**7. Opportunities and challenges**

On contrary to traditional drug discovery program (a complex and time consuming process with high cost of development and risk of failure), drug repositioning, reduces the time and cost of drug development. Drug repositioning is also a low-risk strategy. Computational or machine learning approach has significantly improved the performance of drug repositioning. In comparison to computational approaches, using experimental approaches (such as target protein-based screening, cell-based assay, testing in animal model, and clinical testing) that provide direct evidence-based understanding of links between drugs and diseases are more reliable and credible. However, in recent years computational approaches are usually combined with the experimental approaches to identify new indications for old drugs, called mixed approaches. In this approach, computational methods are validated by biological experiments and clinical tests. Mixed approach of repositioning offers a rational and exhaustive exploration of all possible repositioning opportunities, taking into consideration improved access to available databases and technological advances. Furthermore, the R&D investment required for drug repositioning is lower than that for traditional drug discovery. Thus, drug repositioning offers an opportunity for many pharmaceutical companies to develop drugs with lower investments [27, 28]. Mixed approach of DR offers opportunities for developing repositioned drugs more effectively and rapidly. From the market perspective, a large number of diseases require new drugs to be treated with a potential market demand and economic impacts. For example, the discovery of drugs for rare/neglected diseases has a large potential market to explore. There is, therefore, an opportunity for repurposing of drugs for the treatment of rare, neglected, orphan diseases or difficult to treat diseases. There are over 6000 rare diseases that lack proper treatment. About 5% of them are being researched. Rare diseases have a large potential market to explore. Given the high attrition rates, substantial costs and slow pace of drug discovery and development, repurposing of old drugs to treat both common and rare diseases is increasingly becoming an attractive area of research because it involves the use of drug molecules with reduced risk of failure at shorter time and lower cost development [30–33].

With the advent of technologies such as genomics, proteomics, transcriptomics, metabolomics, etc., and availability of huge databases resources including drug omics data, disease omics data, etc., there are a plenty of opportunities to discover drugs by drug repositioning in a collective and integrated effort of all the above methods/approaches mentioned above. Researchers are currently equipped with the latest reliable tools and data to explore the novel unknown mechanism of actions/ pathways based upon disease-specific target proteins/genes and/or specific biomarkers associated with the progression of the disease [34, 35].

Various databases and software are available publicly for genomics, proteomics, metabolomics and pathway analysis. Several computational strategies are already developed to increase the speed and ease of the repurposing process. Some important databases used in drug repositioning studies are outlined in **Table 4**.

However, opportunities come often with many challenges in drug repositioning. The identification of a new therapeutic indication for an existing drug poses a major challenge in repositioning. However, drug repositioning is a complex process involving multiple factors such as technology, commercial models, patents, and investment and market demands. Some multiple challenges which include choosing the right therapeutic area for the drug under investigation, issues related to clinical trials such as need to run new trials from start if the data from clinical or preclinical trials for the original drug or drug product are outdated or are not satisfactory [34, 35].

**17**

**Table 4.**

*Drug Repurposing (DR): An Emerging Approach in Drug Discovery*

Therapeutic Target Database

Therapeutic Target Database

Pharmacogenetics Knowledge

Human Protein Reference Database (HPRD)

Database of Interacting Proteins (DIP)

NCI Pathway Interaction Database (NCI-PID)

Kyoto Encyclopedia of Genes and Genomes (KEGG)

Adverse Reaction Database

Structured Product Labeling

Cancer Cell Line Encyclopedia

(Canada)

(SPL)

(CCLE)

*Databases used in repositioning studies [34–36].*

Biological General Repository

Base (PharmGKB)

for Interaction

**Database Website**

PubChem http://pubchem.ncbi.nlm.nih.gov Drugbank http://www.drugbank.ca/ Chemspider http://www.chemspider.com ChemDB http://www.chemdb.com

OCA http://oca.weizmann.ac.il/oca-bin/ocamain

RCSB Protein Data Bank (PDB) http://www.rcsb.org

Proteopedia http://proteopedia.org

Drugbank http://www.drugbank.ca/

DrugMap Central (DMC) http://r2d2drug.org/index.html

STRING http://string-db.org/

Clinicaltrial.gov http://clinicaltrials.gov

SIDER http://sideeffects.embl.de/

http://bidd.nus.edu.sg/group/cjttd/

http://bidd.nus.edu.sg/group/cjttd/

http://dip.doe-mbi.ucla.edu/dip/Main.cgi

http://www.pharmgkb.org/

http://www.hprd.org/

http://thebiogrid.org/

http://pid.nci.nih.gov/

PathwayCommons http://www.pathwaycommons.org/about/

FDALABEL (US FDA) http://www.fda.gov/ScienceResearch/

NCBI-GEO http://www.ncbi.nlm.nih.gov/geo/ Sequence Read Archive (SRA) http://www.ncbi.nlm.nih.gov/Traces/sra/ ArrayExpress http://www.ebi.ac.uk/arrayexpress/

