Binding sites on G-protein Coupled Receptors. Implications in Design of Bivalent Ligands and Allosteric modulation

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(1)UNIVERSITA’ DI PISA. Dottorato di Ricerca in Fisiopatologia Medica e Farmacologia (BIO/14, BIO/10, CHIM/08). TESI DI DOTTORATO. Binding Sites on G-Protein Coupled Receptors Implications in Design of Bivalent Ligands and Allosteric Modulation. Dott. Daniele Pietra. Relatori: Prof.ssa Maria Cristina Breschi. Dott.ssa Anna Maria Bianucci. Anni 2003-2005.

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(3) Dedicato a mia mamma. 3.

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(5) Contents. Chapter 1. Introduction. Chapter 2. The. techniques:. 7 Laboratory. animals;. Cell. fractionations;. Pharmacodynamic mathematical models; Radioligand binding assay; Chemical synthesis; Computer in drug discovery and 15. molecular pharmacology Chapter 3. A study of interactions between bivalent ligands and GPCRs validated by means of dimeric theophylline derivatives tested on adenosine A1 receptors. Chapter 4. 31. Enhancer and Competitive Allosteric Modulator Model for G-protein coupled receptors. 49. Chapter 5. Conclusion. 79. Chapter 6. Summary. 89. Chapter 7. Riassunto. 85. Curriculum Vitae. 95. List of publications. 96. Acknowledgments. 97. 5.

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(7) Chapter 1 Introduction. Aim of this thesis The thesis presented herein is aimed at providing an overview about the relevant role played in drug design by the knowledge, at a molecular level, of ligand (drug) – receptor (biological target) interactions as well as paths that ligands should follow to reach their binding site(s) within the receptor. This research led at developing a basic approach for rational design of ligands, which are endowed of particular mechanisms of interaction with the receptor of interest. The starting points of this work were the analysis of relevant structural features of GPCRs and the molecular-pharmacological aspects of the interactions between ligands and these receptors. Bivalent ligands (Chapter 3) and allosteric modulators (Chapter 4) focus on such ligands. The design, and hopefully the discovery, of both bivalent ligands and allosteric modulators for GPCRs may represent a novel approach for affecting the neurotransmitter receptors, both of them offering advantages with respect to design of conventional drugs. In describing the development of the approach presented here it may be worth to start recalling very basic concepts, and afford subsequently the topic of interest.. Basic concepts Medicine is referred to as the branch of health science and the sector of public life which deals with maintaining human health or restoring it through the treatment of diseases and injuries. It both implies an area of knowledge – the science of body. 7.

(8) systems, their diseases and treatment – and the applied practice of that knowledge. Pharmacology (in Greek: pharmacon (φάρμακον) that means drug, and logos (λόγος) that means science) is the study of how chemical substances interact with living systems. If a substance has medicinal properties, it is considered a pharmaceutical agent, that is a drug. This science is considered to have been invented by Arabic physicians in Baghdad during the Golden Age of Islam; pharmacopoeias were penned in Arabic as early as the 7th century (Khairallah, 1946). The study of medicinal agents requires an extensive knowledge of the biological system affected. With the increasing knowledge of biochemistry and cell biology, the field of pharmacology has also changed substantially. It has become possible to design, through molecular analysis of receptors, suitable chemicals that act on specific cellular signaling or metabolic pathways by directly affecting sites located on cell-surface receptors (which modulate and mediate cellular signaling pathways, in turn controlling cellular functions). In biochemistry, a receptor is intended as a protein, located on the cell membrane or within the cytoplasm or cell nucleus, able to bind to a specific molecule (a ligand). In turn, a ligand is defined as an extracellular substance that binds to receptors, and it may consist of a neurotransmitter, hormone, or any other substance, capable of inducing the cellular response to the ligand. Ligand-induced changes in the behavior of receptor proteins result in physiological changes that constitute the biological actions of the ligands themselves. Ligands may be defined as agonists, antagonists or inverse agonists, according to the effect their interaction with the receptor induces on the activity of the receptor itself. An agonist is a substance that binds to a receptor and triggers a response in the cell, while a receptor antagonist is a substance that inhibits the normal physiological function of a receptor.. An inverse agonist is an agent which binds to the same. receptor binding-site as an agonist of such a receptor, but exerts the opposite pharmacological effect. Another kind of ligand, responsible for a wide-spread mechanism of control of protein functions, is represented by allosteric modulators. Such modulators bind to regulatory sites distinct from the active site, so that conformational changes strongly affecting the activity may be induced. With respect to its structure, a ligand may be defined as monovalent, bivalent, etc. In particular, compounds that contain two pharmacophores or one single pharmacophore and a nonpharmacophore recognition unit linked through a connecting spacer have been called bivalent ligands. Bivalent ligands represent an important approach for medicinal chemists to develop new compounds with increased affinity and/or selectivity towards a given receptor, by interacting with multiple receptor domains.. 8.

(9) Many different structural and functional receptor features are known, at present, as well as many different locations in tissues and organs. According to that, a huge number of different mechanisms of interaction and biological effects have to be analyzed. For instance, some receptor proteins are peripheral membrane proteins, while many hormone and neurotransmitter receptors are transmembrane proteins. In particular, transmembrane receptors are embedded in the lipid bilayer of the cell membrane; they allow the activation of signal transduction pathways in response to their interaction with proper binding molecules, or ligands. Metabotropic receptors are coupled to G proteins and indirectly affect the cell through enzymes which control ion channels. Ionotropic receptors contain a central pore which acts as a ligand-gated ion channel. Another major class of receptors includes intracellular proteins such as the steroid hormone receptors. These receptors often can enter the cell nucleus and modulate gene expression in response to the activation by a proper ligand. Understanding the interaction mechanisms between ligands and receptors, at a molecular level, plays a key role in drug design and drug discovery processes, since it allows for obtaining models to be used for rational exploitation of the available experimental data in discovering new molecules that may be used in therapy.. General introduction Most of the extracellular stimuli (both of chemical and physical nature) allowing for communication between cells, such as hormones, neurotransmitters and light interact with integral membrane receptors coupled to G proteins. G protein-coupled receptors (GPCRs) currently account for approximately 50% of small-molecule drug targets (Schoneberg et al., 1999, Hopkins and Groom, 2002), and represent the largest class of tractable therapeutic targets. Agonist-promoted coupling between receptors and G proteins leads to the activation of intracellular effectors, such as enzymes and ion channels. These effectors substantially amplify the production of second messengers feeding into the signal cascade. This cascade may culminate in the physiological response of the cell to the extracellular stimulus (Stadel et al., 1997; Marinissen and Gutkind, 2001). G proteins are heterotrimers, composed of three distinct subunits: α, β and γ. The βand γ-subunit exist and act as a tightly associated complex. The α-subunit has a single high-affinity binding site for guanine nucleosides (GTP or GDP). The GDP-bound αsubunit binds tightly to the βγ-complex and is inactive. The GTP-bound α-subunit dissociates from the βγ-complex and serves as a regulator of above-mentioned. 9.

(10) effectors. More recently, the βγ-complex itself has been shown to regulate effectors, e.g. K+ channel activity (Hepler and Gilman, 1992; Van der Wenden, 1994).. The structure of G protein-coupled receptors GPCRs are characterized by a common structure arising from a polypeptide chain arranged in a bundle made up of seven transmembrane (TM) α-helices, that are connected to each other by extra- and intracellular loops. G protein-coupled receptors are generally made up of 450-600 amino acid, and their molecular weights (not including their glycoside moieties) range from 40 to 50 kDa. The seven hydrophobic fragments, 20-28 amino acid in length (Houslay, 1992), characterizing their structure, span the cell membrane with a whole spatial arrangement that the sequence alignment analysis suggests to be analogous to bactheriorodopsin and mammalian rhodopsin. The 3D structures of these last two proteins have been experimentally obtained in recent years. The GPCR amino-terminal region is located on the extracellular side, and it usually contains the N-glicosylation sites. The seven trasmembrane helices are connected by three short extracellular and three intracellular loops. The intracellular loops between helix 1-2, and 3-4, mostly made up of a short amino acid chains, show high sequence similarity (homology) within the whole receptor family, and are involved in G-protein coupling. The length of the third intracellular loop, between helix 5 and 6, may be considerably different in all the members of the receptor family, and is usually longer than the other two intracellular loops. The amino- and carboxy-terminal segments of this loop show major involvement in G protein coupling and determine the specificity of interaction with the G protein itself. The carboxy-terminus of the whole polypeptide chain, on the cytoplasmic side, is also involved in G protein binding. Receptors that stimulate adenylate cyclase are usually characterized by a long carboxy-terminal region containing serine and threonine residues as potential phosphorylation sites. Phosphorylation is an important mechanism of regulation of receptor activation (Luttrell et al., 1999). Receptors that inhibit adenylate cyclase usually have a large third intracellular loop and a short carboxy-terminal tail, in contrast to receptors that activate phospholipase C which have a short third intracellular loop and large carboxy-terminal tail. Some residues are well conserved among the helices of G protein-coupled receptors. For example, proline residues are conserved in the helices 4, 5, 6, and 7, and they may be responsible for the bending of the helix axis, which plays an important role in giving rise to the binding pocket. The Asp-Arg-Tyr (DRY) sequence is present in the. 10.

