Hot Topic: Unexplored therapeutic opportunities in the human genome

Contemporary drug discovery is dominated by two related  themes. The first of these is target validation upon which the sustainability of pharmaceutical R&D (in both the commercial and academic sectors) crucially depends.  The second is the size of the pool of human proteins that are/could become tractable to being progressed towards clinical efficacy as their final validation step (otherwise known as the druggable proteome).  This usefully detailed review, by a large team of authors, touches on both themes but with a focus on how the community might increase the target pool by data-driven knowledge expansion for hitherto less well characterised proteins [1].

As explained in the paper, this shortfall is being addressed by the NIH Illuminating the Druggable Genome (IDG) project since 2014 [2].  As essential reading for those engaging with the  intersects between pharmacology and drug discovery, just a few aspects can be picked out. One of these is their formalisation of a target development level (TDL) classification scheme of Tclin (clinical evidence), Tchem (chemical modulators), Tbio (biological data)  and Tdark related to the depth of investigation.  This “dark” category encompasses proteins with the least current knowledge (i.e. unvalidated potential targets) and a low number of (if  any) molecular probes.  Included in this are of course the orphan GPCRs that have been the subject of previous Hot Topics in their own right [3].

The authors not only point to many additional resources but also present a wealth of detailed statistics on many aspects of drug targets.  These included (Table 1) that eight olfactory receptors have Tbio level data.  Another nugget was the fact that phenomenological responses following radiation therapy is a bona fide biological functional characterisation approach that few of us are aware of. Last but not least, we were pleased to see GtoPdb [4] cited as one of the sources included in this impressive analysis.

For the record, our own curatorially-supported human druggable target list encompasses 1496 proteins with quantitative ligand interactions.  This can be found via the UniProt cross-reference. (n.b. this number will change slightly as the links from our own latest database release will update in the forthcoming UniProt release).

Comments by Chris Southan, IUPHAR/BPS Guide to PHARMACOLOGY, @cdsouthan
  1. Oprea TI et al. (2018). Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov. doi: 10.1038/nrd.2018.14 [Epub ahead of print] [PMID:29472638]
  2. Illuminating the Druggable Genome (IDG) Program.
  3. Hot topic: The G Protein-Coupled Receptors deorphanization landscape.
  4. Southan C et al. (2016) The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands.  Nucleic Acids Res. 44(D1):D1054-68. doi: 10.1093/nar/gkv1037. [PMID:26464438]


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GtoImmuPdb: technical update March 2018 – beta-release v3

We are pleased to announce the third, beta-release of the Wellcome Trust-funded IUPHAR Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb). Since our last release in August 2017 we have implemented developments that include disease summary pages, graphical browsing features and extensions and improvements to the advanced search. This blog-post details the major developments in the v3.0 release. This release coincides with the latest 2018.1 GtoPdb release.

Portal layout (

Some minor adjustments have been made to the portal with the social media feed panel switching to the right-hand column, updated news items included and changes to support navigation to the new disease page from the disease panel and from a new ‘Diseases’ menu bar item.


GtoImmuPdb beta v3.0 portal.

Disease associations and display

A major new development has been changes to the way disease associations are presented. Previously, we had listed diseases associated to targets and diseases associated to ligands separately. It made sense to consolidate these into a single list of diseases and then to provide specific disease summary pages where all curated information about a disease could be presented. This work was done in conjunction with the Guide to PHARMACOLOGY (GtoPdb) development, as GtoPdb already contains information on target pathophysiology and mutations relating to specific diseases.


New disease list page. Show alphabetical list of disease, with synonyms and count of associate targets and ligands.

The new disease list page, accessed from the new menu-bar item, lists all diseases with curated data in GtoPdb/GtoImmuPdb. A convenient alphabetical list of diseases, with links to the disease summary pages, synonyms and counts of associated targets and ligands.  Our longer-term aim is to provide several disease categories, but currently only two (selected from a tab at the top) can be viewed; all diseases and immuno disease. The immuno diseases category are diseases that have data curated specifically as part of GtoImmuPdb. These are diseases that are relevant to immunology, and/or are associated to targets and ligands of immunological-relevance.

The disease summery pages have been designed to display all pathophysiology, mutation and immunopharmacology data curated in GtoPdb and GtoImmuPdb in one place. See the disease summary for Psoriasis.

General information about the disease is shown, including synonyms, descriptions, links to external disease resources (OMIM, Orphanet, Disease Ontology) and counts of the total associated targets and ligands, alongside whether there is data of immuno-relevance.


