Valproic acid interactions with the NavMs voltage-gated sodium channel.

Valproic acid (VPA) is an anticonvulsant drug that is also used to treat migraines and bipolar disorder. Its proposed biological targets include human voltage-gated sodium channels, among other membrane proteins. We used the prokaryotic NavMs sodium channel, which has been shown to be a good exemplar for drug binding to human sodium channels, to examine the structural and functional interactions of VPA. Thermal melt synchrotron radiation circular dichroism spectroscopic binding studies of the full-length NavMs channel (which includes both pore and voltage sensor domains), and a pore-only construct, undertaken in the presence and absence of VPA, indicated that the drug binds to and destabilizes the channel, but not the pore-only construct. This is in contrast to other antiepileptic compounds that have previously been shown to bind in the central hydrophobic core of the pore region of the channel, and that tend to increase the thermal stability of both pore-only constructs and full-length channels. Molecular docking studies also indicated that the VPA binding site is associated with the voltage sensor, rather than the hydrophobic cavity of the pore domain. Electrophysiological studies show that VPA influences the block and inactivation rates of the NavMs channel, although with lower efficacy than classical channel-blocking compounds. It thus appears that, while VPA is capable of binding to these voltage-gated sodium channels, it has a very different mode and site of action than other anticonvulsant compounds.

Delineating the Ligand-Receptor Interactions That Lead to Biased Signaling at the µ-Opioid Receptor.

Biased agonists, which selectively stimulate certain signaling pathways controlled by a G protein-coupled receptor (GPCR), hold great promise as drugs that maximize efficacy while minimizing dangerous side effects. Biased agonists of the µ-opioid receptor (µOR) are of particular interest as a means to achieve analgesia through G protein signaling without dose-limiting side effects such as respiratory depression and constipation. Rational structure-based design of biased agonists remains highly challenging, however, because the ligand-mediated interactions that are key to activation of each signaling pathway remain unclear. We identify several compounds for which the R- and S-enantiomers have distinct bias profiles at the µOR. These compounds serve as excellent comparative tools to study bias because the identical physicochemical properties of enantiomer pairs ensure that differences in bias profiles are due to differences in interactions with the µOR binding pocket. Atomic-level simulations of compounds at µOR indicate that R- and S-enantiomers adopt different poses that form distinct interactions with the binding pocket. A handful of specific interactions with highly conserved binding pocket residues appear to be responsible for substantial differences in arrestin recruitment between enantiomers. Our results offer guidance for rational design of biased agonists at µOR and possibly at related GPCRs.

Structure and Function of the Su(H)-Hairless Repressor Complex, the Major Antagonist of Notch Signaling in Drosophila melanogaster.

Notch is a conserved signaling pathway that specifies cell fates in metazoans. Receptor-ligand interactions induce changes in gene expression, which is regulated by the transcription factor CBF1/Su(H)/Lag-1 (CSL). CSL interacts with coregulators to repress and activate transcription from Notch target genes. While the molecular details of the activator complex are relatively well understood, the structure-function of CSL-mediated repressor complexes is poorly defined. In Drosophila, the antagonist Hairless directly binds Su(H) (the fly CSL ortholog) to repress transcription from Notch targets. Here, we determine the X-ray structure of the Su(H)-Hairless complex bound to DNA. Hairless binding produces a large conformational change in Su(H) by interacting with residues in the hydrophobic core of Su(H), illustrating the structural plasticity of CSL molecules to interact with different binding partners. Based on the structure, we designed mutants in Hairless and Su(H) that affect binding, but do not affect formation of the activator complex. These mutants were validated in vitro by isothermal titration calorimetry and yeast two- and three-hybrid assays. Moreover, these mutants allowed us to solely characterize the repressor function of Su(H) in vivo.

Elabela/Toddler Is an Endogenous Agonist of the Apelin APJ Receptor in the Adult Cardiovascular System, and Exogenous Administration of the Peptide Compensates for the Downregulation of Its Expression in Pulmonary Arterial Hypertension.

