Talk for conference 14th March 2012 on drug repurposing

1. In silico repositioning of approved drugs
and collaboration for rare and neglected
diseases
Sean Ekins
Collaborations in Chemistry, Fuquay Varina, NC.
Collaborative Drug Discovery, Burlingame, CA.
Department of Pharmacology, University of Medicine & Dentistry of New Jersey-Robert Wood Johnson
Medical School, Piscataway, NJ.
School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD.

2. Just some of the many rare disease groups
Abigail Alliance for Better Access to Developmental Drugs MPD Support
Addi & Cassi Fund National Gaucher Foundation
American Behcet's Disease Association National MPS Society
Amschwand Sarcoma Cancer Foundation National Organization Against Rare Cancers
BDSRA (Batten Disease Support and Research Association) National PKU Alliance
Beyond Batten Disease Foundation National Tay-Sachs & Allied Diseases Association
Blake’s Purpose Foundation New Hope Research Foundation
Breakthrough Cancer Coalition NextGEN Policy
Canadian PKU & Allied Disorders Noah's Hope - Batten disease research fund
Center for Orphan Disease Research and Therapy, University of Our Promise to Nicholas Foundation
Pennsylvania Oxalosis and Hyperoxaluria Foundation
Children’s Cardiomyopathy Foundation Partnership for Cures
Cooley's Anemia Foundation Periodic Paralysis Association
Dani’s Foundation RARE Project
Ryan Foundation for MPS Children
Drew’s Hope Research Foundation Sanfilippo Foundation for Children
EveryLife Foundation for Rare Diseases Sarcoma Foundation of America
GIST Cancer Awareness Foundation Solving Kids' Cancer
Hannah's Hope Fund Taylor's Tale: Fighting Batten Disease
Hope4Bridget Foundation Team Sanfilippo Foundation
Hypertrophic Cardiomyopathy Association - HCMA The Alliance Against Alveolar Soft Part Sarcoma
I Have IIH The Life Raft Group
ISRMD (International Society for Mannosidosis and Related Diseases) The NOMID Alliance
Jacob’s Cure The Transverse Myelitis Association
Jain Foundation The XLH Network, Inc.
Jonah's Just Begun-Foundation to Cure Sanfilippo Inc. United Pompe Foundation
Kids V Cancer
Kurt+Peter Foundation
LGMD2I Research Fund
Lymphangiomatosis & Gorham's Disease Alliance
MAGIC Foundation
Many of these groups are
Manton Center for Orphan Disease Research
MarbleRoad doing R&D on a shoestring how
Mary Payton's Miracle Foundation
Midwest Asian Health Association (MAHA) can we help?

3. One example of why Pharmaceutical R&D needs disrupting
Jonah has Sanfilippo Syndrome
Jonah’s mum, Jill Wood started a foundation, raises money, awareness, funds ground breaking
research happening globally. Willing to sell her house to fund research to save Jonah.
She is in a race against time – what can we do to translate ideas from bench to patient faster?
How do we get more ideas tested, who funds the research
How can we help parents and families ?

4. A starting point is collaboration; software may help
How to do
it better?
What can we
do with
software to A core root of the
current inefficiencies in
facilitate it ? drug discovery are due
to organizations’ and
individual’s barriers to
collaborate effectively
We have tools
Bunin & Ekins DDT
but need 16: 643-645, 2011
integration The future is more
collaborative
• Groups involved traverse the spectrum from pharma, academia, not for
profit and government
• More free, open technologies to enable biomedical research
• Precompetitive organizations, consortia..
• How can it help orphan and rare diseases?

5. Example ; Collaborative Drug Discovery
Platform
• CDD Vault – Secure web-based place for private data – private by default
• CDD Collaborate – Selectively share subsets of data
• CDD Public –public data sets - Over 3 Million compounds, with molecular properties,
similarity and substructure searching, data plotting etc
 will host datasets from companies, foundations etc
 vendor libraries (Asinex, TimTec, ChemBridge)
• Unique to CDD – simultaneously query your private data, collaborators’ data, & public
data, Easy GUI
www.collaborativedrug.com

6. How CDD software has been used: BMGF
 3 Academia/ Govt lab – Industry
screening partnerships
 CDD used for data sharing /
collaboration – along with
cheminformatics expertise
 Previously supported larger groups
of labs – many continued as
customers
More Medicines for Tuberculosis
CDD is a partner on a 5 year project supporting >20 labs and proving cheminformatics
support www.mm4tb.org