Disease summary page for Psoriasis. Top section give overview of disease, including description, synonyms and links. Counts of associated targets and ligands are shown, along with whether the disease is immune relevant.

The detailed information on each target gives a summary of any curated pathophysiology data, including the role of the target along with information on drugs and their therapeutic use and side effects. If any mutation data is available this is indicated, with links back to the relevant section of the targets detail view page. The target information also shows any specific immunopharmacology comments and ligands for which their is interaction data where the ligand is also associated with the disease.


Detailed target and ligand sections of the disease summary pages (here showing for Psoriasis).

The ligand section is currently populated with data only curated through he GtoImmuPdb project. Included is information on whether the ligand is an approved drug, immunopharmacology comments and clinical use information.

Graphical browsing (Cell types)

GtoImmuPdb has been exploring different ways for users to explore and browse data, one of which is via the use of graphics and images. We took an tree diagram of immune system cell types from Wikimedia Commons and adapted it to show the cell types for which we have data. The image was re-labelled and an image map produced to make it interactive and a way to browse to different data types.


New graphical browsing of cell types implemented in GtoImmuPdb (

Advanced Search

The search facility has been extended to cover disease, processes and cell types. This has included ensuring that search on Cell and Gene Ontology terms work by inference. For example a search on ‘cytokine’ will match a GO parent term that contains the word ‘cytokine’ and bring back targets annotated to that term, or any of it’s children.

All immunopharmacology fields (comments, top-level categories, ontology terms, ontology IDs) have now been added to the advanced search for both targets and ligands – so searches can be restricted to these fields.


New immuno feature incorporated into the advanced search

Process Associations – GO evidence display

We have adjusted the display of GO terms in both Process Association to Target pages and the target detailed view pages. On the target detailed view page, the section on process associations only show GO term associated to the target if the have GO evidence other than ‘IEA’  (inferred by electronic annotation).  The IEA evidence is the only evidence used by GO that “is assigned by automated methods, without curatorial judgement”. As such we hide these by default (but users can expand the section to see them). On the process association page, the IEA terms are show, but italicised, to emphasise this difference.


Modifications to show/hide GO associations with IEA evidence.


To reflect the changes made in this release our help pages have been updated (, and we intended to follow this up by putting in place in-line pop-up help, help videos and a revised tutorial.

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

Posted in Guide to Immunopharmacology, Technical

Database release 2018.1

Pharmacology Search results table
The first GtoPdb and GtoImmuPdb beta release of  2018 includes plenty of target and ligand updates as well as announcing some important new features.
As always, full content statistics for release 2018.1 can be found on the database about page.

New website features

Disease listing and disease pages

For the first time, disease information has been gathered together in one place, under a new menu bar option called “Diseases”, which links to a full listing of all the diseases described in GtoPdb. In addition, an “Immuno disease” tab links to a listing of diseases that are relevant to immunology and linked to targets and ligands in GtoImmuPdb.

The table includes the number of targets and ligands that have been associated with the disease by our curators (note, so far only the relationships between ligands relevant to immunological diseases have been formalised in the database structure, so many diseases are not yet linked up to relevant ligands/drugs).
Individual disease pages include information about the disease, such as synonyms and links to Disease Ontology, OMIM or Orphanet where available.
Targets and ligands linked to the disease are listed, with information on disease-causing mutations if known. As noted above, currently the only ligands that have been formally associated with diseases cover the immunopharmacology domain, but we hope to extend this in the future. For further details see the help documentation.
The GtoPdb disease listing

The image shows part of the full disease list. The immunologically-relevant diseases are also shown under a dedicated tab.

Disease list and disease summary pages

Showing a disease summary page with links to external resources and listing the associated targets and ligands in GtoPdb/GtoImmuPdb.

Pharmacology search tool

The new Pharmacology search tool and browser can be found under the Advanced search drop-down menu. This tool allows users to upload target ID sets to retrieve a list of ligands which modulate those targets. Detailed information on how to use it can be found in the help page. After uploading a list of IDs (e.g. UniProtKB accessions or Ensembl gene IDs), select the number of interactions to show, and optionally, the species for the target of the interaction. By default, the results will show the top 5 interactions ordered by decreasing affinity. On the results page, the targets are ordered by how many interactions they have that match the search criteria, with 10 targets per page. A more detailed table of results (including ligand structures and affinity values) is available to download as a CSV file by clicking the “Download” button at the top of the page.
Pharmacology Search results table

Showing a section of the results page following a Pharmacology Search. The default search returns the top 5 interactions for each target.

