DRUG REPOSITIONING FOR BREAST CANCER
1. INTRODUCTION
1.1 DRUG REPOSITIONING:
Drug discovery has always been an interesting domain and, platform for exploration. A
typical drug discovery process would take around 8 – 15 years of time for the introduction of
drug into market; and consumes around 1 billion dollars of money. However, the successes of
the drugs developed are not guaranteed. It is estimated that 90% of all drugs entering various
clinical trials are discontinued, more often due to issues associated with efficacy than safety.
Taking the very fact into consideration, a new field that has come into existence is drug
repositioning. Drug repurposing offers to explore the existing knowledge on drugs, diseases and
targets and helps us to find a novel use of an already available compound or drug lead for the
development of new and better therapies. Since drug repurposing offers a relatively low-risk,
regaining losses, save both money and time, most companies are now heading for reprofiling of
existing drugs. It is because of this reason drug reprofiling is becoming increasingly popular in
the industry. A few older instance of drug repurposing are like Celecoxib, a generic drug,
initially used for osteoarthritis and rheumatoid arthritis by Pfizer was later repositioned and used
for familial adenomatous polyposis, colon & breast cancers. Aspirin was originally meant for
treating pain, is now prescribed as a vasodilator for reducing the risk of heart attack and strokes.
Minoxidil, a generic drug made for hypertension and manufactured by Pharmacia & Upjohn in
Sweden was repositioned by Pfizer for Rogaine, the drug’s trade name, currently used for the
treatment of hair loss.
The emergence of drug resistence is prevalent in mycobacterium tuberculosis,the causative agent
of TB.likewise,mycobacterium tuberculosis(Mtb) strains that are multidrug-resistant or
extremely drug-resistant to first- and second-line drugs have been associated with increased
mortality.
1
Fig 1.1: Drug discovery process in current pharmaceutical drug discovery pipeline.
This increasing drug resistance problems and co-infections lead to the increased demand for
novel anti-tubercular and anti-breast cancer drugs. But being neglected tropical disease and
affecting low income populations, the pharmaceutical companies have not put considerable effort
for new drug discovery. Hence drug repositioning can be a potential method for combating this
dreadful disease.
2
TABLE1:THE
FEW
EXAMPLES
OF
ALREADY
REPOSITIONED
DRUGS:
Drug
Current
New use
Reference
thromboembolic disorders
Ting HJ et al.,
Indication/Target
Glybenclamide
antidiabetic agent
sabcomeline
orthosteric
muscarinic Alzheimer's disease
McArthur
M1 functional agonists
chloroquine
(CQ) Anti-malarial therapy
analogs
Fulvestrant
al.,
anti-breast cancer
Solomon VR et al
property
breast cancer drug
Inhibitor
of
glioblastoma Kotelnikova
al.,
Kinnings SLet al.,
Parkinson's disease
MDR/XDR tuberculosis
Closantel
Broad-spectrum
Onchocerciasis,or
antiparasitic agent used
blindness
Primary kidney cancer, Potential
river Gloeckner C et al.,
therapeutic Hancock MK et
advanced primary liver indication of sorafenib in al.,
cancer
Eet
pathway and Cyr61
Comtan
Sorafenib-Nexavar
RAet
treating
leukemia/myeloproliferative
disorder
3
TABLE2: TOOLS AND DATABASES INVOLVED IN REPOSITIONING:
Databases/
WEB-address
Use
Actions / Role
Drug
it’s a dual function database that
Tools
PDTD (Potential Drug http://www.dddc.ac.cn/
Target Database)
pdtd/
repositioning
associates an informatics database to
a structural database of known and
potential drug targets
CANDI
(Compound Analysis
for
New
Drug
http://www.numedicus.
Drug
A database that combines known
co.uk/index.php?name
repositioning
drugs
=CANDI
with
mechanism
and
indication.
Indications)
EuroTransBio
TarfisDock
http://www.eurotransbi
Drug
Identifies
o.eu/
repositioning
mimic known drugs.
http://www.dddc.ac.cn/
Inverse docking
a web server for identifying drug
tarfisdock/
new
compounds
targets with docking approach
TABLE3: COMPANIES INVOLVED IN REPOSITIONING
The few examples are mentioned below
Sl.No
Company
Strategy
URL
1
BioVista
Reposition drugs in isolation or in www.biovista.com
combination
with
other
drugs.
Concentrate on drugs displaying
plausibility for new indications and
satisfying 4adme criterions.
that
2
Takes
CombinatoRx
combinations
of
known
&
approved drugs. Tries to find out new www.combinatorx.com
uses or patterns of activity of the drug.
3
Mostly concerned with cancer and www.celegene.com
Celegene
inflammatory diseases.
4
Concentrates on discontinued dugs after
Gene Logic
phase II. Mostly focuses on orally www.genelogic.com
available small molecule drugs.
5
Involved in repositioning of unknown
Syntopix
chemical
entities
.Basically
treats www.syntopix.com
dermatological conditions.
1.2 Cancer:
Cancer is the uncontrolled growth of abnormal cells anywhere in a body. The
abnormal cells are called as cancer cells or malignant cells or tumor cells. The abnormal cells
composes the cancer tissue are further identified by the name of the tissue that the abnormal cells
can cause breast cancer, lung cancer, colon cancer depending upon their originated part of body.
The animals and other living organisms can also get cancer. Frequently, cancer cells can break
away from this original mass of cells which can travel through the blood and lymph systems, and
entered into other organs where they can again repeat the uncontrolled growth cycle. This
process of cancer cells leaving an area and growing in another body area is termed metastatic
spread or metastatic disease. There are over 200 types of cancers. For example, prostate cancer,
lung cancer, breast cancer, colon cancer, brain cancer, liver cancer, blood cancer, thyroid cancer
and uterine cancer. The National Cancer Institute divides the following categories:
 Carcinoma cancer begins in the skin or in tissues that line or internal organs.
5
 Sarcoma cancer begins in bone, cartilage, fat, muscle, blood vessels, or
other connective or supportive tissue
 Leukemia cancer starts in blood-forming tissue such as the bone marrow
and causes large numbers of abnormal blood cells to be produced and enter
the blood
 Lymphoma and myeloma cancers begin in the cells of the immune system
 Central nervous system cancers begin in the tissues of the brain and spinal
cord.
FIG1.2: Example of cancer cell growth
1.3 Breast cancer:
The term “breast cancer” refers to a malignant tumor that has developed from cells in
the breast. Usually breast cancer either begins in the cells of the lobules, which are the milkproducing glands, or the ducts, the passages that drain milk from the lobules to the nipple. Less
commonly, breast cancer can begin in the stromal tissue. Cancer cells can invade nearby healthy
breast tissue and make their way into the underarm lymph nodes, small organs that filter out
foreign substances in the body. If cancer cells get into the lymph nodes, they then have a
pathway into other parts of the body.
There are two main types of breast cancer:

Ductal carcinoma starts in the tubes (ducts) that move milk from the breast to the nipple.
Most breast cancers are of this type.

Lobular carcinoma starts in the parts of the breast, called lobules, that produce milk.
In rare cases, breast cancer can start in other areas of the breast.
Breast cancer may be invasive or noninvasive. Invasive means it has spread from the milk duct
or lobule to other tissues in the breast. Noninvasive means it has not yet invaded other breast
tissue. Noninvasive breast cancer is called "in situ."

