Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Abstract

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

Omics-Based Interaction Framework – a systems model to reveal molecular drivers of synergy

“…Synergistic and antagonistic interactions are common in nature and frequently promote efficacy of therapeutic interventions (Chen et al , 2015; Ronzitti et al , 2018; Wei et al , 2018; Zappasodi et al , 2018; Han et al , 2019). While synergy quantification methods from dose-response data, combinatorial screening of molecule libraries, and other predictive exploration models may suggest potentially synergistic conditions or treatments, they do not provide substantive insights into the molecular mechanisms underlying synergy (Chen et al , 2015). Thus, synergy-mediating pathways cannot be strategically targeted in rational drug development.…”

“…Our interest in synergy arose from our observations of the strikingly synergistic interactions of one such empirically derived combination, Pam2-ODN. While we could easily quantify the superiority of protection conferred by the dual treatment, in the absence of a systems theory to interrogate synergistic mechanisms (Chen et al , 2015; Wei et al , 2018), we were limited in our capacity to use available Omics datasets to deduce the mechanisms mediating the synergy. This is important because, although this lack of mechanistic understanding does not limit the utility of the current combination, it precludes development of next generation interventions that more precisely (perhaps, more efficaciously) target the synergy-driving pathways with fewer off-target (potentially toxic) effects.…”

“…Yet, there remains lack of consensus regarding the appropriate analysis of statistical and biological interactions found in non-additive (i.e., antagonistic or synergistic) responses (Wei et al , 2018). Moreover, previously proposed strategies to analyze non-additive interactions frequently lack sufficient generalizability to study these processes outside of their home Omics platforms (Chen et al , 2015). Thus, while synergistic therapeutic combinations may be empirically derived from fortuitous clinical experiences or through screening of bioactive small molecule libraries, the absence of an established means to investigate these favorable drug-drug interactions ultimately precludes understanding of their underlying mechanisms.…”

Pharmacovigilance in the Long-Term use of Medications and the Alternative Based on the Combination of Drugs to Reduce Adverse Effects

“…As science and technology advances, drug combinations have been defined and continues to expand its scope however because biological systems and mathematical analysis of dose-response curves are complex, there are several models, approaches, assumptions and theories analysis drug combination [6,7,9,10].…”

Synergistic Inhibition of Breast Cancer Cell Growth by an Epigenome-Targeting Drug and a Tyrosine Kinase Inhibitor

“…[3][4][5] It is well known that most human complex diseases, especially cancer, are not the result of a single disease gene, but are caused by complex biological networks. 6) As a result, it is reasonable to speculate that targeting multiple disorder factors of disease will have higher medicinal potential than single-targeted agents. In the context of cancer, drug combinations may target multiple pathways and significantly inhibit the growth of tumor cells, and will be more conducive to treating cancer than single therapeutic agents.…”