We’ll be extending the functionality of the tool over the next few months, so please send us your feedback and bug reports to the usual email address!

Target updates

These are some of the targets which have been updated in the new release.


GPR35 (Class A Orphans)
ACKR3 (Chemokine receptors)

Ion channels


ATP-binding cassette transporter family
SLC22 family of organic cation and anion transporters


An update has brought our BACE1 lead inhibitors collection up to 20 with the addition of elenbecestat (E2609) and RO5508887 as clinical candidates, NB-360 with a good brain penetration and Compound 12 [PMID:28626832] as an interesting precedent of a fragment with a PDB structure.  Some of these also have approximate equipotency against with BACE2. Since nearly all BACE1 inhibitors have failed clinically over the last decade (with the Merck verubecestat even having abandoned the prodromal arm) the prospects for this mechanism of action look so bleak as to challenge the central hypothesis of APP secretase target validation.  Our entries now give research groups the option of direct mouse model translational comparisons between these leads in the hope of providing at least some insight into failures and possible progress.

Other new data

From time to time we select entries from relatively new journals that are including quality pharmacology papers.  This release includes two examples. The first of these, the BACE1-binding fragment in PDB, is from ACS-Omega.  The second, a new GtoImmuPdb S1P1 inhibitor entry, is from Pharmacology Research & Perspectives as the new Wiley/ASPET journal.

Two members associated with GtoPdb recently presented at the SAFER project kick-off meeting.  The aim is to provide mechanistic insights and pharmacological tools towards safer treatments for neurological diseases focusing initially on 5-HT2A and the training of PhD students.  As a proof of concept for capturing new relevant structures, we have now added a sub-nanomolar 5-HT2A inhibitor to the database.

New data in the Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb)

Since the last release at least 32 targets and 66 ligands have been added to GtoImmuPdb. The 2018.1 release also coincides with the beta 3 release of the GtoImmuPdb portal – more details of which are available in a separate blog post. Other highlights include +20 ligands associated to immunological diseases, +1 target associated to disease, +57 targets associated to processes, and +8 targets associated to cell types.
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Hot topic: The G Protein-Coupled Receptors deorphanization landscape.

Within the vast GPCR superfamily, orphans are described as receptors devoid of known endogenous ligands. They have been labeled as 7 transmembrane proteins by sequence homology and dispatched accordingly in the different GPCR subfamilies. They have attracted much attention given the recognized potential of GPCRs in terms of drug discovery. It is anticipated that discovering a new (and in the best case: previously unknown) ligand for an elusive receptor will open avenues in terms of innovative physiological concepts as well as unprecedented opportunities for drug discovery. However, after a couple of striking deorphanizations that confirmed their potential, the number of successful pairings between ligands and receptors has decreased.

The present paper by Laschet, Dupuis & Hanson [1] sheds some light on the current state of the field and the phenomenon of reduced discoveries in the orphan landscape. Although it is true that fewer deorphanizations have been reported recently compared to the 1990-2000 period, the authors propose that the rate has reached a “steady-state” stage. Nevertheless, with more than 100 remaining orphans, the daunting task of full deorphanization that lies ahead will require creative approaches both at the technical and conceptual level. Thus, following short historical reminders, the authors provide an extensive description of the current methods applied to deorphanization as well as emerging techniques that should help pharmacologists active in the orphan GPCR field in the near future. In addition, this review lists and discusses the deorphanizations that appeared in the literature since the last comprehensive state of the art issued by the IUPHAR (in 2013) [2] and put these pairings in their contexts, describing the probable outcomes in terms of new drug targets and previously unforeseen physiological loops.

Finally, during the collection of the recent literature about orphans, the authors noticed an important number of unconfirmed pairings and identified this as one of the major issues of the field and an important challenge for the future. Beside the deorphanized receptors that became silent after a single publication, presumably because of the failure of confirmation attempts by other teams, some ligands were openly questioned by recently published negative datasets. The paper proposes tentative explanations for inconsistencies in the literature and suggests recommendations such as critical controls that should be included when reporting a ligand for an orphan receptor.