Ductal carcinoma in situ (DCIS), or intraductal carcinoma, is breast cancer in the lining
of the milk ducts that has not yet invaded nearby tissues. It may progress to invasive
cancer if untreated.

Lobular carcinoma in situ (LCIS) is a marker for an increased risk of invasive cancer
in the same or both breasts.
Many breast cancers are sensitive to the hormone estrogen. This means that estrogen causes the
breast cancer tumor to grow. Such cancers have estrogen receptors on the surface of their cells.
They are called estrogen receptor-positive cancer or ER-positive cancer.
Some women have HER2-positive breast cancer. HER2 refers to a gene that helps cells grow,
divide, and repair themselves.
1.3.1 Causes, incidence, and risk factors:
The most important Risk factors includes:

Age and gender The breast cancer increases when people get older. Most advanced
breast cancer cases are found in women over age 50.

Family history of breast cancer  If people have a close relative who has had breast,
uterine, ovarian, or colon cancer. About 20 - 30% of women with breast cancer have a
family history of the disease.

Genes  Some people have genes that make them to develop breast cancer. The most
common gene defects are found in the BRCA1 and BRCA2 genes. These genes normally
produce proteins that protects from cancer. If a parent passes a defective gene to child,
who have an increased risk for breast cancer. Women with one of these defects have up
to an 80% chance of getting breast cancer sometime during their life.

Menstrual cycle Women who got their periods early (before age 12) or went through
menopause late (after age 55) have an increased risk for breast cancer.
Other risk factors include:


Exposure to previous chest radiation or use of diethylstilbestrol increases the risk of
breast cancer.
Having no children or the first child after age 30 increases the risk of breast cancer.

Breast feeding for one and a half to two years might slightly lower the risk of breast
cancer.

Being overweight increases the risk of breast cancer.

Using hormone replacement therapy after menopause increases the risk of breast cancer.

Alcohol use increases the risk of breast cancer, and this seems to be proportional to the
amount of alcohol used.

Exercise seems to lower the risk of breast cancer.

If radiation therapy treated for cancer of the chest area with child or young ,which brings
higher risk for developing breast cancer. The younger started such radiation especially
the radiation was given during breast development.
1.3.2 Symptoms
Early breast cancer usually does not cause symptoms. The regular breast exams are important.
symptoms may include:

Breast lump or lump in the armpit that is hard, has uneven edges, and usually does not
hurt.

Change in the size, shape, or feel of the breast or nipple for example, it may have redness,
dimpling, or puckering that looks like the skin of an orange

Fluid coming from the nipple may be bloody, clear to yellow, green, and look like pus.
Men can get breast cancer, too. Symptoms include breast lump and breast pain and tenderness.
Symptoms of advanced breast cancer may include:

Bone pain

Breast pain or discomfort

Skin ulcers

Swelling of one arm (next to the breast with cancer)

Weight loss
The symptoms are illustrated in figure1.3 and figure1.4.
FIG 1.3: Symptoms of breast cancer
FIG 1.4: Symptoms of breast cancer
1.3.3 Signs and tests
Tests used to diagnose and monitor patients with breast cancer may include:

Breast MRI to help better identify the breast lump or evaluate an abnormal change on a
mammogram

Breast ultrasound to show whether the lump is solid or fluid-filled

Breast biopsy, using methods such as needle aspiration, ultrasoundguided, stereotactic, or
open

CT scan and lymph node biopsy to see if the cancer has spread

Mammography to screen for breast cancer or help identify the breast lump

PET scan
1.3.4 Treatment
Treatment is based on many factors, including:

Type and stage of the cancer

Whether the cancer is sensitive to certain hormones

Whether the cancer overproduces a gene called HER2
In general, cancer treatments may include:

Chemotherapy medicines to kill cancer cells

Radiation therapy to destroy cancerous tissue

Surgery to remove cancerous tissue especially a lumpectomy removes the breast lump;
mastectomy removes all or part of the breast and possible nearby structures
Hormonal therapy is prescribed to women with ER-positive breast cancer to block certain
hormones that fuel cancer growth.

An example of hormonal therapy is the drug tamoxifen. This drug blocks the effects of
estrogen, which can help breast cancer cells survive and grow. Most women with
estrogen-sensitive breast cancer benefit from this drug.

Another class of hormonal therapy medicines called aromatase inhibitors, such as
exemestane (Aromasin), have been shown to work just as well or even better than
tamoxifen in postmenopausal women with breast cancer. Aromatase inhibitors block
estrogen from being made.
Targeted therapy, also called biologic therapy, is a newer type of cancer treatment. This therapy
uses special anticancer drugs that target certain changes in a cell that can lead to cancer. One
such drug is trastuzumab (Herceptin). It may be used for women with HER2-positive breast
cancer.
Cancer treatment may be local or systemic.

Local treatments involve only the area of disease. Radiation and surgery are forms of
local treatment.

