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.Graphical abstractSpectrum of Systems Biology Approaches for Drug Combinations.
Highlights
Despite increasing investments in pharmaceutical research and development, the rate of introduction of successfully translated drugs has decreased [1]
Pharmaceutical research has increasingly relied on reductionist approaches, even though systemic diseases such as cancer and heart disease are managed by large, interconnected networks with many pathways affecting pathological signaling [4,5]
Review Here, we review computational and experimental methods for accelerating the discovery of effective drug combinations for complex diseases, with special focus on cancer
Summary
Despite increasing investments in pharmaceutical research and development, the rate of introduction of successfully translated drugs has decreased [1]. Reasons for increased attrition rates in drug development include toxicity and inadequate efficacy due to individual variation in therapeutic response and development of drug resistance [2]. This increased attrition rate has coincided with increased interest in seeking highly specific ligands affecting single targets for treatment of disease [3]. The redundancy and feedback in these networks allows for robustness of phenotype and maintenance of homeostasis [6,7]. This network complexity has hindered development of new therapies and
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