Presented at ICCS, June 1-5 2014, Noordwijkerhout, The Netherlands, http://www.int-conf-chem-structures.org/ Will the real drug targets please stand up? Discerning the molecular mechanisms of action (mmoa) for drugs treating human diseases is crucially important. This presentation will provide an overview of target numbers in IUPHAR/BPS Guide to PHARMACOLOGY, the curatorial challenges and compare these to other sources and consider the wider implications for drug discovery. We have developed stringent mapping criteria for primary targets (i.e. identifying those direct protein interactions mechanistically causative for therapeutic efficacy). This includes inter-source corroboration by intersecting multiple drug sources inside PubChem to produce consensus structure sets. An analogous approach is used to intersect published target lists and database subsets at the UniProtKB/Swiss-Prot identity level (a selection of drug and target lists is now hosted on our website http://www.guidetopharmacology.org/lists.jsp) . Our cumulative curation results reveal that structure representation differences, data provenance and variability of assay results, are major issues for experimental pharmacology and global database quality. While our activity mappings encompass some polypharmacolgy (e.g. dual inhibitors and kinase panel screens) our strategic choice is to annotate minimal, rather than maximal target sets. The consequent increased precision gives our database high utility for data mining, linking and cross-referencing. Our own database figures are currently converging to ~200 human protein primary targets for ~900 consensus chemical structures of approved small-molecule drugs. Target lists from other sources are typically larger. Comparative analysis of these lists by their UniProt ID content and Gene Ontology distributions suggests curatorial differences are the main cause of divergence . The global target landscape thus shows paradoxical trends. On the one hand, cumulative drug research output and recent expansions (e.g. epigenetic targets and orphan diseases) have pushed bioactive compounds from papers or patents to above 2 million and chemically modulatable human proteins above 1500. On the other hand, reports of Phase II clinical efficacy failure, with implicit target de-validation, are frequent. In addition, our assessment of drug approval data from 2009 to 2013 indicates new targets (i.e. first-in-class mmoas) are so low as to threaten the sustainability of the pharmaceutical industry. Causes and consequences of these paradoxes, along with utilities for minimal and maximal druggable genomes, will be discussed.

1. www.guidetopharmacology.org
Will the real targets please stand up ?
Chris Southan
IUPHAR/BPS Guide to PHARMACOLOGY Web portal Group, Centre for Integrative Physiology,
School of Biomedical Sciences, University of Edinburgh,
Hugh Robson Building, Edinburgh, EH8 9XD, UK.
cdsouthan@hotmail.com
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2. Outline
• Target considerations
• Comparing lists
• Our approaches to mapping
• UniProt intersects and Genome Ontology (GO) slices
• Atorvastatin examples
• The GToPdb GO distribution
• Conclusions
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3. Target basics
• The most important thing for a drug is clinical proof of efficacy
• However, a data-supported molecular mechanism of action (mmoa) has
crucial (bio and chem) informatics utility
• The concept of primary drug target is familiar in the form of postulating a
necessary, sufficient and causal link between a direct, specific mmoa and
efficacy
• Polypharmacology (multiple efficacy targets) is real but difficult to prove
experimentally or clinically
• As an example, while all statins “target” HMGCR, their in vitro kinetic
parameters and cross-reactivity are different and experimentally variable
• Experimental verification of target engagement in vivo is rare
• In trials that can detect it, different statins have a different clinical
profiles
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4. Triaging activity data: PubChemBioassay for
atorvastatin
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5. Target lists can be large and divergent
• Download: DrugBank 4.0 (May 2014), approved drugs, known
pharmacological action, UniProt target identifiers
• 806 rows > 771 mapped in UniProt, 743 Swiss-Prot and 28TrEMBL (4 human
includingA9UF02 BCR/ABL, 1644 aa)
• Of the 771, 622 were human Swiss-Prot
• Estimate this is at least 2X the primary targets for small-molecule drugs
• Can get lists from other bioactive chemistry databases such asTherapeutic
Target Database and ChEMBL
• Range of published lists available (see our website)
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6. Hosted target lists
http://www.guidetopharmacology.org/lists.jsp
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7. The GToPdb approach to target mapping
• Focus on minimal, rather than maximal relationship capture, to produce a
more concise “drugged genome”
• Stringent primary activity mapping by citable results (e.g. Kd, Ki, IC50)
• Read the papers to resolve the results
• Mask nutraceuticals/metabolites from drug interaction space
• Use consensus target (UniProt IDs) as curation starting points
• Use consensus drug structures (PubChem CIDs) as curation starting points
• Minimise complex subunit mapping to direct interactions
• Don’t use matrix screen results for primary mappings
• Human targets only (currently), mostly small molecules plus Abs
• Pragmatic flexibility i.e. can include multi-mapping, dual inhibitors, proven
polypharmacology and unknown mmoas
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8. The primary target concept
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http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?tab=summary&ligandId=2949

9. Primary target in a complex : gamma secretase
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PSEN1
http://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=2402

10. UniProt slice ‘n dice to compare target sets
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11. UniProt Venn diagram between major target sources
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From an approximately comparable study (on the right) the intersect has only
expanded by two proteins in a year
http://www.ncbi.nlm.nih.gov/pubmed/24533037
2014

12. Gene Ontology in the intersect and differentials
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13. Atorvastatin: mapped to different targets in 4 dbs
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14. Atorvastatin vs DPPIV
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15. Current GToPdb content
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281 primary targets of approved drugs501 protein mappings of approved drugs
354 UniProt
intersect

16. Conclusions
• There are many reasons why drug target lists are discordant
• It is thus useful to have many to compare and discern a consensus (i.e.
getting the real ones to stand up)
• At GToPdb we use consensuses as starting points to activity-map a
minimal set of targets
• Utility of maximal sets include possible polypharmacology and genetic
associations
• Utility of minimal sets include defining basic mmoas, a core drugged
genome, a pocketome , defining data gaps, and as “small (but perfectly
formed) data” to underpin “big (noisy) data”
• First-in class expansions of the minimal set are perilously low
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17. Acknowledgments
and references
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Post-conference note: Organisations
wishing to integrate GToPdb records
are welcome to contact us