BACKGROUND: Elabela/toddler (ELA) is a critical cardiac developmental peptide that acts through the G-protein-coupled apelin receptor, despite lack of sequence similarity to the established ligand apelin. Our aim was to investigate the receptor pharmacology, expression pattern, and in vivo function of ELA peptides in the adult cardiovascular system, to seek evidence for alteration in pulmonary arterial hypertension (PAH) in which apelin signaling is downregulated, and to demonstrate attenuation of PAH severity with exogenous administration of ELA in a rat model. METHODS: In silico docking analysis, competition binding experiments, and downstream assays were used to characterize ELA receptor binding in human heart and signaling in cells expressing the apelin receptor. ELA expression in human cardiovascular tissues and plasma was determined using real-time quantitative polymerase chain reaction, dual-labeling immunofluorescent staining, and immunoassays. Acute cardiac effects of ELA-32 and [Pyr1]apelin-13 were assessed by MRI and cardiac catheterization in anesthetized rats. Cardiopulmonary human and rat tissues from PAH patients and monocrotaline- and Sugen/hypoxia-exposed rats were used to show changes in ELA expression in PAH. The effect of ELA treatment on cardiopulmonary remodeling in PAH was investigated in the monocrotaline rat model. RESULTS: ELA competed for binding of apelin in human heart with overlap for the 2 peptides indicated by in silico modeling. ELA activated G-protein- and ß-arrestin-dependent pathways. We detected ELA expression in human vascular endothelium and plasma. Comparable to apelin, ELA increased cardiac contractility, ejection fraction, and cardiac output and elicited vasodilatation in rat in vivo. ELA expression was reduced in cardiopulmonary tissues from PAH patients and PAH rat models, respectively. ELA treatment significantly attenuated elevation of right ventricular systolic pressure and right ventricular hypertrophy and pulmonary vascular remodeling in monocrotaline-exposed rats. CONCLUSIONS: These results show that ELA is an endogenous agonist of the human apelin receptor, exhibits a cardiovascular profile comparable to apelin, and is downregulated in human disease and rodent PAH models, and exogenous peptide can reduce the severity of cardiopulmonary remodeling and function in PAH in rats. This study provides additional proof of principle that an apelin receptor agonist may be of therapeutic use in PAH in humans.

Highly potent and selective NaV1.7 inhibitors for use as intravenous agents and chemical probes.

The discovery and selection of a highly potent and selective NaV1.7 inhibitor PF-06456384, designed specifically for intravenous infusion, is disclosed. Extensive in vitro pharmacology and ADME profiling followed by in vivo preclinical PK and efficacy model data are discussed. A proposed protein-ligand binding mode for this compound is also provided to rationalise the high levels of potency and selectivity over inhibition of related sodium channels. To further support the proposed binding mode, potent conjugates are described which illustrate the potential for development of chemical probes to enable further target evaluation.

Lateral Fenestrations in K(+)-Channels Explored Using Molecular Dynamics Simulations.

