Drug repurposing involves finding new uses for existing drugs to treat different diseases. It provides a more efficient and lower cost alternative to traditional drug development. Computational approaches like network-based, text mining, and semantic methods are used to discover novel drug-disease relationships for drug repurposing. These include identifying modules in biological networks, propagating information across networks, extracting relationships from literature, and constructing semantic networks to predict new associations. Drug repurposing reduces costs and risks compared to de novo drug development.

1. Drug repurposing
Presented by Aquib Naseer
M pharm 2nd semester pharmacology
SPER , Jamia Hamdard New Delhi

2. Contents :
Introduction to drug repurposing.
Need of drug repurposing.
Drug repurposing vs traditional drug discovery.
Drug repurposing as low cost strategy
Method of drug repurposing.

3. Introduction to drug repurposing :
• Drug repurposing is the process of application of an existing therapeutic
drug to a new disease indication.
Or
• is the application of already approved drugs and compounds to treat a
different disease.
• Also synonymously called drug repositioning however there is difference .
• Drug repositioning is re-investigating existing drugs that failed approval for
new therapeutic indications.

4. Some examples of
drug repurposing :

5. Drug Original indication New indication
Slidenafil Angina Male erectile dysfunction
Eflornithine Sleeping sickness (trypanosomiasis) Reduction of unwanted facial hair in
women
Finasteride Benign prostatic hyperplasia Hair loss
Raloxifene Breast and prostate cancer Osteoporosis
Paclitaxel Cancer Re-stenosis
Zidovudine Cancer HIV/AIDS
Topiramate Epilepsy Obesity
Minoxidil Hypertension Hair loss
Phentolamine Hypertension Impaired night vision
Tadelafil Inflammation & CVS disease Male erectile dysfunction
Mecamylamine Moderate , severe & malignant
hypertension
Attention deficit hyperactive disorder

6. Celecoxib Osteoarthritis Familial adenomatouspolyposis , colon
and breast cancer
Mifepristone Pregnancy termination Psychotic major depression
Thalidomide sedation , nausea & insomnia Erythema nodosum leprosy & multiple
myeloma
Dapoxetine Analgesia , depression Premature ejaculation
Chlorpromazine Antiemetic-anti histaminic Non sedative tranquilizer
Tofisopam Anxiety related conditions Irritable bowel syndrome
Fluoxetine Depression Premenstrual dysphoria
Sibutramine Depression Obesity
Bupropion Depression Smoking cessation
Duloxetine Depression Stress urinary incontinence
Milnacipran Depression Fibromyalgia syndrome
Ropinirole Hypertension Parkinson's disease and idiopathic
restless leg syndrome

7. Term Description
• Drug repositioning Finding new uses outside the scope of the original medical indication for
existing drugs [1] or developing new indications for existing drugs or
biologics .
• Drug repurposing Identifying, developing, and commercializing new uses for existing or
abandoned drugs .
• Drug reprofiling Reducing the risks and costs associated with drug development with the
advantage that the drug has already undergone preclinical and clinical
testing.
• Drug reformulating Finding ways to modify a formulation to allow a drug to enter a new market.
Synonym terms to drug repurposing :-

8. Need of drug repurposing :
• Drug development can be time-consuming and expensive.
• Recent estimates suggest that, on average, it takes 10 years and at least $1
billion to bring a drug to market.
• So pharmaceutical companies have become increasingly interested in
finding new uses for existing drugs—a process referred to as drug
repurposing or repositioning.
Moreover, investment in drug development has been gradually increasing, as reported by
Pharmaceutical Research and Manufacturers of America (PhRMA)

9. The investment in drug development by PhRMA member companies and the number of approved drugs by the FDA from
1995 to 2015

10. The investment in drug development by PhRMA member companies and the number of approved drugs by the FDA from
1995 to 2015

11. The investment in drug development by PhRMA member companies and the number of approved drugs by the FDA from
1995 to 2015

12. The investment in drug development by PhRMA member companies and the number of approved drugs by the FDA from
1995 to 2015

13. Drug repositioning, also known as old drugs for new uses, is an effective strategy to find new indications for
existing drugs and is highly efficient, low-cost and riskless.
Traditional drug development strategies usually include five stages:
1. Discovery and preclinical studies,
2. Safety review,
3. Clinical research,
4. FDA review, and
5. FDA post-market safety monitoring.
However, there are only four steps in drug repositioning:
1. Compound identification,
2. Compound acquisition,
3. Development , and
4. FDA post-market safety monitoring .
Advantages of drug repurposing over Traditional drug discovery :

14. Researchers only need 1-2 years to identify new drug targets and 8 years to develop a
repositioned drug, on average.
Due to the fast growth of bioinformatics knowledge and biology big data, drug repositioning
decreases the time cost of the drug development process significantly.