7. Fitting into the drug discovery
process
Insert your disease here…
Ekins et al,
Trends in
Microbiology
19: 65-74, 2011

8. Searching for TB molecular mimics; collaboration
Modeling – CDD
Biology – Johns Hopkins
Chemistry – Texas A&M
Lamichhane G, et al Mbio, 2: e00301-10, 2011

9. Phase I STTR - NIAID funded collaboration with Stanford
Research International
 Combining cheminformatics methods and pathway analysis
 Identified essential TB targets that had not been exploited
 Used resources available to both to identify targets and molecules that
mimic substrates
 Computationally searched >80,000 molecules - tested 23 compounds in
vitro (3 picked as inactives), lead to 2 proposed as mimics of D-fructose
1,6 bisphosphate, (MIC of 20 and 40 ug/ml)
 POC took < 6mths - - Submitted phase II STTR, Submitted manuscript
 Still need to test vs target - verify hits vs suggested target
Ekins et al,
Trends in
Microbiology
Feb 2011
Sarker et al, submitted 2011

10. Finding Promiscuous Old Drugs for New Uses
 Research published in the last six years - 34 studies - Screened libraries of FDA
approved drugs against various whole cell or target assays in vitro.
 1 or more compounds with a suggested new bioactivity
 13 drugs were active against more than one additional disease in vitro
 Perhaps screen these first?
Ekins and Williams, Pharm Res 28(8):1785-91, 2011

11. Finding Promiscuous Old Drugs for New Uses
 109 molecules were identified by screening in vitro
 Statistically more hydrophobic (log P) and higher MWT than orphan-
designated products with at least one marketing approval for a common
disease indication or one marketing approval for a rare disease from the
FDA’s rare disease research database.
 Created multiple structure searchable databases in CDD
 This work was unfunded
 Data for repurposing in publications is increasing but who is tracking it?
 FDA databases for rare disease research are XL files!!
 After this paper published NCGC released NPC browser….but

12. Analysis of datasets
Dataset ALogP Molecular Number of Number of Number of Number of Number of Molecular Polar
Weight Rotatable Rings Aromatic Hydrogen Hydrogen Surface Area
Bonds Rings bond bond
Acceptors Donors
Compounds 3.1 ± 2.6 428.4 ± 202.8 5.4 ± 3.8 3.8 ± 1.9 2.0 ± 1.4 5.6 ± 4.2 2.0 ± 1.9 89.6 ± 69.3
identified in vitro
with new
activities (N =
109) *
Compounds 3.6 ± 2.7 442.8 ± 150.0 5.1 ± 3.1 4.2 ± 1.5 1.8 ± 1.2 5.5 ± 4.6 2.2 ± 3.3 79.5 ± 78.8
identified in vitro
with multiple new
activities (N = 13)
Orphan 1.4 ± 3.0 b 353.2 ± 218.8 5.3 ± 6.4 2.8 ± 1.7 1.2 ± 1.3 5.3 ± 6.0 2.5 ± 3.0 99.2 ± 110.7
designated a a b
products with at
least one
marketing
approval for a
common disease
indication (N =
79) #
Orphan 0.9 ± 3.3 b 344.4 ± 233.5 5.3 ± 5.3 2.4 ± 1.9 1.3 ± 1.4 6.2 ± 4.2 2.7 ± 2.8 114.2 ± 85.3
designated a b a
products with at
least one
marketing
approval for a
rare disease
indication (N =
52) #
•Promiscuous repurposed compounds are more hydrophobic
•orphan repurposed hits are less hydrophobic
Ekins and Williams, Pharm Res 28(8):1785-91, 2011

13. Dataset Intersection
Orphan +
Common Orphan +
Use 0 Rare use
0
3 5
In vitro hits
Do these represent frequent
actives or promiscuous
compounds?

14. Government Databases Should Come With a Health Warning
Openness Can Bring Serious Quality Issues
Database released and within days 100’s of errors found in structures
Science Translational Medicine 2011 NPC Browser http://tripod.nih.gov/npc/
Science Translational Medicine 2011
Williams and Ekins,
This work was unfunded DDT, 16: 747-750 (2011)

15. Data Errors in the NPC Browser: Analysis of Steroids
Substructure # of # of No Incomplete Complete but
Hits Correct stereochemistry Stereochemistry incorrect
Hits stereochemistry
Gonane 34 5 8 21 0
Gon-4-ene 55 12 3 33 7
Gon-1,4-diene 60 17 10 23 10
Towards a Gold Standard: Regarding Quality in Public Domain Chemistry Databases and Approaches to Improving
the Situation Antony J. Williams, Sean Ekins and Valery Tkachenko, Drug Discovery Today, In Press 2012

16. Need to learn from neglected disease research
Do we really need to screen
massive libraries of compounds as
we have for TB and malaria?
And groups are screening
compounds already screened by
others!
Ekins S and Williams AJ, MedChemComm,
http://www.slideshare.net/ekinssean
1: 325-330, 2010.

