The dominant paradigm in drug discovery has been one of finding small molecules (or more recently, biologics) that bind selectively to one target of therapeutic interest. This reductionist approach conveniently ignores the fact that many drugs do, in fact, bind to multiple targets. Indeed, systems biology is uncovering an unsettling picture for comfortable reductionists: the so-called ‘magic bullet’ of Paul Ehrlich, a single compound that binds to a single target, may be less effective than a compound with multiple targets. This new approach—network pharmacology—offers new ways to improve drug efficacy, to rescue orphan drugs, re-purpose existing drugs, predict targets, and predict side-effects.
Building on work Stuart Armstrong and I did at InhibOx, a spinout from the University of Oxford’s Chemistry Department, and inspired by the work of Shoichet et al. (2007), Álvaro Cortes-Cabrera and I took our ElectroShape method, designed for ultra-fast ligand-based virtual screening (Armstrong et al., 2010 & 2011), and built a new way of exploring the relationships between drug targets (Cortes-Cabrera et al., 2013). Ligand-based virtual screening is predicated on the molecular similarity principle: similar chemical compounds have similar properties (see, e.g., Johnson & Maggiora, 1990). ElectroShape built on the earlier pioneering USR (Ultra-fast Shape Recognition) work of Pedro Ballester and Prof. W. Graham Richards at Oxford (Ballester & Richards, 2007).
Our new approach addressed two Inherent limitations of the network pharmacology approaches available at the time:
- Chemical similarity is calculated on the basis of the chemical topology of the small molecule; and
- Structural information about the macromolecular target is neglected.
Our method addressed these issues by taking into account 3D information from both the ligand and the target.
The approach involved comparing the similarity of each set ligands known to bind to a protein, to the equivalent sets of ligands of all other known drug targets in DrugBank, DrugBank is a tremendous “bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information.” This analysis generated a network of related proteins, connected by the similarity of the sets of ligands known to bind to them.
We looked at two different kinds of ligand similarity metrics, the inverse Manhattan distance of our ElectroShape descriptor, and compared them to 2D Morgan fingerprints, calculated using the wonderful open source cheminformatics toolkit, RDKit from Greg Landrum. Morgan fingerprints use connectivity information similar to that used for the well known ECFP family of fingerprints, which had been used in the SEA method of Keiser et al. We also looked at the problem from the receptor side, comparing the active sites of the proteins. These complementary approaches produced networks that shared a minimal fraction (0.36% to 6.80%) of nodes: while the direct comparison of target ligand-binding sites could give valuable information in order to achieve some kind of target specificity, ligand-based networks may contribute information about unexpected interactions for side-effect prediction and polypharmacological profile optimization.
Our new target-fishing approach was able to predict drug adverse effects, build polypharmacology profiles, and relate targets from two complementary viewpoints:
ligand-based, and target-based networks. We used the DUD and WOMBAT benchmark sets for on-target validation, and the results were directly comparable to those obtained using other state-of-the-art target-fishing approaches. Off-target validation was performed using a limited set of non-annotated secondary targets for already known drugs. Comparison of the predicted adverse effects with data contained in the SIDER 2 database showed good specificity and reasonable selectivity. All of these features were implemented in a user-friendly web interface that: (i) can be queried for both polypharmacology profiles and adverse effects, (ii) links to related targets in ChEMBLdb in the three networks (2D, 4D ligand and 3D receptor), and (iii) displays the 2D structure of already annotated drugs.
Armstrong, M. S., G. M. Morris, P. W. Finn, R. Sharma, L. Moretti, R. I. Cooper and W. G. Richards (2010). “ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics.” J Comput Aided Mol Des, 24(9): 789-801. 10.1007/s10822-010-9374-0.
Armstrong, M. S., P. W. Finn, G. M. Morris and W. G. Richards (2011). “Improving the accuracy of ultrafast ligand-based screening: incorporating lipophilicity into ElectroShape as an extra dimension.” J Comput Aided Mol Des, 25(8): 785-790. 10.1007/s10822-011-9463-8.
Ballester, P. J. and W. G. Richards (2007). “Ultrafast shape recognition to search compound databases for similar molecular shapes.” J Comput Chem, 28(10): 1711-1723. 10.1002/jcc.20681.
Cortes-Cabrera, A., G. M. Morris, P. W. Finn, A. Morreale and F. Gago (2013). “Comparison of ultra-fast 2D and 3D ligand and target descriptors for side effect prediction and network analysis in polypharmacology.” Br J Pharmacol, 170(3): 557-567. 10.1111/bph.12294.
Johnson, A. M., & G. M. Maggiora (1990). “Concepts and Applications of Molecular Similarity.” New York: John Willey & Sons.
Landrum, G. (2011). “RDKit: Open-source cheminformatics.” from http://www.rdkit.org.
Keiser, M. J., B. L. Roth, B. N. Armbruster, P. Ernsberger, J. J. Irwin and B. K. Shoichet (2007). “Relating protein pharmacology by ligand chemistry.” Nat Biotechnol, 25(2): 197-206. 10.1038/nbt1284.
Wishart, D. S., C. Knox, A. C. Guo, S. Shrivastava, M. Hassanali, P. Stothard, Z. Chang and J. Woolsey (2006). “DrugBank: a comprehensive resource for in silico drug discovery and exploration.” Nucleic Acids Res, 34(Database issue): D668-672. 10.1093/nar/gkj067.