Hot Topics: Neurokinin 3 receptor antagonism for menopausal hot flushes

Neurokinin B signalling is increased in menopausal women and has been implicated as an important mediator of hot flushes. A phase 2 trial has assessed the effectiveness of an oral neurokinin 3 receptor antagonist (MLE4901). Results showed it safely and effectively relieved hot flush. The finding that pharmacological blockade of NKB signalling with an oral NK3R antagonist can significantly improve symptoms independently of any hormonal effect fits with the pre-existing data, and indicates promise for such agents. However, larger scale studies of longer duration are needed. Since this condition affects 70% of menopausal women ( i.e. ~ 10 million in the UK) this publication was covered widely in the press, for example in the UK Daily Telegraph with “Could this drug be the key to stopping hot flushes for menopause sufferers?”. Partly as a consequence of press coverage, the paper garnered an impressive Altmetric score of 296 (that will notch up by at least one from this posting).

Our GtoPdb ligand entry shows a number of aspects related to repurposing and data linking problems associated with multiple synonyms for the same structure in multiple clinical contexts. With its original designation as AZD2624 it was on the AZ Open Innovation Clinical Compound Bank repurposing proposal list (but has now been withdrawn) since it failed in its originally tested indication for schizophrenia (PMID 24525659). Unusually, there is no primary publication on the medicinal chemistry but we were able to get the NK3R in vitro binding data from the NCATs AZD2624 data sheet. It was renamed to AZD4901 for the new indication of Polycystic ovary syndrome (PCOS) but was again not progressed (PMID 27459523). In the meantime WO2015033163 was filed by Imperial Innovations for the use of AZD2624 for the treatment of hot flushes. By 2016 rights had been acquired by Millendo where the structure was renamed MLE4901 for the indications of PCOS and vasomotor symptoms (VMS).

Prague et al. (2017). Neurokinin 3 receptor antagonism as a novel treatment for menopausal hot flushes: a phase 2, randomised, double-blind, placebo-controlled trial. Lancet, S0140-6736(17)30823-1. [PMID: 28385352]

The ligand entry was updated in our 2017.3 release. When it gets submitted to PubChem it may be the only source that connects the one structure to its three synonyms and cross-references the publications and clinical trials for the different therapeutic investigations

Comments by Chris Southan (@cdsouthan)

Posted in Hot Topics

Hot Topics: Crystal structures of human AT2 reveal molecular mechanism for lack of desensitization and internalization

The intracellular signal transduction processes activated by the angiotensin AT2 receptor, are atypical for a GPCR and different from the AT1 receptor. Although the classic motifs a GPCR are present in AT2 receptor; it fails to demonstrate classic features of G-protein signalling such as desensitization by phosphorylation, and receptor regulation by internalization. Zhang et al., (2017) [1] report the crystal structures of human AT2 bound to an AT2-selective ligand and to an AT1 /AT2 dual ligand, capturing the receptor in an active-like conformation.

They provide a potential explanations for the poor coupling of AT2 to G proteins and β-arrestins. Helix VIII a very different conformation to other GPCRs; the authors suggest it plays a dual role in the modulation of AT2 function, stabilizing an active like receptor state, while repressing canonical AT2 activity in a self-inhibitory manner by sterically blocking the G protein and β –arrestin binding sites. However, on switching to a membrane-bound conformation, helix VIII can support the recruitment of G proteins and β-arrestins for AT2 signalling. The authors propose helix VIII works as a gatekeeper for either suppression or activation of the receptor depending on its post-translational modifications and interactions with various receptor partners and its environment.

