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Comparison of Compound-to-Target Relationships in Chemogenomic and

Drug Databases

Aprill 2012 update: FYI these two blog posts are on the same theme

Chris Southan

ChrisDS Consulting, Göteborg, Sweden,

Presented to the NCBI PubChem team on 11 April. the BioIT World Chemogenomics and Toxicogenomics Workshop on 12 April Boston, USA, and

as a shorter version, the ChEMBL users meeting at the EBI, 27 may 2011

http://cdsouthan.blogspot.se/2012/01/our-human-beta-lactamase-is-not_09.html

http://cdsouthan.blogspot.se/2011/08/compound-to-target-mappings-part-i.html

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Aknowledgments and Context

• I profoundly appreciate the efforts of those who develop, manage and maintain public resources specified here and many others I enjoy acessing

• I have some history in evaluating the utility, exploitation and content quality of both bioinformatics and cheminformatics databases. I thus enjoy the dual roles (roughly in equal parts) of both fan and critic

• All databases have imperfections. This presentation investigates a selection of these but critical analysis should not be missinterpreted as disparaging either the quality of primary sources or the work of curators and database teams

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Outline

• Mapping concepts sources and challenges• Extremes of the distribution• Atorvastatin, drug-to-targets • Hmg-CoA reductase target-to-drugs• Equivocal mapping examples• Exploring data intersects• Complex targets• Conclusions and outlook

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Activity-to-compound-to-protein Mapping:Capturing Relationships Between four Concepts

MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK

Document Assay Result Compound Protein

Unstructured data Structured data

Expert extraction and curation

Papers & Patents Databases

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The D-A-R-C-P Axis

Pathway/module/system

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Compound and drug-to-target Collations

Targets = 5,662 protein targets, cpds = 284,206 data points = 648,915,

Targets = 8,091 Small Molecules = 658,075, data points = 3,030,317

BioAssays extracted from literature (ChEMBL) = 499,520, Direct screening assays = 3,208, active Compounds = 23,677, Targets = 447

Approved cpds = 1431 , Targets = 1458,

Experimental cpds = 5212, research targets = 3206

Targets = 358 successful, 251 clinical trial and 1,254 research,

Drugs = 1,511 approved, 1,118 clinical trial and 2,331 experimental

D-C-P-S

D-A-R-C-P

D-A-R-C-P

(D)-A-R-C-P-S

D-C-P

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PDB Drug-to-Protein

Mappingsin DrugPort

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Target Mapping: Curatorial Challenges

• Target = (infered) direct binding• Primary (bona fide) target = therapeutic causality• Polytargets = multiple• Para-target = sub-family specificity • Ortho-target = cross-species specificity• Cross-screen = non-homologous• Non-target (e.g. trypsin, albumin)• Off-target = liability (ADR or side effect) • Anti-target = known libaility (e.g. HERG)• Indirect target = non-binding (e.g. APP)• Complex = resolvable to sequence IDs (eg proteosome)• Complex = experimentaly unresolved (e.g. PDE5s)• Ambigous = lack of metadata or curatorial judgment (e.g. BACE)• Non-canonical = where metadata specifies mutation, splice or PTM

• Metabo-target = metabolic interactions• Transport-target = transporters

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Drug-target Networks

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One target-to-many compounds: Dopamine Receptor D2

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One compound-to-(367)-proteins

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Mapping sources for the top selling drug

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Target Matrix for Atorvastatin

Swiss-Prot ChEMBL(BindingD

B)

TTD DrugBank

PubChem

HMDH_HUMAN X X X (PDB) X

HMDH_RAT X X

DPP4_HUMAN X

DPP4_PIG X

AHR_HUMAN X

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Other Statins:

Different BioAssay

Coverages

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Diferent PubChem CIDs map to different submissions, structures and activity profiles

Atorvastatin -> 10 CID name matches

Substances: 19 Links

Substances 397 Links Same structure: 33 Links Mixture: 364 LinksCID 60823 39 canonical

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Vice-versa, Compounds-to-target: HMG-CoA

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Drugs mapped to HMG-CoA as target

Swiss-Prot cross-reference

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Equivocal Mappings

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Swiss-Prot Target Intersects

• 1,627 results for database:(type:drugbank)• 297 results for database:(type:bindingdb)• 45 results for database:(type:bindingdb) AND database:

(type:drugbank) AND organism:"Homo sapiens

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Mixed Mappings

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Mannitol: drug ? yes - ligand ? yes ? target ? no

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Polypropylene Glycol: drug ? no, ligand ? maybe, target ? no

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E-2012: False-negative?

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Antifreeze: drug ?, no, ligand ? no, 154 targets ? no

Wikipedia: Ethylene glycol is moderately toxic with an oral LDLO = 786 mg/kg for humans

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Crowdsourcing Works !

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Curation Challenges

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Secretase matches in TTD

Mixed-concept targets but no small-molecule

true positives

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Gamma Secretase Activity: Variable Subunit Mappings

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APP: Indirect Target, three mechanisms

“for small molecules that suppress the Amyloid Precursor Protein (APP) translation by binding to the 5'Untranslated Region of the APP mRNA

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Proteasome: Target Descriptions and Cross-screens for Bortzemib

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PubChem Compound Intersects:Primary Drug Targets with Screening data

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Mycophenolic acid

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Mycophenolic acid and Prodrug: Complex mappings

• Primary Target human IMPDH2

• IMPDH1 ?

• IMPDH2 hamster

• IMPDH2 Tritrichomonas

• myfortic is an enteric-coated formulation of MPA in a delayed-release tablet.

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Conclusions

• Compared to what we had even a few years ago, let alone in LBPC (life-before-PubChem) these compound-to-protein sources are fantastic

• However, most things that could go wrong have• We don’t often see QC statistics • Data coverage is patchy, ad hoc and can be circular• If you operate on these data at large scale you have no choice

but to ”trust and filter” • If detailed realationships are important you need to ”verify and

judge” back to the primary source • You can only really do this if you have at least some in vitro

background rather than just in silico

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Wouldn’t it be nice if we had ....

• Interpreted mapping distribution statisitcs for each database • Details about extraction triages, curation rules and parsing logic• Harmonisation of mapping rules and cross-comparison of content• Clear declarations and statistics of circularity between databases• Curator judgments overuling document primacy• Consolidated and extended Swiss-Prot cross-references• Assay and target ontologies (Pistoia ? Open Phacts ?)• “Standardization of Enzyme Data” (STRENDA,

http://www.beilstein-institut.de/en/projekte/strenda/) • “Minimum Information About a Bioactive Entity” (MIABE,

http://www.psidev.info/index.php?q=node/394)

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Our Efforts

http://www.jcheminf.com/content/3/1/14

http://www.jcheminf.com/content/1/1/10

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