The use of novel selectivity metrics in kinase research

BMC Bioinformatics

Methodology Article

Pascal Bonnet¹  Nicolas Bosc¹  Christophe Meyer² 

[1] Institut de Chimie Organique et Analytique (ICOA), UMR CNRS-Université d’Orléans 7311, Université d’Orléans BP 6759, 45067, Orléans Cedex 2, France;Janssen-Cilag, Centre de Recherche Pharma Janssen-Cilag, Campus de Maigremont–CS 10615, Chaussée du Vexin, 27106, Val de Reuil, France;

关键词: Selectivity;    Protein kinase;    Kinase inhibitor;    Gini score;    Entropy score;    Partition index;    Window score;    Ranking score;   

DOI  :  10.1186/s12859-016-1413-y

 received in 2016-09-09, accepted in 2016-12-07,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundCompound 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.ResultsTo 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.ConclusionsWe 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.Graphical AbstractTwo novel metrics were developed to better estimate selectivity of compounds screened on multiple proteins.

【 授权许可】

CC BY   
© The Author(s). 2017