Sequence Read Archive (SRA) http://www.ncbi.nlm.nih.gov/Traces/sra/

DailyMed (US FDA) http://dailymed.nlm.nih.gov/dailymed/about.cfm

http://www.genome.jp/kegg/

http://www.fda.gov/Drugs/

http://www.fda.gov/ForIndustry/DataStandards/

http://www.broadinstitute.org/ccle/home

*DOI: http://dx.doi.org/10.5772/intechopen.93193*

(TTD)

(TTD)

**Information available about**

Chemical structure

Target 3D structure

Drug-target information

Protein interaction information

Pathway information

Clinical trial information and adverse effects

FDA label information

Omics data (Target/Drug)


*Drug Repurposing - Hypothesis, Molecular Aspects and Therapeutic Applications*

On contrary to traditional drug discovery program (a complex and time consuming process with high cost of development and risk of failure), drug repositioning, reduces the time and cost of drug development. Drug repositioning is also a low-risk strategy. Computational or machine learning approach has significantly improved the performance of drug repositioning. In comparison to computational approaches, using experimental approaches (such as target protein-based screening, cell-based assay, testing in animal model, and clinical testing) that provide direct evidence-based understanding of links between drugs and diseases are more reliable and credible. However, in recent years computational approaches are usually combined with the experimental approaches to identify new indications for old drugs, called mixed approaches. In this approach, computational methods are validated by biological experiments and clinical tests. Mixed approach of repositioning offers a rational and exhaustive exploration of all possible repositioning opportunities, taking into consideration improved access to available databases and technological advances. Furthermore, the R&D investment required for drug repositioning is lower than that for traditional drug discovery. Thus, drug repositioning offers an opportunity for many pharmaceutical companies to develop drugs with lower investments [27, 28]. Mixed approach of DR offers opportunities for developing repositioned drugs more effectively and rapidly. From the market perspective, a large number of diseases require new drugs to be treated with a potential market demand and economic impacts. For example, the discovery of drugs for rare/neglected diseases has a large potential market to explore. There is, therefore, an opportunity for repurposing of drugs for the treatment of rare, neglected, orphan diseases or difficult to treat diseases. There are over 6000 rare diseases that lack proper treatment. About 5% of them are being researched. Rare diseases have a large potential market to explore. Given the high attrition rates, substantial costs and slow pace of drug discovery and development, repurposing of old drugs to treat both common and rare diseases is increasingly becoming an attractive area of research because it involves the use of drug molecules with reduced risk of failure at shorter time and lower cost devel-

With the advent of technologies such as genomics, proteomics, transcriptomics, metabolomics, etc., and availability of huge databases resources including drug omics data, disease omics data, etc., there are a plenty of opportunities to discover drugs by drug repositioning in a collective and integrated effort of all the above methods/approaches mentioned above. Researchers are currently equipped with the latest reliable tools and data to explore the novel unknown mechanism of actions/ pathways based upon disease-specific target proteins/genes and/or specific bio-

Various databases and software are available publicly for genomics, proteomics, metabolomics and pathway analysis. Several computational strategies are already developed to increase the speed and ease of the repurposing process. Some impor-

However, opportunities come often with many challenges in drug repositioning. The identification of a new therapeutic indication for an existing drug poses a major challenge in repositioning. However, drug repositioning is a complex process involving multiple factors such as technology, commercial models, patents, and investment and market demands. Some multiple challenges which include choosing the right therapeutic area for the drug under investigation, issues related to clinical trials such as need to run new trials from start if the data from clinical or preclinical trials for the original drug or drug product are outdated or are not satisfactory [34, 35].

markers associated with the progression of the disease [34, 35].

tant databases used in drug repositioning studies are outlined in **Table 4**.

**7. Opportunities and challenges**

**16**

opment [30–33].

#### **Table 4.**

*Databases used in repositioning studies [34–36].*
