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.

Abigail Alliance for Better Access to Developmental DrugsAddi & Cassi FundAmerican Behcet's Disease AssociationAmschwand Sarcoma Cancer Foundation BDSRA (Batten Disease Support and Research Association)Beyond Batten Disease FoundationBlake’s Purpose Foundation Breakthrough Cancer Coalition Canadian PKU & Allied DisordersCenter for Orphan Disease Research and Therapy, University of PennsylvaniaChildren’s Cardiomyopathy FoundationCooley's Anemia FoundationDani’s Foundation Drew’s Hope Research Foundation EveryLife Foundation for Rare DiseasesGIST Cancer Awareness FoundationHannah's Hope Fund Hope4Bridget FoundationHypertrophic Cardiomyopathy Association - HCMAI Have IIH ISRMD (International Society for Mannosidosis and Related Diseases)Jacob’s Cure Jain FoundationJonah's Just Begun-Foundation to Cure Sanfilippo Inc.Kids V CancerKurt+Peter FoundationLGMD2I Research FundLymphangiomatosis & Gorham's Disease Alliance MAGIC FoundationManton Center for Orphan Disease ResearchMarbleRoadMary Payton's Miracle Foundation Midwest Asian Health Association (MAHA)

MPD SupportNational Gaucher FoundationNational MPS SocietyNational Organization Against Rare Cancers National PKU AllianceNational Tay-Sachs & Allied Diseases AssociationNew Hope Research Foundation NextGEN Policy Noah's Hope - Batten disease research fundOur Promise to Nicholas Foundation Oxalosis and Hyperoxaluria FoundationPartnership for Cures Periodic Paralysis AssociationRARE ProjectRyan Foundation for MPS Children Sanfilippo Foundation for ChildrenSarcoma Foundation of AmericaSolving Kids' Cancer Taylor's Tale: Fighting Batten Disease Team Sanfilippo FoundationThe Alliance Against Alveolar Soft Part SarcomaThe Life Raft Group The NOMID AllianceThe Transverse Myelitis AssociationThe XLH Network, Inc.United Pompe Foundation

Many of these groups are doing R&D on a shoestring how can we help?

Just some of the many rare disease groups

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 ?

One example of why Pharmaceutical R&D needs disrupting

How to do it better?

What can we do with software to facilitate it ?

The future is more collaborative

We have tools but need integration

• 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?

A starting point is collaboration; software may help

A core root of the current inefficiencies in drug discovery are due to organizations’ and individual’s barriers to collaborate effectivelyBunin & Ekins DDT

16: 643-645, 2011

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

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

How CDD software has been used: BMGF

CDD is a partner on a 5 year project supporting >20 labs and proving cheminformatics support www.mm4tb.org

More Medicines for Tuberculosis

Ekins et al,Trends in Microbiology

19: 65-74, 2011

Fitting into the drug discoveryprocess

Insert your disease here…

Searching for TB molecular mimics; collaboration

Lamichhane G, et al Mbio, 2: e00301-10, 2011

Modeling – CDDBiology – Johns HopkinsChemistry – Texas A&M

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

Phase I STTR - NIAID funded collaboration with Stanford Research International

Sarker et al, submitted 2011

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

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

Dataset ALogP Molecular Weight

Number of Rotatable

Bonds

Number of Rings

Number of Aromatic

Rings

Number of Hydrogen

bond Acceptors

Number of Hydrogen

bond Donors

Molecular Polar Surface Area

Compounds identified in vitro

with new activities (N =

109) *

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

Compounds identified in vitro with multiple new activities (N = 13)

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

Orphan designated

products with at least one marketing

approval for a common disease

indication (N = 79) #

1.4 ± 3.0 b 353.2 ± 218.8 a

5.3 ± 6.4 2.8 ± 1.7 a

1.2 ± 1.3 b

5.3 ± 6.0 2.5 ± 3.0 99.2 ± 110.7

Orphan designated

products with at least one marketing

approval for a rare disease

indication (N = 52) #

0.9 ± 3.3 b 344.4 ± 233.5 a

5.3 ± 5.3 2.4 ± 1.9 b

1.3 ± 1.4 a

6.2 ± 4.2 2.7 ± 2.8 114.2 ± 85.3

Ekins and Williams, Pharm Res 28(8):1785-91, 2011

Analysis of datasets

•Promiscuous repurposed compounds are more hydrophobic •orphan repurposed hits are less hydrophobic

Dataset Intersection

Orphan +CommonUse

Orphan + Rare use

In vitro hits

0

53

0

Do these represent frequent actives or promiscuous compounds?