(11) second intracellular loop of almost all G protein-coupled receptors: a role of this sequence in mediating G protein activation has been suggested (Palmer and Stiles, 1995). The aspartate residue in the helix 2 is present in almost all G protein-coupled receptors, and it is involved in the regulation by sodium ions (Horstman et al., 1990; Martin et al., 1999). Furthermore, some cisteine residues, in the helix 6 and in the extracellular loops, are well-conserved. The (endogenous) agonist containing a cationic amine has been shown to bind to another Asp residue in the helix 3. This interaction is enhanced when hydroxyl moieties on the phenyl ring of the relevant ligand bind to one (serotonin receptor) or two (adrenergic and dopaminergic receptors) Serine residues located in helix 5 (Strader et al., 1989). In the recent years, it has been suggested that GPCRs are able to form homooligomeric and also heterooligomeric complexes (Gouldson et al., 2000). A homooligomeric complex is made up of repeating units of the same receptor, the smallest one being the homodimeric complex, made of two units. A heterooligomeric complex is made up of more than one kind of receptor subtypes, the smallest possible complex being the heterodimeric one, made of two units each one from a different receptor subtype. Two basic models of dimerization have been recently proposed. One model implies the possibility for two independently folded receptors to face each other giving rise to intermolecular contacts. In this case, the resulting complex is called “contact dimer”. The other model refers to the domain swapping, suggesting that different domains of each receptor, for example the first five transmembrane helices domain of one receptor and the last two transmembrane helices of the other one, can exchange to generate the functional interactions. The resulting complex is called “swapped dimer”. Based on certain key sequences, the G protein-coupled receptor can be grouped into three major subfamilies: the class A (that includes receptors related to rhodopsin), the class B (that includes receptors related to the calcitonin receptor), and the class C (that includes receptors related to the metabotropic receptor). In the case of already known receptors belonging to the class A, the endogenous ligand (neurotransmitter or hormone) is believed to bind a site located in a cavity within the bundle which results from the seven TM domains, so that the receptor is activated. Different mechanisms are believed to exist in the case of receptors belonging to the classes B and C, where the endogenous ligands generally bind to N-terminal domains, leaving the TM cavity essentially unoccupied (Schoneberg et al., 1999). For the class A receptors, there are also evidences that a ligand could penetrate into the receptor’s active site located within the transmembrane helices thanks to previous interaction at other sites located nearer to the extracellular boundary of the plasma membrane. Such a ligand may. 11.

(12) subsequently reach its proper binding site driven by fluctuations of receptor itself (Colson et al., 1998).. References Colson A-O, Perlman JH, Smolyar A, Gersengorn MC and Osman R (1998) Static and Dynamic Roles of Extracellular Loops in G-Protein-Coupled receptors: A mechanism for sequential binding of thyrotropin-releasing hormone to its receptors. Biophys J 74:1087-1100 Gouldson PR, Higgs C, Smith RE, Dean MK, Gkoutos GV, Reynolds CA (2000) Dimerization and domain swapping in G-protein-coupled receptors: a computational study Neuropsychopharmacology, S4:S60-S77 Hepler JR and Gilman AG (1992) G proteins. TiBS. 17:383-387 Hopkins Al and Groom CR (2002) The druggable genome. Nat Rev Drug Discov 1:723-730 Horstman DA, S Brandon, Wilson AL, Guyer CA, Cragoe EJ Jr., Limbird LE (1990) An aspartate conserved among G-protein receptors confers allosteric regulation of α2adrenergic receptors by sodium. J Biol Chem. 265:21590-21595 Houslay MD (1992) G-protein linked receptors: a family probed by molecular cloning and mutagenesis procedures. Clin Endocrinol. 36:525-534 Khairallah AA (1946) Outline of Arabic Contributions to Medicine: Chapter X, Chemistry and Pharmacy. Luttrell LM, Ferguson SS, Daaka Y, Miller WE, Maudsley S, della Rocca GJ, Lin F Kawakatsu H, Owada K, Luttrel DK, Caron MG, Lefkowitz RJ (1999) β-Arrestindependent formation of β2 adrenergic receptor-Src protein kinase complexes. Science 283:655–66 Marinissen MJ and Gutkind JS (2001) G-protein-coupled receptors and signaling networks: emerging paradigms. TiPS 22:368-376 Martin S, Botto JM, Vincent JP, Mazella J (1999) Pivotal role of an aspartate residue in sodium sensivity and coupling to G proteins of neurotensin receptors. Mol Pharmacol. 55:210-215 Palmer TM and Stiles GL (1995) Adenosine receptors. Neuropharmacology, 34:683694 Schoneberg T, G Shultz and Gudermann T (1999) Structural basis of G proteincoupled receptor function. Mol Cell Endocrinol. 151:181-193 Stadel JM, Wilson S and Bergsma DJ (1997) Orphan G protein-coupled receptors: a neglected opportunity for pioneer drug discovery. TiPS. 18:430-437 Strader CD, Candelore MR, Hill WS, Dixon RAF, Sigal IS (1989) A single amino acid substitution in the β-adrenergic receptor promotes partial agonist activity from antagonists. J Biol Chem. 264:16470-16477. 12.

(13) van der Wenden EM (1994) Thesis "Structural requirements for the interaction between ligands and the adenosine A1 receptor", chapter 2. 13.

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(15) Chapter 2 The Techniques. Introduction In this chapter, the experimental and computational techniques used in this thesis work will be described. These techniques were exploited to investigate extracellular binding sites on GPCRs in a study of interactions between bivalent ligands and GPCRs, validated by means of dimeric theophylline derivatives tested on adenosine A1 receptors (Chapter 3), and in the development of the Enhancer and Competitive Allosteric Modulator (ECAM) model for GPCRs (Chapter 4). Due to the interdisciplinary character of the research work presented here, the description of such techniques is quite detailed, so that the needed technical aspects referring to different research fields are briefly recalled for readers with different backgrounds. In particular, this chapter would provide a wide range of bibliographic and web references which allow the interested reader to obtain more information about the topics presented in this work. It should also be pointed out that the techniques described in the following sections only represent a part of the large amount of methods available in the art and useful to investigate the extracellular binding sites on GPCRs, both under the theoretical and applicative points of view.. Laboratory animals The investigation of pharmacological properties of a compound requires that suitable. 15.

(16) bioassays are available. Biological tests should be fast, easy, and applicable to a large number of compounds, especially in the first steps of the drug discovery process. Obviously the preliminary steps of biological assays cannot be performed on humans, until good efficacy and safety indicators are already obtained. Reasonably, initial tests are performed in vitro, by using cells, tissues, enzymes, receptors or other biological material partially or completely integer, or purified. Later on tests are performed in vivo, that is on animals. Cells, tissues, enzymes used for in vitro tests may come from human or, more often, from animals. It can be understood that nowadays the use of laboratory animals is still necessary in the research for new drugs, in spite an increasing use of “in silico” models is strongly suggested by regulatory authorities. The special attention paid in the last decades on animal experimentation is the result of a recent cultural attitude that requires the human species to be more respectful with regard to the other species. It also reflects the increasing awareness, within different scientific areas, about general ethical questions related to the development of the research itself. It is now a commonly shared opinion that the principle of freedom of research must coexist with the principle of “science with conscience”. In this perspective, several rules were given both at the European, by the guidelines of the European Community Council Directive 86-609, and at the Italian level, by the Decreto Legislativo n. 116 del 27 gennaio 1992. When the bioassay has to be carried out by using animal material, the laboratory animal is sacrificed, its organs or tissues of interest are withdrawn and directly used for the bioassay, or they are frozen (usually at -70°C, -86°C or even lower temperatures), and stored until their use.. References http://www.unipi.it/ricerca/animali Hulme EC (1992) Receptor-Ligand Interactions: A Practical Approach Oxford University Press, USA Roling BE, Kessel ML (1990) The Experimental Animal in Biomedical Research CRC Press. Cell fractionation As mentioned in the above section, the primary bioassay is usually performed on cells, tissues, enzymes, receptors or other biological material more or less integer or purified. Whenever the assay requires to be carried out on a subcellular component,. 16.