Systemic treatments affect the entire body. Chemotherapy is a type of systemic treatment.
1.4 HDAC:
Histone deacetylases (HDACs) are a class of enzymes found in bacteria, fungi, plants
and animals that remove the acetyl group from the ε-amino groups of lysine residues located in
the NH2 terminal tails of core histones.
There are 18 known human histone deacetylases, grouped into four classes based on the structure
of their accessory domains.
Class I includes HDAC1, HDAC2, HDAC3, and HDAC8 and have homology to yeast RPD3. HDAC4,
HDAC5, HDAC7, and HDAC9 belong to class II and have homology to yeast HDA1. HDAC6 and
HDAC10 contain two catalytic sites and are classified as class IIa, whereas HDAC11 has conserved
residues in its catalytic center that are shared by both class I and class II deacetylases and is sometimes
placed in class IV. In these types,HDAC1 and HDAC3 are involved in breast cancer.
1.4.1 HDAC1:
Histone deacetylase 1 is an enzyme that in humans is encoded by the HDAC1 gene.
Histone acetylation and deacetylation, catalyzed by multisubunit complexes, play a key role in
the regulation of eukaryotic gene expression. The protein encoded by this gene belongs to the
histone deacetylase/acuc/apha family and is a component of the histone deacetylase complex. It
also interacts with retinoblastoma tumor-suppressor protein and this complex is a key element in
the control of cell proliferation and differentiation. Together with metastasis-associated protein-2
[MTA2], it deacetylates p53 and modulates its effect on cell growth and apoptosis.
1.4.2 Role of HDACs in Cancer:
The role of HDACs in cancer is not restricted to their contribution to histone
deacetylation, but also to their role in deacetylation of non-histone proteins. For example,
HDAC1 interacts with the tumor suppressor p53 and deacetylates it in vivo and in vitro.These
HDACs play important roles in the regulation of gene expression, apoptosis, stress responses,
DNA repair, cell cycle, genomic stability, etc, indicating that this group of HDACs are key
regulators of normal cell growth and proliferation.
Another way by which HDACs are recruited to DNA independently of DNA methylation
involves the interaction with transcription factors and nuclear receptors. Focusing on the
interaction with transcription factors, HDAC1 and HDAC2 are involved in transcriptional
repression regulated by the retinoblastoma protein Rb. E2F is a family of transcription factors
involved in cell-cycle control. E2F-containing promoters are repressed by members of the Rb
family that are recruited by a physical interaction with the E2F protein. One possibility is that the
repression of E2f-regulated promoters by Rb implies the recruitment of HDACs to the E2F-
containing promoters
Treatment with TSA, a classical HDAC inhibitor, prevents the Rb-
mediated repression of gene transcription HDACs also participate in gene expression regulation
mediated by nuclear receptors. Estrogen receptors (ERs) belong to a large superfamily of nuclear
receptors that modulate the expression of genes regulating critical breast and ovary functions.
HDAC1 interacts with ER-a and suppresses its transcriptional activity. The interaction of
HDAC1 with ER-a is mediated by the activation function-2 (AF-2) domain and DNA-binding
domain of ER-a, and this interaction is weakened in the presence of estrogens.Furthermore,
another study indicates that the ER-gene transcription is regulated by a multiprotein complex that
includes HDACs, DNMTs, and retinoblastoma protein Rb.
A typical characteristic of human cancer is the deregulation of DNA methylation and
posttranslational histone modifications, in particular histone acetylation, which has the fatal
consequence of gene transcription-deregulation. The data from our studies of a panel of normal
tissues, primary tumors, and human cancer cell lines indicate that a loss of acetylated
Lys16 (K16-H4) and trimethylated Lys20 (K20-H4) of histone H4 is a common event in human
cancer.
2. LITERATURE REVIEW:
Drug repositioning strategy is an interesting approach for pharmaceutical companies;
especially to increase their productivity. SELNERGY(tm) is a reverse docking based-program
able to virtually screen thousands of compounds on more than 2000 3D biological targets. This
program was successfully applied to tofisopam and revealed that the isomers of tofisopam are
able to fit with phosphodiesterase 4. This old drug was used as a racemic mixture to treat anxiety
in the eighties and was recently shown to act as a PDE4 inhibitor. It has been demonstrated that
tofisopam acts via the inhibition of PDE4 in the submicromolar range. In this paper the
identification of the biochemical mechanism of tofisopam isomers allows repositioning of this
drug in new therapeutic indications where modulation of cAMP via PDE4 inhibitors are
possible.[ Bernard P, Dufresne-Favetta C, Favetta P, 2008].
Drug repositioning helps fully explore indications for marketed drugs and clinical candidates.
Here the clinical side-effects (SEs) for a drug which provides a human phenotypic profile and
this profile suggests the additional disease indications. It has been extracted that 3,175 SEdisease relationships are combined from drug labels and the drug-disease relationships from
Pharm GKB. Many relationships provide explicit repositioning hypotheses, such as drugs
causing hypoglycemia are potential candidates for diabetes. It has been built along with Naïve
Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was
above 0.8 in 92% of these models. The method was extended to predict indications for clinical
compounds, 36% of the models achieved AUC above 0.7. This concludes that closer attention
should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to
rationally explore the repositioning potential based on this "clinical phenotypic assay".
[Yang L, Agarwal P, 2011 Dec 21].
Identifying new indications for existing drugs (drug repositioning) is an efficient way of
maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death
among hospitalized patients. As both new indications and ADRs are caused by unexpected
chemical–protein interactions on off-targets, which is reasonable to predict those interactions by
mining the chemical–protein interactome (CPI). Making such predictions has recently been
facilitated by a web server named DRAR-CPI. This server has a representative collection of drug
molecules and targetable human proteins built up from our work in drug repositioning and ADR.
When a user submits a molecule, the server will give the positive or negative association scores
between the user’s molecule and server library drugs based on their interaction profiles towards
the targets. Users can thus predict the indications or ADRs of their molecule based on the
association scores towards servers library drugs. It has been matched our predictions of drug–
drug associations with those predicted via gene-expression profiles, achieving a matching rate as
high as 74%. It has been successfully predicted the connections between anti-psychotics and
anti-infectives, indicating the underlying relevance of anti-psychotics in the potential treatment
of infections, vice versa. This server is freely available at http://cpi.bio-x.cn/drar/ [Heng Luo,
Jian Chen, May 10, 2011].
Here it is described as an efficient computational methodology to discover leads to a protein
target from safe marketed drugs. They have applied an in silico ‘‘drug repurposing’’ procedure
for identification of nonsteroidal antagonists against the human androgen receptor (AR), using
multiple predicted models of an antagonist-bound receptor. The library of marketed oral drugs
was then docked into the best-performing models, and the 11 selected compounds with the
highest docking score were tested in vitro for AR binding and antagonism of
dihydrotestosterone-induced AR transactivation. The phenothiazine derivatives acetophenazine,
fluphenazine, and periciazine, used clinically as antipsychotic drugs, were identified as weak AR
antagonists. This in vitro biological activity correlated well with endocrine side effects observed
in individuals taking these medications. Further computational optimization of phenothiazines,
combined with in vitro screening, led to the identification of a nonsteroidal antiandrogen with
improved AR antagonism and marked reduction in affinity for dopaminergic and serotonergic
receptors that are the primary target of phenothiazine antipsychotics.
A.B.Cheltsov, Nov 2, 2006].
[W.H.Bisson,
They have developed a computational drug repositioning pipeline to perform large-scale
molecular docking of small molecule drugs against protein drug targets, in order to map the
