Abstract
Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations.
Original language | English |
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Article number | 7 |
Journal | Journal of Cheminformatics |
Volume | 7 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2015 |
Bibliographical note
Funding Information:This work is partly supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award T32CA17468 (K.A.R.), the National Institutes of Health P50CA058187, Cancer League of Colorado, the Department of Defense Award W81XWH-11-1-0527 and the David F. and Margaret T. Grohne Family Foundation. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the funders.
Publisher Copyright:
© 2015 Ryall and Tan; licensee Springer.
Keywords
- Cancer
- Computational modeling
- Drug combinations
- Drug discovery
- Systems biology
ASJC Scopus subject areas
- Computer Science Applications
- Physical and Theoretical Chemistry
- Computer Graphics and Computer-Aided Design
- Library and Information Sciences