alzheimer's in guide to pharmacology
DESCRIPTION
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=rankTRANSCRIPT
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www.guidetopharmacology.org
Alzheimer’s in GToPFront-loading clinical ligands and targets to be retrievable by a major disease term
Chris Southan, Edinburgh Meeting, April 2014
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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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Sources
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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?
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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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Results II
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Target Example: BACE1
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Clinical Example:LY2811376
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Social Media as a Curation Source
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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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Questions ?