title: A theoretical entropy score as a single value to express inhibitor selectivity.
abstract: BACKGROUND
  Designing 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.
  
  
  RESULTS
  Here we propose a new theoretical entropy score that can be calculated from a set of IC(50) data. In contrast to previous measures such as the 'selectivity score', Gini score, or partition index, the entropy score is non-arbitary, fully exploits IC(50) 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.
  
  
  CONCLUSIONS
  For 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.
authors.affiliation: Merck Research Laboratories, Department of Molecular Pharmacology, Oss, The Netherlands. joost.uitdehaag@merck.com
authors.forename: Joost C M
authors.initials: JC
authors.lastname: Uitdehaag
authors.forename: Guido J R
authors.initials: GJ
authors.lastname: Zaman
mesh_terms: D055808:Drug Discovery / Q000379:methods*; D019277:Entropy; D057166:High-Throughput Screening Assays; D006801:Humans; D008024:Ligands; D047428:Protein Kinase Inhibitors / Q000737:chemistry / Q000302:isolation & purification* / Q000378:metabolism*; D018160:Receptors, Cytoplasmic and Nuclear / Q000378:metabolism; D012985:Software Design
chemical_list: D008024:Ligands; D047428:Protein Kinase Inhibitors; D018160:Receptors, Cytoplasmic and Nuclear