The goal is to determine the degree of the adverse relationship between the MoAs and the tumor marker genes expression that reveals how likely the com pound is to reverse the expression of tumor marker genes. From the perspective of algorithm development, predic tion of drug effect selleck and compound screening are essentially the same. The only difference is the distance criteria When similar prediction is applied, the MoA is first ranked for the largest positive distance and then each drugs within the MoA are then ranked with the same cri teria. when reverse prediction is applied, then the MoA is first ranked for the smallest negative distance and then each drugs within each MoA are ranked the same. Background Drug development in general is time consuming, expensive with extremely low success and relatively high attrition rates.

To overcome or by pass this productivity gap and to lower the risks associated with drug develop ment, more and more companies are resorting to approaches, commonly referred to as Drug Reposition ing or Drug Repurposing . Drug repositioning is nothing but identifying and developing new uses for existing or abandoned pharmacotherapies. Since the starting point is usually approved compounds with known bioavailability and safety profiles, proven formulation and manufacturing routes, and well characterized pharmacol ogy, repositioned drugs can enter clinical phases more rapidly and at a fraction of costs incurred in the discov ery and development of completely novel compounds.

This new indication discovery has already yielded several successes that include the repositioning of sildenafil from an anti angina drug to erectile dysfunction treatment and repositioning thalidomide, a withdrawn drug, for leprosy and multiple myeloma. Indeed, it is not surprising that in recent years, repositioned drugs account for 30% of the new medicines that reach their first markets. Although there are several advantages, rational drug repositioning poses formidable challenges primarily because the mole cular basis and the underlying mechanisms of most diseases and drug actions are either elusive or poorly understood, intricate, or are not readily amenable to human or computational data mining techniques. Drug repositioning is predominantly dependent on two principles i the promiscuous nature of the drug and ii targets relevant to a specific disease or pathway may also be critical for other diseases or pathways.

The latter may be represented as a shared gene or fea ture between a disease disease, drug drug, or a disease drug. Based on this principle, some computational approaches have been developed and applied to identify drug repositioning candidates ranging from mapping gene expression profiles with drug response profiles, to side effect based similarities. An increasing number of network Dacomitinib based methods built on guilt by association principle have also been used to selleckchem 17-AAG identify drug repositioning candidates.