Presented to Prof Mike Sternberg's Structural Bioinformatics Group, Imperial College London, March 2018

1. Coverage of the evidence-supported druggable
genome in the IUPHAR/BPS Guide to
Pharmacology (GtoPdb) and other databases
Christopher Southan
Senior Cheminformatician, representing the IUPHAR/BPS Guide to Pharmacology
team, Deanery of Biomedical Sciences, University of Edinburgh, UK.
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Imperial College March 2018

2. Outline
• Concept of the druggable genome
• Sources for the druggable proteome
• Comparing coverage
• Inner and outer limits
• Distribution of target attributes
• Chemistry intersects
• Future expansion
• Discussion points
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3. The druggable genome concept:16 years on
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Hopkins and Groom, 2002 PMID 12209152
Oprea et. al. 2018 PMID: 29472638

4. Introducing GtoPdb
http://www.guidetopharmacology.org
• IUPHAR = International Union of Basic and Clinical Pharmacology, BPS = British
Pharmacological Society
• Formerly know as IUPHAR-DB for receptors and channels since 2003
• Since 2012 funded by WellcomeTrust to cover all targets in the human genome
• Since 2015 WellcomeTrust “fork” as Guide to IMMUNOPHARMACOLOGY
• Molecular mechanism of action (mmoa) mapping primary & secondary targets
• Release cycle time (with PubChem refreshes) down to ~ 2 months
• Described in six Nucleic Acids ResearchAnnual Database issues, latest as PMID
26464438 (2016) and PMID 29149325 (2018)
• Distilled into the 2-yearly BritishJournal of Pharmacology “Concise Guide to
PHARMACOLOGY” as a nine-paper series (see PMID 29055037) with outlinks
• Presents users with selected quality compounds for pharmacology research in
silico, in vitro, in cellulo, in vivo, and in clinic
• A an ELIXIR UK Node resource since 2016
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5. 5
GtoPdb data relationship model:
Quantitative parameter capture with literature provenance,
expert-selected, curated and commented
Document > assay > result > compound > location > protein target
D- A- R - C- L- P

6. Selected primary sources of drugability mappings
• GtoPdb D- A- R - C- L- P
• ChEMBL D- A- R - C- P
• BindingDB D-A- R - C- P
• DrugBank (some DAR) C-P
• PubChem BioAssay. from ChEMBL D-A- R - C- P
• PubChem BioAssay MLSCN Screening centres A- R - C- P
• SureChEMBL patents D A R C L P
• Exelra (formerly GVKBIO) D- A- R - C- L- P
• SciFinder D-C
There are other drug informatics databases but they are generally secondary
sources i.e. not doing de novo curation
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7. 7

8. DrugBank
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9. ChEMBL
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10. BindingDB
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11. Selecting UniProt curated druggable sources
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12. Four-way
Venns
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Mar 2018

13. Druggable inner and outer limits
(Swiss-Prot human proteome at 20,136)
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Source-unique 1,421
4-way 738
3-way 1096
2-way 966
All sources (sum) 4223 = 21% of proteome
4-way = 3.4% of proteome
4-way = 22% of the sum

14. Intersects by GO-function splits
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Uniques 2-way
3-way 4-way

15. Attribute distributions in the (2016) 4-way target set
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16. Inter-source PubChem compound intersects (CIDs)
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• Advanced functionality to property filter slice ’n dice inside PubChem
• Need to be aware of circularity w.r.t. compounds, targets, patent extractions and assays
• 81% overlap of BindingDB with ChEMBL
• DrugBank has 41% PDB ligands
• ChEMBL has 6115 target-mapped substances (non-CIDs)
• GtoPdb has 2015 target-mapped substances, mostly peptides plus 230 antibodies

17. Initiatives for expansion
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NIH Illuminating the Druggable Genome (IDG) Program
objective is to improve our understanding of the
properties and functions of proteins that are currently
unannotated within the four most commonly drug-
targeted protein families: the G-protein coupled
receptors, nuclear receptors, ion channels, and protein
kinases.

18. Glass half-full Summary
• The data-supported druggable proteome is expanding
• UniProt chemistry cross-referencing shows complementary selectivity
• Steady expansion of 3 and 4 way intersects
• Expanding choice of experimental perturbagens for systems
pharmacology, dug discovery, chemical biology and synthetic biology
• The phenotypic screening push should < deconvolution of new targets
• All major diseases are likely to get robust GWAS data
• 1000s of new rare diseases should yield new targets
• It is hoped the druggable expansion will translate into
– novel validated targets
– broader potential therapeutic coverage
– new approved medicines
– new combinations and hybrids
– more mechanistic repurposing via target-hopping 18

19. Glass half-empty Summary
• Caveats for the listings w.r.t. false positives and false negatives
• Ligand modulation starting points are variable w.r.t. real-world
tractability for lead generation and optimisation
• Primary targets of marketed drugs only > 350 proteins
• Very slow appearance of successful new targets
• Balanced by the de-validation of targets that cost billions, e.g. ACAT,
CETP, CATK, LpPLA2, even BACE1 for AD looking equivocal
• Nearly all genetic validation support results go the “wrong way” with
predominant Loss-of-function (LOF), i.e. same as inhibitors
• GOF chemical modulators very rare (have we thrown away the
activators?)
• Massive GWAS mechanistic confirmation backlog
• Problem of many targets for small disease effects
• The reproducibility crisis
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20. Thank you and further info
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Current Protocols in Bioinformatics, in press, March 2018