Network Pharmacology Strategies Toward Multi-Target Anticancer Therapies: From Computational Models to Experimental Design Principles

Abstract

Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.

Emerging therapeutic targets in myeloproliferative neoplasms and peripheral T-cell leukemia and lymphomas

“…It is known that cancer cells may harbor a variety of somatic alterations in various biological pathways, but only part of these mutations are related to cancer initiation and development and only a few are driving the cancer progression. Accordingly, upon treatment with a single targeted drug, it is often difficult to predict the outcome because of the compensatory pathways and feedback loops that are still poorly understood in most cancers [10–12]. …”

Network-based approaches for drug response prediction and targeted therapy development in cancer

“…Applying network-based methods will allow to reason on intervention schemes at the level of the whole cell signaling and to take into account specific vulnerabilities in tumors of each patient [63]. Development of robust pipelines providing various methods for prediction of drug response and suggestion of intervention schemes will ameliorate patient-specific treatment.…”

Network analysis: a new approach to study endocrine disorders

“…Computational modelling of these interactions is now possible and has facilitated a shift towards network-centric strategies in new drug design (Nacher & Schwartz 2008). As a consequence, a novel concept has now emerged in drug discovery for improving treatment responsiveness termed 'polypharmacology' (Barabasi et al 2011) and has been implicated in the design of novel anticancer drugs (Tang & Aittokallio 2013) and antimicrobial agents (Rohde et al 2012).…”