Design and Synthesis of Novel Serotonin Receptor Ligands

Novel and potent ligands to the serotonin7 (5-HT7) receptor have been synthesized. The synthesized compounds include a set of substituted pyrimidines which show high affinity to the 5-HT7 receptor, synthesized by previously described methods [1,2] in high yield. Comparing the affinities of substituted pyrimidines to previously calculated models [3,4] yielded new hypotheses about the nature of interaction between the pyrimidine ligands and the 5-HT7 binding site. Several new series of compounds were synthesized by various methods to validate these hypotheses, including a conjugate addition to vinylpyrimidines [5]. These compounds include benzofurans, oximes, hydrazones, as well as a group of substituted piperazines. All series of compounds show affinity to the 5-HT7 receptor comparable to previously synthesized 5-HT7 ligands. Several of the synthesized ligands show affinity which exceeds that of currently available ligands. The synthesized compounds were evaluated quantitatively by calculating a three-dimensional quantitative structure-affinity relationship (3D-QSAR) for the 5-HT7 receptor. Evaluation of the calculated model validated qualitative assumptions about the data set as well as described regions of interaction in greater detail than previously available. These observations give further insight on the nature of ligand-binding site interactions with highly potent ligands such as 4-(3-furyl)-2-(N-methylpiperazino)pyrimidine which will lead to more potent 5-HT7 receptor ligands. Additionally, a model was calculated for affinity to the 5-HT2a receptor. Comparing this model to that calculated for affinity to the 5-HT7 receptor identified two regions which may be exploited in future sets of ligands to increase selectivity to the 5HT7 receptor

Mind the Gap - Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies.

HIGHLIGHTS Many CNS targets are being explored for multi-target drug designNew databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compoundsQSAR, virtual screening and docking methods increase the potential of rational drug design The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer's disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D1-R/D2-R/5-HT2A -R/H3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs

A Miniaturized Screen of a Schistosoma mansoni Serotonergic G Protein-Coupled Receptor Identifies Novel Classes of Parasite-Selective Inhibitors

Schistosomiasis is a tropical parasitic disease afflicting ~200 million people worldwide and current therapy depends on a single drug (praziquantel) which exhibits several non-optimal features. These shortcomings underpin the need for next generation anthelmintics, but the process of validating physiologically relevant targets (‘target selection’) and pharmacologically profiling them is challenging. Remarkably, even though over a quarter of current human therapeutics target rhodopsin-like G protein coupled receptors (GPCRs), no library screen of a flatworm GPCR has yet been reported. Here, we have pharmacologically profiled a schistosome serotonergic GPCR (Sm.5HTR) implicated as a downstream modulator of PZQ efficacy, in a miniaturized screening assay compatible with high content screening. This approach employs a split luciferase based biosensor sensitive to cellular cAMP levels that resolves the proximal kinetics of GPCR modulation in intact cells. Data evidence a divergent pharmacological signature between the parasitic serotonergic receptor and the closest human GPCR homolog (Hs.5HTR7), supporting the feasibility of optimizing parasitic selective pharmacophores. New ligands, and chemical series, with potency and selectivity for Sm.5HTR over Hs.5HTR7 are identified in vitro and validated for in vivo efficacy against schistosomules and adult worms. Sm.5HTR also displayed a property resembling irreversible inactivation, a phenomenon discovered at Hs.5HTR7, which enhances the appeal of this abundantly expressed parasite GPCR as a target for anthelmintic ligand design. Overall, these data underscore the feasibility of profiling flatworm GPCRs in a high throughput screening format competent to resolve different classes of GPCR modulators. Further, these data underscore the promise of Sm.5HTR as a chemotherapeutically vulnerable node for development of next generation anthelmintics

Newly Synthesized Fluorinated Cinnamylpiperazines Possessing Low In Vitro MAO-B Binding

