A presentation made by database team member Chris Southan at the International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification (NC-IUPHAR) Joint Meeting with the British Pharmacological Society and the Editors of ‘The Concise Guide to PHARMACOLOGY’ (April, 2014, Edinburgh University) Results can be directly acessed via the following link: http://guidetopharmacology.org/GRAC/LigandTextSearchForward?page=4&searchString=Alzheimer&searchCategories=all&order=rank

1. www.guidetopharmacology.org
Alzheimer’s in GToP
Front-loading clinical ligands and targets to be
retrievable by a major disease term
Chris Southan, Edinburgh Meeting, April 2014
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2. Objectives
• Take a sweep through the reviews for a snapshot of AD
clinical candidates
• Resolve these to structures, molecular mechanisms of action
(mmoas), protein targets and citable activity data
• Curate these into the database
• Explore optimisations and issues
• Assess utility for AD, other diseases and tagged collections
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3. Sources
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4. Curatorial Challenges
• Most reviews and lists were not curation-friendly
• Blinding of lead structures (e.g. for BACE1 )
– E2609, PF-05297909, HPP854, RG7129, AZD3293, CTS-21166, MK-8931
– journals violating principle of reproducibility
– may find key structure clues in patents, but not easily
• Unknown or indirect mmoas
– alpha secretase stimulation
• Surfacing of development and clinical data largely ad hoc
– no pointers from clinicaltrials.gov to PubMed
– results in either, both or neither and with poor comparability
– date leap-frogging between clinicaltrials.gov, press releases and papers
• Difficult to interpret, distil and standardise author semantics to insightful
free-text curator comments e.g.
– why is this compound not being progressed for AD?
– what did “termination” in this clinical trials.gov record mean? 4

5. Results I
• http://guidetopharmacology.org/GRAC/LigandTextSearchForward?page=4
&searchString=Alzheimer&searchCategories=all&order=rank
• 39 ligands with an eclectic mix of mmoas
– two imaging reagents
– four anti A-beta peptide antibodies
– Cognition enhancers and secretase inhibitors in the majority
• Unfortunately, nothing that would classify as “successful” against the
underlying pathology
• Old targets e.g. the cholinesterases (ACHE, BCHE)
• New targets e.g. Corticosteroid 11-beta-dehydrogenase (HSD11B1)
• Usual suspects e.g. beta and gamma APP secretases (BACE1, PSEN1)
• Unexpected targets e.g. LpPLA2 (PLA2G7)
• Repurposing attempts e.g. Liraglutide (diabetes) and Bepridil (vasodilator)
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6. Results II
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7. Target Example: BACE1
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8. Clinical Example:LY2811376
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9. Social Media as a Curation Source
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10. Going Forward
• Publicise
• User feedback and committee crowdsourcing
• Fill in remaining mmoa gaps
• Extend patent mapping for SAR data sets not in the literature
• Keep on top of new clinical candidates
• Extend research level capture for new mmoas
• Assess current query recall and specificity (e.g. targets vs ligands)
• Assess future disease ontologies for query recall
• Explore general meta-tagging options such as:
– European College of Neuropsychopharmacology (ECNP)
– “repurposed” or (available for) “repurposing” (NCATS, AstraZeneca/MRC)
– Company portfolios
• Lobby for clinical trial structure un-blinding and data transparency
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11. Questions ?
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