Comments by Julien Hanson, University of Liege

  1. Laschet C, Dupuis N, Hanson J. (2018) The G Protein-Coupled Receptors deorphanization landscape. Biochem Pharmacol. pii: S0006-2952(18)30073-X. doi: 10.1016/j.bcp.2018.02.016. [Epub ahead of print] [PMID:29454621]
  2. Davenport AP, et al. (2013) International Union of Basic and Clinical Pharmacology. LXXXVIII. G Protein-Coupled Receptor List: Recommendations for New Pairings with Cognate Ligands. Pharmacol Rev. 65: 967-86. [PMID:23686350]

Note, the GtoPdb latest pairings page tracks reports of novel pairings between orphan receptors and their ligands.

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Hot topic: Pharmacogenomics of GPCR Drug Targets

A system of rigorous clinical trials and regulation exist to ensure that a new drug is safe and effective when reaching the market. However, natural human genetic variation(s) may cause individuals to respond differently to the same medication. A collaboration between the MRC Laboratory of Molecular Biology, Cambridge (UK), the Scripps Research Institute in Florida and the Department of Drug Design and Pharmacology, University of Copenhagen (home of the GPCRdb team) has now published a new detailed study on the effects of genetic variation in G protein-coupled receptors on responses to FDA-approved drugs [1].

The authors address the following main questions:

  • How variable are GPCR drug targets in the human population?
  • Are individuals with variant receptors likely to respond differently to drugs?
  • What is the estimated economic burden associated with variation in GPCR drug targets?

To address these questions, the authors have analysed datasets from multiple sources including genotype information from the 1,000 Genomes project, exome sequencing data from the exome aggregation consortium (ExAC), which contains aggregated information on genetic variants for ~60,000 ‘healthy’ individuals, structural information of receptors in complex with diverse ligands, data on functional effect of mutants and information on drug sales from the UK National Health Service.

The study reports that on average, an individual carries 68 missense variations in approximately one-third of the 108 GPCR drug targets. Many FDA approved drugs target a number of highly variable GPCRs. For example, several genetic variants for the mu-opioid receptor selected for experimental characterisation show an altered response for FDA-approved drugs, which could potentially lead to no or adverse reactions in the human population. Several variants occur within drug-binding sites and other functionally important positions, such as for the CCR5 drug-binding pocket of maraviroc, an antiretroviral drug for HIV treatment.

Based on an economic model, the authors estimated the potential economic burden due to ineffective prescribing of GPCR targeting drugs to be between 14 million and half-a-billion pounds annually in the UK alone.

This work might inspire many scientists to characterise human variants from multiple angles similar to the ENCODE project.

Key highlights:

  • GPCRs targeted by FDA-approved drugs show genetic variation in the human population
  • Genetic variation occurs in functional sites and may result in altered drug response
  • We present an online resource of GPCR genetic variants for pharmacogenomics research
  • Understanding variation in drug targets may help alleviate economic healthcare burden

(1) Hauser AS et al. (2017). Pharmacogenomics of GPCR Drug Targets. Cell, 172(1-2):41-54.e19. doi:10.1016/j.cell.2017.11.033. [PMID:29249361]

Comments by Alexander Hauser, University of Copenhagen and GPCRdb

While the above is a tour de force for GPCRs note also the genetic variation from 1K Genomes and/or ExAC can be accessed for every target protein in GtoPdb via the Ensembl gene ID we cross-reference.  

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PubMed Commons and Altmetrics

This has been split from an updated older post on Citation profiles for our NAR and Concise Guide papers  and this section will be updated soon

PubMed Commons
In addition to keeping an eye on citations per se we also folow up on some of the newer ways of increasing the findability and connectivity of our work in the ever more complex bibleometrics/Social Media ecosystem.  These efforts are modest (compared to what can be done) since we have our heads down for the Day Job but some of them have become necessary  house-keeping . These include grant linking,  the addion of  ORCHIDs  for team members (both of these as  EPMC functionality) and making sure papers are entered into our very own Edinburgh Research Explorer (actually highly ranked in Google for title searches).

Two other aspects may be of  interest (they can’t be detailed here but background is in the links). The first of these is the use of PubMed Commons. that has several utilities for us, including being able to “daisy chain” forward citaion pointers (but you wont see them in EPMC yet).  For example, amoung the 73 PubMed  citations for our 2009 NAR paper, 7 are 2015 and 2 from 2016. Thus, some recent authors are still citing our oldest paper (we see this across the series in fact  but, to be fair, some of them could be giving us the courtesy of multiple NAR cites although I have not checked). We therefore came up with the strategy of adding discrete pointers in PubMed Commons. As it happened, the last one (pictured below) was added most recently, even thought it is first in the chain by abstract date.


