Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug
- Hee Sook Lee†1, 6,
- Taejeong Bae†1, 2, 6,
- Ji-Hyun Lee†1, 2, 6,
- Dae Gyu Kim1,
- Young Sun Oh1,
- Yeongjun Jang3,
- Ji-Tea Kim3,
- Jong-Jun Lee1,
- Alessio Innocenti4,
- Claudiu T Supuran4,
- Luonan Chen5,
- Kyoohyoung Rho3Email author and
- Sunghoon Kim1, 2Email author
© Lee et al.; licensee BioMed Central Ltd. 2012
Received: 5 January 2012
Accepted: 31 May 2012
Published: 2 July 2012
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.
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.
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/).
KeywordsTripartite network Drug repositioning Shared Neighborhood Scoring (SNS) algorithm
Modern drug discovery is time-consuming and expensive, involving coordinated multi-disciplinary research in multiple stages, each requiring intensive and specialized resources . Although rapid advancement of “omics” approaches, computational systems biology and accumulation of digital data resources have provided a vast array of significant information in life science , data relevant to drug discovery are not easily identified and recruited for application to pharmaceutical research . Despite the technological advances in drug discovery such as HTS, the approval of new drugs has remained stagnant in the past decade, resulting in an overall decline in the productivity of` the pharmaceutical industry.
In efforts to save development time and minimize the risk of failure during drug develo pment, repositioning of currently available drugs to new therapeutic indications is considered an alternative route . To date most repositioned drugs have been the consequence of serendipitous observations of unexpected efficacy and side effects of drugs in development or on the market. However, recently, systems biology approaches have been applied in efforts to discover unknown effects for existing drugs. For instance, drug repositioning approaches have incorporated in silico approaches for analyzing large data sets such as gene expression profiles [4,5], literature mining , chemical similarity , side-effect similarity , disease-drug network , pathway-based disease network , and phenotypic disease network . To establish a more logical approach to repositioning a known drug to a new indication, we established a knowledge platform comprising binary linkages between diseases, drugs, and proteins, from which new and previously unknown connections can be drawn between drugs and diseases of interest. This integrated database was designated PharmDB.
For probing the database and identifying disease-drug linkages, we have developed the Shared Neighborhood Scoring (SNS) algorithm, which predicts relationships between drugs, proteins and diseases. While the relationship data are collected from experiments, coverage of the data is still incomplete. Thus there may be undetected links and hidden nodes in the network. Up to now, a number of prediction methods and measures have been proposed to find these undetected associations from topological or structural properties of various complex networks [12,13]. To date, most of these algorithms and measures are applicable only to a monopartite network that consists only of one type of node. Therefore, multipartite network composed of more than a type of nodes cannot be analyzed using these measures. To solve this problem, researchers have used projection methods that convert multipartite networks into monopartite ones. Unfortunately, any projection method can result in information loss, especially in low-degree nodes. Accordingly projecting the PharmDB tripartite network into monopartite drug, protein and disease networks can distort many well-known network measures, such as average path length < l>, average clustering coefficient < C>, degree-dependent clustering coefficient C(k), degree distribution P(k), assortativity coefficient r , and degree-degree correlation coefficient knn(k) . To overcome these limits of the projection technique, we designed a new prediction method called Shared Neighborhood Scoring (SNS) algorithm which calculates the probability of a link existence between two nodes of interest. This can be done by evaluating the connections of their neighbors in PharmDB tripartite network.
Data sources of PharmDB
Shared neighborhood scoring (SNS) algorithm
The prediction performance of the SNS algorithm was measured by plotting receiver operating characteristic (ROC) curves (Figure 3D, 3E, 3 F). For calculating SN scores, “simple algorithm” considers only “Shared Nodes Count” but “extended algorithm” includes both “Shared Nodes Count” and “Shared Nodes Weight”. As shown in the Figure 3, the extended algorithm shows better performance than simple one. AUC values of simple algorithm are 0.679, 0.778, and 0.602, in Drug-Protein relation, Drug-Disease relation, and Protein-Disease relation, respectively. And AUC values for extended algorithm are 0.937, 0.868, and 0.871. According to the result, prediction performances with extended scope of shared neighborhood nodes were improved by 38%, 12%, and 45%, respectively.
