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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
1
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
2
Sources
3
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
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)
5
Results II
6
Target Example: BACE1
7
Clinical Example:LY2811376
8
Social Media as a Curation Source
9
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
10
Questions ?
11

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Alzheimer’s in the Guide to PHARMACOLGY: front-loading ligands and targets

  • 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 1
  • 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 2
  • 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) 5
  • 9. Social Media as a Curation Source 9
  • 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 10