(17) the purification of it from the corresponding organ, tissue or cells is made. This treatment can be performed by means of cell fractionation techniques. Cell fractionation is the separation of disrupted cells into fractions according to a variety of physical and/or chemical properties, i.e. mass, size, specific gravity, etc. The first step is the disruption of the biological material to release the desired organelles. It may be obtained by several methods, such as a mechanical disruption due to shear forces and local pressure changes. An example of an apparatus used with such a purpose is the Potter-Elvehjem homogenizer, equipped with teflon pestle/glass tube. Clearances between pestle and walls of tube only lead the cells to break down, while organelles don't. Homogenization is usually performed in isotonic/hypertonic (0.250.32 M sucrose) solution in order to prevent disruption of organelles by osmotic lysis. Ionic (salt) solutions are not used since they can cause aggregation. Rotating pestle breaks cells by shear, forcing cells through pestle leads to an increase in the intracellular hydrostatic pressure, when cells come free, the external hydrostatic pressure is suddenly reduced and the pressure difference causes cell lysis. A different homogenizer type is, for example, the so-called Dounce homogenizer, with glass pestle/glass tube, its main lysis method being the pressure; in the case of Ground glass, with fitted ground glass pestle/tube, the main lysis method is shear. Cell disruption can also be carried out by sonication, i.e. through high frequency sound waves, or by pressure. In this last case, for instance by French Press, the material is squeezed through a small orifice. Other methods used in cell fractionation are the following: grinding with abrasives, which uses shear forces, freeze/thaw cycles, whose disruption is given by ice damage, nitrogen bomb, which uses pressure, and osmotic lysis, when a hypotonic medium is used to swell (and burst) cells, especially useful for cells which are hard to lyse otherwise. Subsequently, the homogenate is subjected to several steps of centrifugation, with or without density gradient to separate the different organelles by exploiting their different sedimentation rates. As an example, the homogenate is filtered through gauze (cheese cloth, 3-4 layers) to remove connective tissue and large debris and its fractions are separate by differential centrifugation, as illustrated in Fig. 2.1. This technique does not give very pure fractions, so that further purification of organelles may be required. This can be obtained by extensive washing, re-suspending the pellets and re-centrifuging (which may result in a significant loss of material), or by density gradient centrifugation (two types - discontinuous and continuous). It also will result in some loss of material but not at the same extent as washing. Other purification techniques consist of chromatography, which is beyond the purposes of this thesis.. 17.

(18) Fig. 2.1 A general cell fractionation procedure. References Hulme EC (1992) Receptor-Ligand Interactions: A Practical Approach, Oxford University Press, USA Voet D and Voet JG (1997) Biochimica, Zanichelli Editore. Pharmacodynamic mathematical models Mathematics is used in every scientific fields to develop mathematical models, with the purpose of accurately simulate the reality. The use of the proper mathematical language plays an important role in describing the behavior of any system, specifically a pharmacological system of interest. In pharmacology, a number of mathematical models able to rationalize and quantify drug responses have been developed, especially in the field of pharmacodynamics, in order to study biochemical and physiological effects of a drug and relationships between its concentrations and effects. With regard to the pharmacodynamics, which concerns the study of drugs binding the receptors, it has been postulated that ligand-receptor interactions take. 18.

(19) place according to the mass action law and the more general kinetic theory of interaction of chemical compounds. In order to develop a model accounting for drugreceptor interactions at equilibrium, an algebraic system of equations needs to be formulated. They could be linear or non linear equations, and they need to be solved when interaction parameters have to be extracted from experimental data. Differential equations may account for the analyzed biological system in a state far from the equilibrium, or still under evolution. Generally speaking, the formulation of a pharmacodynamic model requires each phenomenon, responsible for the effect of the drug under investigation, to be translated into a mathematical expression, either mechanistic or empirical. Such expressions will constitute a mathematical system which, once solved, is able to provide the pharmacodynamic model. A quite simple example is given by the interaction described below: a compound, L, acts at a binding site of a receptor, R, according to a 1:1 stoichiometry. The formation of the RL complex, both at the equilibrium and as a function of the time, as well as the dissociation of the RL complex, as a function of time, have to be described. Moreover, an analogous system under equilibrium condition in the presence of an inhibitor, I, will be analyzed. Several models are available to explain the ligand-receptor association. The simplest model describes the interaction on the basis of the “law of mass action” and it assumes that binding is reversible.. [R ] + [L ]. Kon Koff. [RL ]. where [L] is the concentration of the ligand, [R] is the concentration of the receptor, and [LR] is the concentration of the ligand-receptor complex. The simulations required in order to describe the system outlined above, are referred to as “saturation experiments”. Binding occurs when ligand and receptor collide due to diffusion, and when the collision happens with the correct orientation and the proper energy amount. The rate of association is:. [ ] [ ]. Number of association events per time unit = L ⋅ R ⋅ K on. (2.1). Once binding has occurred, the ligand and the receptor remain bound together for a random amount of time. The probability of dissociation is the same at every time. The rate of dissociation is:. 19.

(20) [ ]. Number of dissociation events per time unit = RL ⋅ K off. (2.2). After dissociation, the ligand and receptor should be the same as at they were before binding. If either the ligand or receptor were chemically modified, then the binding does not follow the law of mass action. Equilibrium is reached when the rate of association equals the rate of dissociation of a ligand-receptor complex. This equilibrium would be more properly called “apparent equilibrium” or “steady state”, since in pharmacological systems is often impossible to have evidence that equilibrium has been reached.. [L ] ⋅ [R ] ⋅ K on. = [RL ] ⋅ K off. (2.3). The above equation can be rearranged according to the following:. [L ] ⋅ [R ] = [RL ]. K on = Kd K off. (2.4). Kd is the equilibrium dissociation constant. It represents the ligand concentration corresponding to the equilibrium condition where half receptors are occupied. Kd is usually expressed as molar concentration (M). We can express the total concentration of the receptor in terms of the concentration of. [ ]tot. its free and its bound forms: R. = [RL ] + [R ] . The combination of the above. relationship with equation 2.4 gives:. [RL ] = [L ] ⋅ B max K d + [L ] % specific binding =. [RL ] B max. =. (2.5). [L ]. K d + [L ]. (2.6). where Bmax represents Rtot . This latter equation is graphically represented by an equilateral hyperbole and explains the asymptotic trend of [LR] with respect to [LR]max or % specific binding with respect to 100 % (Fig. 2.2).. 20.

(21) % specific binding. 100 80 60 40 20 0 0. 2. 4. 6. 8. 10. Conc NxKd Fig. 2.2 Saturation binding experiments. The system may also comprise another competitive species. The simulations required in order to describe it are referred to as “competition experiments”. In this last case two simultaneous equilibria are established, one for the first ligand and the other for second one, which is the competitor. For the first ligand:. [L ] + [R ]. [RL ]. where:. Kd =. For the competitor:. [R ] ⋅ [L ] [RL ]. (2.4). [RI]. [I] + [R ]. where:. Ki =. [I ] ⋅ [R ] [RI ]. (2.7). In this case. B max = [RL ] + [RI ] + [R ]. 21. (2.8).

