Background In recent years, the kinase field has developed the prac tice of monitoring inhibitor selectivity through profiling on panels of biochemical assays, and other fields are NSC 697286following this example. It would be powerful to have a good single selectivity value for quantitatively steering the drug discovery process, nearlyfor measuring progress of series within a program, for com putational drug design, and for establishing when a compound is sufficiently selective. However, in contrast to, for instance, lipophilicity and potency, where values such as logP or binding constant are LY450139 Gamma-secretase inhibitorguiding, quantitative measures for selectivity are still under debate. Another is that it performs sub optimally with smaller profiling panels. In addition, the use of % inhibition data makes the value more dependent on experimental conditions than a Kd based score. For instance, profiling with 1 uM inhibitor concentration results in higher percentages inhibition than using 0. 1 uM of inhibitor. The 1 uM test therefore yields a more promiscuous Gini value, requiring the arbitrary 1 uM to be mentioned when calculating Gini scores. The same goes for concentrations of ATP or other co factors. This is confusing and limits compari sons across profiles. A recently proposed method is the partition index. This selects a reference kinase, and calculates the fraction of inhibitor molecules that would bind this kinase, in an imaginary pool of all panel kinases. The partition index is a Kd based score with a thermodynamical underpinning, and performs well when test panels are smaller.
However, this score is still not ideal, since it doesnt characterize the complete inhibitor distribu tion in the imaginary kinase mixture, but just the frac tion bound to the reference enzyme. Consider two inhibitors A binds to 11 kinases, one with a Kd of 1 nM and ten others at 10 nM. Inhibitor B binds to 2 kinases, seen as containing more information about which active site to bind than a promiscuous inhibitor. The selectivity difference between the inhibitors can therefore be quan tified by information entropy. The distribution of a compound across energy states is given by the Boltzmann formula both with Kds of 1 nM. The partition index would score both inhibitors as equally specific, whereas the second is intuitively more specific. Another down side is the necessary choice of a reference kinase. If an inhibitor is relevant in two projects, it can have two dif ferent Pmax values. Moreover, because the score is rela tive to a particular kinase, the error on the Kd of this reference kinase dominates the error in the partition index. Ideally, in panel profiling, the errors on all Kds are equally weighted. Here we propose a novel selectivity metric without these disadvantages. Our method is based on the princi ple that, when confronted with multiple kinases, inhibi tor molecules will assume a Boltzmann distribution over the various targets. The broadness of this distribution can be assessed through a theoretical entropy calculation. We show the advantages of this method and some applications. Because it can be used with any activity profiling dataset, it is a universal parameter for expressing selectivity.