Herein, we report on the synthesis and pharmacological evaluation of ten novel fluorinated cinnamylpiperazines as potential monoamine oxidase B (MAO-B) ligands. The designed derivatives consist of either cinnamyl or 2-fluorocinnamyl moieties connected to 2-fluoropyridylpiperazines. The three-step synthesis starting from commercially available piperazine afforded the final products in overall yields between 9% and 29%. An in vitro competitive binding assay using l-[3H]Deprenyl as radioligand was developed and the MAO-B binding affinities of the synthesized derivatives were assessed. Docking studies revealed that the compounds 8–17 were stabilized in both MAO-B entrance and substrate cavities, thus resembling the binding pose of l-Deprenyl. Although our results revealed that the novel fluorinated cinnamylpiperazines 8–17 do not possess sufficient MAO-B binding affinity to be eligible as positron emission tomography (PET) agents, the herein developed binding assay and the insights gained within our docking studies will certainly pave the way for further development of MAO-B ligands

Dopamine D3 receptor ligands—Recent advances in the control of subtype selectivity and intrinsic activity

AbstractVarious pharmacological studies have implicated the dopamine D3 receptor as an interesting therapeutic target in the treatment of different neurological disorders. Because of these putative therapeutic applications, D3 receptor ligands with diverse intrinsic activities have been an active field of research in recent years. Separation of purely D3-mediated drug effects from effects produced by interactions with similar biogenic amine receptors allows to verify the therapeutic impact of D3 receptors and to reduce possible side-effects caused by “promiscuous” receptor interactions. The requirement to gain control of receptor selectivity and in particular subtype selectivity has been a challenging task in rational drug discovery for quite a few years. In this review, recently developed structural classes of D3 ligands are discussed, which cover a broad spectrum of intrinsic activities and show interesting selectivities

Part I, Unified Pharmacophore Protein Models of the Benzodiazepine Receptor Subtypes ; Part II, Subtype

Part I. New models of unified pharmacophore/receptors have been constructed guided by the synthesis of subtype selective compounds in light of recent developments both in ligand synthesis and structural studies of the binding site itself. The evaluation of experimental data in combination with comparative models of the α1β2γ2, α2β2γ2, α3β2γ2 and α5β2γ2 GABA(A) receptors has led to an orientation of the pharmacophore model within the benzodiazepine binding site (Bz BS). These results not only are important for the rational design of new selective ligands, but also for the identification and evaluation of possible roles which specific residues may have within the benzodiazepine binding pocket. More importantly, the process summarized here may be used as a general template to help scientists develop novel ligands for receptors for which the three dimensional structure has not yet been confirmed by X-ray crystallography or cryo-electron microscopy. Presented here are new models of the α1β2γ2, α2β2γ2, α3β2γ2 and α5β2γ2 GABA(A) receptors which have incorporated homology models built based on the acetylcholine binding protein. These new models will further our ability to understand structural characteristics of ligands which act as agonists, antagonists, or inverse agonists to the Bz BS of the GABA(A) receptor. This approach will also serve as a powerful model for structure based approaches carried out using ligand-protein docking methods. Part II. An effective strategy to alleviate memory deficits would be to enhance memory and cognitive processes by augmenting the impact of acetylcholine released from cholinergic neurons of the hippocampus. Using the included volume pharmacophore presented in Part I, a number of a5 selective compounds were synthesized, notably PWZ-029. PWZ-029 was examined in rats in the passive and active avoidance, spontaneous locomotor activity, elevated plus maze and grip strength tests which are indicative of the effects on memory acquisition, locomotor activity, anxiety, and muscle tone. Improvement of task learning was shown at a dose of 5mg/kg in passive avoidance test without effect on anxiety or muscle tone. Moderate negative modulation at GABA(A) receptors containing the α5 subunit using a moderate inverse agonist such as PWZ-029, is a sufficient condition for eliciting enhanced encoding/consolidation of declarative memory. Using low temperature NMR and X-ray analysis, it was shown that enhanced selectivity and potent in vitro affinity of α5 selective benzodiazepine dimers was possible with aliphatic linkers of 3 to 5 carbons in length. Although originally proposed to enhance solubility, oxygen-containing linkers caused the dimer to fold back on itself leading to the inability of dimers to enter the binding pocket. In addition, studies of a series of PWZ-029 analogs found that the electrostatic potential near the ligands\u27 terminal substituent correlated with its binding selectivity toward the α5β2γ2 versus α1β2γ2 Bzr/GABA(A) ergic isoform. Investigations further found that compound PWZ-029, which exhibits reasonable binding selectivity toward GABA(A) receptors containing the a5 subunit and possesses a favorable electrophysiological profile, was able to attenuate scopolamine induced contextual memory impairment in mice. This compound appears to be useful (Harris, Delorey et al.) for the treatment of cognitive deficits in rodents as well as primates (Rowlett et al.) and may well be a compound for the treatment of patients with Alzheimers disease

