Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27881431
&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215
Predictive approaches for drug combination discovery in cancer
#MMPMID27881431
Madani Tonekaboni SA
; Soltan Ghoraie L
; Manem VSK
; Haibe-Kains B
Brief Bioinform
2018[Mar]; 19
(2
): 263-276
PMID27881431
show ga
Drug combinations have been proposed as a promising therapeutic strategy to
overcome drug resistance and improve efficacy of monotherapy regimens in cancer.
This strategy aims at targeting multiple components of this complex disease.
Despite the increasing number of drug combinations in use, many of them were
empirically found in the clinic, and the molecular mechanisms underlying these
drug combinations are often unclear. These challenges call for rational,
systematic approaches for drug combination discovery. Although high-throughput
screening of single-agent therapeutics has been successfully implemented, it is
not feasible to test all possible drug combinations, even for a reduced subset of
anticancer drugs. Hence, in vitro and in vivo screening of a large number of drug
combinations are not practical. Therefore, devising computational methods to
efficiently explore the space of drug combinations and to discover efficacious
combinations has attracted a lot of attention from the scientific community in
the past few years. Nevertheless, in the absence of consensus regarding the
computational approaches used to predict efficacious drug combinations, a
plethora of methods, techniques and hypotheses have been developed to date, while
the research field lacks an elaborate categorization of the existing
computational methods and the available data sources. In this manuscript, we
review and categorize the state-of-the-art computational approaches for drug
combination prediction, and elaborate on the limitations of these methods and the
existing challenges. We also discuss about the recent pan-cancer drug combination
data sets and their importance in revising the available methods or developing
more performant approaches.
|*Drug Discovery
[MESH]
|Animals
[MESH]
|Antineoplastic Combined Chemotherapy Protocols/*therapeutic use
[MESH]
free
free
free