(22) The combination of equation 2.5 with 2.7 followed by rearrangement leads to:. [R ]tot ⋅ [L ] ⎛ [I] ⎞⎟ + [L ] ⎜1 +. [RL ] =. Kd ⎜ ⎝. (2.9). K i ⎟⎠. This equation points out that [LR] values depend on Kd, Ki, [L] and [I]. In the absence of inhibitor, equation 2.9 can be reduced to the equation 2.5. Equation 2.9 may be rearranged into the equation 2.10, that gives rise to the curve shown in Fig. 2.3.. % specific binding =. K d + [L ] ⋅ 100 K d 1 + 10 ( log [I ]− log K i ) + [L ]. (. ). (2.10). When [I] = IC50 (the concentration of unlabeled drug that causes the radioligand binding to be half way between the upper and lower plateaux), [LR] (in the presence of [I]) is half of the [LR] observed in the absence of [I], according to:. [RL ] =. 0.5 ⋅ [R ]tot ⋅ [L ] = K d + [L ]. [R ]tot ⋅ [L ] ⎛ IC K d ⎜⎜ 1 + 50 Ki ⎝. ⎞ ⎟⎟ + [L ] ⎠. (2.11). After simplification of equation 2.11, and its resolution with respect to Ki:. Ki =. IC 50 ⋅ K d K d + [L ]. (2.12). It is worth to observe that the curve represented in Fig. 2.3 decreases from 90% to 10% specific binding when the concentration of the unlabeled drug increases by 81fold. In other words, the entire curve will cover two log units (100-fold change in concentration).. 22.

(23) % specific binding. 100%. 90%. 10% 0% 81 fold. log [unlabeled ligand] Fig. 2.3 Competitive binding experiments. Association binding experiments allow measuring the dependence on the time of association between a ligand, L, and a receptor, R. Such type of experiments are performed in order to measure the association rate constant (Kon). Experimental conditions implying [L] » [R] are highly plausible, since they reflect most of the experimental ones (the so-called “pseudo-first order” conditions). That makes the estimate of Kon quite simple by maintaining [L] at a constant value during the formation of the complex, while [R] decreases. Under the above conditions:. [RL ] [RL ]eq. (. = 1 − e − K ob ⋅ t. ). (2.13). where Kob is the observed rate constant, expressed in units of inverse time (min-1). This constant has a different physical meaning with respect to the association constant Kon. The two variables are related to each other by the equation:. K on =. K ob − K off [L ]. (2.14). -1 -1 Kon is expressed as M min . Obviously, only known values of Koff and [L] allow the. accurate estimate of Kon. Fig. 2.4 shows the association rate of the LR complex, expressed as radioligand binding %, (i.e. ([LR] / [LR]eq)100), versus the time (Fig. 2.4).. 23.

(24) specific binding %. 100 80 60 40 20 0. Time Fig. 2.4 Association rate of LR complex. Dissociation experiments allow measuring the “off rate” for a ligand dissociating from the receptor. The rate of concentration change (formation and dissociation) of ligandreceptor complex can be expressed as follows:. d [RL ] = K on ⋅ [R ] ⋅ [L ] − K off [RL ] dt. (2.15). Thus, under the condition of a negligible association phenomenon (that may be obtained in particular type of experiments):. d[RL ] = − K off ⋅[RL ] dt. (2.16). The integration of the above equation gives:. [RL ] [RL ]eq. = e − K off. 24. (2.17).

(25) specific binding % =. [RL ] [RL ]eq. ⋅ 100. (2.18). Koff is expressed in units of inverse time (min-1) (Fig. 2.5).. specific binding %. 100 80 60 40 20 0. Time Fig. 2.5 Dissociation rate of LR complex. References Christopoulos A (2001) Biomedical Applications of Computer Modeling Edited by CRC Press 2001 Hulme EC (1992) Receptor-Ligand Interactions: A Practical Approach, Oxford University Press, USA Ninfa AJ and Ballou DP (2004) Metodologie di Base per la Biochimica e la Biotecnologia, edizione italiana Zanichelli. Radioligand binding assay Radoligand binding assays constitute the primary biological assays which are performed to test therapeutic potentialities of a compound or, in general, for its pharmacological (or toxicological or metabolic) characterization. In these assays, the affinity of a given labeled molecule for a given receptor preparation is measured; if the compound is labeled by a radioactive isotope, the experiment is referred to as a radioligand binding assay. With regard to the receptor preparation, it may come from a whole organism, such as a laboratory animal, or from organs, tissues, cells, subcellular. 25.

(26) components, more or less purified as mentioned above. The extent of binding of the labeled compound may be estimated in the presence or in the absence of additional substances or compounds which can interact with the receptor itself, and which can affect the extent of binding of the labeled compound to the receptor. Moreover, binding may be evaluated both under equilibrium conditions and as a function of the time. Once mechanisms of interaction between the investigated compounds and the receptors are hypothesized, in a biological system deeply analyzed by the experimental assay, ligand affinities for the receptor or other interaction parameters may be extracted from the experimental data. They may consist of thermodynamic constants, in the case of equilibrium mechanistic models, or kinetic reaction constants in the case of mechanistic models monitored during elapsing time. In the most general cases they may consist of both thermodynamic and empirical parameters. In radioligand binding assays, the measure of the amount of radioligand bound to the receptor is usually performed by physically separating the receptor preparation (bound to the radioligand) from the unbound radioligand. The bound receptor preparation turns out to be radioactive. Such a separation is usually carried out by means of a filtration device able to hold the receptor-radioligand complex over the filter, while the free radioligand goes into the filtrate. It may be worth to mention that a certain amount of radioligand might also interact with the filter itself or with other components of the receptor preparation by a non-specific manner, causing the so-called non-specific binding, that constitutes a sort of background noise. Non-specific binding must be considered in the model describing the experiment. More precisely, non-specific binding may include several different phenomena that can derive from interaction of the ligand with membranes, with receptors and transport proteins not involved in the investigated phenomena, with the filters used to separate bound from free ligand, and so on. Non-specific (or aspecific) binding is usually, but not necessarily, proportional to the concentration of radioligand, within the range it is used. Aspecific binding is detected by measuring radioligand binding in the presence of a saturating concentration of an unlabeled drug (exploited as ”cold ligand”) (100 – 1000 times its Kd) that binds to the receptors. Under those conditions, virtually all the receptors are occupied by the unlabeled drug, so that the radioligand can only bind to non-specific sites. By subtraction of the non-specific binding at a particular concentration of radioligand from the total binding at the same concentration, the specific radioligand binding toward the receptor of interest is obtained. Thus: total binding – non-specific binding = specific binding = [LR], where L represents the radioligand and LR represents the radioligand-receptor complex. Usually, the specific binding is expressed as percentage (% specific binding), where the total binding is 100% and the non-. 26.

(27) specific one is 0%.. References Hulme EC (1992) Receptor-Ligand Interactions: A Practical Approach, Oxford University Press, USA Ninfa AJ and Ballou DP (2004) Metodologie di Base per la Biochimica e la Biotecnologia. Edizione italiana Zanichelli. Chemical synthesis In chemistry, chemical synthesis is the purposeful execution of chemical reactions in order to get a product, or several products. This happens by physical and chemical manipulations usually involving one or more reactions. In the modern laboratory usage, this tends to imply that the process is reproducible, reliable, and well established to work in multiple laboratories. Organic synthesis plays a fundamental role in the pharmaceutical and medical research, in that most drugs nowadays available have been obtained by means of chemical synthesis, in particular organic synthesis dealing with carbon derivatives. A chemical synthesis begins with selection of compounds that are known/thought as reagents or reactants. Various reaction types can be applied to them in order to synthesize the final or an intermediate product. The amount of product in a chemical synthesis is referred to as the reaction yield. Typically, yields are expressed as a weight (in grams) or as a percentage of the total theoretical quantity of product that should be produced according to the reactant amounts. Organic synthesis may be considered as the construction of organic molecules via chemical processes. Organic molecules can often contain a higher level of complexity compared to purely inorganic compounds, so the synthesis of organic compounds has developed into one of the most important aspects of organic chemistry.. Reference Freeman WH and Company (1990) Chimica organica, versione italiana. Editore Zanichelli. Computer in drug discovery and molecular pharmacology Computers supply essential tools in modern medicinal chemistry and present a strong. 27.

