Computer-aided design of multi-target ligands at A1R, A2AR and PDE10A, key proteins in neurodegenerative diseases.

Compounds designed to display polypharmacology may have utility in treating complex diseases, where activity at multiple targets is required to produce a clinical effect. In particular, suitable compounds may be useful in treating neurodegenerative diseases by promoting neuronal survival in a synergistic manner via their multi-target activity at the adenosine A1 and A2A receptors (A1R and A2AR) and phosphodiesterase 10A (PDE10A), which modulate intracellular cAMP levels. Hence, in this work we describe a computational method for the design of synthetically feasible ligands that bind to A1 and A2A receptors and inhibit phosphodiesterase 10A (PDE10A), involving a retrosynthetic approach employing in silico target prediction and docking, which may be generally applicable to multi-target compound design at several target classes. This approach has identified 2-aminopyridine-3-carbonitriles as the first multi-target ligands at A1R, A2AR and PDE10A, by showing agreement between the ligand and structure based predictions at these targets. The series were synthesized via an efficient one-pot scheme and validated pharmacologically as A1R/A2AR–PDE10A ligands, with IC50 values of 2.4–10.0 μM at PDE10A and Ki values of 34–294 nM at A1R and/or A2AR. Furthermore, selectivity profiling of the synthesized 2-amino-pyridin-3-carbonitriles against other subtypes of both protein families showed that the multi-target ligand 8 exhibited a minimum of twofold selectivity over all tested off-targets. In addition, both compounds 8 and 16 exhibited the desired multi-target profile, which could be considered for further functional efficacy assessment, analog modification for the improvement of selectivity towards A1R, A2AR and PDE10A collectively, and evaluation of their potential synergy in modulating cAMP levels

ALTERED EXPRESSION AND FUNCTIONALITY OF A2A ADENOSINE RECEPTORS IN HUNTINGTON’S DISEASE AND OTHER POLYGLUTAMINE DISORDERS

Several studies have suggested the possible involvement of A2A adenosine receptors in the pathogenesis of neuronal disorders, including Huntington’s disease. Huntington’s disease is an inherited neurodegenerative disease clinically characterized by motor, cognitive and behavioural impairments. The genetic cause of the disease is the expanded CAG triplet in a gene coding for huntingtin, a protein involved in several physiological processes. Huntington’s disease affects primarly GABAergic neurons in the basal ganglia that express adenosine A2A and dopamine D2 receptors. The present study describes a functional alteration of A2A adenosine receptor in striatal cells engineerized to express full length or truncated, wild type or mutant huntingtin. The data obtained demonstrate that the presence of mutant huntingtin induce an amplification of the transduction signal mediated by adenylyl cyclase and an aberrant coupling of A2A receptor to this transduction pathway. The expression and functionality of A2A adenosine receptor were subsequently evaluated in transgenic mice R6/2, an animal model of Huntington’s disease that express exon 1 of the human huntingtin gene. Saturation binding experiments revealed an increase of A2A receptor levels in striatum of R6/2 mice until 14 post natal days. In addition, also the potency of a typical A2A agonist was increased in striatal membranes of R6/2 mice when compared to wild type mice. The subsequent study aimed the evaluation of the presence and functionality of A2A adenosine receptors in peripheral blood cells from patients affected by Huntington’s disease compared with control subjects. The results revealed a statistically significant increase of the A2A receptor density in platelets, lymphocytes and neutrophils of Huntington’s disease patients and presymptomatic carriers of the mutation when compared to control subjects. In order to verify the specificity of A2A receptor alteration in polyglutamine disease, the same study was conducted in blood cells from patients affected by Spinocerebellar ataxia, characterized by an expanded CAG triplet in the ataxin gene and in patients affected by Friedreich’s ataxia, characterized by an expansion of the GAA triplet. Saturation binding experiments in peripheral blood cells from Spinocerebellar ataxia showed altered A2A binding parameters similar to those obtained in Huntington’s disease patients. In addition, data obtained in Friedreich’s ataxia patients showed affinity and density values for A2A receptors similar to those obtained from control subjects, demonstrating the involvement of the CAG but not of the GAA triplet. Overall these data demonstrate that an aberrant A2A receptor phenotype is present in polyglutamine disorders and this seems to be related with the expanded CAG triplet. The amplification of the signal transduction system of A2A receptors suggests that the use of selective A2A antagonists could be beneficial in the treatment of Huntington’s disease as well as in other related polyglutamine diseases. In addition, the alteration of A2A receptors in peripheral blood cells of patients with polyglutamine diseases suggests that this receptor could be an easily accessible biomarker for the evaluation of the efficacy of potential new therapies

