title: Forecasting roles of combinatorial chemistry in the age of genomically derived drug discovery targets.
abstract: Genomics has caused an explosion in the number of potential therapeutic targets with varying degrees of validated pathophysiology. Among the first applications of combinatorial chemistry in genomics-driven drug discovery is the search for surrogate ligands or substrates. In the event that no surrogate is found for molecular assays, more exotic functional screens in whole cells or model organisms are used. Protein-protein interaction mapping by yeast and mammalian two-hybrid systems dominates empirical functional genomics, and this will lead to a bias for screening projects targeting this type of interaction. Drug discovery for protein-protein interactions has a poor track record, and this will challenge prevailing views on the design of combinatorial libraries. Genomics based on structural homology will yield many putative kinases, receptors, enzymes, transporter proteins, ion channels and GPCRs. Most of these projects will require new surrogate agonists, ligands or substrates, and then pharmaceutically useful agonists or antagonists will need to be found. Again, combinatorial chemistry might be essential to these studies. Given the need to screen hundreds of targets at great risk of irrelevance to pathophysiology, combined with the challenge of finding surrogate or natural ligands for these new targets, there is an urgent need for efficiency. Different groups are addressing these concerns by developing biologically-driven combinatorial libraries in order to achieve a higher density of bioactivity. Early efforts in this regard will be described.
authors.affiliation: Departments of Chemistry & Discovery Biology, Selectide, the Combinatorial Chemistry Subsidiary of Aventis Pharmaceuticals, 1580 E. Hanley, Tucson, AZ 85737, USA. david.thorpe@aventis.com
authors.forename: D S
authors.initials: DS
authors.lastname: Thorpe
mesh_terms: D020650:Combinatorial Chemistry Techniques; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods; D023281:Genomics / Q000639:trends*; D008128:Logic