drug-target interaction space and find novel interactions. Their method emphasizes on removing
false positive interaction predictions using criteria from known interaction docking, consensus
scoring, and specificity. In all, the database they used contained 252 human protein drug targets
that they classified as reliable-for-docking as well as 4621 approved and experimental small
molecule drugs from Drug Bank. These were cross-docked, and then filtered through stringent
scoring criteria to select top drug-target interactions. In particular, they have used MAPK14 and
the kinase inhibitor BIM-8 as examples where their stringent thresholds enriched the predicted
drug-target interactions with known interactions up to 20 times compared to standard score
thresholds. After which, they have validated nilotinib as a potent MAPK14 inhibitor in vitro
(IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The
published literature indicated experimental evidence for 31 of the top predicted interactions,
highlighting the promising nature of their approach. Novel interactions discovered may lead to
the drug being repositioned as a therapeutic treatment for its off-target’s associated disease,
added insight into the drug’s mechanism of action, and added insight into the drug’s side
effects.[Yvonne Y.Li, Jianghong An, Sep 2011].
Heterogeneous high-throughput biological data become readily available for various diseases.
The amount of data points generated by such experiments does not allow manual integration of
the information to design the most optimal therapy for a disease. It has been described a novel
computational workflow for designing therapy using Ariadne Genomics Pathway Studio
software. Publically available microarray experiments are used for glioblastoma and
automatically constructed ResNet and ChemEffect databases to exemplify how to find
potentially effective chemicals for glioblastoma--the disease yet without effective treatment. The
first approach involved construction of signaling pathway affected in glioblastoma using
scientific literature and data available in ResNet database. Compounds known to affect multiple
proteins in this pathway were found in ChemEffect database. Another approach involved
analysis of differential expression in glioblastoma patients using Sub-Network Enrichment
Analysis (SNEA). SNEA identified angiogenesis-related protein Cyr61 as the major positive
regulator upstream of genes differentially expressed in glioblastoma. Through these
observations, breast cancer drug Fulvestrant was identified as a major inhibitor of glioblastoma
pathway as well as Cyr61. This suggested Fulvestrant as a potential treatment against
glioblastoma. It is further shown how to increase efficacy of glioblastoma treatment by finding
optimal combinations of Fulvestrant with other drugs.[ Kotelnikova E, Yuryev A,8 june 2010]
In this the knowledge, understanding and prediction of so-called off-target effects allow a
rational approach to the understanding of side-effects. With PROMISCUOUS they have
provided an exhaustive set of drugs (25 000), including withdrawn or experimental drugs,
annotated with drug–protein and protein–protein relationships (21 500/104 000) compiled from
public resources via text and data mining including manual curation. Measures of structural
similarity for drugs as well as known side-effects can be easily connected to protein–protein
interactions to establish and analyze networks responsible for multi-pharmacology. This
network-based approach can provide a starting point for drug-repositioning. PROMISCUOUS is
publicly available at http:// bioinformatics.charite.de/promiscuous. It contains three different
types of entities: drugs, proteins and side-effects. The database not only contains drug–target
interaction data, but also protein–protein interaction and drug side effect data, which have proven
to be useful for drug repositioning. This information is mapped to its biological context via
KEGG pathways. Mirtazapine is an antidepressant for which 184 side-effects are detailed in
PROMISCUOUS. Based on this side-effect information, a search for related drugs can be
performed in the network exploration tool.[ Joachim von Eichborn, Manuela S. Murgueitio,
October 11, 2010]
They have built an integrated dataset of around 120,000 concepts and 570,000 relations to
visualize the links between drugs, proteins and diseases. And they have included information
from a wide variety of publicly available databases, allowing analysis on the basis of: drug
molecule similarity; protein similarity; tissue specific gene expression; metabolic pathways and
protein family analysis. When analysed this integrated dataset to highlight known examples of
repositioned drugs, and their connectivity across multiple data sources. It is also suggested that,
methods of automated analysis for discovery of new repositioning opportunities on the basis of
indicative semantic motifs. By using Ondex integration backend; mappers and transformers are
then used to join different data sets, remove unconnected nodes and add additional information
to the network. As a final step, the network is analyzed for interesting examples of repositioning
by manually traversing the data using Ondex. The data included in the drug repositioning dataset
were limited to publicly available sources, to enable their wide redistribution. The databases and
analysis methods used to generate the dataset were: Drug Bank, UniProt, HPRD, KEGG, PFam,
SymAtlas, G Sesame, OpenBabel and BLAST. Celecoxib was used as a treatment for arthritis,
but more recently it has been shown to also be effective against colo-rectal cancer.
Chlorpromazine goes beyond simple sedation; patients also demonstrate improvements in
emotional behavior. It was this observed activity that led to it being trailed as an anti-psychotic.
Chlorpromazine was eventually approved, and is used, for both purposes.[Simon J Cockell,
Jochen Welie, Phillip Lord,Oct-2010].
Here it is illustrated on how system biology strategies for repositioning existing FDA-approved
drugs may accelerate the therapeutic capacity to eliminate CSC traits in pre-invasive
intraepithelial neoplasias. First, they have described a signalling network signature that overrides
bioenergetics stress- and oncogene-induced senescence (OIS) phenomena in CSCs residing at
pre-invasive lesions. Second, it has been functionally mapped the anti-malarial chloroquine and
the anti-diabetic metformin ("old drugs") to their recently recognized CSC targets ("new uses")
within the network. By discussing the preclinical efficacy of chloroquine and metformin to
inhibiting the genesis and self-renewal of CSCs they finally underscore the expected translational
impact of the "old drugs-new uses" repurposing strategy to open a new CSC-targeted
chemoprevention era.[Vazquea Martin, Lopea Bonetec.A, May 19,2011].
In this the binding site of a commercially available drug is extracted or predicted from a 3D
structure or model of the target protein. Off-targets with similar ligand binding sites are
identified across the proteome using an efficient and accurate functional site search algorithm
Atomic interactions between the putative off-targets and the drug are evaluated using proteinligand docking. Only those off-targets that do not experience serious atomic clashes with the
drug are selected for further analysis. The drug is further optimized to enhance its potency,
selectivity and ADME properties by taking into account both the primary target and the offtargets across the genome. They then demonstrate the efficiency and efficacy of our chemical
systems biology approach through the discovery of safe chemical compounds with the potential
to treat MDR-TB and XDR-TB. The identified compounds are entacapone and tolcapone. These
drugs primarily target human catechol-Omethyltransferase (COMT), which is involved in the
breakdown of catecholamine neurotransmitters such as dopamine. They are used as adjuncts to
treat Parkinson’s disease by increasing the bioavailability of the primary drug levodopa, which is
a substrate of COMT [Sarah L. Kinnings, Nina Liu, July 3, 2009].
Here, they have presented a novel method for the large-scale prediction of drug indications
(PREDICT) that can handle both approved drugs and novel molecules. This method is based on
the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–
drug and disease–disease similarity measures for the prediction task. On cross-validation, it
obtains high specificity and sensitivity (AUC¼0.9) in predicting drug indications, surpassing
existing methods. They have validated the predictions by their overlap with drug indications that
