Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products

Abstract

It is widely accepted that drug discovery often requires a systems-level polypharmacology approach to tackle problems such as lack of efficacy and emerging resistance of single-targeted compounds. Network pharmacology approaches are increasingly being developed and applied to find new therapeutic opportunities and to re-purpose approved drugs. However, these recent advances have been relatively slow to be translated into the field of natural products. Here, we argue that a network pharmacology approach would enable an effective mapping of the yet unexplored target space of natural products, hence providing a systematic means to extend the druggable space of proteins implicated in various complex diseases. We give an overview of the key network pharmacology concepts and recent experimental-computational approaches that have been successfully applied to natural product research, including unbiased elucidation of mechanisms of action as well as systematic prediction of effective therapeutic combinations. We focus specifically on anticancer applications that use in vivo and in vitro functional phenotypic measurements, such as genome-wide transcriptomic response profiles, which enable a global modelling of the multi-target activity at the level of the biological pathways and interaction networks. We also provide representative examples of other disease applications, databases and tools as well as existing and emerging resources, which may prove useful for future natural product research. Finally, we offer our personal view of the current limitations, prospective developments and open questions in this exciting field.

A review of connectivity map and computational approaches in pharmacogenomics

“…Another possible limitation in CMap is the presence of potential batch effect, the similarity of gene expression profiles observed for unrelated stimuli in grown or processed cells at the same time. Batch effects have been identified as a significant source of systematic error that can be corrected [82]. Attempts to solve the problem of batch effects have been made in the methods proposed.…”

“…The authors partitioned the DN using graph theory tools to identify groups of drugs (communities) that are densely interconnected [63]; the same method was also applied by [82,83]. Their results revealed that these groups were significantly enriched with drugs of a similar MoA and therapeutic purpose and, hence, can be used for such predictive purposes.…”

Discovery and development of natural product oridonin-inspired anticancer agents

“…It is known that the polypharmacology and network pharmacological approach have been widely recognized in drug discovery to enhance efficacy and overcome drug resistance of singular-target drugs [98–100]. Such approaches have been successfully applied in the field of natural product research based upon the systematic elucidation and prediction of mechanisms, as well as functional phenotypic measurements in vivo and in vitro [101, 102]. These relevant advances inspire researchers to identify new and unexplored targets of oridonin and its derivatives (e.g.…”

Identification of drug candidates and repurposing opportunities through compound–target interaction networks

“…However, due to the exponentially increasing number of combinations, effective computational approaches are required for exploring the most potent multi-target regimens on a network level [11]. Another exciting application area involves multitargeted mixtures of natural products, where network pharmacology approaches may enable systematic mapping of yet unexplored target space of natural compounds [77].…”