Government Databases Should Come With a Health Warning

Openness Can Bring Serious Quality Issues

NPC Browser http://tripod.nih.gov/npc/Database released and within days 100’s of errors found in structures

Williams and Ekins, DDT, 16: 747-750 (2011)

Science Translational Medicine 2011

This work was unfunded

Science Translational Medicine 2011

Substructure # of

Hits

# of

Correct

Hits

No

stereochemistry

Incomplete

Stereochemistry

Complete but

incorrect

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

Data Errors in the NPC Browser: Analysis of Steroids

http://www.slideshare.net/ekinsseanEkins S and Williams AJ, MedChemComm, 1: 325-330, 2010.

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!

2D Similarity search with “hit” from screening

Export database and use for 3D searching with a pharmacophore or other model

Suggest approved

drugs for testing - may also

indicate other uses if it is

present in more than one database

Suggest in silico hits for in vitro screening

Key databases of structures and bioactivity data FDA drugs

database

Repurpose FDA drugs in silico

Ekins S, Williams AJ, Krasowski MD and Freundlich JS, Drug Disc Today, 16: 298-310, 2011

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?

PXR Antagonist Pharmacophore Compounds can “switch off” PXR 3 azoles shown to antagonize PXR ~ equipotent (10-20M) mutagenesis

data indicates they bind outer surface of PXR – AF-2 binding pocket

Can a pharmacophore infer features needed to antagonize hPXR?

Ekins et al., Mol Pharmacol 72:592–603, (2007)

Huang et al., Oncogene 26: 258-268 (2007), Wang et al., Clin Cancer Res 13: 2488-2495

Hydrophobe / ring aromatic

H-bond acceptors

Antagonists require a balance between hydrophobic and hydrogen bonding features.

PXR Antagonist Binding Site/s - Docking

Ekins et al., Mol Pharmacol 72:592–603, (2007)

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

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

PXR Antagonist Database Searching

Ekins et al., Mol Pharmacol, 74(3):662-72 , (2008)

SPB00574 2.14 24.8

SPB03255 2.22 6.3

Catalyst fit IC50 (M)

PXR Antagonist Database Searching Finds New Hits

Further similarity searching retrieved 4 active analogs of SPB03255 Also tested leflunomide – FDA approved drug

O

N

O

NH

FF

F 6.8 M

Ekins et al., Mol Pharmacol, 74(3):662-72 , (2008)

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

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

LundbeckPfizer

Merck

GSKNovartis

Lilly

BMS

AllerganBayer

AZ

Roche BI

Merk KGaA

Massive models – using open tools

Gupta RR, et al., Drug Metab Dispos, 38: 2083-2090, 2010

CDK +fragment descriptors MOE 2D +fragment descriptorsKappa 0.65 0.67

sensitivity 0.86 0.86specificity 0.78 0.8

PPV 0.84 0.84

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

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.

•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?

Mobile Apps for Drug Discovery

Williams et al DDT 16:928-939, 2011

Apps for collaborationODDT – Open drug discovery teamsFlipboard-like app for aggregating social media for diseases etcCreate virtual drug discovery teams link to open notebook science

Alex Clark, Molecular Materials Informatics, Inc

Williams et al DDT 16:928-939, 2011Clark et al submitted 2012Ekins et al submitted 2012

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 potentialMon, Aug 23 2010By Kate KellandLONDON (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.

Contacts: Travis Wager (travis.t.wager@pfizer.com) Paul Galatsis (paul.galatsis@pfizer.com)

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.

The Evolving Pfizer R&D EcosystemFound on the internet http://dl.dropbox.com/u/14511423/VRU.pptx

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

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