The use of novel selectivity metrics in kinase research.
BACKGROUND
Compound selectivity is an important issue when developing a new drug. In many instances, a lack of selectivity can translate to increased toxicity. Protein kinases are particularly concerned with this issue because they share high sequence and structural similarity. However, selectivity may be assessed early on using data generated from protein kinase profiling panels.


RESULTS
To guide lead optimization in drug discovery projects, we propose herein two new selectivity metrics, namely window score (WS) and ranking score (RS). These metrics can be applied to standard in vitro data-including intrinsic enzyme activity/affinity (Ki, IC50 or percentage of inhibition), cell-based potency (percentage of effect, EC50) or even kinetics data (Kd, Kon and Koff). They are both easy to compute and offer different viewpoints from which to consider compound selectivity.


CONCLUSIONS
We performed a comparative analysis of their respective performance on several data sets against already published selectivity metrics and analyzed how they might influence compound selection. Our results showed that the two new metrics bring additional information to prioritize compound selection. Two novel metrics were developed to better estimate selectivity of compounds screened on multiple proteins.
['Bosc|Nicolas|N|', 'Meyer|Christophe|C|', 'Bonnet|Pascal|P|http://orcid.org/0000-0001-6485-138X']
D045744:Cell Line, Tumor D019295:Computational Biology D016208:Databases, Factual D055808:Drug Discovery D006801:Humans D008962:Models, Theoretical D047428:Protein Kinase Inhibitors D011494:Protein Kinases
Selectivity Protein kinase Window score Ranking score Entropy score Gini score Kinase inhibitor Partition index