are currently under clinical trials, and by their agreement with tissue-specific expression
information on the drug targets. And they have further shown that disease specific genetic
signatures can be used to accurately predict drug indications for new diseases (AUC¼0.92). This
lays the computational foundation for future personalized drug treatments, where gene
expression signatures from individual patients would replace the disease-specific signatures
[Assaf Gottlieb, Roded Sharan, April 12, 2011].
They have tried to overcome the problems faced earlier by developing an OTE [off target
effects]-based method to repurpose drugs for cancer therapeutics, based on transcriptional
responses made in cells before and after drug treatment. Specifically, it has been defined as a
new network component called cancer-signaling bridges (CSB) and integrated it with a
Bayesian factor regression model (BFRM) to form a new hybrid method termed CSB-BFRM.
Proof-of-concept studies were conducted in breast and prostate cancer cells and in promyelocytic
leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses
to more than 90% of drugs approved by the U.S. Food and Drug Administration and more than
75% of experimental clinical drugs that were tested. Mechanistic investigation of OTEs for
several high-ranking drug–dose pairs suggested repositioning opportunities for cancer therapy,
based on the ability to enforce retinoblastoma-dependent repression of important E2F-dependent
cell-cycle genes. Together, the findings establish new methods to identify opportunities for drug
repositioning or to elucidate the mechanisms of action of repositioned drugs.[ Guangxu Jin,
Changhe Fu,Hong Zhao,Oct 17,2011].
They have presented a network-based signature and a comprehensive signaling map for
identification of candidates of drug repositioning and combinations for breast TIC [tumor
initiating cells]. The network-based signature is based on an extended concept of network motifs,
known as cancer-signaling bridges (CSBs), which can be used to expand the cancer drugtargets of known signaling pathways. They have used the profiles of TIC derived from
CD44+/CD24-/low breast cancer cells and mammospheres (MS) cells to establish network-based
signatures. Facilitated by the signaling pathways that are highly connected with CSBs, e.g.,
MAPK, NOTCH, ECM-receptor, Jak-STAT, and Wnt, they first identified the high-confidence
signaling paths automatically chosen out of CSBs by two scoring systems, namely, Differential
Expression Score (DES) and Signaling Pathway Score (SPS). The high-confidence signaling
paths are used to build the network-based signature that characterizes the breast TIC. The
network-based signature for CD44+/CD24-/low cells is composed by 140 proteins and 132
protein-protein interactions and that for mammospheres contains 153 proteins and 119 proteinprotein interactions. The FDA-approved drugs whose targets are included in the network-based
signatures are repositioned to breast TIC as the candidates for the repositioning. Furthermore,
they have curated a comprehensive signaling map for breast TIC by using the available signaling
transductions in BioCarta, KEGG, and IPA and include seven signaling pathways, PI3K/AKT,
JAK/STAT, Notch, HH, Wnt, P53, and ECM. The signaling map enabled them to further refine
the repositioning candidates and eventually propose combination candidates by using the crosstalks of signaling pathways in the map. [Guangxu Jin and Hong Zhao, July 12, 2011].
Drug repositioning strategy is an interesting approach for pharmaceutical companies; especially
to increase their productivity. SELNERGY(tm) is a reverse docking based-program able to
virtually screen thousands of compounds on more than 2000 3D biological targets. This program
was successfully applied to tofisopam and revealed that the isomers of tofisopam are able to fit
with phosphodiesterase 4. This old drug was used as a racemic mixture to treat anxiety in the
eighties and was recently shown to act as a PDE4 inhibitor. It has been demonstrated that
tofisopam acts via the inhibition of PDE4 in the submicromolar range. In this paper the
identification of the biochemical mechanism of tofisopam isomers allows repositioning of this
drug in new therapeutic indications where modulation of cAMP via PDE4 inhibitors are
possible.[ Bernard P, Dufresne-Favetta C, Favetta P, 2008].
Drug repositioning helps fully explore indications for marketed drugs and clinical candidates.
Here the clinical side-effects (SEs) for a drug which provides a human phenotypic profile and
this profile suggests the additional disease indications. It has been extracted that 3,175 SEdisease relationships are combined from drug labels and the drug-disease relationships from
Pharm GKB. Many relationships provide explicit repositioning hypotheses, such as drugs
causing hypoglycemia are potential candidates for diabetes. It has been built along with Naïve
Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was
above 0.8 in 92% of these models. The method was extended to predict indications for clinical
compounds, 36% of the models achieved AUC above 0.7. This concludes that closer attention
should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to
rationally explore the repositioning potential based on this "clinical phenotypic assay".
[Yang L, Agarwal P, 2011 Dec 21].
Cell migration is a key step for deterioration of many in situ or metastasis malignant tumours.
Tumour anti-migration is a promising strategy to treat cancer, but corresponding drugs
developed under such a strategy are still in dire poverty, partly due to the lengthy process of drug
trials and approval required by the US Food and Drug Administration (FDA). Given there are
thousands of FDA approved drugs in the market, It is believed that drug repositioning may
provide a fast and cost-effective way to identify potential anti-migration drugs. In this paper, an
in-silico drug screening method using a genomic strategy is proposed for the goal, in which
genomic signature identification combined with support vector machine modelling is adopted to
estimate drug efficacy. And a high-throughput, sensitive, 3-dimensional invasion assay by
quantitative bioluminescence imaging proved the performance of proposed method on in vitro
disease models [Yong Mao; Kemi Cui; Wang Lulu, 21 april 2011].
Identifying new indications for existing drugs (drug repositioning) is an efficient way of
maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death
among hospitalized patients. As both new indications and ADRs are caused by unexpected
chemical–protein interactions on off-targets, which is reasonable to predict those interactions by
mining the chemical–protein interactome (CPI). Making such predictions has recently been
facilitated by a web server named DRAR-CPI. This server has a representative collection of drug
molecules and targetable human proteins built up from our work in drug repositioning and ADR.
When a user submits a molecule, the server will give the positive or negative association scores
between the user’s molecule and server library drugs based on their interaction profiles towards
the targets. Users can thus predict the indications or ADRs of their molecule based on the
association scores towards servers library drugs. It has been matched our predictions of drug–
drug associations with those predicted via gene-expression profiles, achieving a matching rate as
high as 74%. It has been successfully predicted the connections between anti-psychotics and
anti-infectives, indicating the underlying relevance of anti-psychotics in the potential treatment
of infections, vice versa. This server is freely available at http://cpi.bio-x.cn/drar/ [Heng Luo,
Jian Chen, May 10, 2011].
A series of chloroquine (CQ) analogs were designed and synthesized in a repositioning approach
to develop compounds with high anti-breast cancer property. The compounds were then
examined for their antiproliferative effects on two human breast tumor cell lines and a matching
non-cancer cell line. Although many of them showed substantial antiproliferative effects on
breast cancer cells examined, two compounds, 7-chloro-N-(3-(4-(7-(trifluoromethyl)quinolin-4yl)piperazin-1-yl)propyl)quinolin-4-amine and {3-[4-(7-chloro-quinolin-4-yl)-piperazin-1-yl]propyl}-(7-trifluoromethyl-quinolin-4-yl)-amine,emerged as the most active among this series.
They were particularly potent against MCF7 cells when compared to CQ and cisplatin, a widely
prescribed anti-cancer drug. The results suggest that these CQ analogs could serve as bases for
