Social media for GtoPdb and GPCRDB
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This document discusses using social media to promote the Guide to PHARMACOLOGY (GtoPdb) and G Protein-Coupled Receptor (GPCR) databases. It notes that social media, altmetrics, open access, and open data are central to the current revolution in scholarly communication. The document then provides details on the social media presence and growth of the GtoPdb Facebook page and Twitter account. It also discusses using individual Twitter accounts and reciprocal interactions to expand reach. Other topics covered include using altmetrics for immediate impact assessment, open access statistics, maintaining blogs, leveraging networks on LinkedIn, using SlideShare for post-conference exposure, and rankings on Google.
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Basic information about these two targets and the lead compounds against them were sourced via the IUPHAR/BPS Guide to Pharmacology (GtoPdb) as Target ids: 2330 and 2331, for BACE1 and 2, respectively. This was consolidated by a literature and patent review as well as following them in other databases. The most recent information on clinical trials was sourced from press releases.
Results/Discussion
GtoPdb annotates 24 lead compounds against BACE1 and 12 against BACE2. The corresponding counts mapped to these targets in ChEMBL are 8741 and 1377 making BACE1 one of the most actively pursued enzyme targets ever. Notwithstanding the massive global effort during 2018 Merck’s verubecestat and J&J’s atabecestat BACE1 inhibitors not only failed their Phase III endpoints but even appeared to worsen cognition in prodromal patients. In 2019 Amgen/Novartis stopped Phase II/III trials of umibecestat that also showed more cognitive decline in the treatment group compared to controls. BACE2 presented an anomalous situation in several ways. By 2016 both Novartis and Amgen declared their inability to reproduce the TMEM27 secretase turnover reported in 2011. Notwithstanding, Novartis and other companies have published patents on BACE2-specific inhibitors over several years and paradoxically verubecestat is more potent against BACE2 rather than 1 but was never tested for glucose-lowering. Equally puzzling is that one academic group is still publishing BACE2 inhibitors for T2D even post de-validation. One thing both targets have in common is the complete absence of genetic support from genome-wide disease association studies but this warning sign went unheeded.
Conclusions
The massive waste of resources on the pursuit of BACE1 as an AD target over the last two decades is catastrophic. This tale of de-validation is compounded for this paralogous pair of enzymes by the fact that the original evidence for BACE2 as a T2D target was eventually refuted. The story of these targets highlights a range of crucial pharmacological pitfalls that must be avoided in the future.
Reference(s)
[1] Southan C, Hancock J.M. (2013) A tale of two drug targets: the evolutionary history of BACE1 and BACE2. Front Genet. 4:293.
Guide to Pharmacology database: ELIXIR updae
The document describes several pharmacology databases and resources maintained by IUPHAR/BPS including:
1) The Guide to PHARMACOLOGY database which contains drug targets, ligands, and approved drugs. It is updated quarterly and accessible online.
2) The Guide to MALARIA PHARMACOLOGY database which contains information on antimalarial compounds and their molecular targets.
3) Efforts to add semantic mark-up and link entities between pharmacology databases and other resources like ChEMBL, PubChem, and UniProt to improve interoperability.
In silico 360 Analysis for Drug Development
Introduction:
Consequent to a memorandum of understanding between the Karolinska Institutet and the International Union of Basic and Clinical Pharmacology (IUPHAR) in 2018 a report on academic drug development, including guidelines (ADEV) has been drafted [1]. As part of this exercise, we conceived a triage for comprehensive informatics profiling around the compound, target, disease axis. We have termed this “in slico 360” (INS360) the aim of which was to support ADEV teams since they may lack either internal expertise or external support to do this on their own. Indeed, some past SciLifeLab Drug Discovery and Development Platform projects had been halted because of overlooked competitive impingements or insufficient target validation evidence.
Methods
We assessed the current database landscape, mostly public but including commercial, for potential utility for INS360. We were guided primarily by content coverage, usability, and reputation. We also explored some open property prediction resources for assay interference and toxicological inferences.
Results:
As a first-stop-shop, we selected the IUPHAR/BPS Guide to PHARMACOLOGY with ~900 ligand-target relationships captured via expert curation of journal papers Moving up in scale we evaluated ChEMBL at 1.8 million compounds with 1.1 million assay descriptions and 7,000 targets. With yet another jump we could search the patent corpus with 18 million extracted compounds in SureChEMBL. We explored PubChem that integrates these three with over 500 other sources linked to 96 million compounds, BioAssay results and connectivity into the NCBI Entrez system. The final jump in scale for document-to-chemistry navigation was represented by SciFinder with 155 million structures. On the target side, 360-exploration has the need to encompass literature, structure, genetic variation, splicing, interactions, and disease pathways. From their UniProt links, both GtoPdb and ChEMBL provide these entry points. Navigating genetic association data in support of target validation was enabled by the OpenTargets portal and the GWAS Catalog. We also fount servers that could produce prediction scores from chemical structures for a range of features important for de-risking development.
