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Online databases containing high throughput screening and other property data continue to proliferate in number. Many pharmaceutical chemists will have used databases such as PubChem, ChemSpider, DrugBank, BindingDB and many others. This work will report on the potential value of these databases for providing data to be used to repurpose drugs using cheminformatics-based approaches (e.g. docking, ligand-based machine learning methods). This work will also discuss the potentially related applications of the Open PHACTS project, a European Union Innovative Medicines Initiative project, that is utilizing semantic web based approaches to integrate large scale chemical and biological data in new ways. We will report on how compound and data quality should be taken into account when utilizing data from online databases and how their careful curation can provide high quality data that can be used to underpin the delivery of molecular models that can in turn identify new uses for old drugs.
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Mining public domain data as a basis for drug repurposing
Antony J Williams, Sean Ekins and Valery Tkachenko
ACS Philadelphia August 2012
http://tinyurl.com/d6wodsl
Drug Repurposing
Drug repurposing commonly means data reexamination also!
Lots of data mining occurs
Then more screening which creates more data..
LOTS of public databases used to examine repurposing…
A LOT of data coming online
Interlinked on the semantic web
Where do you get your data?
Databases? Patents? Papers? Your own lab? Collaborators? All of the above?
What is likely common to all sources? Data Quality issues. There is no perfect database.
Public Domain Databases
Our databases are a mess…
Non-curated databases are proliferating errors
We source and deposit data between databases
Original sources of errors hard to determine
Curation is time-consuming and challenging
Availability of libraries of FDA drugs
Johns Hopkins Clinical Compound library- made compounds available at cost
The FDA Drug Database
The DailyMed Database
Government Databases Should Come With a Health Warning
Williams and Ekins, DDT, 16: 747-750 (2011)
What is Neomycin?
Not this…
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
Williams, Ekins and TkachenkoDrug Disc Today 17: 685-701 (2012)
Data Errors in the NPC Browser: Analysis of Steroids
Drug Disambiguation Project
NCATS Discovering “New Therapeutic Uses for Existing Molecules”
58 Molecule names and identifiers. Where are the “structures”?
NCATS dataset• Several groups tried to collate molecules• Chris Lipinski provided approximately 30 unique molecules
• Simple molecule descriptors shows no difference between compounds classified as discontinued (N= 15) or those in clinical trials (n = 14).
• Where is the definitive set of publicly accessible molecules for computational repurposing and analysis?
Drug structure quality is important..
Many groups ARE doing in silico repositioning
Integrating or using sets of FDA drugs..and if structures are incorrect predictions will be
Where is the definitive set of FDA approved drugs with correct structures?
Ideally we need linkage between in vitro data and clinical data
We have a problem…
Lots of data available but quality is suspect Errors proliferate database to database Data continues to flow in unabated When errors are identified hard to get fixed! Data licensing is confusing – “Open Data” We are “takers” not “givers” mostly… Standards are lacking:
Data licensing Data processing – structure standardization
• Let’s agree collaboration and crowdsourcing can help
• Provide SIMPLE ways to provide feedback• Contribute when possible – databases should
provide feedback mechanisms• Adopt standards for structure handling and
representation• Adopt standards for data interchange• Allow machine handling of data – use the
power of the semantic web
So what needs to happen to improve?
Williams, Ekins and Tkachenko, Drug Disc Today 17: 685-701 (2012)
Collaboration on Curation Collaborate on curation…share through standards
and open interfaces
All DBs should take comments!
Standardize
Use the SRS as guidance for standardization
“Appify” curation and collaboration
• The data network is complex• “Appify” collaboration and
curation networks • Increasing crowdsourcing role
for data analysis
Ekins & Williams, Pharm Res, 27: 393-395, 2010.
Mobile Apps for Drug Discovery
Open Drug Discovery Teams
Free iOS app used to expose repurposing data All of this data has been tweeted
http://tinyurl.com/6l9qy4f
Ekins, Clark and Williams, Mol Informatics, in Press 2012
Open Drug Discovery Teams
Gather stakeholders. Decide if goals are primarily scientific, commercial or mixed.
Explore benefits of open licensing and drawbacks of enclosure. Hold closely to open definitions and standards. Do not write your own IP licenses!
Provide simple explanations for terms of use. Use metadata to indicate licensing terms explicitly - the Creative Commons Rights Expression Language is a good tool.
Do not lock up metadata. If you can’t make the data public domain, make the metadata public domain.
Simple Rules for licensing “open” data
Williams, Wilbanks and Ekins. PLoS Comput. Biol. in Press Sept.2012
Open PHACTS Project Develop a set of robust standards… Implement the standards in a semantic integration hub Deliver services to support drug discovery programs in
pharma and public domain 22 partners, 8 pharmaceutical companies, 3 biotechs 36 months project
Guiding principle is open access, open usage, open source- Key to standards adoption -
Guiding principle is open access, open usage, open source- Key to standards adoption -
To facilitate THIS process!
What’s the structure?What’s the structure?
Are they in our file?
Are they in our file?
What’s similar?What’s
similar?
What’s the target?
What’s the target?Pharmacology
data?Pharmacology
data?
Known Pathways?
Known Pathways?
Working On Now?
Working On Now?Connections
to disease?Connections to disease?
Expressed in right cell type?Expressed in
right cell type?
Competitors?Competitors?
IP?IP?
It’s not JUST structures of course…
Taxol: Paclitaxel Bioassay Data
Most Bioassay data associated with structure with one ambiguous stereocenter
Hydrophobic
features (HPF)
Hydrogen
bond acceptor
(HBA)
Hydrogen
bond donor
(HBD)
Observed vs.
predicted IC50
r
Acoustic mediated process 2 1 1 0.92
Disposable tip mediated process 0 2 1 0.80
Data from 2 AstraZeneca patents - Ephrin pharmacophores developed using data for 14 compounds with IC50. Different dispensing methods give different results. Impact hypotheses and could impact drug discovery.
Ekins, Olechno and Williams, Submitted 2012
Acoustic Disposable tip
Measuring data: dispensing dependencies
Acoustically-derived IC50 values were 1.5 to 276.5-fold lower than for tip-based dispensing
• Pharmacophores and other computational models are used to guide medicinal chemistry.
• Non tip-based methods may improve HTS results and avoid misleading computational and statistical models.
• No analysis of influence of dispensing processes on data.
• Public databases should annotate metadata to create larger datasets for comparing different computational methods. How much data is reproducible, accurate, valid? The challenge of high-throughput science.
Measuring data: dispensing dependencies
Conclusions
Acknowledgments
Sean Ekins Christopher Lipinski Joe Olechno John Wilbanks Drug Disambiguation project team RSC Cheminformatics Team
Thank you
Email: [email protected] Twitter: @chemconnector Blog: www.chemconnector.com SLIDES: www.slideshare.net/AntonyWilliams
Email: [email protected]: collabchemBlog: http://www.collabchem.com/