the development of a new group of effective cancer chemotherapeutics.[ Solomon VR, Hu C,
Lee H, 2010 May 31].
Decitabine and azacitidine, two DNA methyltransferase (DNMT) inhibitors, are the current
standard of treatment for myelodysplastic syndrome (MDS). Histone deacetylase (HDAC)
inhibitors are also being tested against MDS. Both drug classes synergize in their gene
reactivating and anticancer activities. The combination of hydralazine and valproate
(Transkrip®), a DNMT and HDAC inhibitor, respectively), has been developed as epigenetic
therapy under the drug repositioning concept. To evaluate the clinical efficacy and safety of
hydralazine and valproate against MDS, an open phase-II study for previously treated patients
with MDS was conducted. The hydralazine dose was given according with the acetylator
phenotype, and valproate was dosed at 30 mg/kg/day. Response was graded with International
Working Group criteria. Toxicity was evaluated by the Common Toxemia Criteria-National
Cancer Institute version 3 scale. Refractory cytopenia with multilineage dysplasia was diagnosed
in ten cases, and refractory anemia with excess of blasts in two. Main toxicities were mild,
including somnolence and nausea. Preliminary results of this phase-II study suggest that the
combination of hydralazine and valproate is a promising non-toxic and effective therapy for
MDS.[ Candelaria M, Herrera A, Labardini J, González-Fierro A, Trejo-Becerril C, April2011 ].
Several targets are co-crystallized with their corresponding drugs, e.g,HSP90 structure with
radicicol, protein kinase B-RAF with sorafenid. In these cases, MED-SuMo can be used for drug
repurposing. When a very similar binding site is found in the PDB , it is possible that the
corresponding drug also binds the similar target site. Here,they have described an example of
using MED-SuMo in repurposing sorafenib from BRAF in other protein kinases’ binding sites.
The B-RAF-sorafenib complex 3D structure is available in the PDB. It was used as the input of
MED-SuMo to query the PDB binding site database. The database contains redundant protein
kinase structures. But, in most cases, the redundant structures are co-crystallized with different
ligands. [Olivia Doppelt-Azeroual, Fabrice Moriaud, Stewart A. Adcock and François
Delfaud-2009].
The development of HIV drugs is an expensive and a lengthy process. In this paper, it has been
used as drug repositioning; a process whereby a drug approved to treat one condition is used to
treat a different condition, to identify clinically approved drugs that have anti-HIV activity. The
data presented here shows a combination of two clinically approved drugs, decitabine and
gemcitabine, reduced HIV infectivity by 73% at concentrations that had minimal antiviral
activity when used individually. Decreased infectivity coincided with a significant increase in
mutation frequency and a shift in the HIV mutation spectrum. These results indicate that an
increased mutational load is the primary antiviral mechanism for inhibiting the generation of
infectious progeny virus from provirus. Similar results were seen when decitabine was used in
combination with another ribonucleotide reductase inhibitor. The results suggest that HIV
infectivity can be decreased by combining a nucleoside analog that forms noncanonical base
pairs with certain ribonucleotide reductase inhibitors.This observations support a model in which
increased mutation frequency decreases infectivity through lethal mutagenesis [Christine L.
Clouser, Steven E. Patterson & Louis M. Mansky, 29 June 2010].
Decitabine and azacitidine, two DNA methyltransferase (DNMT) inhibitors, are the current
standard of treatment for myelodysplastic syndrome (MDS). Histone deacetylase (HDAC)
inhibitors are also being tested against MDS. Both drug classes synergize in their gene
reactivating and anticancer activities. The combination of hydralazine and valproate
(Transkrip®), a DNMT and HDAC inhibitor, respectively), has been developed as epigenetic
therapy under the drug repositioning concept. To evaluate the clinical efficacy and safety of
hydralazine and valproate against MDS, an open phase-II study for previously treated patients
with MDS was conducted. The hydralazine dose was given according with the acetylator
phenotype, and valproate was dosed at 30 mg/kg/day. Response was graded with International
Working Group criteria. Toxicity was evaluated by the Common Toxemia Criteria-National
Cancer Institute version 3 scale. Refractory cytopenia with multilineage dysplasia was diagnosed
in ten cases, and refractory anemia with excess of blasts in two. Main toxicities were mild,
including somnolence and nausea. Preliminary results of this phase-II study suggest that the
combination of hydralazine and valproate is a promising non-toxic and effective therapy for
MDS.[ Candelaria M, Herrera A, Labardini J, González-Fierro A, Trejo-Becerril C, April2011 ].
3. MATERIALS AND METHODS:
3.1 TOOLS AND SOFTWARES:
3.1.1 NCBI:
The National Center for Biotechnology Information (NCBI) is a multi-disciplinary
research group that serves as a resource for molecular biology information. It was formed in
1988 as a complement to the activities of the National Institutes of Health (NIH) and the
National Library of Medicine (NLM). NCBI creates public databases, conducts research in
computational biology, develops software tools for analyzing genome data, and disseminates
biomedical information. This tool is used for collecting protein [HDAC1]information with
HDAC1 FASTA sequence.
3.1.2 BLAST:
Basic Local Alignment Search Tool, or BLAST, is an algorithm for comparing primary
biological sequence information, such as the amino-acid sequences of different proteins or the
nucleotides of DNA sequences. A BLAST search enables a researcher to compare a query
sequence with a library or database of sequences, and identify library sequences that resemble
the query sequence above a certain threshold. The BLAST program was designed by Eugene
Myers, Stephen Altschul, Warren Gish, David J. Lipman and Webb Miller in 1990.BLAST
tool is to find regions of sequence similarity, which will yield functional and evolutionary clues
about the structure and function of your novel sequence. Sequences similar to a query can be
found in a database sequence in turn and returning the highest scoring sequences. This can be
achieved by Dynamic Programming algorithms but in practice faster approximate methods are
often used.
3.1.3 PROTEIN DATA BANK:
The Protein Data Bank (PDB) is a repository for the 3-D structural data of large biological
molecules, such as proteins and nucleic acids.The data, typically obtained by X-Ray
crystallography or NMR spectroscopy and submitted by biologists and biochemists from around
the world, are freely accessible on the Internet via the websites of its member organisations
(PDBe, PDBj, and RCSB). The PDB is overseen by an organization called the Worldwide
Protein Data Bank, wwPDB.
The PDB is a key resource in areas of structural biology, such as structural genomics. Most
major scientific journals, and some funding agencies, such as the NIH in the USA, now require
scientists to submit their structure data to the PDB. If the contents of the PDB are thought of as
primary data, then there are hundreds of derived (i.e., secondary) databases that categorize the
data differently. For example, both SCOP and CATH categorize structures according to type of
structure and assumed evolutionary relations; GO categorize structures based on genes.PDB is
generally used for retrieving structure of protein.
3.1.4 AUTODOCK 1.4.6:
AutoDock is a suite of automated docking tools. It is designed to predict how small molecules,
such as substrates or drug candidates, bind to a receptor of known 3D structure,simply termed as
protein-ligand docking.AutoDock consist of two generations of software: AutoDock 4 and
AutoDockVina.AutoDock4 actually consists of two main programs: autodock performs the
docking of the ligand to a set of grids describing the target protein; autogrid pre-calculates these
grids.In addition to using them for docking, the atomic affinity grids can be visualized.
AutoDockVina does not require choosing atom types and pre-calculating grid maps for them.
Instead, it calculates the grids internally, for the atom types that are needed, and it does this
virtually instantly.It has also developed a graphical user interface called AutoDockTools, or
ADT for short, which amongst other things helps to set up which bonds wil3l treated as rotatable
in the ligand and to analyze dockings. Usage of AutoDock has contributed to the discovery of
several drugs.
AutoDock has applications in:

