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www.guidetopharmacology.org

Analysing targets and drugs to populate the GToP database

Chris Southan, presented at the University of Capetown, July 2014

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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Outline

• Target considerations• Target numbers in databases and the literature• The GToPdb approach to primary target mapping• Target proteins and Gene Ontology comparisons• The GToPdb approved target function distribution• Drug consensus sets• Conclusions

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Target considerations

• The capture (in GToPdb and other sources) of the data-supported molecular mechanisms for a drug has crucial pharmacological, bioinformatics and cheminformatics utility

• The concept of “primary target” postulates a causal, necessary and sufficient link between a direct drug binding and clinical efficacy

• Polypharmacology (multiple efficacy targets) is important but difficult to prove experimentally or clinically

• The in vitro kinetic parameters and cross-reactivity for the same mechanisms are different and experimentally variable

• Verification of target engagement and residence time in vivo is rare • Many proteins in listings are not actual drug targets ; (e.g. albumin,

trypsin, HERG, APP, P450s) or were not (e.g. ACE2, BACE2)• Phenotypic screening may be on the ascendance, but deconvolution

to mechanism of action remains important

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The spread of target numbers

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Literature and patent target growth

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Slow growth in new small-molecule targets

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The GToPdb approach to target mapping

• Focus on minimal, rather than maximal relationship capture, to produce a concise “drugged genome”

• Read the papers to resolve the mechanisms• Stringent mapping by citable data (e.g. Kd, Ki, IC50)• Mask nutraceuticals/metabolites/hormones from these mappings• Use consensus sets (human UniProt/Swiss-Prot IDs) to map “out”

to drugs• Use consensus drug structures (PubChem CIDs) to map “in” to

targets• Reduce complex subunit mapping to direct interactions• Avoid matrix screening results for primary mappings• Pragmatically flexible i.e. can include dual inhibitors, proven

secondary targets, non-human mappings or unknown mechanisms

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Primary target annotation

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Selecting a primary target from a complex

PSEN1

Gamma secretase

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Comparing target sets in the protein database

Intersects and differences between target sets

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Selects made from June 2014 UniProt(no species filter)

ChEMBLDrugB = DrugBankBdb = BindingDBGTP = Guide to PHARMACOLOGY

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Gene Ontology for intersects and differentials

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GToPdb targets for approved drugs

281 primary targets of approved drugs501 protein mappings of approved drugs

354 UniProt intersect

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GToPdb approved targets: class and pathway splits

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Getting to the real drugs: developing a consensus triage

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Issues for approved drug structure discordance

• Our analysis is being extended to include additional drug sources and we have constituted a new NC-IUPHAR expert subcommittee to support us in resolving challenging cases

• We have introduced new relationships such as drug <-> prodrug and active metabolites

• Representational discordance between the same canonical drugs in documents and databases is a serious issue in pharmacology, medicinal chemistry and cheminformatics

• Contributory factors include naming inconsistencies, salt permutations,

stereo forms, tautomerism, legacy dominance of PDF images and errors

• Definitive sets will require not only more inter-source collaboration for standardisation (including wider use of InChI) but also that regulatory bodies and pharmaceutical companies directly engage with provenancing public database structures for clinically used drugs

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Conclusions

• Published and database target numbers diverge mainly because of different curatorial selectivity

• Current inner limit ~ 300, outer limit ~ 2000 • New mechanisms for new approved drugs are low • GToPdb uses consensus lists as starting points for curation • Utilities of our minimal primary target set:

– Validated mechanisms– Defining the core drugged genome and pocketome– Use as “small (but perfectly formed) data” to underpin “big (noisy)

data”

• Drug structure discordance in global databases is a serious issue

Acknowledgments and

referencesQuestions ?

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