So, if  the scholars in question happen to check PubMed (n.b. but not  BJP authors, since the most recent NAR reference would have been added by the Editors anyway) we have now have a set of  comments to point the four older papers forward to the fifth 2016 paper (and should we be fortunate enough to get accepted for a future NAR Database issue, we would then add a new comment to the chain).  Consequent to the posting above, an unexpectedly prominent  ping appeared  below, on the 2nd of Feb, highlighted in yellow.


In a nutshell, “Featured comments” just happend to automatically select ours (but it seems like an actual human edited it)  which consequently featured on the PubMed front page, no less, giving us 24 hours of micro-fame!  As icing on the cake, the concomitant dailly auto-tweet of the heuristic chart-toppers, shown below, reached 4394 followers of the PMCom account (and was re-tweeted by us of course)


Continuing on the metrics theme, in the panel below you can see Altmetrics scores for our same five NAR papers, plus the one Kudos entry.


These outlinks can be found under the right-most “External links” tab on any EPMC entry that has them. The Altmetrics Rosette and sub-scores give a general measure of interest asssociated with a paper (but dont forget this may not necessarily be completely positive)  broken down by category, as you can see for our 2016 NAR below;


Interesting aspects of Almetic scores include that they are faily immediate (i.e. accumulating within the first month or so)  and tend to move in the oposite direction to the slower accumulation of cites (i.e.they flatten off).  Here again, we alow ourselves a little warmth of feeling to see that the Altmetrics hueristics (while not incontravertable)  puts us close to the top-10% of comparable publications for both our GtoPdb NARs (i.e. we got the word out). The older papers, published during LBA (Life Before Altmetrics), clearly pick up lower scores. To conclude by putting it on the  record, we are most appreciative of colleagues and compatriots who explicitly draw positive attention to our work in both traditional and altenative ways.

Posted in Uncategorized

Hot topic: Trends in GPCR drug discovery: new agents, targets and indications

New avenues for GPCR drug discovery have emerged owing to recent advances in receptor pharmacology, technological breakthroughs in structural biology and innovations in biotechnology. A collaboration between the Department of Drug Design and Pharmacology, University of Copenhagen (home of the GPCRdb team) and the Uppsala University have published a detailed analysis of all GPCR drugs and agents in clinical trials, which reveals current trends across molecule types, drug targets and therapeutic indications [1].

By manually curating CenterWatch’s Drugs in Clinical Trials database and cross-referencing with public sources (such as Drugbank, Pharos and Open Targets), they were able to identify 475 approved drugs that target 108 unique GPCRs (~34% of all FDA-approved drugs) ( Additionally, there are approximately 321 agents that are currently investigated in clinical trials, of which ~20% target 66 potentially novel GPCR targets with no approved drug yet. Of these, 37 are peptide or protein-activated GPCRs.

Other relevant highlights:

  • Based on this data, the authors calculated GPCR-targeted agent success rates of 78%, 39% and 29% for phases I, II and III, respectively — slightly higher than the FDA’s average for all investigated agents.
  • There are early indications that the proportion of GPCR-targeted biologics such as monoclonal antibodies (mAbs) and other recombinant proteins is increasing in early stage clinical trials.
  • There is increasing focus on target selectivity rather than polypharmacology.
  • More allosteric modulators in early stage clinical trials.
  • CNS disorders remain highly represented among the indications of GPCR-targeted agents.
  • Diabetes is highly represented among the GPCR-targeted agents currently in clinical trials.
  • Opportunities are emerging for GPCR-targeted agents in oncology.
  • The GPCR structures are starting to impact drug discovery.

Currently, established GPCR drug targets are used by an average of 10.3 (median = 4) distinct approved agents. This indicates a near saturation of the current target space, and emphasizes the need to identify new druggable receptors in order to develop novel medications. The 224 (56%) non-olfactory GPCRs that are yet to be explored in clinical trials have broad untapped therapeutic potential, particularly in genetic and immune system disorders.

Comments by Alexander Hauser and David Gloriam, University of Copenhagen and GPCRdb.

[1] Hauser AS, Attwood MM, Rask-Andersen M, Schiöth HB, Gloriam DE. (2017) Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev Drug Discov. 16(12):829-842. doi: 10.1038/nrd.2017.178. [PMID:29075003]

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