(28) relevance both in drug discovery and development. In order to investigate drugreceptor interactions at a molecular level, a number of different techniques have been (and are still being) developed. Some of these techniques are based on theoretical models that may be handled by computer programs. Many kinds of programs are required for “in silico” representing molecular structures in two or three dimensions. The list below reports some of the most common available softwares: ChemDraw, IsisDraw, VMD, InsightII, Sybyl, Chimera, Discover. Some of them are able to draw the chemical structures either at a 2D or 3D level, or to convert a 2D structure in a 3D structure. Moreover, theoretical structures may be optimized in order to make them able of accurately representing molecular systems (either free ligands or target macromolecules) in their experimental environment (and hopefully, in the physiological one). That is achieved by means of Molecular Mechanics (MM) techniques and Molecular Dynamics (MD) simulations. In the molecular mechanics approach the molecule is treated as a series of spheres (representing the atoms) connected by springs (representing the bonds). Equations derived from classical mechanics are used to calculate the different contributions to the molecular energy resulting from bond stretching, angle bending, torsion energies, and non-bonded interactions. These calculations require molecular parameters which have been previously obtained by spectroscopic experiments of by Quantum Mechanical calculations on molecular fragments. Such parameters describe more or less approximately the interactions between different sets of atoms (sub-molecular fragments), and are stored in tables within the program (the so-called “force fields”). The energies calculated by Molecular Mechanics mainly mimic the formation enthalpies (even though they are able of approximately calculating other molecular properties): because of that, they do not represent absolute quantities but are only useful when comparing different conformations of the same molecular system. A quite simple optimization of molecular structures is achieved by minimization of the molecular energy obtained from the force filed for a given initial 3D structure. Several minimization algorithms are available in the relevant software (e.g. Discover). The structure corresponding to an energy minimum has a good probability of being a stable structure, depending on the goodness of the starting one. In the most general cases, however, the simple energy minimization process doesn’t ensure that a highly stable conformation is found, as it is only capable of finding “local” energy minima. Therefore, in order to identify really plausible conformations, the possibility of generating many different conformations is required, so that several “local” energy minima are identified and compared to each other. One of the most common approaches, exploited for generating huge ensembles of molecular configurations for a given system (a small molecule, a protein, as well as a ligand-receptor complex), consists of Molecular. 28.

(29) Dynamics (MD) simulations, based on a suitable combination of the Langevin equation and a selected “force field”. That allows the generation of a number of different conformations from which a suitable ensemble of plausible structures is sampled. Quite stable configurations are selected, from such an ensemble, for the system of interest. The MD protocols imply heating the molecule at temperatures up to 900 K (starting from very low temperatures, such as 80 K). During the heating step the simulation allows the structure to undergo more energetic motions, that, in turn, allow it to overcome energy barriers between different conformations corresponding to “local” energy minima, or to cross energy saddles. More in detail, the molecular system is usually “heated” at a very high temperature (up to 900K) for a certain period of time (e.g. 5 picoseconds), then “cooled” (down to 300 K, i.e. near to the “room” temperature) for another period of time (e.g. 50 picoseconds) to provide a molecular trajectory from which the ensemble of plausible structures is obtained, by sampling many different configurations of the system. Further energy minimization of the sampled configurations is often carried out. Such a protocol usually allows identifying conformations possessing a high probability of accurately representing the system of interest. Since most receptors for drugs consist of macromolecules, such as proteins, endowed of a complex three-dimensional structure, experimental measurements (for instance coming from the X-ray crystallography) should represent the most straightforward approach that enables obtaining the needed structural information. Unfortunately, not all proteins are easy to be crystallized (e.g. membrane proteins). In the case of the membrane receptors, the molecular modeling techniques allow obtaining suitable theoretical (“in silico”) models. That may only be achieved if the primary structure (i.e. the amino acid sequence) is known and the X-ray structure of at least a homologous protein has been determined. The amino acid sequences of the protein to be modeled and the one, that will be exploited as 3D template, are aligned and compared by suitable softwares (ClustalW, Blast, and so on). On this basis, a rough 3D model for the protein of interest is built by replacing the amino acids of its sequence into the known 3D structure of the template protein. The rough initial model, as well as models for complexes, involving the protein of interest and ligands of it, may be optimized by using the protocols described above. Accurate 3D models allow analyzing molecular interactions that turn out to greatly help in drug design.. References Burger’s Medicinal Chemistry and Drug Discovery fifth edition, Edited by Wolff ME (1995). 29.

(30) http://www.ndsu.nodak.edu/qsar_soc/ http://www.molecules.org/. 30.

(31) Chapter 3 A Study of Interactions between Bivalent Ligands and GPCRs Validated by means of Dimeric Theophylline Derivatives Tested on Adenosine A1 Receptors. Abstract General binding properties of bivalent ligands toward GPCRs were investigated by analyzing relative pKi values [relative pKi=(pKi bivalent ligand/pKi monomer)*100] as a function of the spacer lengths. The analysis showed a common trend in the behaviour of these ligands at GPCRs. In order to support this observation, a new series of bivalent theophylline derivatives were synthesized and tested on the adenosine A1 receptor. The results obtained for these new compounds showed the same general affinity trend. Moreover, molecular modeling of adenosine A1 receptor in monomer and dimer states, followed by the definition of the cavities (which reflect possible ligand binding sites) was done. It allowed us to speculate different binding modes of bivalent ligands at GPCRs as a function of the spacer lengths.. 31.

(32) Introduction Compounds that contain two pharmacophores or one single pharmacophore and a nonpharmacophore recognition unit linked through a connecting spacer have been called “bivalent ligands” (Neumeyer et al., 2003). Bivalent ligands are an important approach for medicinal chemists to develop new compounds with increased affinity and/or selectivity toward a given receptor, by interacting with multiple receptor domains. During the years, a number of bivalent ligands have been developed. They may contain. two. identical. pharmacophores. (homobivalent),. two. non-identical. pharmacophores, or a pharmacophore and a nonpharmacophore recognition unit (heterobivalent). These compounds have been studied for a variety of biological targets such as enzymes, ion channels and G-protein coupled receptors (GPCRs) (Morphy and Rankovic, 2005). Our interest focused on GPCRs, as they represent the largest class of cell surface receptors and are the target for about the 50% of all modern drugs (George et al., 2002). Adenosine receptors belong to the superfamily of GPCRs and may be grouped into four subtypes: A1, A2A, A2B and A3, as classified on the basis of the International Union of Pharmacology (Fredholm et al., 2001). As all GPCRs, adenosine receptors are characterized by a common structure with a polypeptide chain giving rise to seven transmembrane (TM) α-helices, connected to each other by extra- and intracellular loops, a cytoplasmatic carboxy-terminal tail and an extracellular amino-terminal tail (Schoneberg et al., 1999). In the recent years, it has been suggested that adenosine A1 receptor, as well as other GPCRs, is able to form homooligomeric and also heterooligomeric complexes, (Gouldson et al., 2000) and the presence of homodimeric complexes of adenosine A1 receptor in brain cortex has been shown (Ciruela et al., 1995). Generally, a homooligomeric complex is made up of repeating units of the same receptor, the smallest one being the homodimeric complex, made of two units. A heterooligomeric complex is made up of more than one kind of receptor, the smallest possible complex being the heterodimeric one, made of two units each one from a different receptor subtype. Two basic models of dimerization have been recently proposed. One model illustrates the possibility for two independently folded receptors to face each other giving rise to intermolecular contacts. In this case, the resulting complex is called “contact dimer”. The other model refers to the domain swapping, suggesting that different domains of. 32.

(33) each receptor, for example the first five transmembrane helices domain of one receptor and the last two transmembrane helices of the other, can exchange to generate the functional interactions. The resulting complex is called “swapped dimer” (Gouldson et al., 2000). The literature concerning the possible interaction modes of bivalent ligands with GPCRs, reports that such compounds may interact with neighboring binding sites on a single receptor (Messer, 2004). On the other hand, some recent publications also illustrate the possibility for such compounds (e.g. KDN21 for δ e κ receptors) (Xie et al., 2005) to bridge two receptors that are organized as heterodimers. Further possibilities of binding modes have been reported by Portoghese (Portoghese, 2001). Nevertheless, no studies that analyze possible interaction modes of bivalent ligands as a function of their spacers are available up to now. The goal of this work consists of analyzing the general binding properties of already known bivalent ligands for GPCRs and experimentally validate the hypotheses coming out from this study by means of a new series of bivalent derivatives of theophylline tested on the adenosine A1 receptor. Despite theophylline is known to possess a moderate affinity for the adenosine A1 receptor, it has been chosen as a basic nucleus to obtain new bivalent compounds, since it is commercially available, cheap and easy to be synthetically manipulated. The part of study referring to theophylline derivatives is not focused on the research of new high affinity ligands. It is focused instead on supporting some hypotheses of ligandreceptor interactions that came out from other analysis techniques used in this work, by exploiting such derivatives as probe compounds for validation purposes. Summarizing, the study presented herein was developed over two lines. The first one consisted of identifying simple requirements for the spacer length of bivalent ligands, also speculating about different possible binding modes. The second one consisted of performing preliminary experiments aimed at supporting the analysis previously carried out.. Results and Discussion Molecular modeling studies of the adenosine A1 receptor, both in the monomeric and the dimeric states were performed. In order to construct the dimeric complexes of the receptor, the models for two monomeric units were approached to each other so that two pairs of transmembrane helices, belonging to two different subunits, faced to each other, according to previous hypotheses suggesting that the most probable subunit interface is made up of two pairs of helices (Gouldson et al., 2000, Fanelli and De. 33.

