A theoretical entropy score as a single value to express inhibitor selectivity

Joost Cm Uitdehaag,Guido Jr Zaman

doi:10.1186/1471-2105-12-94

Abstract

BackgroundDesigning maximally selective ligands that act on individual targets is the dominant paradigm in drug discovery. Poor selectivity can underlie toxicity and side effects in the clinic, and for this reason compound selectivity is increasingly monitored from very early on in the drug discovery process. To make sense of large amounts of profiling data, and to determine when a compound is sufficiently selective, there is a need for a proper quantitative measure of selectivity.ResultsHere we propose a new theoretical entropy score that can be calculated from a set of IC50 data. In contrast to previous measures such as the 'selectivity score', Gini score, or partition index, the entropy score is non-arbitary, fully exploits IC50 data, and is not dependent on a reference enzyme. In addition, the entropy score gives the most robust values with data from different sources, because it is less sensitive to errors. We apply the new score to kinase and nuclear receptor profiling data, and to high-throughput screening data. In addition, through analyzing profiles of clinical compounds, we show quantitatively that a more selective kinase inhibitor is not necessarily more drug-like.ConclusionsFor quantifying selectivity from panel profiling, a theoretical entropy score is the best method. It is valuable for studying the molecular mechanisms of selectivity, and to steer compound progression in drug discovery programs.

Highlights

Designing maximally selective ligands that act on individual targets is the dominant paradigm in drug discovery
Theory Imagine a theoretical mixture of all protein targets on which selectivity was assessed
No competing factors are present such as ATP. To this mixture we add a small amount of inhibitor, in such a way that approximately all inhibitor molecules are bound by targets, and no particular binding site gets saturated

Summary

Introduction

Designing maximally selective ligands that act on individual targets is the dominant paradigm in drug discovery. The kinase field has developed the practice of monitoring inhibitor selectivity through profiling on panels of biochemical assays [1,2,3,4,5,6,7], and other fields are following this example [8,9]. Such profiling means that scientists are faced with increasing amounts of data that need to be distilled into human sense. Both are ranked specific by both S(3 μM) and S(10x), whereas the first compound is clearly more specific

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Results

Conclusion