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

Abstract

Introduction: System-wide identification of both on- and off-targets of chemical probes provides improved understanding of their therapeutic potential and possible adverse effects, thereby accelerating and de-risking drug discovery process. Given the high costs of experimental profiling of the complete target space of drug-like compounds, computational models offer systematic means for guiding these mapping efforts. These models suggest the most potent interactions for further experimental or pre-clinical evaluation both in cell line models and in patient-derived material.

Areas covered: The authors focus here on network-based machine learning models and their use in the prediction of novel compound–target interactions both in target-based and phenotype-based drug discovery applications. While currently being used mainly in complementing the experimentally mapped compound–target networks for drug repurposing applications, such as extending the target space of already approved drugs, these network pharmacology approaches may also suggest completely unexpected and novel investigational probes for drug development.

Expert opinion: Although the studies reviewed here have already demonstrated that network-centric modeling approaches have the potential to identify candidate compounds and selective targets in disease networks, many challenges still remain. In particular, these challenges include how to incorporate the cellular context and genetic background into the disease networks to enable more stratified and selective target predictions, as well as how to make the prediction models more realistic for the practical drug discovery and therapeutic applications.

Keywords:

• cell-based models
• drug repositioning
• drug–target interactions
• machine learning
• network pharmacology
• phenotypic screening
• target validation

Acknowledgmentst

The authors would like to thank Mrs. Janica Wakkinen and Dr. Simon Anders for many useful discussions about different types of experimental assays and computational models.

Declaration of interest

A Cichonska is financially supported by the Biocentrum Helsinki Foundation. T Aittokallio is financially supported from the Academy of Finland (grants 269862, 272437, 279163 and 292611) and the Cancer society of Finland. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Notes

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