17. Repurpose FDA drugs in silico
Key databases of
structures and 2D Similarity search with “hit”
bioactivity data FDA drugs from screening
database
Export database and Suggest
use for 3D searching approved
with a pharmacophore drugs for testing
or other model - may also
indicate other
uses if it is
present in more
than one
database
Suggest in silico hits
for in vitro screening
Ekins S, Williams AJ, Krasowski MD and Freundlich JS, Drug Disc Today, 16: 298-310, 2011

18. PXR antagonist drug discovery
 Cancer drugs act as PXR agonists,
increasing own metabolism and transport
out of cells
 How could we block this?
 Preferably find a clinically used drug?

19. PXR Antagonist Pharmacophore
 Compounds can “switch off” PXR
 3 azoles shown to antagonize PXR ~ equipotent (10-20µM) mutagenesis
data indicates they bind outer surface of PXR – AF-2 binding pocket
Huang et al., Oncogene 26: 258-268 (2007), Wang et al., Clin Cancer Res 13: 2488-2495
 Can a pharmacophore infer features needed to antagonize hPXR?
H-bond acceptors
Hydrophobe / ring aromatic
Antagonists require a balance between
hydrophobic and hydrogen bonding features.
Ekins et al., Mol Pharmacol 72:592–603, (2007)

20. PXR Antagonist Binding Site/s - Docking
2 separate binding sites on either side of Lys277- identified with GOLD
rigid docking in 1NRL chain A
azoles would interfere with SRC-1 binding in the AF-2 site. One site is
predominantly hydrophobic -15 amino acids.
Lys277 most likely serves as a “charge clamp” for interaction between
the co-activator SRC1 (His687) and PXR
Azoles compete with SRC-1 for AF-2
Piperazine etc may not be necessary
- Solvent exposed
Ekins et al., Mol Pharmacol 72:592–603, (2007)

21. PXR Antagonist Database Searching
 Screened four databases – known drugs and
commercially available molecules, N = 3533
 67 hits retrieved
 We tested in vitro a small number based on
their pharmacophore fit values and mapping to
the pharmacophore features
 Followed up hits with similarity searching using
ChemSpider.com, emolecules.com
Ekins et al., Mol Pharmacol, 74(3):662-72 , (2008)

22. PXR Antagonist Database Searching Finds New Hits
Catalyst fit IC50 (µM)
SPB00574 2.14 24.8
SPB03255 2.22 6.3
Further similarity searching retrieved 4 active analogs of SPB03255
Also tested leflunomide – FDA approved drug
F
F
6.8 µM
O
F
N
O H
N
Ekins et al., Mol Pharmacol, 74(3):662-72 , (2008)

23. We can do the same for rare diseases: Searching for
Potential Chaperones for Sanfilippo Syndrome
 Pshezhetsky et al showed Glucosamine rescues
HGSNAT mutants
 Glucosamine used to create a 3D common features
pharmacophore using Discovery Studio.
 The pharmacophore + ligand van der Waals shape
was used to search multiple 3D databases
 FDA drugs, natural products, orphan drugs, KEGG,
CSF metabolome etc.
 The pharmacophore consists of a positive ionizable
(red) and 3 hydrogen bond donor groups (purple).
 Selected hits for experimental testing
 Collaboration ongoing!
e.g. Isofagomine maps pharmacophore

24. Crowdsourcing Project “Off the Shelf R&D”
All pharmas have assets on shelf that reached clinic
“Off the Shelf R&D”
Get the crowd to help in repurposing / repositioning
these assets
How can software help?
- Create communities to test
- Provide informatics tools that are accessible to the
crowd - enlarge user base
- Data storage on cloud – integration with public data
- Crowd becomes virtual pharma-CROs and the
“customer” for enabling services

