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, IC or percentage of inhibition), cell-based potency (percentage of effect, EC) 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217660 | PMC |
| http://dx.doi.org/10.1186/s12859-016-1413-y | DOI Listing |
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