Proc Natl Acad Ssi / PNAS    2014 

   "Exploiting polypharmacology for drug target deconvolution"

 by Taranjit S. Gujral*, Leonid Peshkin*, and Marc W. Kirschner

Title: Modeling technique predicts untested protein kinase inhibitor specificity:

Keywords: 
systems pharmacology | regularized regression | perturbation biology | predictive modeling | cencer cell migration 

Abstract: Polypharmacology (action of drugs against multiple targets)
represents a tempting avenue for new drug development; unfortunately,
methods capable of exploiting the known polypharmacology of drugs for
target deconvolution are lacking. Here, we present an ensemble
approach using elastic net regularization combined with mRNA
expression profiling and previously characterized data on a large set
of kinase inhibitors to identify kinases that are important for
epithelial and mesenchymal cell migration. By profiling a selected
optimal set of 32 kinase inhibitors in a panel against six cell lines,
we identified cell-type specific kinases that regulate cell migration.
Our discovery of several `informative' kinases with a previously
uncharacterized role in cell migration (such as Mst and Taok family of
MAPK kinases in mesenchymal cells) may represent `novel' targets that
warrant further investigation. Target deconvolution using our ensemble
approach has the potential to aid in the rational design of more
potent but less toxic drug combinations.


Although protein kinase inhibitors (PKIs) represent a promising class
of anti-cancer therapeutics, these drugs act in a notoriously
nonspecific manner. Recent advances in high-throughput screening have
allowed researchers to profile the drug target specificities for
hundreds of PKIs, however instead of simplifying the picture these
studies have shown that even well-characterized inhibitors that were
thought to be specific can interact with off-target kinases. Seeking a
different approach, Taranjit Gujral et al. mathematically modeled two
key steps currently needed to analyze PKI specificity-kinase
expression and kinase profiling-and identified kinases central to
epithelial and mesenchymal cell migration in previously characterized
data from a large set of real-world kinase inhibitors. Using these
results, the authors constructed a set of 32 optimal kinase inhibitors
spanning a broad range of kinase specificities and profiled them
against six cell lines, thus revealing cell-type specific kinases
predicted to regulate cancer cell migration. To assess the results,
the authors then validated the role of these kinases by depleting them
in gene knockdown experiments. The findings offer a means to predict
cell-type specific responses to previously untested PKIs and should
contribute to identifying new cancer treatments, according to the
authors.