Protein kinases, mediate most of the signal transduction to control cellular processes, have become primary drug targets, especially in cancers. To date, over thirty thousand kinase inhibitors have been identified; however, only 27 small molecule drugs have been approved by US FDA. The low clinical development success rates for investigational inhibitors may result from difficulty in drug target validation for particular diseases, as well as incorrect evaluation without considering the roles of target kinases in particular diseases for inhibitor selectivity.

Here, we propose “Approvance scores” to quantify the clinical development success rates of inhibitors for particular cancers, and utilized KIDFamMap to provide the optimizing guidance for enhancing the success rate of drugs for particular cancers. Approvance scores considered not only inhibitor potency of targeting kinases but also the role of target kinases in a particular disease. Our results show that the kinase candidates identified from expression data for computing approvance score were highly correlated with cancer-related genes and biological processes of gene ontology, as well as approvance scores are consistent with the efficiencies of inhibitors. Kinase profiling results also show that the optimizing guidance of KIDFamMap is able to design kinase inhibitors with high approvance scores.

We believe that the approvance scores reflect an index to design the drugs of a particular disease and provide personalized medicine according to the patient’s gene expressions. According to KIDFamMap and approvance scores, we can design kinase inhibitors for particular diseases with high clinical development success rates.