The specificities of nine approved tyrosine kinase inhibitors (imatinib, dasatinib, nilotinib, gefitinib, erlotinib, lapatinib, sorafenib, sunitinib, and pazopanib) were determined by activity-based kinase profiling using a large panel of human recombinant active kinases. This panel consisted of 79 tyrosine kinases, 199 serine/threonine kinases, three lipid kinases, and 29 disease-relevant mutant kinases. Many potential targets of each inhibitor were identified by kinase profiling at the K(m) for ATP. In addition, profiling at a physiological ATP concentration (1 mm) was carried out, and the IC(50) values of the inhibitors against each kinase were compared with the estimated plasma-free concentration (calculated from published pharmacokinetic parameters of plasma C(trough) and C(max) values). This analysis revealed that the approved kinase inhibitors were well optimized for their target kinases. This profiling also implicates activity at particular off-target kinases in drug side effects. Thus, large-scale kinase profiling at both K(m) and physiological ATP concentrations could be useful in characterizing the targets and off-targets of kinase inhibitors.

As a drug used to treat imatinib-resistant and -intolerant, chronic and advanced phase chronic myelogenous leukaemia, nilotinib is well characterised as a potent inhibitor of the Abl tyrosine kinase activity of wild-type and imatinib-resistant mutant forms of BCR-Abl. Here we review the profile of nilotinib as a protein kinase inhibitor. Although an ATP-competitive inhibitor of Abl, nilotinib binds to a catalytically inactive conformation (DFG-out) of the activation loop. As a consequence of this, nilotinib exhibits time-dependent inhibition of Abl kinase in enzymatic assays, which can be extrapolated to other targets to explain differences between biochemical activity and cellular assays. Although these differences confound assessment of kinase selectivity, as assessed using a combination of protein binding and transphosphorylation assays, together with cellular autophosporylation and proliferation assays, well established kinase targets of nilotinib in rank order of inhibitory potency are DDR-1textgreaterDDR-2textgreaterBCR-Abl (Abl)textgreaterPDGFRalpha/betatextgreaterKITtextgreaterCSF-1R. In addition nilotinib has now been found to bind to both MAPK11 (p38beta) and MAPK12 (p38alpha), as well as with very high affinity to ZAK kinase. Although neither enzymatic nor cellular data are yet available to substantiate the drug as an inhibitor of ZAK phosphorylation, modeling predicts that it binds in an ATP-competitive fashion.

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.