(34) Benedetti, 2005). In particular, we considered a receptor complex where the transmembrane helices 5 and 6 are at the interface between the two receptor subunits (5,6-dimer) and a receptor complex where the transmembrane helices 1 and 2 are at the interface between the two receptor subunits (1,2-dimer). These two complexes were selected as the two extremes of different possibilities, because they respectively reflect the lowest and the highest distance between the two binding sites, on the basis of the measured size of the three-dimensional (3D) model of the monomeric receptor. A. B. C. Figure 3.1. Models of the analyzed theophylline-adenosine A1 receptor complexes. In all cases, monomer theophylline molecules are arranged in the binding site referred to as the “main” binding site in the text. The monomeric state of the receptor is shown along with the distances between theophylline and extracellular top of the receptor (~25 Å) and the distance between theophylline and the loop area nearest to the cell membrane (~7 Å). The view is taken from the plane of the plasma membrane (A). The figure also shows the model for a complex involving the 5,6-dimer (B), along with the distance between two theophylline molecules, each arranged within a different subunit (~26 Å). The view is taken along the axis of the transmembrane helix bundle. Last, the figure shows the model for a complex involving 1,2-dimer (C), along with the distance between two theophylline molecules, each arranged within a different subunit (~36 Å). The view is the same as (B). In all models, the surfaces of the amino acid residues forming the cavities are colored in grey.. 34.

(35) The modeling task was followed by the analysis of cavities which may give rise to plausible binding sites, by providing, from the amino acid residues surrounding them, the atoms required for the interaction (Tsodikov et al., 2002). From such analysis, a wide cavity located into the helix bundle, giving rise to the commonly recognized binding site, was identified, as expected. Moreover, the presence of further cavities was observed. They are located both in the extracellular loop of the monomeric model, and at the interface between the two receptor subunits in the 5,6-dimeric complex. All these cavities are believed to be potential binding sites (Fig. 3.1).. Figure 3.2. Some hypothesized binding modes of bivalent ligands with monomeric and dimeric receptors. A shows two possible interaction modes of a bivalent ligand with a monomeric receptor. In particular, the two pharmacophoric units of the ligand may be located either within the transmembrane helix bundle of the receptor (“main” binding site) or one within it and the other outside the receptor. B shows that the same binding modes are possible, in principle, even when bivalent ligands bind to the dimeric receptor complex. Each ligand molecule interacts only with one monomeric receptor subunit. C and D show that a single molecule of the bivalent ligand may interact at the same time with both the receptor subunits of the dimeric complex (“main” binding site), or with one receptor subunit in its “main” binding site and in a site located at the interface between the two receptor subunits. In particular, in C the spacer is supposed to intercalate the helices of the receptor subunits, while in D the spacer is long enough to form a “bridge” between the two receptor subunits through the extracellular space.. These observations allowed us to speculate different possible binding modes of bivalent ligands at GPCRs as a function of their spacer lengths (Fig. 3.2). More in detail, a bivalent ligand, after a first interaction with the receptor surface, can penetrate into the binding pocket, and the two pharmacophoric units of the ligand may interact. 35.

(36) with two different sites, the one deeply located into the helix bundle, and the other located at the receptor extracellular surface. This may occur when the spacer, which determines the distance between the two pharmacophoric moieties, has a proper length, otherwise the second molecular moiety can stay outside the receptor subunit. This can be conceived, in principle, for both the monomeric and the dimeric receptors. There may be cases where the two monomeric subunits of a dimeric complex can bind two different ligand molecules independently from each other. Other possibilities imply that the two pharmacophoric moieties of one ligand bind to two receptor subunits. The possibility that a first ligand moiety binds to a first receptor subunit, while the second moiety is arranged at the interface between the two receptor subunits, may also be conceived. Depending upon the length and nature of the spacer, it may act by intercalating the transmembrane helices, or it may form a “bridge” connecting the two receptor units. Since, in principle, many interaction modes are conceivable, the binding properties of some already known dimeric ligands for GPCRs (Neumeyer et al., 2003, LeBoulluec et al., 1995, Zheng et al., 2000, Descampe-Francois et al., 2003) (Fig. 3.3) were analyzed, in order to investigate whether some preferred interaction modes do exist, and in order to identify them. A. B H N. CH2. n. O. H N. O. HN. NH. O. O. CH2. O. NH2. H2N. C. D. N. N H. NH. H. O. N. N. N. n. H H. H N O. OH. CH2. n. H N. N. H. H H. H. N H O. CH2. n. O. O HO. Figure 3.3. General structures of bivalent ligands acting at the 5HT1A and 5HT1D serotonin receptors (A), the MT1 and MT2 melatonin receptors (B), the α2a and α2b cathecolamine receptors (C), and the μ, κ and δ opioid receptors (D) used to investigate the binding properties of the known dimeric ligands at GPCRs.. 36.

(37) The starting point was the definition of relative pKi values, that may be obtained as a function of the spacer lengths, according to the formula: relative pKi=(pKi bivalent ligand/pKi monomer)*100 In particular, bivalent ligands for cathecolamine, melatonin, serotonin, and opioid receptors, containing spacers of different lengths, and showing activity values ranging from sub-nanomolar to micromolar were considered. Then, the means of the relative pKi values were calculated over the different ligand-receptor systems, so that a mean value for each spacer length could be obtained. The spacer length is given by the number (n) of skeleton atoms in the spacer itself. The average value of relative pKis as a function of n was referred as kn (Fig. 3.4). A general behavior for all the analyzed bivalent ligands was observed. This trend appears to be characterized by a minimum of affinity corresponding to n = 3-4 atoms, followed by an increase in the affinity when n = 8-12 atoms; in the latter case the affinity shows values comparable to the monomer.. Relative pKi. 120 Bivalent theophyllines for A 1 receptor kn. 110 100 90 80 0. 1. 2. 3. 4. 5. 6. 7. 8. 9 10 11 12. Number of spacer methylenic units Figure 3.4. Relative pKi values of bivalent theophylline derivatives interacting with adenosine A1 receptor, calculated by means of the equation in parentheses [relative pKi = (pKi bivalent ligand / pKi monomer)*100] (○), and the average of the relative pKi values of several bivalent ligands acting on the cathecolamines, melatonin, serotonin, and opioid receptors (■) (referred as kn) versus the length of the spacer.. 37.

(38) In order to experimentally support this observation, a new series of bivalent theophylline derivatives was synthesized. Compounds 2-10 were prepared by onestep synthesis from theophylline 1 and the appropriate dibromoalkane, in the presence of NaH (Scheme 3.1) (Itahara and Imamura, 1994).. O NH. N O. N. N. O. NaH Br(CH2)nBr DMF. N O. N. N. N. N. N. 2 3 4 5 6. 1. O. (CH2)n. n=2 n=3 n=4 n=5 n=6. 7 8 9 10. N N. O. n=7 n=8 n=10 n=12. Scheme 3.1. Synthetic pathway for the preparation of bivalent theophylline derivatives.. Such derivatives were tested on adenosine A1 receptors by means of radioligand binding assay (Table 3.1), and the experimental results showed to be in agreement with the general trend observed for the already known ligands of other GPCRs (Fig. 3.4). The differences between the affinity values of the bivalent theophylline derivatives and the theophylline itself were tested for the statistical significance, as expressed in Table 3.1 by the probability (p). In order to compare such experimental results with our observation regarding the general behavior of GPCR bivalent ligands above reported, theoretical pKi values of the bivalent theophylline derivatives (pKi calc-biv-theo) were calculated on the basis of the following equation: pKi calc-biv-theo = (kn * pKi theophylline) / 100 where the pKi of monomeric theophylline and the previously defined kn parameter are involved. The good agreement between the predicted and experimentally obtained pKi values for the new theophylline derivatives (Table 3.2) was estimated by calculating the minimum absolute error (= 0.153), the mean absolute error (= 0.316), and the maximum absolute error (= 0.543). In order to get a deeper insight about binding modes of the above bivalent ligands, we. 38.