Potassium channels are of paramount physiological and pathological importance and therefore constitute significant drug targets. One of the keys to rationalize the way drugs modulate ion channels is to understand the ability of such small molecules to access their respective binding sites, from which they can exert an activating or inhibitory effect. Many computational studies have probed the energetics of ion permeation, and the mechanisms of voltage gating, but little is known about the role of fenestrations as possible mediators of drug entry in potassium channels. To explore the existence, structure, and conformational dynamics of transmembrane fenestrations accessible by drugs in potassium channels, molecular dynamics simulation trajectories were analyzed from three potassium channels: the open state voltage-gated channel Kv1.2, the G protein-gated inward rectifying channel GIRK2 (Kir3.2), and the human two-pore domain TWIK-1 (K2P1.1). The main results of this work were the identification of the sequence identity of four main lateral fenestrations of similar length and with bottleneck radius in the range of 0.9-2.4 Å for this set of potassium channels. It was found that the fenestrations in Kv1.2 and Kir3.2 remain closed to the passage of molecules larger than water. In contrast, in the TWIK-1 channel, both open and closed fenestrations are sampled throughout the simulation, with bottleneck radius shown to correlate with the random entry of lipid membrane molecules into the aperture of the fenestrations. Druggability scoring function analysis of the fenestration regions suggests that Kv and Kir channels studied are not druggable in practice due to steric constraining of the fenestration bottleneck. A high (>50%) fenestration sequence identity was found in each potassium channel subfamily studied, Kv1, Kir3, and K2P1. Finally, the reported fenestration sequence of TWIK-1 compared favorably with another channel, K2P channel TREK-2, reported to possess open fenestrations, suggesting that K2P channels could be druggable via fenestrations, for which we reported atomistic detail of the fenestration region, including the flexible residues M260 and L264 that interact with POPC membrane in a concerted fashion with the aperture and closure of the fenestrations.

Analysis of Differential Efficacy and Affinity of GABAA (α1/α2) Selective Modulators.

Selective modulators of the γ-amino butyric acid (GABAA) family of receptors have the potential to treat a range of disease states related to cognition, pain, and anxiety. While the development of various α subunit-selective modulators is currently underway for the treatment of anxiety disorders, a mechanistic understanding of the correlation between their bioactivity and efficacy, based on ligand-target interactions, is currently still lacking. In order to alleviate this situation, in the current study we have analyzed, using ligand- and structure-based methods, a data set of 5440 GABAA modulators. The Spearman correlation (ρ) between binding activity and efficacy of compounds was calculated to be 0.008 and 0.31 against the α1 and α2 subunits of GABA receptor, respectively; in other words, the compounds had little diversity in structure and bioactivity, but they differed significantly in efficacy. Two compounds were selected as a case study for detailed interaction analysis due to the small difference in their structures and affinities (ΔpKi(comp1_α1 - comp2_α1) = 0.45 log units, ΔpKi(comp1_α2 - comp2_α2) = 0 log units) as compared to larger relative efficacies (ΔRE(comp1_α1 - comp2_α1) = 1.03, ΔRE(comp1_α2 - comp2_α2) = 0.21). Docking analysis suggested that His-101 is involved in a characteristic interaction of the α1 receptor with both compounds 1 and 2. Residues such as Phe-77, Thr-142, Asn-60, and Arg-144 of the γ chain of the α1γ2 complex also showed interactions with heterocyclic rings of both compounds 1 and 2, but these interactions were disturbed in the case of α2γ2 complex docking results. Binding pocket stability analysis based on molecular dynamics identified three substitutions in the loop C region of the α2 subunit, namely, G200E, I201T, and V202I, causing a reduction in the flexibility of α2 compared to α1. These amino acids in α2, as compared to α1, were also observed to decrease the vibrational and dihedral entropy and to increase the hydrogen bond content in α2 in the apo state. However, freezing of both α1 and α2 was observed in the ligand-bound state, with an increased number of internal hydrogen bonds and increased entropy. Therefore, we hypothesize that the amino acid differences in the loop C region of α2 are responsible for conformational changes in the protein structure compared to α1, as well as for the binding modes of compounds and hence their functional signaling.

A combination of computational and experimental approaches identifies DNA sequence constraints associated with target site binding specificity of the transcription factor CSL.

