Drug repositioning framework by incorporating functional information

Abstract

As a shortcut for drug development, drug repositioning draws more and more attention in pharmaceutical industry to identify new indications for marketed drugs or drugs failed in late clinical trial phase. At the same time, the abundant highthroughput data pushes the computationally repositioning drugs a hot topic in the area of systems biology. Here, the authors propose a general framework for repositioning drug by incorporating various functional information. The framework starts with the identification of differentially expressed gene sets under disease state and drug treatment. Then the disease and drug are associated by the overlap of these two gene sets via biological function. The authors provide two strategies to assess the functional overlap. In the first strategy, functional relevance are evaluated by leveraging genes' lethality information to reveal drug's potential of curing diseases. In the second strategy, biological process perturbation profiles are identified by mapping differentially expressed genes into pathways and gene ontology (GO) terms. Their associations are assessed and used to rank drugs' potential of curing diseases. The preliminary results on prostate cancer demonstrate that our new framework improves the drug repositioning efficiency and various function information could complement each other. Importantly, the new framework will enhance the biological interpretation and rationale of drug repositioning and provide insights into drug action mechanisms.

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

“…88 Unique top target families are labeled for natural products (protein phosphatases and glycosyl hydrolases) and for FDA-approved drugs (cytokine receptors and isomerases). Notably, the method of Jahchan et al 31 incorporates information on both the known targets and enriched pathways of the top-scoring small molecules; Laenen et al 32 incorporate a functional protein association network into their model and Wu et al 33 incorporate a side effect score based on differential expression of essential genes. For example, one may discover the unknown MoA of a compound via the comparison of its gene expression prole to those of small molecules of known MoA (see Fig.…”

Meta-path Based Prioritization of Functional Drug Actions with Multi-Level Biological Networks

“…The therapeutic hypothesis refers to the hypothesis that a drug cures a specific disease. First, it provides biological interpretations that support the therapeutic hypothesis 3,8 . For example, vasodilation is the functional drug action of captopril, which is an anti-hypertensive drug, and it provides biological interpretation of how the drug attenuates high blood pressure more than its target protein, the ACE protein 9 .…”

“…Functional drug actions have also been studied. They can be observed via wet experiments or be inferred from molecular drug actions such as DEGs 3–6 (differentially expressed genes). While no database provides information on the functional drug actions using standardized identifiers such as GO terms, the DrugBank database provides information about them in plain text.…”

Turning genome-wide association study findings into opportunities for drug repositioning

“…In addition, limitations in data sources could affect the performance of network-based analysis methods, for example the reliability and completeness of interactome data [123] and the difficulty in constructing accurate PPI networks [124] . Finally, the complicated nature of network structures makes parameter optimization a key issue when analyzing the data with network-based algorithms [125] .…”