X-ray crystallography;

structure-based drug design;

lead optimization;

virtual screening (HTS);

combinatorial library design;

protein-protein docking;

chemical mechanism studies.
AutoDock 4 is free and is available under the GNU General Public License. AutoDock Vina is
available under the Apache license, allowing commercial and non-commercial use and
redistribution.
The ligand and protein were uploaded in autodock.the grid box is then aligned accordingly.It is
then allowed to autogrid.the auotodock file was created by procedure.It is then allowed toauto
dock. After completion of docking of protein with each entered ligand ,binding energy values that is
contained in .dlg format is acquired,along with the csv that is generated . The same procedure is
followed for the entire set of ligands that is taken for the work.
3.1.5 PyMOL VIEWER 1.5:
PyMOL is an open-source, user-sponsored, molecular visualization system. It can produce high
quality 3D images of small molecules and biological macromolecules, such as proteins.PyMOL
is one of a few open source visualization tools available for use in structural biology.
The Py portion of the software's name refers to the fact that it extends, and is extensible by
the Python programming language. It also covers aspects of the program likely to be of use in
medicinal chemistry, such asvisualizing protein−ligand interactions, working with prepared
session files, and creating figures. It contains a number of presets that can be of immediate use
in visualizing protein / ligandcomplexes as well as in preparation of several common types of
figures. PyMOL can be used to measure distances, angles, and dihedrals. By default, PyMOL
images are saved at screen resolution with a width and height equal to that of the graphics area.
The protein-dlg file is opened using the file options in pymol.this displays the docked structure
of protein with the ligand.the ligand is then rendered in solid surface,which enables us to view
the interactions between the protein and the ligand. The angle as well the distance between the
amino acids in protein and ligand is noted.
3.1.6 DISCOVERY STUDIO®VISUALIZER 3.1
DS Visualizer is a free, feature-rich molecular modeling environment, for both small
moleculeand macromolecule applications. DS Visualizer provides functionality for visualizing,
analyzing,and sharing biological and chemical data. It allowsto viewmolecular data from
multiple perspectives by providing the optionsto view data through 3D structures, sequences, and
data tables. It helps to interact with and rapidly analyze your data using the MoleculeWindow,
which supports visualization, data table and hierarchyviews of the data. It also enables to view
and manipulate publication quality 3D molecularstructures ranging from atomic-level to large
macromolecularcomplexes, and generate a variety of charts such as 3D point plots, heat mapsand
Ramachandran plots to analyze the data.DS Visualizer can handle a range of data types,
including 2D and 3D structures. The structures and sequences can be directly downloaded
from the PDB, or NCBI.
3.2 METHODOLOGY:
4.RESULTS AND DISCUSSION
Target Information:
Histone deacetylase 1 is an enzyme that in humans is encoded by the HDAC1
gene. Histone acetylation and deacetylation, catalyzed by multisubunit complexes, play a key
role in the regulation of eukaryotic gene expression. The protein encoded by this gene belongs to
the histone deacetylase/acuc/apha family and is a component of the histone deacetylase complex.
It also interacts with retinoblastoma tumor-suppressor protein and this complex is a key element
in the control of cell proliferation and differentiation. The target protein HDAC1 sequence was
retrieved from NCBI.
TARGET SEQUENCE FOR PROTEIN HDAC1:
>gi|49456395|emb|CAG46518.1| HDAC1 [Homo sapiens]
MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSD
DYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLH
HAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGD
LRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKG
HAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNM
TNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEF
SDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA
BLAST RESULT:
Table 4: Blast result
BLAST is help to find identity between target and template .Here the maximum identity is
93%,So the 3MAX_A[Crystal Structure of Human HDAC2 complexed with an N-(2aminophenyl)benzamide] was taken as the template for Modelling of HDAC1 protein. The
HDAC1 protein structure was modeled by using MODELLER 9v5.
FIG 4.1HDAC1 PROTEIN STRUCTURE:
PyMOL viewer is help to view the protein structure.Here the HDAC1 protein was visualized in
pyMOL viewer.
DOCKING RESULTS:
A total of about 1204 FDA-approved drugs from drugbank was taken and were docked with
the modeled protein HDAC1 by using AUTODOCK Software.These docking results were
aligned according to their highest binding energy.teh lowest binding energy and mean binding
energy values were obtained from docking logarithmic file(dlg).
It is found that, drug that belongs to their lowest binding energy values, nitrofurantoin acquires
the highest binding energy value of -11.79. A bacteriostatic or bactericidal agent depending on
the concentration and susceptibility of the infecting organism. Nitrofurantoin is active against
some gram positive organisms such as S. aureus, S. epidermidis, S. saprophyticus, Enterococcus
faecalis, S. agalactiae, group D streptococci, viridians streptococci and Corynebacterium. Its
spectrum of activity against gram negative organisms includes E. coli, Enterobacter, Neisseria,
Salmonella and Shigella. It may be used as an alternative to trimethoprim/sulfamethoxazole for
treating urinary tract infections though it may be less effective at eradicating vaginal bacteria.
May also be used in females as prophylaxis against recurrent cystitis related to coitus.
Nitrofurantoin is highly stable to the development of bacterial resistance, a property thought to
be due to its multiplicity of mechanisms of action.the taxonomy class of nitrofurantoin is
Nitrofurans.
The drug Tiaprofenic acid acquires the highest binding energy value of –11.68.It is a
non-steroidal anti-inflammatory drug of the arylpropionic acid (profen) class, used to treat pain,
especially arthritic pain Benzoyl Derivatives is the taxonomic class of Tiaprofenic acid.
Furazolidone,which drug is a nitrofuran derivative with antiprotozoal and antibacterial
activity. Furazolidone binds bacterial DNA which leads to the gradual inhibition of monoamine
oxidase. Carbamates and Derivatives are the taxonomic class of Furazolidone, which acquires
the lowest binding energy value of -11.58.
Bromfenac,which drug is a nonsteroidal anti-inflammatory drug (NSAID) for ophthalmic
use. Ophthalmic NSAIDs are becoming a cornerstone for the management of ocular pain and
inflammation. Their well-characterized anti-inflammatory activity, analgesic property, and
established safety record have also made NSAIDs an important tool to optimize surgical
outcomes. Benzophenones are the taxonomic class of Bromfenac drug,which acquires the
highest binding energy value of -11.34.
Ketoprofen,which drug is a propionic acid derivative, is a nonsteroidal anti-inflammatory
agent (NSAIA) with analgesic and antipyretic properties.the taxonomic class of ketoprofen is a
Benzophenones. Ketoprofen acquires the highest binding energy value of -11.27.
The Autodock and autogrid images are depicted in picture, which taken while running.
FIGURE 4.2:HDAC1 PROTEIN AND NITROFURANTOIN COMPOUND IN AUTODOCK
TOOL
FIGURE 4.3: AUTOGRID PREPARING FOR AUTOGRID CALCULATION
FIGURE 4.4: AUTOGRID CALCULATION FOR NITROFURANTOIN
FIGURE 4.5: AUTODOCK PREPARING FOR DOCK CALCULATION
FIGURE 4.6:AUTODOCK CALCULATION FOR NITROFURANTOIN
The interaction between protein and ligand was depicted in picture according to highest mean
binding energy.
FIG4.7INTERACTION BETWEEN PROTEIN HDAC1 AND NITROFURANTOIN:
FIG 4.8INTERACTION BETWEEN PROTEIN HDAC1 AND TIAPROFENIC ACID:
FIG 4.9 INTERAC TION BETWEEN PROTEIN HDAC1 AND FURAZOLIDONE:
FIG 4.10INTERACTION BETWEEN PROTEDIN HDAC1 AND BROMFENAC:
FIG 4.11 INTERACTION BETWEEN PROTEIN HDAC1 AND KETOPROFEN:
These protein-ligand interactions were visualized in PyMOL viewer.It can produce high quality
3D images of small molecules and biological macromolecules such as proteins.The python
programming language play a important role in PyMOL viewer,to visualize the interaction of the
protein and drug.
CONCLUSION:
There is a tremendous commercial and medical value in drug repositioning. This presents
an excellent strategy to achieve optimal potential and maximize the value of therapeutic
drug.Our focus is on improving existing drugs and repurposing drugs to treat HDAC1 for breast
cancer. In this project,1024 FDA approved drugs were taken from drug bank and docked with
HDAC1 protein in autodock. The best compound was screened according to their highest
binding energy value and best interaction with HDAC1 protein.It has been arrived to conclusion
that, the drug Nitrofurantoin[DB00698]showed best interaction with HDAC1 protein with the
highest binding energy of -11.79 kcal/mol. Tiaprofenic acid which drug showed best interaction
with HDAC1 and given second highest binding energy of -11.68. Likewise, the drugs,
Furazolidone [DB00614], Bromfenac [DB00963],and Ketoprofen [DB01009] showed best
interaction and given highest binding energy values are
-11.58kcal/mol, -11.34kcal/mol,
-11.27kcal/mol.So that we suggesting these compounds are best to treating with HDAC1 for
breast cancer. This when carried out invivo and invitro might prove to be one of the best
repositioned drug.
.
REFERENCES:
1. Alexis Nzila, Zhenkun Ma , Kelly Chibale Drug repositioning in the treatment of malaria
and TB
Future Med. Chem. (2011) 3(11), 1413–1426.
2. Sarah L. Kinnings, Nina Liu., Nancy Buchmeier, Drug Discovery Using Chemical
Systems Biology:
Repositioning the Safe
Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis
PLoS Computational Biology July 2009 Volume 5 | Issue 7 | e1000423.
3. Sarah L. Kinnings, Li Xie, Richard M. Jackson,The Mycobacterium tuberculosis
Drugome and Its Polypharmacological Implications,PLoS Computational Biology
November 2010 | Volume 6 | Issue 11 | e1000976
4. W. H. Bisson, A. V. Cheltsov, N. Bruey-Sedano,Discovery of antiandrogen activity of
nonsteroidal scaffolds of marketed drugs ,PNAS _ July 17, 2007 _ vol. 104 _ no. 29 _
11927–11932
5. Yvonne Y. Li, Jianghong An, Steven J. M. Jones,A Computational Approach to Finding
Novel Targets for Existing Drugs,PLoS Computational Biology, September 2011 |
Volume 7 | Issue 9 | e1002139
6. Assaf Gottlieb, Gideon Y Stein, Eytan Ruppin and Roded Sharan1,PREDICT: a method
for inferring novel drug indications with application to personalized and medicine,
EMBO Macmillan Publishers Limited april 2011
7. Heng Luo, Jian Chen, Leming Shi, Mike Mikailov, Huang Zhu, Kejian Wang ,DRARCPI: a server for identifying drug repositioning potential and adverse drug reactions via
the
chemical–protein
interactome
Nucleic
Research
Acids,
2011,
Vol.
39,
doi:10.1093/nar/gkr299
8. Yvonne Y. Li Jianghong An Steven J.M. Jones , A Large-Scale Computational Approach
to Drug Repositioning Genome Informatics 17(2): 239{247 (2006)
9. Christine L. Clouser, Steven E. Patterson and Louis M. Mansky, Exploiting Drug
Repositioning for Discovery of a Novel HIV Combination Therapy, JOURNAL OF
VIROLOGY, Sept. 2010, p. 9301–9309
10. Simon J Cockell, Jochen Weile, Phillip Lord, Claire Wipat, An integrated dataset for in
silico drug discovery, Journal of Integrative Bioinformatics, 7(3):116, 2010
11. Lun Yang, Pankaj Agarwal, Systematic Drug Repositioning Based on Clinical SideEffects :PLoS ONE , December 2011 | Volume 6 | Issue 12 | e28025
12. Joachim von Eichborn1, Manuela S. Murgueitio, Mathias Dunkel, Soeren Koerner
:PROMISCUOUS: a database for network-based drug-repositioning ,Nucleic Acids
Research, 2011, Vol. 39, doi:10.1093/nar/gkq1037
13. Sachin Mathur, MS1, Deendayal Dinakarpandian, Drug Repositioning using Disease
Associated Biological Processes and Network Analysis of Drug Targets :AMIA Annu
Symp Proc 2011;2011:305-311, PMCID:PMC3243256
14. Guanghui Hu, Pankaj Agarwal :Human Disease-Drug Network Based on Genomic
Expression Profiles :PLoS ONE , August 2009 | Volume 4 | Issue 8 | e6536
15. Marina Sirota,Joel T. Dudley, Jeewon Kim ,Discovery and Preclinical Validation of Drug
Indications
Using Compendia of Public Gene Expression Data: Science Translational medicine,17
August 2011 Vol 3 Issue 96 96ra77
16. Divya Sardana, Cheng Zhu ,Minlu Zhang, udivadaRanga C. G, Lun Yang ,Anil G. Jegga
:Drug repositioning for orphan diseases, PMID:21504985 [PubMed - indexed for
MEDLINE] : April 18, 2011
17. Guangxu Jin, changhe Fu,Hong Zhao,Kemi Cui: A Novel Method of Transcriptional
Response
Analysis
to
Facilitate
Drug
Repositioning
for
Cancer
Therapy,PMCID:PMC3251651[PubMed - indexed for MEDLINE], 2011 Nov 22.
18. Yang L, Agarwal P: A novel method of transcriptional response analysis to facilitate drug
repositioning for cancer therapy, [PubMed - indexed for MEDLINE] PMCID:
PMC3251651,November 9, 2011.
19. Bernard.P, Dufresne-Favetta.C, Favetta.P, Himbert.F:Application of Drug Repositioning
Strategy to TOFISOPAM Current medicinal chemistry:Volume 15, Number 30,2008.
20. Ekins S, Williams AJ, Krasowski MD, Freundlich JS:In silico repositioning of approved
drugs for rare and neglected diseases, Drug Discov Today 2011 Apr;16(7-8):298-310.
Epub 2011 Mar 1.
21. Yong Mao,Kemi Cui,Wang Lulu, Hong Zha,Fang Nie, Brandl, M: An in-silico approach
for drug repositioning to tumour anti-migration using an integrated genomic strategy, Life
sciences systems and application workshop (LiSSA) 2011,IEEE/NIH.
22. Joel Dudley, Tarangini Deshpande, Atul Butte: Exploiting drug–disease relationships for
computational drug repositioning, Brief Bioinform (2011) 12 (4): 303-311. doi:
10.1093/bib/bbr013.
23. Deftereos SN, Andronis C, Friedla EJ, Persidis A, Persidis A:Drug repurposing and
adverse event prediction using high-throughput literature analysis,2011 MayJun;3(3):323-34. doi: 10.1002/wsbm.147. Epub 2011 Feb 16.
24. Diamond PF. Drug Repositioning gains in popularity. Genetic Engineering &
biotechnology news. 2010; 30.
25. Buckley CE, Marguerie A, Roach AG, Goldsmith P, Fleming A, Alderton WK, et al.
Drug reprofiling using zebra fish identifies novel compounds with potential promyelination effects. Neuropharmacology.59:149-159.
26. Solomon VR, Hu C, Lee H. Design and synthesis of chloroquine analogs with anti-breast
cancer property. Eur J
Med Chem.45:3916-3923.
27. Ha S, Seo YJ, Kwon MS, Chang BH, Han CK, Yoon JH. IDMap: facilitating the
detection of potential leads with therapeutic targets. Bioinformatics. 2008;24: 1413-1415.
28. Sannella AR, Casini A, Gabbiani C, Messori L, Bilia AR, Vincieri FF, et al. New uses for
old drugs. Auranofin, a clinically established antiarthritic metallodrug, exhibits potent ant
malarial effects in vitro: Mechanistic and pharmacological implications. FEBS Lett.
2008;582: 844-847.
29. Boolell M, Allen MJ, Ballard SA, Gepi-Attee S, Muirhead GJ, Naylor AM, et al.
Sildenafil: an orally active type 5 cyclic GMP-specific phosphodiesterase inhibitor for the
treatment of penile erectile dysfunction. Int J Impot Res. 1996;8: 47-52.
30. Doppelt-Azeroual O, Moriaud F, Adcock SA, Delfaud F. A review of MED-SuMo
applications. Infect Disord Drug Targets. 2009;9 :344-357.
31. Clouser CL, Patterson SE, Mansky LM. Exploiting drug repositioning for discovery of a
novel HIV combination therapy. J Virol.84:9301-9309.
32. Tobinick EL. The value of drug repositioning in the current pharmaceutical market. Drug
News Perspect. 2009;22 :119-125.
33. Tobinick EL. The value of drug repositioning in the current pharmaceutical market. Drug
News Perspect. 2009; 22:119-125.
34. Hohman M, Gregory K, Chibale K, Smith PJ, Ekins S, Bunin B. Novel web-based tools
combining chemistry
informatics, biology and social networks for drug discovery.
Drug Discov Today. 2009; 14:261-270.