Case study – benzthiazide as a potential agent for lung cancer
CA9, a carbonic anhydrase isoenzyme, is a transmembrane protein that plays an important role in pH regulation . The expression of CA9 is highly induced in various cancers under hypoxic conditions, which is functionally important for the growth and survival of tumor cells . We confirmed whether CA9 is actually induced in hypoxic conditions by Western blotting with its specific antibody in HCC-1588. As expected, CA9 levels were significantly increased in hypoxic conditions (1% O2) compared with those in normoxic conditions (20% O2) (Figure 6C). We also confirmed that TBZT induced cell death by measuring the activation of caspase 3 (Figure 6D). To confirm the drug-protein pair relationship between CA9 and TBZT, we tested whether the forced expression of CA9 would compensate for the anti-proliferative activity of CA9 by the treatment of TBZT under hypoxic conditions. Cell proliferation was reduced to 70% of the control cells by the treatment of TBZT in the cells transfected with EV, but 35% of the control cells in the cells transfected with CA9. Therefore, the exogenous supplementation of CA9 recovered the proliferation by up to 35% (Figure 6E). This result validates that the anti-proliferative activity of TBZT against HCC-1588 cells mainly involves CA9. Perhaps, the remaining part could be contributed by other CA isozymes that are also involved in the regulation of cancer. Even if further chemical optimization of TBZT is required to improve efficacy and specificity, these results suggest a possible application of TBZT for further development against lung cancer through its CA9 inhibitory activity.
This study demonstrates that drug repositioning can be rapidly guided by a knowledge platform PharmDB, a pharmacological network database comprising protein, drug, and disease data which we are providing as a web-based service. As an ever-increasing amount of biological and pharmacological data are scattered throughout the literature and in proprietary databases, the integrated data of PharmDB provides a valuable tool by consolidating certain valuable sets of data. We adopted a tripartite pharmacological network-based analysis, and developed a novel neighborhood scoring algorithm to predict previously unknown relationships between drugs, proteins and diseases. The theoretical foundation of algorithm is that a connection probability between two nodes is proportional to the number of nodes commonly shared between them. So the connection probability of two indirectly linked nodes was computed, which is called the shared neighborhood score. This score can highlight missing linkages which may either result from “no actual connection” or “lack of information” and help to differentiate between these two possibilities.
We experimentally validated the usefulness of the shared neighborhood score by identifying a hitherto unknown drug-protein relationship and potential new indication based on this connection. Aside from drug repositioning, the network map of PharmDB composed of not only the data integrated from diverse databases but also the predicted data using the shared neighborhood algorithm can applied to other purposes, such as the prediction of drug mode-of-action, off-target effects, and even the design of optimal drug combinations for a disease of interest.
PharmDB, an integrated tripartite database, coupled with Shared Neighborhood Scoring (SNS) algorithm, would provide much more enriched information than general integrated databases and give us clues for finding new indication of known drugs. Furthermore, these data can be easily explored with phExplorer and accessed via BioMart web service (http://www.i-pharm.org/, http://biomart.i-pharm.org/).
Construction of PharmDB
To integrate the data in the existing databases that contain different identifiers, we assigned the following standard identifiers (IDs): PubChem CID for drug, GeneID for protein, and MeSH descriptor for disease. We constructed a comprehensive drug-protein-disease tripartite network by integrating the link information from the nine databases, namely, EntrezGene interaction (ftp://ftp.ncbi.nih.gov/gene/GeneRIF/), MINT (ftp://mint.bio.uniroma2.it/pub/release/mitab26/2011-07-08/), CTD (http://ctd.mdibl.org/), TTD (http://bidd.nus.edu.sg/group/cjttd/TTD_Download.asp), ChemBank (http://chembank.broadinstitute.org), DIP (http://dip.doe-mbi.ucla.edu/dip/Download.cgi), PharmGKB (http://www.pharmgkb.org/resources/downloads_and_web_services.jsp), OMIM (ftp://ftp.ncbi.nih.gov/repository/OMIM/ARCHIVE/), and GAD (http://geneticassociationdb.nih.gov/). As the existing databases have their own unique ID systems, we tagged the standard IDs using an in-house script. For the entities that were not tagged by the in-house script, we manually assigned them with appropriate IDs.
Shared neighborhood scoring algorithm
The shared neighborhood score then becomes where ,, and . Here, the 1st-order term is added when some nodes are linked directly to node i (or j) but linked indirectly to node j (or i). The 2nd-order term is considered only when some nodes are linked indirectly to both node i and node j. In Additional file 1: Figure S1, as node m is the only shared neighbor of node i and node j, . To obtain the 1st-order shared neighborhood score or the 2nd-order shared neighborhood score , connection probability is calculated beforehand. As a pair (i, k) is mediated by two nodes, , Path (i, k, j) is composed of an indirect link ( i, k) and a direct link ( k, j). Similarly, and . Path (i, l, j) is composed of both indirect links ( i, l) and ( l, j). The total shared neighborhood score is thus . When calculating , we omitted a link between node i and j to remove the dependency of the measure on the existence of a link between i and j, which is the so-called “leave-one-out approach” .
The shared neighborhood score is proportional to the number of shared neighborhood nodes. So there is no an upper limit on score. The problem is that the amount of data is not evenly distributed on each relation category. So, even if two different types of relations have identical score, their connecting possibility can’t be regarded as identical. For that reason, we normalized the shared neighborhood score using connecting probability function (Additional file 1: Figure S3).
