The use of novel selectivity metrics in kinase research View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Nicolas Bosc, Christophe Meyer, Pascal Bonnet

ABSTRACT

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. More... »

PAGES

17

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12859-016-1413-y

    DOI

    http://dx.doi.org/10.1186/s12859-016-1413-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1016546465

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28056771


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