A Collaborative Web-based Architecture for! Fragment-Based Drug Discovery Data! Michael Siani-Rose1, Barry Bunin, Ph.D.1, Norah MacCuish, Ph.D.2! Drug Discovery, 1633 Bay Shore Hwy, Suite 342, Burlingame, CA 94010! 2Mesa Analytics & Computing, Inc. 212 Corona Street, Santa Fe, NM 87501!
1Collaborative
Abstract!
Hsp90 fragment with assay data!
The Collaborative Drug Discovery (CDD) Vault™ for private Fragment-Based Drug Discovery (FBDD) data also contains Fragment-Based Public data (such as the ChemBridge “Astex Rule of 3” compliant Fragment Library). The CDD platform provides a secure, cloud-based database with a web interface that permits users to access and search chemical FBDD collections of structures with SAR data, as well as screening and preclinical study data. ! An open call is being made to all FBDD researchers, for input into a general purpose resource of not only fragments, but also associated, published or patented bioactivity data associated with published fragments. It is envisioned that this will be a unique data source to answer common questions in the field, such as why certain fragments bind better than others, chemical property trends, 2D vs. 3D fragment properties, and other useful similar insights that can only be addressed with a critical mass of data. The Public resource would complement researchersʼ private mining of IP-sensitive data in the secure Vaults. Publications, patents, or data may be provided for consideration, formatting and free hosting with attribution for the public, scientific good conveniently via email to
[email protected]. !
Introduction!
Analysis of Fragments and ! Bound Ligand Crystal Structures!
Screening against ChemBridge Astex RO3 Fragment Library!
Figure 3. Example Shape-Pharmacophore Analysis. 2D Hsp90 ligands structures were downloaded from the CDD Vault. Crystal structures (PDB 2012, Murray 2010) were utilized for 3D shapepharmacophore clustering, showing additional binding complexity over the 2D series in Murray, et al. (Murray 2010).!
Figure 4. Results from a similarity search in the CDD Vault for an active query (shown in the top left) against the ChemBridge Fragment Database; results (including structure and Tanimoto similarity measures) are shown in blue on the right side of the figure.!
Methods! Figure 2. Hsp90 fragments loaded into CDD Project (Murray 2010).!
Collaboration in FBDD! Figure 1. Fragment-based drug discovery. Fragments are used to gradually fill the site through growth (top) or linking of adjacent fragments (bottom), then linked or augmented further to build tighter-binding ligands.! Fragment-based drug discovery is complementary to traditional drug discovery methods, (1) utilizing intelligently designed libraries which obviate the need for High Throughput Screening (HTS) of 103-106 compounds, (2) using a more flexible approach to expanding a drug lead than either human- or algorithm-driven medicinal chemistry. And (3) making use of actual protein structure techniques (e.g., X-ray crystallography, NMR-assisted or a variant), building up a better drug in the context of the protein binding site.! Converting a fragment to a lead molecule can be done using the following methods:! 1. Fragment optimization through extending molecules by standard medicinal chemistry techniques.! 2. Merging and linking of fragments by combining multiple fragments, which have been shown to bind separately near each other.!
For Further Information! Please contract
[email protected]!
To date, drug discovery pharma and biotech companies have maintained proprietary libraries of compounds, cautiously guarding their methods for building the libraries, which give them an advantage based on techniques of fragment-based diversity as well as their preferred medicinal chemistry. Academic labs, while open about their approaches, individually lack the resources to build and refine (over years and decades) large optimized libraries for screening. ! A repository for fragments and binding for lead or drug development data would provide a tremendous resource for cross-pollination of fragmentbased discovery, which is often hidden behind closed doors today. As with all drug discovery efforts (including HTS of large compound libraries) published data is often limited to positive results. Negative data can greatly augment our understanding of what a good drug should look like by allowing us to avoid certain pitfalls.! Several issues can act as barriers to effective cross-laboratory collaborations; heterogeneity of data (wide range of variability observed for in vitro IC50 calculations between laboratories, lot to lot differences in compounds tested which both require normalization or monitoring) and intellectual property (IP) ownership. Data obtained in different laboratories are often stored in incompatible formats and site-licensed databases, preventing effective data mining across labs, especially for accessing negative data. In addition, data persistence is uncertain when employees and students leave or projects wind down, resulting in data often remaining fragmented and inaccessible.!
We have built a database of fragments from the literature for drug discovery applied to the chaperone Hsp90, which plays a role in conformational stability, maturation and functionality of proteins in the cell. Inhibitors have been developed against Hsp90 as chemotherapeutic agents in cancer. Murray et al. (Murray 2010) found two leads with high Ligand Efficiency (LE) which bind in different configurations against the Hsp90 active site using fragment based discovery. We built a database within the CDD database environment, capturing 2D-chemical structure, ITC (isothermal titration calorimetry) and IC50 (uM) data. This database facilitates analysis of Murray et al.ʼs two lead series against Hsp90: aminopyrimidines and phenols.
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References! 1. 2.
Balloon v 1.3.1.983, URL users.abo.fi/mivainio/balloon/index.php.! ChemBridge Fragment Library URL www.chembridge.com/screening_libraries/ fragment_library/! Erlanson DA. Fragment-based lead discovery: a chemical update. Curr Opin Biotechnol. 2006 17(6):643-52.! 4. Fingerprint Module v. 2.0, Mesa Analytics & Computing, Inc., URL www.mesaac.com/ site_media/uploads/files/FingerprintModule2_0.html (2012). ! 5. Grouping Module v. 2.0, Mesa Analytics & Computing, Inc., URL www.mesaac.com/ site_media/uploads/files/GroupingModule2_0.html (2012).! 6. Jmol: an open-source Java viewer for chemical structures in 3D. http://www.jmol.org/! 7. Murray CW et al. Fragment-based drug discovery applied to Hsp90. Discovery of two lead series with high ligand efficiency. J Med Chem. 2010 53(16):5942-55.! 8. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR, "Open Babel: An open chemical toolbox." J. Cheminf. 2011, 3, 33. DOI:10.1186/1758-2946-3-33.! 9. The Open Babel Package, version 2.3.1 openbabel.org.! 10. PDB, URL http://www.rcsb.org/pdb/ (2012).! 11. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL www.R-project.org.! 12. ShapeBase Module 2.0, Mesa Analytics & Computing, Inc., URL www.mesaac.com (2012)! 3.
13. Woodhead AJ et al. Discovery of (2,4-dihydroxy-5-isopropylphenyl)-[5-(4-methylpiperazin-1ylmethyl)-1,3-dihydrois oindol-2-yl]methanone (AT13387), a novel inhibitor of the molecular chaperone Hsp90 by fragment based drug design. J Med Chem. 2010 53(16):5956-69.!
Figure 5. Example Shape-Pharmacophore Similarity Search. A single ligand crystal structure was used as a query (upper left image) against 26 thousand conformations generated from the ChemBridge Fragment library, finding four similar conformations of four different structures. Image on lower right shows ChemBridge structures aligned to crystal structure.!
Acknowledgments! The authors would like to thank Dan Erlanson, Carmot, Inc. for productive conversations and Open Source Software packages: Balloon (Balloon 2012), OpenBabel, R (R 2008), Jmol (Jmol 2012).!