The QSARome of the receptorome: Quantitative structure-activity relationship modeling of multiple ligand sets acting at multiple receptors

Recent advances in High Throughput Screening (HTS) led to the rapid growth of chemical libraries of small molecules, which calls for improved computational tools and predictive models for Virtual Screening (VS). Thus this dissertation focuses on both the development and application of predictive Quantitative Structure-Activity Relationship (QSAR) models and aims to discover novel therapeutic agents for certain diseases. First, this dissertation adopts the combinatorial QSAR framework created by our lab, including the first application of the Distance Weighted Discrimination (DWD) method that resulted in a set of robust QSAR models for the 5-HT7 receptor. VS using these models, followed by the experimental test of identified compounds, led to the finding of five known drugs as potent 5-HT7 binders. Eventually, droperidol (Ki = 3.5 nM) and perospirone (Ki = 8.6 nM) proved to be strong 5-HT7 antagonists. Second, we intended to enhance VS hit rate. To that end, we developed a cost/benefit ratio as an evaluation performance metric for QSAR models. This metric was applied in the Decision Tree machine learning method in two ways: (1) as a benchmarking criterion to compare the prediction performances of different classifiers and (2) as a target function to build QSAR classification trees. This metric may be more suitable for imbalanced HTS data that include few active but many inactive compounds. Finally, a novel QSAR strategy was developed in response to the polygenic nature of most psychotic disorders, related mainly to G-Protein-Coupled Receptors (GPCRs), one class of molecular targets of greatest interest to the pharmaceutical industry. We curated binding data for thousands of GPCR ligands, and developed predictive QSAR models to assess the GPCR binding profiles of untested compounds that could be used to identify potential drug candidates. This comprehensive study yielded a compendium of validated QSAR predictors (the GPCR QSARome), providing effective in silico tools to search for novel antipsychotic drugs. The advances in results and procedures achieved in these studies will be integrated into the current computational strategies for rational drug design and discovery boosted by our lab, so that predictive QSAR modeling will become a reliable support tool for drug discovery programs

Benchmarking and Developing Novel Methods for G Protein-coupled Receptor Ligand Discovery

G protein-coupled receptors (GPCR) are integral membrane proteins mediating responses from extracellular effectors that regulate a diverse set of physiological functions. Consequently, GPCR are the targets of ~34% of current FDA-approved drugs.3 Although it is clear that GPCR are therapeutically significant, discovery of novel drugs for these receptors is often impeded by a lack of known ligands and/or experimentally determined structures for potential drug targets. However, computational techniques have provided paths to overcome these obstacles. As such, this work discusses the development and application of novel computational methods and workflows for GPCR ligand discovery. Chapter 1 provides an overview of current obstacles faced in GPCR ligand discovery and defines ligand- and structure-based computational methods of overcoming these obstacles. Furthermore, chapter 1 outlines methods of hit list generation and refinement and provides a GPCR ligand discovery workflow incorporating computational techniques. In chapter 2, a workflow for modeling GPCR structure incorporating template selection via local sequence similarity and refinement of the structurally variable extracellular loop 2 (ECL2) region is benchmarked. Overall, findings in chapter 2 support the use of local template homology modeling in combination with de novo ECL2 modeling in the presence of a ligand from the template crystal structure to generate GPCR models intended to study ligand binding interactions. Chapter 3 details a method of generating structure-based pharmacophore models via the random selection of functional group fragments placed with Multiple Copy Simultaneous Search (MCSS) that is benchmarked in the context of 8 GPCR targets. When pharmacophore model performance was assessed with enrichment factor (EF) and goodness-of-hit (GH) scoring metrics, pharmacophore models possessing the theoretical maximum EF value were produced in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases). Lastly, chapter 4 details a method of structure-based pharmacophore model generation using MCSS that is applicable to targets with no known ligands. Additionally, a method of pharmacophore model selection via machine learning is discussed. Overall, the work in chapter 4 led to the development of pharmacophore models exhibiting high EF values that were able to be accurately selected with machine learning classifiers