Computational methods that predict residence times of GPCR ligands

This thesis describes the development and analysis of different computational meth- ods that predict drug-target residence time, the duration of ligand binding at its target. Residence time has been shown to be a better surrogate of in vivo efficacy than equilib- rium binding affinity. All methods were applied to ligands acting at G protein-coupled receptors (GPCRs). GPCRs are a massive pharmaceutical target, with approximately one third of approved drugs acting at a GPCR. Three sets of computational methods were used to predict residence time. The first set are machine learning (ML) approaches trained only on ligand descriptors, the second set are molecular dynamics (MD) approaches and the final set of methods combine ML and MD. All three sets of methods were developed against a database of GPCR ligand kinetic data compiled from published sources. Different ML approaches were applied to ligands of 20 GPCRs. Both the principle component analysis and the multi-linear regression revealed properties relating to size of the ligand have a correlation to residence time. The first of two MD-based approaches was a steered MD method. By applying this method to 17 A2A receptor ligands, it was found that the changing interaction energies made by the dissociating ligand to both the receptor residues and to water correlated strongly with residence time values. The second MD-approach is a recently- published method, τ-RAMD. Results from τ-RAMD were found to correlate more strongly with molecular weight than residence time. Finally, ML models were trained on ensembles of short MD simulations of 259 GPCR-ligand complexes. The model with the highest accuracy was a gradient boosted regressor model trained on a combination of ligand molecular descriptors and the non- bonded interaction energy between the receptor-bound ligands and water

Measuring ligand concentration where it matters: Assessing the “micro pharmacokinetic/pharmacodynamics” of adenosine receptor ligands

The methods used to determine fundamental pharmacological parameters almost exclusively assume that the concentration of drug in the local environment of the target receptor is equal to the concentration of drug that has been added to the system. It has, however, recently been shown that, dependent upon their physiochemical properties, β2-adrenoceptor ligands can interact directly with phospholipids, increasing their local concentration and directly influencing the measured association rate constant at the receptor. This local concentrating effect also been demonstrated directly using a fluorescent β2-ligand with fluorescence correlation spectroscopy (FCS). In this study we expand these early observations by investigating multiple ligands at a different G protein-coupled receptor, the adenosine A2a receptor. In particular, we probe the importance of physicochemical properties on membrane interaction and observed pharmacology by utilising eight fluorescent adenosine receptor ligands with identical pharmacophores (xanthine amine congener (XAC)), but varying fluorophores and linker regions to modulate their properties. These ligands were assessed for kinetic binding profiles, phospholipid affinity, and local concentrations above cell membranes. The binding kinetics of the eight fluorescent ligands was assessed by measuring the time resolved fluorescence energy transfer (TR-FRET) between the terbium-labelled A2a receptor and fluorescent ligand over time. From this series, three ligands with distinct kinetic profiles were chosen for analysis by FCS (XACXBY, kon=24100±6860 min-1mM-1; CA200645, kon=1330±175 min-1mM-1; AV075 kon=791±36.4 min-1mM-1), where their local concentration was measured at distances 2-200µm above live CHO cells. The concentration of all ligands was higher close to the cells, with XAC-X-BY630 having the highest concentration at 2µm above the membrane (1024.6±347.4 nM) compared to CA200645 (62.3±9.5 nM) and AV075 (111.3±30.1 nM). This was consistent XACXBY displaying the fastest association rate and supports previous observations. These studies were then extended to investigate the kinetics and phospholipid interaction of 57 commercially available compounds known to bind at least one adenosine receptor. Binding kinetics were measured at all four adenosine receptors using a competition association assay, and phospholipid affinity (KIAM) was assessed in an Immobilised Artificial Membrane High Performance Liquid Chromatography assay. In this cohort, there was a statistically significant relationship between kon and KIAM (p=0.03), but surprisingly a better correlation with koff (p=0.0012), which may suggest that hydrophobic interactions are important for modulating dissociation rate in this receptor family. In general, the data in this study support the hypothesis that lipophilic ligands have a greater concentration in the local receptor environment close to the cell membrane, which may in turn influence observed pharmacological parameters. This reinforces the importance of considering “micro pharmacokinetics/pharmacodynamics” when determining the pharmacology of novel receptor ligands