Regulation of transcription is fundamental to development and physiology, and occurs through binding of transcription factors to specific DNA sequences in the genome. CSL (CBF1/Suppressor of Hairless/LAG-1), a core component of the Notch signaling pathway, is one such transcription factor that acts in concert with co-activators or co-repressors to control the activity of associated target genes. One fundamental question is how CSL can recognize and select among different DNA sequences available in vivo and whether variations between selected sequences can influence its function. We have therefore investigated CSL-DNA recognition using computational approaches to analyze the energetics of CSL bound to different DNAs and tested the in silico predictions with in vitro and in vivo assays. Our results reveal novel aspects of CSL binding that may help explain the range of binding observed in vivo. In addition, using molecular dynamics simulations, we show that domain-domain correlations within CSL differ significantly depending on the DNA sequence bound, suggesting that different DNA sequences may directly influence CSL function. Taken together, our results, based on computational chemistry approaches, provide valuable insights into transcription factor-DNA binding, in this particular case increasing our understanding of CSL-DNA interactions and how these may impact on its transcriptional control.

Prediction of cytochrome P450 xenobiotic metabolism: tethered docking and reactivity derived from ligand molecular orbital analysis.

Metabolism of xenobiotic and endogenous compounds is frequently complex, not completely elucidated, and therefore often ambiguous. The prediction of sites of metabolism (SoM) can be particularly helpful as a first step toward the identification of metabolites, a process especially relevant to drug discovery. This paper describes a reactivity approach for predicting SoM whereby reactivity is derived directly from the ground state ligand molecular orbital analysis, calculated using Density Functional Theory, using a novel implementation of the average local ionization energy. Thus each potential SoM is sampled in the context of the whole ligand, in contrast to other popular approaches where activation energies are calculated for a predefined database of molecular fragments and assigned to matching moieties in a query ligand. In addition, one of the first descriptions of molecular dynamics of cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9 in their Compound I state is reported, and, from the representative protein structures obtained, an analysis and evaluation of various docking approaches using GOLD is performed. In particular, a covalent docking approach is described coupled with the modeling of important electrostatic interactions between CYP and ligand using spherical constraints. Combining the docking and reactivity results, obtained using standard functionality from common docking and quantum chemical applications, enables a SoM to be identified in the top 2 predictions for 75%, 80%, and 78% of the data sets for 3A4, 2D6, and 2C9, respectively, results that are accessible and competitive with other recently published prediction tools.

Critical Assessment of State-of-the-Art Ligand-Based Virtual Screening Methods.

The availability of large chemical libraries containing hundreds of millions to billions of diverse drug-like molecules combined with an almost unlimited amount of compute power to achieve scientific calculations has led investors and researchers to have a renewed interest in virtual screening (VS) methods to identify biologically active compounds. The number of in silico screening tools and software which employ the knowledge of the protein target or known bioactive ligands is increasing at a rapid pace, creating a crowded computational landscape where it has become difficult to assess the real advantages and disadvantages in terms of accuracy and efficiency of each individual VS technology. In the current work, we evaluate the performance of several state-of-the-art commercial software for 3D ligand-based VS against well-known 2D methods using an internally curated benchmarking data set. Our results show that the best individual methods can differ significantly based on the data set, and that combining them using data fusion techniques results in improved enrichment in the top 1 % of retrieved hits. Although 2D methods alone can already provide a significant enrichment in the number of predicted active compounds, the combination of data-fused 2D results with just one out of the best 3D methods (ROCS, FLAP or Blaze) further improves early enrichment and the likelihood of identifying additional chemotypes.

Investigating dynamic and energetic determinants of protein nucleic acid recognition: analysis of the zinc finger zif268-DNA complexes.

BACKGROUND: Protein-DNA recognition underlies fundamental biological processes ranging from transcription to replication and modification. Herein, we present a computational study of the sequence modulation of internal dynamic properties and of intraprotein networks of aminoacid interactions that determine the stability and specificity of protein-DNA complexes. RESULTS: To this aim, we apply novel theoretical approaches to analyze the dynamics and energetics of biological systems starting from MD trajectories. As model system, we chose different sequences of Zinc Fingers (ZF) of the Zif268 family bound with different sequences of DNA. The complexes differ for their experimental stability properties, but share the same overall 3 D structure and do not undergo structural modifications during the simulations. The results of our analysis suggest that the energy landscape for DNA binding may be populated by dynamically different states, even in the absence of major conformational changes. Energetic couplings between residues change in response to protein and/or DNA sequence variations thus modulating the selectivity of recognition and the relative importance of different regions for binding. CONCLUSIONS: The results show differences in the organization of the intra-protein energy-networks responsible for the stabilization of the protein conformations recognizing and binding DNA. These, in turn, are reflected into different modulation of the ZF's internal dynamics. The results also show a correlation between energetic and dynamic properties of the different proteins and their specificity/selectivity for DNA sequences. Finally, a dynamic and energetic model for the recognition of DNA by Zinc Fingers is proposed.

