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. 2012 Jul 2;6:80.
doi: 10.1186/1752-0509-6-80.

Rational Drug Repositioning Guided by an Integrated Pharmacological Network of Protein, Disease and Drug

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Free PMC article

Rational Drug Repositioning Guided by an Integrated Pharmacological Network of Protein, Disease and Drug

Hee Sook Lee et al. BMC Syst Biol. .
Free PMC article

Abstract

Background: The process of drug discovery and development is time-consuming and costly, and the probability of success is low. Therefore, there is rising interest in repositioning existing drugs for new medical indications. When successful, this process reduces the risk of failure and costs associated with de novo drug development. However, in many cases, new indications of existing drugs have been found serendipitously. Thus there is a clear need for establishment of rational methods for drug repositioning.

Results: In this study, we have established a database we call "PharmDB" which integrates data associated with disease indications, drug development, and associated proteins, and known interactions extracted from various established databases. To explore linkages of known drugs to diseases of interest from within PharmDB, we designed the Shared Neighborhood Scoring (SNS) algorithm. And to facilitate exploration of tripartite (Drug-Protein-Disease) network, we developed a graphical data visualization software program called phExplorer, which allows us to browse PharmDB data in an interactive and dynamic manner. We validated this knowledge-based tool kit, by identifying a potential application of a hypertension drug, benzthiazide (TBZT), to induce lung cancer cell death.

Conclusions: By combining PharmDB, an integrated tripartite database, with Shared Neighborhood Scoring (SNS) algorithm, we developed a knowledge platform to rationally identify new indications for known FDA approved drugs, which can be customized to specific projects using manual curation. The data in PharmDB is open access and can be easily explored with phExplorer and accessed via BioMart web service (http://www.i-pharm.org/, http://biomart.i-pharm.org/).

Figures

Figure 1
Figure 1
Overview of PharmDB. Nine different databases were integrated using standard IDs (Entrez Gene ID for protein, PubChem CID for drug and MeSH Descriptor ID for disease) to construct PharmDB. The integrated network was analyzed using the shared neighborhood scoring algorithm, providing a predictive capacity for PharmDB to suggest functional relationships between diseases, proteins, and rugs. These data are provided through a web browser, phExplorer (network visualization software) and web service (http://www.i-pharm.org/, http://biomart.i-pharm.org/).
Figure 2
Figure 2
Shared neighborhood scoring algorithm. SN score can be calculated by summing up "Shared Nodes Count" and "Shared Nodes Weight". First, “Shared Nodes Count” is defined as the number of shared nodes to consider the effect of direct connectors. Similary, “Shared Nodes Weight” is defined as the product of each weight of links bridging two end nodes to trace the effect of indirect neighbors (Bottom right). Here weight is a measure for connecting probability of each pair. For all possible pairs of the network, firstly weight 1 is assigned to each connected pairs directly linked between two nodes. Weight for unconnected pairs is assigned the connection probability, the fraction of directly connected pairs among the total number of pairs having the given “Shared Nodes Count”. For example, (1) the weight product of two indirect links (2), (3) weight of the upper (direct link / indirect link) multiplied by weight of the lower one (indirect link / direct link). The sum of (1), (2), and (3) is "Shared Weight" of the unconnected pair (protein, drug) (bottom left).
Figure 3
Figure 3
Shared neighborhood node distribution and evaluation of the shared neighborhood scoring algorithm. Shared neighborhood node distribution comparison between connected links and unconnected links in Drug-Protein relation ( A), Drug-Disease relation ( B) and Protein-Disease relation ( C) (Rectangle: Connected links, Triangle: Unconnected links). ROC analysis of simple form of SNS algorithm and extended form of SNS algorithm in Drug-Protein relation ( D), Drug-Disease relation ( E) and Protein-Disease relation (F) (Rectangle: Simple algorithm, Triangle: Extended algorithm).
Figure 4
Figure 4
Drug repositioning pipeline overview. Schematic representation of drug repositioning pipeline for squamous cell carcinoma (SCC). First, cancer-related proteins and drugs were extracted from PharmDB using cancer terms (such as “Carcinoma”, “Neoplasm”, and “Cancer”). Second, inferred SCC-related drugs were extracted using the shared neighborhood scoring algorithm. Among the candidates, any known cancer agents were filtered out; leaving only drugs that had not been previously implicated as anti-cancer drugs. Then the FDA approved drugs which known to be related with cancer-related proteins were maintained for further analysis as SCC drug candidates in this study.
Figure 5
Figure 5
The hypoxia-dependent TBZT effect against SCC. ( A) Antiproliferative activity of TBZT was monitored by [3 H] thymidine incorporation under normoxic and hypoxic conditions. (B) The effect of TBZT on cell death was monitored by counting sub-G1 cells.
Figure 6
Figure 6
TBZT as an inhibitor of CA9. ( A) To validate predictions by PharmDB analysis for SCC, TBZT is tested for inhibitory activity against its potential targets, CA isozymes (CA1, CA2, and CA9). ( B) In vitro inhibition of TBZT and the AZA control against CA isoforms (i.e., 1, 2, and 9). ( C) Cellular levels of CA9 in the SCC cell line, HCC-1588, under normoxic and hypoxic conditions. ( D) The effect of TBZT on cell death was monitored by caspase-3 activation. (E) HCC-1588 cells, transfected with an empty vector (EV) or CA9, were treated with TBZT under normoxic and hypoxic conditions.

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