(39) looked at the distances between the ending of the spacers. The distance corresponding to a fully extended spacer of 12 carbon atoms is 13 Å. Moreover, the models of the theophylline-adenosine A1 receptor complexes show that the distance between theophylline bound in the site within the helix bundle, commonly recognized as the main binding site, and the extracellular top of the receptor is ~25 Å, while the distance between theophylline and the nearest loop is ~7 Å. Furthermore, in the 5,6dimer model, the distance between two theophylline molecules, each located at the two above mentioned binding sites of different subunits, is ~26 Å. Finally, the model for the 1,2-dimer shows that the distance between the two theophyllines located at the two same binding sites is ~36 Å (Fig. 3.1).. Table 3.1. Affinities of theophylline and its bivalent derivatives at adenosine A1 receptor, expressed as Ki values.. Compound. na Ki (μM) ± SEMb. 1 (theophylline). 0. 10 ± 1.8. 2. 2. 32 ± 10 *. 3. 3. 50 ± 4.2 ***. 4. 4. 124 ± 18 **. 5. 5. 62 ± 8.1. 6. 6. 9.3 ± 3.6 *. 7. 7. 21 ±1.9. 8. 8. 14 ± 8.0. 9. 10. 7.6 ± 3.3. 10. 12. 6.2 ± 0.4. a. n is the number of spacer methylenic units. 3 Ki values were obtained from binding competition assays using [ H]DPCPX as the radioligand. Data represent the means (± SEM) from at least three experiments. * p<0.05, ** p<0.01, *** p<0.001 with respect to theophylline b. This means that the spacer of a bivalent ligand should be longer than n = 12 in order to ensure its capability of interacting with both receptor units at the same time, possibly giving rise to a link that may contribute to dimerization. More in detail, the measure of the spacer should be at least 24 methylenic units for the 5,6-dimer, and at least 36 methylenic units for the 1,2-dimer. These values correspond to ligands with the spacer intercalated between the helices of the two receptor subunits, while an even different interaction mode, implying that the ligand “bridges” the two receptor subunits through. 39.

(40) the extracellular space, requires even longer distances. Furthermore the bivalent ligands synthesized for this study do not appear likely to simultaneously interact with two sites, the one located within the helix bundle of one receptor subunit (previously referred to as the “main” site) and the other located at the interface between two receptor subunits. In fact, in these derivatives the spacer is linked to the pharmacophoric molecular fragment so that, when a theophylline moiety is arranged in the already mentioned “main” binding site, it is directed toward the extracellular surface of the receptor. In any case the affinity values of the bivalent theophylline derivatives were found to be in good agreement with the general trend showed by all the already known GPCR ligands analyzed in this work.. Table 3.2. Experimental and predicted pKi values, referring to the affinity of bivalent theophylline derivatives at the adenosine A1 receptor, and their differences.. Compound na Experimental pKi Predicted pKi. Difference. 2. 2. 4.500. 4.408. 3. 3. 4.302. 4.149. 0.153. 4. 4. 3.904. 4.183. -0.279. 5. 5. 4.210. 4.652. -0.442. 6. 6. 5.030. 4.670. 0.360. 7. 7. 4.669. 4.845. -0.176. 8. 8. 4.838. 5.024. -0.186. 9. 10. 5.120. 4.853. 0.267. 10. 12. 5.203. 5.052. 0.151. 0.092. a. n is the number of spacer methylenic units. Minimum absolute error = 0.092, mean absolute error = 0.234, maximum absolute error = 0.442. All the experimental results obtained here, coupled to the above described speculations, allowed us to suggest that only one binding mode can be taken as the most probable one. It implies that the two pharmacophoric moieties of a single ligand are located within a single monomeric receptor (or within a monomeric unit of a dimeric receptor complex). Our results have highlighted that there are no possibilities for a bivalent ligand with pharmacophores spaced less than 13 Å to interact with a dimeric GPCR, without a. 40.

(41) very strong distortion of the receptor complex itself, compared to the monomer. The minimum affinity is shown when the spacer length corresponds to n = 3-4 atoms (distance between the ending of the spacer: ~ 4-5 Å). That may be due to the narrow, even in some way flexible, path encountered by the ligand in arranging within the receptor. Such narrow path, while allowing, through possible temporary conformational transitions, the arrangement of the “head” pharmacophoric moiety in the already mentioned “main” binding site, may account for the strong difficulty encountered by the “tail” pharmacophoric moiety of a shortly spaced bivalent ligand in finding a place to bind the receptor. It makes the binding of the whole ligand more difficult in comparison with the corresponding monomeric ligand. The increase in the affinity of the bivalent ligands up to values comparable to the corresponding monomer, when the spacer length is n = 8-12 atoms (distance between the ending of the spacer: ~ 10-13 Å), can arise because the second moiety of the bivalent ligand lies within a larger area located in the extracellular loop at plasma membrane surface. A development of this study is expected to give rise to bivalent ligands well suited for therapeutical applications. It has been recently reported that bivalent ligands, especially the heterobivalent ones, may exist endowed with a moiety which is able to establish strong and specific interactions at the level of the extracellular loops (Messer, 2004). Since the differences in amino acid composition between various receptor subtypes can be especially found in the loops, compared to the more conserved transmembrane regions, thus allowing significant differences in the affinity of a molecule toward a give receptor site, the interaction with the loops could be used to design new selective ligands. Thus, the knowledge of the optimal spacer length becomes a key element in molecular design.. Conclusions The study reported here mainly focused on analyzing the affinities of already known bivalent ligands of different GPCRs. The results suggested a common trend in the behavior of such ligands with respect to the length of the spacer. This observation was supported by experimental data obtained for a new series of bivalent theophylline derivatives. From the general trend observed, a simple model for predicting the affinity toward a given GPCR of newly designed bivalent ligands can be extracted. Moreover, a number of different binding modes for bivalent ligands of adenosine A1 receptors was proposed and analyzed. Only few of them are likely to exist for ligands with spacer lengths up to 12 carbon atoms. In this case, one of the two pharmacophoric moieties may interact with the binding site deeply located in the. 41.

(42) transmembrane helices (referred to as the “main” binding site), while the other moiety may arrange somewhere at the surface of the plasma membrane. A development of this study is expected to give rise to bivalent ligands suitable for therapeutic applications, as highly selective interactions with the loops at the surface of the plasma membrane could be used to design new selective ligands. In this perspective, the knowledge of the optimal spacer length becomes a key element in molecular design.. Experimental General [3H]DPCPX (120 Ci/mmol), was obtained from NEN. CPA and teophylline were from Sigma. All other chemicals, including starting materials, were from standard commercial sources at the highest purity commercially available. Thin-Layer Chromatography (TLC) was carried out using aluminium sheets (20x20 cm) with silica gel F254 from Merck. Spots were visualised under UV light (254 nm). 1. H-NMR spectra were measured at 200 MHz with a Varian Gemini 200 instrument.. Chemical shifts for 1H are given in ppm (δ) relative to tetramethylsilane (TMS) as internal standard. For the NMR assignments of the products in the experimental part, the atom numbering known for theophylline was used.. Synthesis General protocol for the preparation of bivalent theophylline derivatives containing alkyl spacers. Theophylline (1 eq.) is poured into a flask containing 3.5 mL of N,Ndimethylformamide, obtaining a white suspension partially dissolved. NaH 60% in mineral oil (1.1 eq.) is added and the suspension stirred at room temperature for 15 min, then 0.5 eq. of the appropriate dibromoalkane is added dropwise. The white suspension is stirred overnight (18-24 hours) at room temperature. The reaction mixture is poured into water (20 mL) and left standing for 1-2 hours at r.t. The formed white precipitate is collected by filtration. After washing with EtOAc and drying in vacuo, the product results pure enough for being submitted to biological tests. Spectral properties of the products, reported in the following, are in complete agreement with previous literature (Itahara and Imamura, 1994). 1,2-Di(7-theophyllinyl)ethane (2, n = 2). 42.