Cosolvent-Enhanced Sampling and Unbiased Identification of Cryptic Pockets Suitable for Structure-Based Drug Design.

Modulating protein activity with small-molecules binding to cryptic pockets offers great opportunities to overcome hurdles in drug design. Cryptic sites are atypical binding sites in proteins that are closed in the absence of a stabilizing ligand and are thus inherently difficult to identify. Many studies have proposed methods to predict cryptic sites. However, a general approach to prospectively sample open conformations of these sites and to identify cryptic pockets in an unbiased manner suitable for structure-based drug design remains elusive. Here, we describe an all-atom, explicit cosolvent, molecular dynamics (MD) simulations-based workflow to sample the open states of cryptic sites and identify opened pockets, in a manner that does not require a priori knowledge about these sites. Furthermore, the workflow relies on a target-independent parametrization that only distinguishes between binding pockets for peptides or small molecules. We validated our approach on a diverse test set of seven proteins with crystallographically determined cryptic sites. The known cryptic sites were found among the three highest-ranked predicted cryptic sites, and an open site conformation was sampled and selected for most of the systems. Crystallographic ligand poses were well reproduced by docking into these identified open conformations for five of the systems. When the fully open state could not be reproduced, we were still able to predict the location of the cryptic site, or identify other cryptic sites that could be retrospectively validated with knowledge of the protein target. These characteristics render our approach valuable for investigating novel protein targets without any prior information.

DeltaDelta neural networks for lead optimization of small molecule potency.

The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking congeneric series based on deep 3D-convolutional neural networks. Furthermore we prove its effectiveness by blindly testing it on datasets provided by Janssen, Pfizer and Biogen totalling over 3246 ligands and 13 targets as well as several well-known openly available sets, representing one the largest evaluations ever performed. We also performed online learning simulations of lead optimization using the approach in a predictive manner obtaining significant advantage over experimental choice. We believe that the evaluation performed in this study is strong evidence of the usefulness of a modern deep learning model in lead optimization pipelines against more expensive simulation-based alternatives.

Discovering New Casein Kinase 1d Inhibitors with an Innovative Molecular Dynamics Enabled Virtual Screening Workflow.

The value of including protein flexibility in structure-based drug design (SBDD) is widely documented, and currently, molecular dynamics (MD) simulations represent a powerful tool to investigate protein dynamics. Yet, the inclusion of MD-derived information in pre-existing SBDD workflows is still far from trivial. We recently published an integrated MD-FLAP (Fingerprints for Ligands and Proteins) approach combining MD, clustering and Linear Discriminant Analysis (LDA) for enhancing accuracy, efficacy, and for protein conformational selection in virtual screening (VS) campaigns. Here we prospectively applied the MD-FLAP workflow to discover novel chemotypes inhibiting the Casein Kinase 1 delta (CSNK1D) enzyme. We first obtained a VS model able to separate active from inactive compounds, with a global AUC of 0.9 and a partial ROC enrichment at 0.5% of 0.18, and use it to mine the internal Pfizer screening database. Seven active molecules sharing a phenyl-indazole scaffold, not yet reported among CSNK1D inhibitors, were found. The most potent inhibitor showed an IC50 of 134 nM.

Network-Based Drug Discovery: Coupling Network Pharmacology with Phenotypic Screening for Neuronal Excitability.

