Polypharmacology Directed Compound Data Mining: Identification of Promiscuous Chemotypes with Different Activity Profiles and Comparison to Approved Drugs

Increasing evidence that many pharmaceutically relevant compounds elicit their effects through binding to multiple targets, so-called polypharmacology, is beginning to change conventional drug discovery and design strategies. In light of this paradigm shift, we have mined publicly available compound and bioactivity data for promiscuous chemotypes. For this purpose, a hierarchy of active compounds, atomic property based scaffolds, and unique molecular topologies were generated, and activity annotations were analyzed using this framework. Starting from ∼35 000 compounds active against human targets with at least 1 μM potency, 33 chemotypes with distinct topology were identified that represented molecules active against at least 3 different target families. Network representations were utilized to study scaffold-target family relationships and activity profiles of scaffolds corresponding to promiscuous chemotypes. A subset of promiscuous chemotypes displayed a significant enrichment in drugs over bioactive compounds. A total of 190 drugs were identified that had on average only 2 known target annotations but belonged to the 7 most promiscuous chemotypes that were active against 8-15 target families. These drugs should be attractive candidates for polypharmacological profiling.

[1]  J. Mestres,et al.  Conciliating binding efficiency and polypharmacology. , 2009, Trends in pharmacological sciences.

[2]  B. E. Evans,et al.  Methods for drug discovery: development of potent, selective, orally effective cholecystokinin antagonists. , 1988, Journal of medicinal chemistry.

[3]  J. Bajorath,et al.  Systematic analysis of public domain compound potency data identifies selective molecular scaffolds across druggable target families. , 2010, Journal of medicinal chemistry.

[4]  J. Bajorath Computational analysis of ligand relationships within target families. , 2008, Current opinion in chemical biology.

[5]  Xin Wen,et al.  BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities , 2006, Nucleic Acids Res..

[6]  G. V. Paolini,et al.  Global mapping of pharmacological space , 2006, Nature Biotechnology.

[7]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[8]  Mindy I. Davis,et al.  A quantitative analysis of kinase inhibitor selectivity , 2008, Nature Biotechnology.

[9]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[10]  A. Hopkins Network pharmacology: the next paradigm in drug discovery. , 2008, Nature chemical biology.

[11]  Jürgen Bajorath,et al.  Molecular Scaffolds with High Propensity to Form Multi-Target Activity Cliffs , 2010, J. Chem. Inf. Model..

[12]  Jürgen Bajorath,et al.  Structural and Potency Relationships between Scaffolds of Compounds Active against Human Targets , 2010, ChemMedChem.

[13]  G. Bemis,et al.  The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.

[14]  R. Morphy Selectively nonselective kinase inhibition: striking the right balance. , 2010, Journal of medicinal chemistry.

[15]  David S. Wishart,et al.  DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..

[16]  Michael J. Keiser,et al.  Predicting new molecular targets for known drugs , 2009, Nature.

[17]  Jordi Mestres,et al.  A chemogenomic approach to drug discovery: focus on cardiovascular diseases. , 2009, Drug discovery today.

[18]  P. Hajduk,et al.  Rational approaches to targeted polypharmacology: creating and navigating protein-ligand interaction networks. , 2010, Current opinion in chemical biology.

[19]  Michael J. Keiser,et al.  Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.