25. Massive models – using open tools
Allergan
Bayer
Merk KGaA
CDK +fragment descriptors
Merck
MOE 2D +fragment descriptors
Lilly
Kappa 0.65 0.67 Pfizer
sensitivity 0.86 0.86 Lund
specificity 0.78 0.8 Roche BI
PPV 0.84 0.84
Novartis
GSK
AZ
BMS
Can we get pharmas to share models rather than data – precompetitive?
What can be developed with very large training and test sets?
training 194,000 and testing 39,000
Open molecular descriptors / models vs commercial descriptors
Potential to share models selectively with collaborators e.g. academics,
rare & neglected disease researchers
Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010

26. Future Drug Discovery
Pharma R&D already looking like this – a big
network
I think we are seeing something like this with all
the orphan disease networks too
Massive collaboration networks – software
enabled. We are in “Generation App”
Crowdsourcing will have a role in R&D. Drug
discovery possible by anyone with “app access”
Ekins & Williams, Pharm Res, 27: 393-395, 2010.

27. Mobile Apps for Drug Discovery
•Make science more accessible =
>communication
•Mobile – take a phone into field /lab and
do science more readily than on a laptop
•MolSync + DropBox + MMDS = Share
molecules as SDF files on the cloud =
collaborate
•How could orphan disease research
leverage apps?
Williams et al DDT 16:928-939, 2011

28. Apps for collaboration
ODDT – Open drug discovery teams
Flipboard-like app for aggregating social media for diseases etc
Create virtual drug discovery teams link to open notebook science
Alex Clark, Molecular Materials Informatics, Inc
Williams et al DDT 16:928-939, 2011
Clark et al submitted 2012
Ekins et al submitted 2012

29. Found on the internet http://dl.dropbox.com/u/14511423/VRU.pptx
The Evolving Pfizer R&D Ecosystem
 Evolving paradigm for the discovery of medicines (Collaborative)
 A vision that points towards open innovation and collaborations
 Open research model to collectively share scientific expertise
 Enhance speed of drug discovery beyond individual resource capabilities (Speed)
 Limited research budgets and capabilities driving greater shared resources
 Goal to see all partners succeed by accelerating the SCIENCE
 Synergize Pfizer’s strengths with Research Partners (Knowledge)
 Pair Pfizer’s design, cutting edge tools, synthetic excellence with research partners (academics, not-for-profits,
venture capitalists, or biotechs) to develop break through science, novel targets, and indications of unmet medical
need
 Current example of academic and not-for-profits partners (Discover and Publish)
 Drive to publish in top journal with science receiving high visibility and interest
Body clock mouse study suggests new drug potential
Mon, Aug 23 2010
By Kate Kelland
LONDON (Reuters) - Scientists have used experimental drugs being developed
by Pfizer to reset and restart the body clock of mice in a lab and say their work
may offer clues on a range of human disorders, from jetlag to bipolar disorder.
a few months ago we entered into a collaboration with
the giant pharmaceutical industry Pfizer to test some of
their leading molecules for potential relevance to HD.
Contacts:
 Travis Wager (travis.t.wager@pfizer.com)
 Paul Galatsis (paul.galatsis@pfizer.com)

30. The newest drug discovery reality
Gone full circle
Pharma now becoming more like rare disease groups
Working on a shoestring, limited resources, leverages academics,
partners with disease foundations, funded by them – open innovation
Collaboration is a core element
If Jill Wood or others can become a virtual pharma, if they have enough
domain knowledge and drive
Pfizer and other pharmas can be more like Jill, smaller, leaner, working
on many more diseases as collaborators
In silico approaches and collaboration = central to rare disease drug
discovery

31. Acknowledgments
 Jill Wood
 Antony J. Williams (RSC)
 Rishi Gupta, Eric Gifford, Ted Liston, Chris Waller (Pfizer)
 Joel Freundlich (Texas A&M), Gyanu Lamichhane (Johns
Hopkins)
 Carolyn Talcott, Malabika Sarker, Peter Madrid, Sidharth
Chopra (SRI International)
 MM4TB colleagues
 Matthew D. Krasowski (University of Iowa)
 Sridhar Mani (Albert Einstein College of Medicine)
 Alex Clark (Molecular Materials Informatics, Inc)
 Vladyslav Kholodovych, Ni Ai, Dima Chekmarev, Sandhya
Kortagere, Chia-Wei Li, J Don Chen, William J. Welsh
(UMDNJ)
 Accelrys
 CDD – Barry Bunin
 Funding BMGF, NIAID.
 Everyone that has shared data in CDD..
 Email: ekinssean@yahoo.com
 Slideshare: http://www.slideshare.net/ekinssean
 Twitter: collabchem
 Blog: http://www.collabchem.com/
 Website: http://www.collaborations.com/CHEMISTRY.HTM