[1] Zhang et al. (2017). Structural basis for selectivity and diversity in angiotensin II receptors. Nature, 544(7650):327-332. [PMID: 28379944]

Comments by Anthony Davenport

Posted in Hot Topics

Hot Topics: Structural insights into adiponectin receptors suggest ceramidase activity

Adiponectin receptors, divided into Adipo1 and Adipo2, were initially classed as GPCR on the basis of hydropathy analysis suggesting seven transmembrane domains.  However, they appear to be present in the cell membrane in a topology inverted compared to the 7TM GPCR.  The study from Vasiliauskaite-Brooks [1] and colleagues suggests that Adipo1 and Adipo2 exhibit ceramidase activity.  They report that adiponectin binding to Adipo2 enhances this ceramidase activity, providing in silico evidence for this mechanism. It is possible, therefore, that adiponectin receptors may belong to a unique class of catalytic receptor rather than GPCR.

[1] Vasiliauskaité-Brooks et al. (2017). Structural insights into adiponectin receptors suggest ceramidase activity. Nature, 544(7648):120-123. [PMID: 28329765]

Comments by Steve Alexander (@mqzspa)

Posted in Hot Topics

GtoImmuPdb: technical update March 2017

The 4th alpha-release (v4.0) of the Guide to IMMUNOPHARMACOLOGY was released on 23rd March 2017. This blog post summarises some of the main features of the release and other developments as we moved toward our first public, beta-release in Spring 2017

An early synopsis of the project can be found in this blog post. You can also review our previous technical blogs on GtoImmPdb.

Development Progress

Alpha-Release v4.0 Portal

The disease panel on the portal is now active (Fig. 1). This contains two links, which link to different views of the Immuno Disease List page.


Fig 1. GtoImmuPdb v4.0 portal.
New disease panel (lower left-hand side) is now functional. New disease menu item is also included

The Immuno Disease List pages provide an overview of the disease-target and disease-ligand associations, curated in the database specifically for GtoImmuPdb

Alpha-Release v4.0 Navigation

In conjunction with adding the Immuno Disease List pages we have extended the menu-bar navigation to contain a Disease menu. This holds two sub-menu items, one points to the disease list associations to targets and one to the disease list associations to ligands.

Disease List Page

The disease list page is designed to display all disease associations curated as part of the GtoImmuPdb. One single page, it is divided into two views, one showing disease associations to targets in the database and the other showing disease associations to ligands (Fig. 2).


Fig. 2. Immuno Disease List page. Showing target to disease associations.

Users can switch between the two views (Targets or Ligands) using a tab at the top of the page.

The format of the list of disease associations is similar for both targets and ligands. Both show one section or row per disease. Along with the disease name any external references to other disease resources (OMIM, Disease Ontology and Orphanet) are shown.

Next to each disease name are the total number of either targets or ligands associated in GtoImmuPdb to that disease. By default, the full details of the target or ligand associations are hidden. These can be displayed by clicking the ‘display all ….’ link.

At the top of the page are two toggle buttons that can be used to show or hide all the associations for all disease, if users so wish.

For targets, when the associations are displayed they show the name of the target and curated comments about the association. It also lists any ligands for which that target is a primary target and highlights if the ligand is an approved drug.

For ligands, it shows the name of the ligand, comments and any literature references for the association.

Immuno-Relevance Searching

The ranking of search results has been developed to apply a weighting to targets and ligands that are returned from a search that are consider of greater immunological relevance. The weighting is only applied when searching from a GtoImmuPdb page, not from the standard GtoPdb pages.

The criteria used to determine the immune-relevancy (and it’s weighting) of a given target or ligand is based on the amount of immunological data curated against it. For example, targets that have process, cell type and disease data annotated against them will rank higher than targets with only process data. The weighting is applied in addition to existing search weightings – so exact matches (to target or ligand name for example) will still score highest. We will be refining this relevancy scoring during testing, both in alpha and beta releases.

Cell Type Associations – Definitions

We have extended the submission tool and database to better capture and store definitions of cell type categories. This enable reference and ligands to be tagged in the definition text. We have also further developed the cell type list page to display these definitions at the top of the table (Fig. 3) – with the ability to toggle their display.


Fig. 3. Cell type list view – show toggle-able definitions

This project is supported by a 3-year grant awarded to Professor Jamie Davies at the University of Edinburgh by the Wellcome Trust (WT).