Diseases such as chronic pain with complex etiologies are unlikely to respond to single, target-specific therapeutics but rather require intervention at multiple points within a perturbed disease system. Such approaches are being enabled by the rise of computational methods to identify key points of intervention and by new screening techniques that focus on a relevant condition or phenotype, rather than a specific target. Here we apply an in silico network pharmacology approach to identify small-molecule compounds with the potential to selectively disrupt the structure of a chronic-pain specific disease network, which we validate using a novel phenotypic screen that recapitulates key aspects of neuronal and pain biology by measuring changes in neuronal excitability in native sensory neurons. The combination of network pharmacology with a phenotypic screen is a powerful approach; we show that hit rates increase from 26% to 42%. This represents a rational approach to the discovery of compounds with a poly-pharmacology based therapeutic value, which will be vital for the discovery of treatments for complex disease.

An Intracellular Allosteric Modulator Binding Pocket in SK2 Ion Channels Is Shared by Multiple Chemotypes.

Small conductance potassium (SK) ion channels define neuronal firing rates by conducting the after-hyperpolarization current. They are key targets in developing therapies where neuronal firing rates are dysfunctional, such as in epilepsy, Parkinson's, and amyotrophic lateral sclerosis (ALS). Here, we characterize a binding pocket situated at the intracellular interface of SK2 and calmodulin, which we show to be shared by multiple small-molecule chemotypes. Crystallization of this complex revealed that riluzole (approved for ALS) and an analog of the anti-ataxic agent (4-chloro-phenyl)-[2-(3,5-dimethyl-pyrazol-1-yl)-pyrimidin-4-yl]-amine (CyPPA) bind to and allosterically modulate via this site. Solution-state nuclear magnetic resonance demonstrates that riluzole, NS309, and CyPPA analogs bind at this bipartite pocket. We demonstrate, by patch-clamp electrophysiology, that both classes of ligand interact with overlapping but distinct residues within this pocket. These data define a clinically important site, laying the foundations for further studies of the mechanism of action of riluzole and related molecules.

Ensemble- and Rigidity Theory-Based Perturbation Approach To Analyze Dynamic Allostery.

Allostery describes the functional coupling between sites in biomolecules. Recently, the role of changes in protein dynamics for allosteric communication has been highlighted. A quantitative and predictive description of allostery is fundamental for understanding biological processes. Here, we integrate an ensemble-based perturbation approach with the analysis of biomolecular rigidity and flexibility to construct a model of dynamic allostery. Our model, by definition, excludes the possibility of conformational changes, evaluates static, not dynamic, properties of molecular systems, and describes allosteric effects due to ligand binding in terms of a novel free-energy measure. We validated our model on three distinct biomolecular systems: eglin c, protein tyrosine phosphatase 1B, and the lymphocyte function-associated antigen 1 domain. In all cases, it successfully identified key residues for signal transmission in very good agreement with the experiment. It correctly and quantitatively discriminated between positively or negatively cooperative effects for one of the systems. Our model should be a promising tool for the rational discovery of novel allosteric drugs.

Residues W320 and Y328 within the binding site of the µ-opioid receptor influence opiate ligand bias.