Conclusion:
This work scoped out initial resource choices for the INS360. We propose that not only ADEV operations but essentially any pharmacology research team has much to gain from this approach and many potential pitfalls can consequently be avoided when approaching key checkpoints, such as preparing a publication. However, support may be needed for both institutions and teams to get the best out of these complex and feature-rich databases.
[1] Southan C, (2019) Towards Academic Drug Development Guidelines, ChemRxiv pre-print no. 8869574
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Progress in drug discovery and chemical biology is hugely enabled by curated document-assay-result-compound-target relationships (D-A-R-C-P) in open databases from resources such as the Guide to Pharmacology and ChEMBL. These are synergistically integrated into PubChem which pre-computes chemical similarity and connectivity between over 95 million structures and 5.6 million BioAssay results. It also links chemistry to documents via various additional routes including MeSH and large scale submissions from publishers. However, these efforts are patchy and very few journals facilitate such connectivity. There thus remains a massive shortfall in public D-A-R-C-P capture from decades of papers and patents. This presentation will cover these aspects and discuss their partial amelioration by options such as author-driven depositions and open lab-book approaches as used by Open Source Malaria
GtoPdb June 2019 poster
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PubChem as a source of systems biology perturbagens
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Will the real proteins please stand up
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- 1. www.guidetopharmacology.org Getting it out there: Social media for GtoPdb and GPCRDB Dr Christopher Southan For the Gloriam Group, University of Copenhagen May 2015 1
- 2. Introduction • Scientists are like genes; they only get noticed when they express themselves • We are in the midst of a web-enabled revolution in scholarly communication • This revolution cannot be precisely circumscribed but Social Media, Altmetrics, Open Access and Open Data are central strands • There are successful scientists who to appear to ignore all this (but they probably delegate) • The rest of us need to engage as teams and individuals • Adeptness at this is becoming career-enhancing 2
- 3. Tools of the revolution 3
- 4. Team Social Media: TheApril 2015 GtoPdb report • 2913 ‘likes’, up from 532 in October 2014 • 589 followers, up from 496 in October 2014 • We now have a blog on WordPress, with 23 posts as of April 2015 • Our SlideShare account includes slide sets and posters from the team • We also have a Guide to PHARMACOLOGY company page on • We maintain a presence on Wikipedia, with a page describing the background, development, content and features of the resource
- 5. Team Twitter • A GtoPdb retweet of my post reaches extra 550 • My RT of a GtoPdb post reaches extra 177 5 550 45 177 GtoPdb intersect Me
- 6. Individual (professional) Twitter presence 6
- 7. Twitter accounts I follow 7
- 8. Reciprocal contacts with RT networks < 1000 8
- 9. Between relevant and “push” followers • Many twitter accounts I follow have over 50% spurious followers • I purge ~ 2-4 per week by “blocking” • A general Social Media problem • Includes pushes into your Google + circles • LinkedIN connection requests from strangers with no context 9
- 10. Familiar? 10
- 11. Altmetrics: Immediate impact assessment 11
- 12. Open access stats and citations 12 Citations in PMC = 245 In Google Scholar = 53 Citations in PMC = 4 In Google Scholar = 14
- 13. 13
- 14. Team blog 14
- 15. Personal blog 15
- 16. The “In the Pipeline” effect 16
- 17. Google +: Useful but in decline ? 17
- 20. LinkedIN “Likes” flash across multiple networks 20
- 21. LinkedIN groups: Patchy utility 21
- 22. LinkedIN: The network advantage 22
- 23. Team Slideshare: Post-conference exposure 23
- 26. Figshare: better than supplementary data 26
- 27. 27
- 28. 28
- 29. Conclusions • Papers, citations, meeting presentations, grant apps and database entries remain our core business • However, its wise to be cognisant of the revolution I have outlined in these slides • Exploiting the individual and team utilities can pay off in real outreach and impacts • There are no rules or “correct” ways of doing any of this (its anarchy out there) • There are many creative ways of working the systems • Notwithstanding, its probably better to be succinct, professional and polite • Yes, it can waste some time, but then so can many things…. 29
- 30. Acknowledgements, Info and Qs GtoPdb database team: • Adam Pawson, • Helen Benson • Elena Faccenda • Joanna Sharman • Jamie Davies See “Beyond the paper cv and developing a scientific profile online, Antony Williams” (Slideshare) 30