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www.guidetopharmacology.org

Analysing the drug targets in the human genome

Chris Southan17th World Congress of Basic and Clinical Pharmacology (WCP2014), Cape Town

Track 6, Tues 15th July, 15.30 -17.00

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 and definitions• Target numbers in databases and the literature• The GToPdb approach to target mapping• Target proteins and Gene Ontology comparisons• The GToPdb approved drug target function distribution• 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 bona fide drug targets ; (e.g.

albumin, trypsin, HERG, APP, P450s) or were not initially (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 of citable data (e.g. Kd, Ki, IC50)• Mask nutraceuticals/metabolites/hormones from these mappings• Use consensus sets (human UniProt/Swiss-Prot identifiers) to map “out” to drugs• Use consensus drug structures (PubChem compounds) to map “in” to targets• Reduce complex subunit mapping to direct interactions• Avoid matrix screening results for primary mappings• Small-molecule and peptide focused but includes approved antibodies• Pragmatically flexible for including dual inhibitors, proven secondary targets,

non-human mappings, or effective drugs of unknown mechanism• Encompass special relationships e.g. prodrug > drug or drug > active

metabolites• Considering new capture types (e.g. protein-protein interaction inhibitors,

conjugates and nucleotide therapeutics)

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Primary target annotation =

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Comparing target sets

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Gene Ontology for intersects and differentials

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GToPdb approved drug interactions

281 primary targets 501 protein interaction mappings

354 UniProt intersect

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GToPdb approved targets: class and pathway splits

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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 include:

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

data”

Acknowledgments and

referencesQuestions ?

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GToPdb Hosted Target Listshttp://www.guidetopharmacology.org/lists.jsp

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Short student glossary for “Analysing the drug targets in the human genome”

• Target = protein to which a small-molecule drug, research compound or antibody binds for activity modulation and therapeutic effect

• UniProt = global protein database with identifiers for curated (Swiss-Prot) or automatically classified (TrEMBL) defined protein sequences

• Ligand = generic term for a pharmacologically important and specific interaction• PubChem = global chemistry and bioactivity database with defined identifiers for

small molecule and peptide chemical structures • Approved drugs = approved by national authorities as prescription medicines• Mechanism of action = defined molecular description (e.g. enzyme inhibitor or

receptor antagonist)• Curation (for GToPdb) = abstracting published information into concise annotations

for database records, including links to other resources• Activity mapping = linking database entries between a ligand, its protein target, a

publication reference , and the affinity results (e.g. IC50, Ki or Kd)• Genome Ontology (GO) = standardised representation of different classifications of

protein function and other attributes• Intersect = proteins in-common between certain sets (result of a Boolean AND)• Differential = proteins unique to certain sets (in a Venn diagram)• For reference, the canonical human proteome (i.e. excluding sequence variants of

any type) has 20,213 Swiss-Prot entries