A systems pharmacology approach to the adenosine A1 receptor

The majority of drugs are prescribed on the premise that their desired and undesired effects are well characterised. However, the mechanisms underlying these effects can be elusive and are of interest to the pharmaceutical industry in terms of rational drug design. G protein-coupled receptors are a significant class of drug target that are capable of influencing multiple signalling processes, and downstream effects, simultaneously through a variety of effectors, such as G proteins or –β-arrestins. The effector activated by a given receptor is often a function of the ligand. This is termed functional selectivity and can contribute to adverse drug effects. Understanding functional selectivity in a mammalian setting is hindered by cross-talk between many competing signalling components. The Sc. cerevisiae pheromone response can be modified to isolate individual mammalian receptor- G protein interactions. Therefore, this simple organism represents an excellent tool to study functional selectivity. Further, the simplicity of this organism allows this pathway to be mathematically modelled. By applying mathematical models to mammalian GPCR signalling in yeast it is possible to extract experimentally inaccessible quantitative parameters underlying functional selectivity. This interdisciplinary approach to pharmacological mechanisms is an example of systems pharmacology. Here a systems pharmacology approach is applied to adenosine receptor signalling in yeast with a view to understanding the contribution of the ligand, receptor and G protein to functional selectivity. The first stage of this process was expression and characterisation of adenosine A1R, A2AR, A2BR and A3R subtypes in yeast. Here, the A1R and A2R subtypes were shown to be functional in yeast, but the A3R response was limited. The A1R signals through G proteins representing the inhibitory G αi family in yeast, while the A2AR and A2BR signal through both inhibitory and stimulatory G protein equivalents. Here ligand bias is quantified but further extended to describe adenosine receptor selectivity. Further, the yeast system was used to inform novel fluorescent compound development. Fluorescent ligand-binding rates would ultimately inform modelling studies. A minimal mathematical framework was developed to described A1R signalling in yeast. Ordinary differential equation models recreate dynamic cellular processes. Here an ODE model was applied to experimental time course data to predict rate constants throughout the yeast G protein cycle in the presence of the mammalian A1R. This model predicts that G protein subtype influences the ligand-receptor-G protein interactions of the A1R in yeast. Further modification of the system and fluorescent technologies may help validate these predictions

Computer-Aided Design of Ligands at Multiple Protein Targets for Multifactorial Diseases

Today, drug discovery predominately focuses on the design of ligands with high selectivity towards a specific biological target. A significant limitation in the case of multi-factorial diseases (e.g. neurodegenerative disorders) is that effective therapy may require multi-target drugs addressing the complexity of multi-factorial pathologies. Here, single- and multi-target ligand design was investigated to discover novel compounds active at multiple proteins/multiple binding sites including allosteric ligands. Calpain-1, a challenging target, was selected to develop and evaluate computational approaches to the discovery of novel ligands. Current selective calpain-1 inhibitors are reported to bind to an allosteric site and their mode of action has remained elusive. To elucidate this, a structure-based virtual screening protocol was implemented to find chemically novel compounds with improved selectivity and a reduced side-effects profile. To develop methods for the discovery of multi-target ligands, a multi-target design approach, which could be beneficial in the treatment of Lung Carcinoma and Neurodegenerative diseases, was investigated. A novel ensemble of proteins was targeted to elevate intracellular cAMP, deemed to be beneficial in these diseases resulting in the discovery of ligands with high binding affinity at three targets, PDE10A, A1 and A2A receptors. In tandem, functional activity at the A2A receptor and PDE10A was investigated, resulting in the discovery of novel compounds, which exhibited anti-proliferative effects in lung carcinoma cell-lines correlating with the co-expression of the two targets and increased cAMP levels. Critically, the dynamics of one amino acid residue, Val84, was identified as a novel conformational descriptor of A2A receptor activation. Overall, novel single- and multi-target ligand design approaches are presented in this work, which could be applicable to a wide range of ligand design problems, across (multi-factorial) disease areas and target families. The findings may facilitate improved design of allosteric calpain-1 inhibitors using the PEF(S) domain, and encourage investigating the therapeutic benefits of dual ligands at the A2A receptor and PDE10A against lung cancer in vivo.IDB Cambridge International Scholarship and ERC Starting Grant (No. 336159

DISCOVERY OF INDOLYL PIPERAZINYLPYRIMIDINES WITH DUAL-TARGET PROFILES ADENOSINE A2¿ AND DOPAMINE D2 RECEPTORS FOR PD TREATMENT

Ph.DDOCTOR OF PHILOSOPH

Exploring protein flexibility during docking to investigate ligand-target recognition

Ligand-protein binding models have experienced an evolution during time: from the lock-key model to induced-fit and conformational selection, the role of protein flexibility has become more and more relevant. Understanding binding mechanism is of great importance in drug-discovery, because it could help to rationalize the activity of known binders and to optimize them. The application of computational techniques to drug-discovery has been reported since the 1980s, with the advent computer-aided drug design. During the years several techniques have been developed to address the protein flexibility issue. The present work proposes a strategy to consider protein structure variability in molecular docking, through a ligand-based/structure-based integrated approach and through the development of a fully automatic cross-docking benchmark pipeline. Moreover, a full exploration of protein flexibility during the binding process is proposed through the Supervised Molecular Dynamics. The application of a tabu-like algorithm to classical molecular dynamics accelerates the binding process from the micro-millisecond to the nanosecond timescales. In the present work, an implementation of this algorithm has been performed to study peptide-protein recognition processes