(43) 1. H-NMR (CDCl3): δ = 7.54 (s, 2H, H-8), 4.79 (s, 4H, NCH2CH2N), 3.60, 3.46 (2 x s, 2 x. 6H, N(1)-CH3 and N(3)-CH3). 1,3-Di(7-theophyllinyl)propane (3, n = 3) 1. H-NMR (CDCl3): δ = 7.78 (s, 2H, H-8), 4.40 (m, 4H, NCH2CH2CH2N), 3.60, 3.41 (2 x. s, 2 x 6H, N(1)-CH3 and N(3)-CH3), 2.60 (m, 2H, NCH2CH2CH2N). 1,4-Di(7-theophyllinyl)butane (4, n = 4) 1. H-NMR (CDCl3): δ = 7.59 (s, 2H, H-8), 4.34 (m, 4H, NCH2CH2CH2CH2N), 3.58, 3.40. (2 x s, 2 x 6H, N(1)-CH3 and N(3)-CH3), 1.92, (m, 4H, NCH2CH2CH2CH2N). 1,5-Di(7-theophyllinyl)pentane (5, n = 5) 1. H-NMR (CDCl3): δ = 7.74 (s, 2H, H-8), 4.30 (m, 4H, NCH2CH2CH2CH2CH2N), 3.61,. 3.41 (2 x s, 2 x 6H, N(1)-CH3 and N(3)-CH3), 1.93 (m, 4H, NCH2CH2CH2CH2CH2N), 1.37 (m, 2H, NCH2CH2CH2CH2CH2N). 1,6-Di(7-theophyllinyl)hexane (6, n = 6) 1. H-NMR (CDCl3): δ = 7.53 (s, 2H, H-8), 4.27 (m, 4H, NCH2CH2CH2CH2CH2CH2N),. 3.59,. 3.41. (2. x. s,. 2. x. 6H,. N(1)-CH3. and. N(3)-CH3),. 1.90. (m,. 4H,. NCH2CH2CH2CH2CH2CH2N), 1.38 (m, 4H, NCH2CH2CH2CH2CH2CH2N). 1,7-Di(7-theophyllinyl)heptane (7, n = 7) 1. H-NMR (CDCl3): δ = 7.61 (s, 2H, H-8), 4.28 (m, 4H, NCH2CH2CH2CH2CH2CH2CH2N),. 3.61,. 3.41. (2. x. s,. 2. x. 6H,. N(1)-CH3. and. N(3)-CH3),. 1.86,. (m,. 4H,. NCH2CH2CH2CH2CH2CH2CH2N), 1.35 (m, 6H, NCH2CH2CH2CH2CH2CH2CH2N). 1,8-Di(7-theophyllinyl)octane (8, n = 8) 1. H-NMR. (CDCl3):. δ. =. 7.55. (s,. 2H,. H-8),. 4.28. (m,. 4H,. NCH2CH2CH2CH2CH2CH2CH2CH2N), 3.60, 3.43 (2 x s, 2 x 6H, N(1)-CH3 and N(3)CH3),. 1.88. (m,. 4H,. NCH2CH2CH2CH2CH2CH2CH2CH2N),. 1.33. (m,. 8H,. NCH2CH2CH2CH2CH2CH2CH2CH2N). 1,10-Di(7-theophyllinyl)decane (9, n = 10) 1. H-NMR. (CDCl3):. δ. =. 7.56. (s,. 2H,. H-8),. 4.27. (m,. 4H,. NCH2CH2CH2CH2CH2CH2CH2CH2CH2CH2N), 3.60, 3.41 (2 x s, 2 x 6H, N(1)-CH3 and N(3)-CH3), 1.87 (m, 4H, NCH2CH2CH2CH2CH2CH2CH2CH2CH2CH2N), 1.28 (m, 12H, NCH2CH2CH2CH2CH2CH2CH2CH2CH2CH2N).. 43.

(44) 1,12-Di(7-theophyllinyl)dodecane (10, n = 12) 1. H-NMR. (CDCl3):. δ. =. 7.56. (s,. 2H,. H-8),. 4.29. (m,. 4H,. NCH2CH2CH2CH2CH2CH2CH2CH2CH2CH2CH2CH2N), 3.61, 3.42 (2 x s, 2 x 6H, N(1)CH3. and. N(3)-CH3),. 1.88. NCH2CH2CH2CH2CH2CH2CH2CH2CH2CH2CH2CH2N),. (m, 1.28. 4H, (m,. 16H,. NCH2CH2CH2CH2CH2CH2CH2CH2CH2CH2CH2CH2N).. Molecular modeling studies The present model of the adenosine A1 receptor was built on the basis of bovine rhodopsin 3-D structure (PDB entry 1HZX, chain A) (Teller et al, 2001) using the Biopolymer module of InsightII and the Discover programs (Accelrys, San Diego, CA, USA). The alignment of the amino acid sequence of rat adenosine A1 receptor (SwissProt accession numbers: P25099) and the amino acid sequence of bovine rhodopsin (Swiss-Prot accession number: P02699) were retrieved from tGRAP (Beukers et al, 1999). This alignment ensured that the highly conserved motifs (DRY/ERY and NPxxY), as well as Cys 110 of the rhodopsin and Cys 80 of the A1 receptor matched exactly. Then, the N-terminal and C-terminal regions were blocked by means of Nacetyl and N-methyl amide groups, respectively, in order to avoid electrostatic interactions. Moreover, a disulfide bridge was built between Cys 80 and Cys 169. The obtained model was solvated with a water molecule layer of 5 Ǻ, and the pH was set to the physiological value of 7.4. The cff91 force-field was selected for energy minimizations and molecular dynamics simulations. A distance-dependent (ε = 1r) dielectric function was used. The model was subjected to sequential energy minimizations, first with helices backbone constraints, then without any constraints. In order to fix the position of the constrains, the transmembrane regions were obtained from GPCRDB (Beukers et al, 1999). Minimizations were carried out by means of 1000 steps of steepest descent followed by conjugate gradient until the rms gradient of the potential energy was less than 0.01 kcal/mol.Å. A twin cut-off (15.0, 20.0 Å) was used to calculate non-bonded electrostatic interactions at every minimization step. In order to obtain an antagonist-bound model for the receptor complex, theophylline was first manually docked into the active site so that a H-bond interactions between the carbonyl oxygens of theophylline C-2 and C-6 and the hydroxyl groups of Ser94 (TM3) and Ser281 (TM7), respectively, occur (Ivanov et al, 2002). The resulting protein-ligand complex was then refined by energy minimization, by means of 1000 steps of steepest descent followed by conjugate gradient until the rms gradient of the. 44.

(45) potential energy was less than 0.01 kcal/mol.Å. In the dimeric complexes, the loops, the N-terminus, and the C-terminus were not included; models able to approximate either the contact dimeric complexes or the domain swapping complexes were obtained. Omitting the loops should not significantly affect the measure of the distance between the two theophylline molecules each one of them located in the binding site of each receptor unit. The complex with the transmembrane helices 5 and 6 at the interface between the two receptor units and the complex with the transmembrane helices 1 and 2 at the interface between the two receptor units, both with and without the ligand, were generated by relaxing the system made up of the two monomers in their appropriate orientation and taken at a distance of 9 Å. This was performed by energy minimization, followed by a molecular dynamics simulation carried out by heating the systems to 300 K to reach the equilibration after 10 ps. Then structures were collected after 50 ps of molecular dynamics simulation, and the best ones were submitted to a further energy minimization. The structures of the resulting monomer, dimers and of the respective complexes with theophylline were used to make assumptions on the distance between the ligand, located at the large binding site in the helix bundle, and other cavities possibly present in the receptor, for example between theophylline and the top of the monomeric receptor, as well as between the two theophyllines themselves in the dimeric complexes. In order to investigate the possible cavities in the obtained models, we used the program Surface Racer 1.1, using van der Waals radii from Richard (probe radius of 1.7 Å) (Tsodikov et al., 2002). Finally, bivalent theophylline derivatives, with different lengths of the spacer, were modeled and submitted to energy minimization following the same protocol, in order to measure the distance (Å) between the two xanthine cores in their optimized geometries. All the structure pictures were obtained by the VDM-Visual Molecular Dynamics program (Humphrey et al, 1996).. Radioligand binding assay Crude sinaptosomal membranes were prepared by using a modification of the method described by Lohse et al. (Lohse et al., 1984). Male Wistar rat brain cortex was homogenized in 10 volumes of ice-cold 0.32 M sucrose, 20 mM Tris HCl buffer pH 7.4 with 30 strokes in Dounce homogenizer. The homogenate was centrifuged at 1000 g for 10 min to remove the nuclear fraction, and the resulting supernatant centrifuged at. 45.