106. Kooistra, A. J.; Roumen, L.; Leurs, R.; de Esch, I. J. P.; de Graaf, C. From heptahelical bundle to hits from the haystack: structure-based virtual screening for GPCR ligands. Methods Enzymol., 2012, in press.

107. Istyastono, E. P.; de Graaf, C.; de Esch, I. J.; Leurs, R. Molecular determinants of selective agonist and antagonist binding to the histamine H receptor. Curr Top Med Chem, 2011, 11, 661-679.

108. Jongejan, A.; Lim, H. D.; Smits, R. A.; de Esch, I. J.; Haaksma, E.; Leurs, R. Delineation of agonist binding to the human histamine H4 receptor using mutational analysis, homology modeling, and ab initio calculations. Journal of chemical information and modeling, 2008, 48, 1455-1463.

109. Shin, N.; Coates, E.; Murgolo, N. J.; Morse, K. L.; Bayne, M.; Strader, C. D.; Monsma, F. J., Jr. Molecular modeling and site-specific mutagenesis of the histamine-binding site of the histamine H4 receptor. Mol Pharmacol, 2002, 62, 38-47.

110. Kiss, R.; Kiss, B.; Konczol, A.; Szalai, F.; Jelinek, I.; Laszlo, V.; Noszal, B.; Falus, A.; Keseru, G. M. Discovery of novel human histamine H4 receptor ligands by large-scale structure-based virtual screening. Journal of medicinal chemistry, 2008, 51, 3145-3153.

111. Sander, K.; Kottke, T.; Tanrikulu, Y.; Proschak, E.; Weizel, L.; Schneider, E. H.; Seifert, R.; Schneider, G.; Stark, H. 2,4-Diaminopyrimidines as histamine H4 receptor ligands--Scaffold optimization and pharmacological characterization. Bioorganic & medicinal chemistry, 2009, 17, 7186-7196.

112. Tanrikulu, Y.; Proschak, E.; Werner, T.; Geppert, T.; Todoroff, N.; Klenner, A.; Kottke, T.; Sander, K.; Schneider, E.; Seifert, R.; Stark, H.; Clark, T.; Schneider, G. Homology model

Introduction

22

adjustment and ligand screening with a pseudoreceptor of the human histamine H4 receptor. ChemMedChem, 2009, 4, 820-827.