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Posted in Guide to Immunopharmacology, Technical

Database release 2017.2

Our 2nd database release of 2017 was published on 23rd March 2017. It now includes 15019 interactions between 2809 targets and 8832 ligands. For full release statistics see the About the Guide to PHARMACOLOGY page.

Target updates

The major part of the work to update the target family summary pages has been completed in advance of producing the Concise Guide to PHARMACOLOGY 2017/18 from the database, which is due out later this year. For the next version, we have been working towards trying to make the information more concise, and limiting both ligands and further reading to the 5 most useful in many cases. Obviously there are some targets where it makes sense to have more or less than 5 displayed on the summary page, but in any case, all the ligands can still be viewed on the detailed target page, and the website contains more further reading references than are included in the published Concise Guide. We are very grateful to all the contributors and the editors who have provided information.

Since most of our curation effort has gone into these updates, the only GPCR detailed page updates this time are the Gonadotrophin-releasing hormone receptors.

Ligand updates

We have refreshed our PDB ligand links and now have 1283 links from ligands to individual RCSB PDB ligand pages and the crystal structures they are found in, e.g. LSD recently crystalised with 5-HT2B.

Meanwhile, our development team has prepared the following new website features and updates:

Web services updates

The REST web services have been updated and now include interactions web services  providing lists of target-ligand pairs which can be filtered by target/ligand type and properties, binding affinity etc., and references web services which can retrieve references by id or the full interaction reference set.

Graphs comparing ligand activity across species

We have developed new ligand activity graphs comparing activity ranges across species using data extracted from GtoPdb and ChEMBL. These are available via the ‘biological activity’ tab (screenshot 1) on ligand pages but currently only for ligands that are also in ChEMBL. For example, DPCPX (screenshot 2) shows similar activity at A1 receptors across a range of species tested.


Screenshot 1. New link to view charts of activity data on the DPCPX ligand page biological activity tab


Screenshot 2. Chart showing DPCPX ligand activity data from ChEMBL and GtoPdb across 4 species. Mouse-over a plot to see the median, lower and upper quartiles, and minimum and maximum data points for each activity type.

Mouse-over a plot to see the median, interquartile range, low and high data points. A value of zero indicates that no data are available. A separate chart is created for each target, and where possible the algorithm tries to merge ChEMBL and GtoPdb targets by matching them on name and UniProt accession, for each available species. However, please note that inconsistency in naming of targets may lead to data for the same target being reported across multiple charts.

At the end of the page, below the charts, is a table listing all the data points that were used to build the charts, the source databases, assay details, and links to the original references and PubMed.

The graphs can be useful for comparing data across species when choosing model organisms to use for experiments. For example, the ligand palosuran is known to have 100-fold lower binding inhibitory potency on rat versus human UT receptor (screenshot 3).


Screenshot 3. Palosuran activity at human and rat UT receptors.

Extracting ChEMBL activities

Since ChEMBL contains an enormous amount of data (>14.3 million activities in ChEMBL 22) we have filtered and extracted the most useful data and tried to standardise them to the terms used in GtoPdb. Data are selected according to the following criteria:

  1. The target must have a type of ‘SINGLE PROTEIN’, ‘PROTEIN COMPLEX’, or ‘PROTEIN COMPLEX GROUP’
  2. Affinity types are combined and normalised as follows:
    Kd = Dissociation constant, Kd, K app, K Bind, K calc, Kd’, KD app, KD’, Kd(app), KD50, Kdiss, Relative Kd, Binding constant, K aff, K diss, KD/Ki
    pKd = -Log Kdiss, -Log KD50, pKd, pKD, logKd, -Log Kd, Log Kd, -Log KD, Log KD
    Ki = Adjusted Ki, Ki, ki, Ki app (inact), Ki app, Ki(app), Ki_app, Ki’, Ki”, KI’, K’i, Kiact, Ki high, Ki low, KiH, KiL, Kii, KII, Kic, Ki.c, Ki comp, Ki’ uncomp
    pKi = pKi(app), pKi, -Log K0.5, Log Ki, logKi, -Log Ki, pKiH, pKiL
    IC50 = IC50 app, IC50, IC50 max, I50, Mean IC50, IC50H, IC50L
    pIC50 = pIC50, pIC50(app), -Log I50, logIC50, log IC50, Log IC50, -Log IC50, pI50, pIC50(calc)
    EC50 = EC50
    pEC50 = pEC50 diss, pEC50, -Log EC50, Log EC50, logEC50
    A2 = A2
    pA2 = pA2, pA2(app), pA2 app, pA2/pKB
  3. Raw data (e.g. Kis are converted into their negative log to base 10 values (e.g. pKis)
  4. Activities deemed by ChEMBL curators to be “outside typical range” are ignored (to prevent skew)
  5. Only binding (‘B’) and functional (‘F’) assays are included (no large-scale screening data)

We have tried to be as inclusive as possible with the ChEMBL data, but please note that due to the sheer volume, there will be data that have not yet been manually checked by the ChEMBL curators and we always ask users to refer back to the original references when using the data.

We hope this new feature will be useful to our users, and we welcome any feedback you may have.

Posted in Concise Guide to Pharmacology, Database updates, Technical

Recent IUPHAR reviews on Ang(1-7) coupling with GPCRs, treating systemic autoimmune diseases, and small molecule modulators of adenylyl cyclases

The latest ‘state of the field’ IUPHAR reviews are out in the British Journal of Pharmacology:

Karnik SS, Khuraijam D, Tirupula K, Unal H. (2017) Significance of Ang(1-7) coupling with MAS1 and other GPCRs to the Renin-Angiotensin System: IUPHAR Review 22. Br J Pharmacol. doi: 10.1111/bph.13742. [Epub ahead of print] [PMID:28194766]

Ishii M. (2017) Immunology provides a great success for treating systemic autoimmune diseases – a perspective on immunopharmacology – IUPHAR Review 23. Br J Pharmacol. doi: 10.1111/bph.13784. [Epub ahead of print] [PMID:28299772]

In Pharmacological Reviews, the latest IUPHAR review article is:

Dessauer CW, Watts VJ, Ostrom RS, Conti M, Dove S, Seifert R. (2017) International Union of Basic and Clinical Pharmacology. CI. Structures and Small Molecule Modulators of Mammalian Adenylyl Cyclases. Pharmacol Rev. 69: 93-139. [PMID:28255005]

These come hot on the heels of the 100th article in Pharm Revs, and the 21st in BJP, discussed in this blog post.



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Posted in Publications

Hot Topics: Structural Basis of Substrate Recognition by the Multidrug Resistance Protein MRP1

Despite a flurry of mammalian ATP binding cassette (ABC) transporter structures in the last 2 years the Holy Grail has still been to determine how these diverse proteins interact with their transport substrates. Jue Chen and colleagues at the Rockefeller have now accomplished this for  the multidrug resistance protein-1 (MRP1/ABCC1) using advances in high resolution cryo-electron microscopy to show the structures of substrate-free and leukotriene C4 bound protein [1]. The paper also lays the foundation for revealing the structural basis for multidrug transport by MRP1 (which is a confounding factor for some chemotherapies) as the flexible substrate binding cavity in the membrane has both polar and a hydrophobic sub-pockets enabling it to interact with chemically diverse drugs. Whether this structural data enables the design of clinically-relevant MRP1 inhibitors will now be the focus of much research.

[1] Johnson Z.L., Chen J. (2017). Structural Basis of Substrate Recognition by the Multidrug Resistance Protein MRP1. Cell. pii: S0092-8674(17)30131-9. [PMID: 28238471]

Comments by Prof. Ian Kerr, University of Nottingham (@iankerr_science)

Posted in Hot Topics