The development of G protein-biased agonists for the µ-opioid receptor (MOR) offers a clear drug discovery rationale for improved analgesia and reduced side-effects of opiate pharmacotherapy. However, our understanding of the molecular mechanisms governing ligand bias is limited, which hinders our ability to rationally design biased compounds. We have investigated the role of MOR binding site residues W320 and Y328 in controlling bias, by receptor mutagenesis. The pharmacology of a panel of ligands in a cAMP and a ß-arrestin2 assay were compared between the wildtype and mutated receptors, with bias factors calculated by operational analysis using ΔΔlog(τ/KA) values. [3H]diprenorphine competition binding was used to estimate affinity changes. Introducing the mutations W320A and Y328F caused changes in pathway bias, with different patterns of change between ligands. For example, DAMGO increased relative ß-arrestin2 activity at the W320A mutant, whilst its ß-arrestin2 response was completely lost at Y328F. In contrast, endomorphin-1 gained activity with Y328F but lost activity at W320A, in both pathways. For endomorphin-2 there was a directional shift from cAMP bias at the wildtype towards more ß-arrestin2 bias at W320A. We also observe clear uncoupling between mutation-driven changes in function and binding affinity. These findings suggest that the mutations influenced the balance of pathway activation in a ligand-specific manner, thus identifying residues in the MOR binding pocket that govern ligand bias. This increases our understanding of how ligand/receptor binding interactions can be translated into agonist-specific pathway activation.

Discovery of Compounds that Positively Modulate the High Affinity Choline Transporter.

Cholinergic hypofunction is associated with decreased attention and cognitive deficits in the central nervous system in addition to compromised motor function. Consequently, stimulation of cholinergic neurotransmission is a rational therapeutic approach for the potential treatment of a variety of neurological conditions. High affinity choline uptake (HACU) into acetylcholine (ACh)-synthesizing neurons is critically mediated by the sodium- and pH-dependent high-affinity choline transporter (CHT, encoded by the SLC5A7 gene). This transporter is comparatively well-characterized but otherwise unexplored as a potential drug target. We therefore sought to identify small molecules that would enable testing of the hypothesis that positive modulation of CHT mediated transport would enhance activity-dependent cholinergic signaling. We utilized existing and novel screening techniques for their ability to reveal both positive and negative modulation of CHT using literature tools. A screening campaign was initiated with a bespoke compound library comprising both the Pfizer Chemogenomic Library (CGL) of 2,753 molecules designed specifically to help enable the elucidation of new mechanisms in phenotypic screens and 887 compounds from a virtual screening campaign to select molecules with field-based similarities to reported negative and positive allosteric modulators. We identified a number of previously unknown active and structurally distinct molecules that could be used as tools to further explore CHT biology or as a starting point for further medicinal chemistry.

Design, characterization, and first-in-human study of the vascular actions of a novel biased apelin receptor agonist.

[Pyr(1)]apelin-13 is an endogenous vasodilator and inotrope but is downregulated in pulmonary hypertension and heart failure, making the apelin receptor an attractive therapeutic target. Agonists acting at the same G-protein-coupled receptor can be engineered to stabilize different conformational states and function as biased ligands, selectively stimulating either G-protein or ß-arrestin pathways. We used molecular dynamics simulations of apelin/receptor interactions to design cyclic analogues and identified MM07 as a biased agonist. In ß-arrestin and internalization assays (G-protein-independent), MM07 was 2 orders of magnitude less potent than [Pyr(1)]apelin-13. In a G-protein-dependent saphenous vein contraction assay, both peptides had comparable potency (pD2:[Pyr(1)]apelin-13 9.93±0.24; MM07 9.54±0.42) and maximum responses with a resulting bias for MM07 of ≈350- to 1300-fold for the G-protein pathway. In rats, systemic infusions of MM07 (10-100nmol) caused a dose-dependent increase in cardiac output that was significantly greater than the response to [Pyr(1)]apelin-13. Similarly, in human volunteers, MM07 produced a significant dose-dependent increase in forearm blood flow with a maximum dilatation double that is seen with [Pyr(1)]apelin-13. Additionally, repeated doses of MM07 produced reproducible increases in forearm blood flow. These responses are consistent with a more efficacious action of the biased agonist. In human hand vein, both peptides reversed an established norepinephrine constrictor response and significantly increased venous flow. Our results suggest that MM07 acting as a biased agonist at the apelin receptor can preferentially stimulate the G-protein pathway, which could translate to improved efficacy in the clinic by selectively stimulating vasodilatation and inotropic actions but avoiding activating detrimental ß-arrestin-dependent pathways.