FDA approved drugs
We have downloaded Drugs@FDA data files (Last updated: 19/09/2011)(http://www.fda.gov/downloads/Drugs/InformationOnDrugs/UCM163762). Then we extracted single active ingredient from Product table and tagged PubChem ID for them. The total number of FDA approved drugs tagged with PubChem ID is 23,191.
Cell culture and materials
The HCC-1588 cell line was obtained from the Korean cell line bank and was maintained in RPMI (Hyclone) containing 10% fetal bovine serum and 1% antibiotics. Antibody against caspase-3 and tublin (Cell Signaling Technology) were purchased. M73 monoclonal antibody to CA9 was obtained from Dr. S. Pastorekova (Slovak Academy of Science, Slovak Republic). TBZT and AZA were purchased from Sigma.
Thymidine incorporation assay
To determine the effect of TBZT on cell proliferation, HCC-1588 cells were treated with TBZT in 2% serum-containing media for 48 h under normoxic (20% O2) and hypoxic (1% O2) conditions. AZA was used as positive control. pcDNA3-CA9 vector and empty vector (Dr. J.-Y. Kim, National Cancer Center, Korea) were transfected into HCC-1588 cells using Lipofectamine 2000 (Invitrogen). After 24 h incubation, TBZT was added to 2% serum-containing media for 48 h under hypoxic conditions. [3 H] thymidine at 1 μ Ci/ml was added to the culture medium and was incubated for 4 h. The incorporated thymidine was measured by liquid scintillation counter (Wallac).
HCC-1588 cells were treated with TBZT (0.4, 2, 10 μM) in 2 % serum-containing medium for 48 h under normoxic and hypoxic conditions. AZA was used as positive control. The treated cells were fixed with 70% ethanol for 1 h at 4°C, washed twice with ice-cold PBS, and stained with propidium iodide (50 μg/ml) containing 0.1% sodium citrate, 0.3% NP-40 (nonylphenoxylpolyethoxylethanol 40), and 50 μg/ml RNase A for 40 min. The cells were subjected to flow cytometry (FACSCalibur, Becton-Dickinson) to evaluate the apoptotic cells by counting the sub-G1 cells. For each sample, 20,000 cells were analyzed using Cell Quest Pro software.
An applied photophysics stopped-flow instrument was used for assaying CA-catalyzed CO2 hydration activity . Following the initial rates of the CA-catalyzed CO2 hydration reaction for a period of 10–100 s, phenol red (at a concentration of 0.2 mM) was used as the indicator, working at the absorbance maximum of 557 nm in 20 mM HEPES buffer (pH 7.5) and 20 mM Na2SO4 (to maintain the constant ionic strength). For the determination of the kinetic parameters and inhibition constants, the CO2 concentrations used ranged from 1.7–17 mM. For each inhibitor, at least six traces of the initial 5–10% of the reaction were used for determining the initial velocity. The uncatalyzed rates were determined in the same manner and were subtracted from the total observed rates. Stock solutions of the inhibitor (0.1 mM) were prepared in distilled-deionized water and diluted to 0.01 nM with distilled-deionized water. Inhibitor and enzyme solutions were preincubated together for 15 min–72 h at room temperature (15 min) or 4°C (all other incubation times) prior to assay to allow the formation of the enzyme-inhibitor complex or the eventual active site mediated hydrolysis of the inhibitor. The inhibition constants were obtained by non-linear least-squares methods using PRISM 3 as previously described. The mean values were represented from at least three different determinations [31,34].
Current positions: KR, YJ - Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 305–806, Korea
Shared Neighborhood Scoring
- SN score:
Shared Neighborhood score
the Molecular INTeraction database
the Database of Interacting Proteins
The Comparative Toxicogenomics Database
Therapeutic Target Database ChemBank
The Pharmacogenomics Knowledge Base
Online Mendelian Inheritance in Man
Genetic Association Database
Receiver Operating Characteristic
Area Under Curve
Squamous Cell Carcinoma
- KS test:
The authors thank to Sangho Oh and Nam-Jin Koo at Korean Bioinformation Center for updating PharmDB data. This study was supported by the grants of the Global Frontier (NRF-M1AXA002-2010-0029785) and the Research Information Center Supporting Program (2012–0000350) and the WCU project (R31-2008-000-10103-0) of the Ministry of Education, Science, and Technology and Korea Healthcare Technology (A092255-0911-1110100), the Ministry of Health and Welfare Affairs, and Gyonggi-do to SK, an EU project of the 7th framework programme (METOXIA) to CTS, and by the Korean Ministry of Education, Science and Technology (MEST) under grant number 20110002321.
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