Computational Analysis of Kinase Inhibitors Identifies Promiscuity Cliffs across the Human Kinome

Kinase inhibitors are high-priority drug candidates for a variety of therapeutic applications. Accordingly, there has been a rapid growth in the number of kinase inhibitors and volumes of associated activity data. A paradigm for the use of kinase inhibitors in oncology is that these compounds have multitarget activities and elicit their therapeutic effects through polypharmacology. An analysis of kinase inhibitors and associated activity data from medicinal chemistry has so far only identified small subsets of highly promiscuous kinase inhibitors. In this study, we have collected inhibitors of human kinases and their activity data from seven public repositories, curated, and combined these data, yielding more than 112 000 inhibitors with well-defined activity measurements from which qualitative target annotations were derived. An analysis of these unprecedentedly large data sets revealed that nearly 40% of human kinase inhibitors have multikinase activities but that only 4% are known to be active against five or more kinases. However, structurally analogous inhibitors often displayed significant differences in the number of kinase annotations, leading to the formation of nearly 16 000 promiscuity cliffs. Moreover, 2236 promiscuity cliffs (14.03%) were formed by kinase inhibitors at different stages of clinical development. Overall, these cliffs suggested many target hypotheses for kinase inhibitors, taking data incompleteness into consideration, as well as hypotheses for structural modifications leading to kinase selectivity. Furthermore, from network representations, pathways comprising sequences of promiscuity cliffs were extracted that revealed unexpected structure-promiscuity relationships. To enable follow-up investigations, all promiscuity cliffs formed by human kinase inhibitors will be made freely available.