Rational Design of High-Affinity Peptide Drug Candidates

DESCRIPTION (provided by applicant): The RandD of new drugs is a protracted and expensive process. The typical drug discovery process evolves from target identification to preclinical development and marketing. Several studies estimate the average cost of development of a single new drug in excess of 800 million. A large portion of this expense is attributed to abandoned lead candidates initially obtained from screening vast random combinatorial libraries of compounds for activity against the target but which fail for various reasons. Recent vast strides in the unraveling of the human interactive have allowed the mapping of a large numbers of protein-protein interactions, several of which are key to the pathology of disease. The identification of modulators of protein function and the process of transforming these into high-content lead series are key activities in modern drug discovery. A rapid methodology to identify high-affinity peptide drug candidates able to modulate key protein interactions, and having high potential for success as drug leads, will have huge benefits. Short high-affinity peptide ligands are highly desirable drug candidates because they are robust, easily synthesized in large quantities, and readily purified. Several short peptides have demonstrated significant ability to modulate protein-protein interactions - e.g. octreotide (SSTR2) and RGD peptides (1v23 integrins), and several others have led to the development of highly potent peptidomimetics. In preliminary studies, Lynntech has demonstrated that it is feasible to obtain high-affinity peptide ligands to target proteins based on their known amino acid sequence alone using a modular approach that combined computational biology and bioinformatics tools with a unique, advanced, high-density peptide microarray for high throughput screening of candidate ligands. Several high-affinity peptide ligands (20-30 nM affinity) were obtained after a single array screening, with the potential to refine the affinity further using iterative arrays. The methodology used conveys itself readily for automation. This study proposes the creation of a new bioinformatics engine for High-Affinity Ligand Optimization (HALO) that will enable the rapid, automated, and customized generation, screening, and identification of high affinity peptide ligands to any protein-protein interaction integral to disease pathology. HALO is a highly potent tool that will be useful for rapid hit-to-lead peptide drug candidate generation and is expected to revolutionize and expedite the process of drug discovery.