15. B
Traditional drug discovery

16. B
Traditional drug discovery

17. Drug repositioning a low-risk strategy.
In addition, some repositioned drugs may be marketed as molecular entities and have
more opportunities to be pushed into the market once a new indication is discovered.
Repositioned drugs have passed all clinical tests in Phase I, Phase II, and Phase III, their
safety has been confirmed
Drug repositioning holds a higher reward with a lower risk , because

18. A risk-reward diagram to
compare repositioning
and traditional drug
development strategies

19. A risk-reward diagram to
compare repositioning
and traditional drug
development strategies

20. A risk-reward diagram to
compare repositioning
and traditional drug
development strategies

21. Method of drug repositioning :
The main issue in drug repositioning is the detection of novel drug-
disease relationships.
-Mostly done by serendipity over the years.
-It is only recently that more systematic
approaches based on computational analyses
are being used.

22. Computational
approaches

23. 1.The core methodologies of drug repositioning approaches are divided into three categories:
2. 1) Network-based approaches. 2) Text-mining approaches and
3.3) Semantic approaches
Most existing computational approaches are based on the gene
expression response of cell lines after treatment or merging several types
of information about disease-drug relationships
The main hypothesis of such approaches is that ‘if gene expression
signature of a particular drug is opposite to the gene expression signature of a disease’, that drug may have
a potential therapeutic effect on the disease.

24. 1)Network-based approaches :
• Network-based approaches are widely used in drug repositioning due
to the associated ability to integrate multiple data sources.
• In this section, two types of network-based approaches are reviewed:
1. Network-based cluster approaches and
2. Network-based propagation approaches

25. A) Network-based cluster approaches :
Inspired by the fact that biologic entities (disease, drug, protein, etc.) in the same
module of biological networks share similar characteristics, network-based cluster
approaches have been proposed to discover novel drug-disease relationships or drug-
target relationships.
These approaches aim to find several modules (also known as subnetworks, groups or
cliques) using cluster algorithms according to the topology structures of networks.
These modules include various relationships such as drug-disease, drug-drug or drug-
target relationships. The most common network-based cluster approaches, include
DBSCAN , CLIQUE , STING , and OPTICS .

26. B) Network-based propagation approaches:
• The workflow of these approaches is that , prior information propagates from the source node to all network
nodes and some subnetwork nodes.
• According to the different propagation ways, these approaches can be divided into two types:
1. Local approaches and
2. Global approaches.
• Local propagation approaches only take the limited information of the network into account
and may fail to make correct predictions in some cases.
• By contrast, global approaches containing information from the entire network perform
better than local approaches.

27. 2) Text mining-based approaches :
Literature
sources
Relevant
documents
Relevant
concepts
knowledges
IR BNER
BIE
BKD
Defined as ‘discovery by computer of ; new, or previously unknown information, by
automatically extracting information from different written resources’
The main pipeline of biological text mining includes four phrases:
1. Information retrieval (IR),
2. Biological name entity recognition (BNER),
3. Biological information extraction (BIE) and
4. Biological knowledge discovery (BKD).

28. Steps included :
1. In the IR step, relevant documents are extracted from the literature. These relevant
documents need to be filtered because there are some useless concepts in documents.
2. In the BNER step, valuable biological concepts are identified with controlled
vocabularies.
3. In the BIE and BKD steps, useful information is extracted to discover knowledge about
biological concepts and build a knowledge graph. At the same time, potential associations
between knowledge, such as drug-disease and drug-target relationships, can also be
detected.

29. 3) Semantics-based approaches :
• Semantics – ‘the branch of linguistics and logic concerned with meaning’.
• Semantics-based approaches were widely used in information retrieval, image retrieval and other fields.
• Recently, these methods have been applied to drug repositioning.
• The workflow of these methods mainly includes three steps :
1. First, biological entity relationships are extracted from prior information in massive medical databases to
build the semantic network.
2. Then, semantics networks based on existing ontology (study of existance of an entity) networks are
constructed by adding the prior information obtained in the previous step.
3. Finally, mining algorithms are designed to predict novel relationships in the semantic network.

30. The workflow of a
semantic network
inference.

31. • Conclusion : Developing a new drug is costly and time consuming &
drug repurposing comes forth as an excellent alternative.

32. References :
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097480/#B4
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5568558/
• https://www.sciencedirect.com/topics/medicine-and-dentistry/drug-
repositioning