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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5407_Библиотеки_им_академика_М_И_Перельмана
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40
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
TNF-α (tumor necrosis factor), MMP-9 (matrix metalloprotien), and MPO
human myelo peroxidase. Druglikliness (Molinspiration and Molsoft) and
ADMET (pkCSM) were evaluated by online computational tools. The
active site on target proteins was analyzed (CASTp 3.0). Molecular dynamic
simulations (GROMACS 2018.1 software and amber algorithms) revealed
a stable ligand–protein complex for mesalamine and coumarin derivatives
in the aqueous system along with highest binding affinity toward all the
proteins investigated in comparison with mesalamine alone. Further, these
computational results were confirmed by in vivo studies on the acetic acid
induced ulcerative colitis rat model which revealed that the synthesized
mesalamine-coumarin diazo derivatives were potent in reducing ulcerative
109
colitis.
2.3.1 TARGET-BASED APPROACH
There are several computational processes, including visualization tools,
which assist the drug design/discovery process decision systems using
110–112
target-based drug design methodologies.
The prospective lead or
therapeutic compounds are shaped mostly by biological targets. Target-based
drug discovery has had some success. It is not only for small molecules but
also has an active role in identifying antibody medicines, protein biologics,
gene therapies, and nucleic acid-based treatments.
113,114
Moreover, these
approaches are often quicker, simpler, and less expensive to build and
operate. It would target specific active site of the candidate by the principle
of SARs.
115
Figure 2.3 depicts the schematic representation of a target-based
drug discovery approach.
2.3.2 KNOWLEDGE-BASED APPROACH
The information-driven recent trends in the drug development method are
becoming more prevalent. When combined with structural knowledge about
their target proteins, structural, physicochemical, and ADMET property
with standard or potent ligands have shown to be greatly significant for
116,117
early-stage drug development etiquette.
The integument of advance
chemo-informatics tools, which are used to evaluate the structures and
characteristics of effective compounds, has also grown dramatically in the
41 Computational Drug Discovery and Drug Repurposing
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last several years. Recently, authentic databases are playing a key role in
the knowledge-based approach system.
118
These are categorized with various
forms in Table 2.5. Preliminarily, emphasis on the physicochemical characteristics of drug is most important for clinical approval. These parameters
are molecular weight, H-bond acceptor, donor, cLogP, TPSA; number of
rotatable bonds and rings. Among all, H-bond accepter parameter is a major
factor in determining efficacies of drug.
119
Sl No. Database Link
World drug index https://www.daylight.com/about/index.html
MDDR database http://www.akosgmbh.de/accelrys/databases/mddr.
htm
SuperDrug http://bioinf.charite.de/superdrug
Comprehensive medicinal
chemistry (CMC)
GPCR-PEnDB https://gpcr.utep.edu/database
http://www.akosgmbh.de/accelrys/databases/cmc-3d.
htm
42
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
2.3.3 SIGNATURE-BASED APPROACH
In the beginning, the drug discovery and development depends upon the
disease of interest. The screening of a leading compound is now the most
important stage by the identification of their drug-likeness properties and
113,120
toxicity using high-throughput technology.
Moreover, one of the most
important requirements in compound profiling and drug discovery is to
evaluate the effects of these compounds on cellular activities by utilizing
a large number of key proteins. Firstly, the unique molecular state of a
phenotypic that differs from the wild-type or healthy state is referred to
as the disease signature. Generally, heterogeneous biological data, such as
gene expression in organs and protein composition in the microbiome, may
be used to describe these signatures. Second, drug signatures are similar to
disease signatures (Fig. 2.4). Drug fingerprints are perturbed by therapy
exposure rather than disease condition.
113,120,121
FIGURE 2.4 Schematic representation of the disease of interest.
2.3.4 NETWORK-BASED APPROACH
For the development of effective inhibitors, a new technique that incorporates
networks-based methods is being explored. For the purpose of determining
43 Computational Drug Discovery and Drug Repurposing
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the quantitative structure–activity relationship (QSAR) among the known
inhibitors by using the first-principles quantum mechanical approach, physical characteristics of neural networks, such as electronegativity and molar
122
volume, would be plausible.
This would be effective for early-stage drug
development. Figure 2.5 shows schematic representation of network-based
drug design.
FIGURE 2.5 Schematic representation of network-based drug design.
Moreover, the integration of omics experiments with route- and networkbased methods for early drug development is intended to bridge the gap
between fundamental research on pathway models and the actual requirements of the early drug development pipeline, evidently. Topology-based
route analysis techniques may aid in the reduction of false positives during
the target analysis process, the prioritization of target validation, the selection of optimum hits, and the progression from hit to lead during the hit to
lead step. There are very few limits and difculties to overcome, despite
the fact that including pathway- and network-based drug development may
increase the rate of success for nding new drugs. For example, network
topology databases provide a large number of contradictory ndings, and
the accuracy of these reports is questionable in many cases. Network-based
computational techniques will benet from the advances made possible by
the development and distribution of disease- and drug-specic omics data.
123
This will provide a new route toward a more evolved drug development
pipeline with reduced attrition rates.
124
44
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
2.3.5 TARGET MECHANISM-BASED APPROACH
Considering that it is a multifactorial disease, the paradigm shifts from “one
drug-one target” called target-based drug development. Moreover, the main
goal of either the multitarget strategy is to provide single-molecular entities
with a broader pharmacological spectrum through the use of multiple drugs.
When it comes to the development of new therapeutic armaments against
disease, target-based drug design offers a strong strategy.
125,126
However, the druggability of hybrid compounds that have been created
by rationally integrating different pharmacophoric characteristics may
be severely compromised, particularly if their molecular weight is raised
and their water solubility is reduced. In this regard, recent advancements
in early-stage ADMET will help to ease some of the difculties associated
with target-based drug design and development. On the other hand, it must
still conrm the efcacy of multitarget treatment in fact before it can be
implemented. This makes this task very appealing, but it also explains
why “Pharmaceutical industries” are disengaged from target-based drug
creation, particularly for different indications. However, the integration of
a chemobioinformatic method may aid in bridging the gap that currently
exists between the demand for disease-oriented drug design and the desire
127,128
for target-based drug design.
When developing a multitarget drug, it
is important to consider the fundamental molecular characteristics that are
required for successful interaction with each intended biological target. To
accomplish this objective, several drug design methods, most of which are
inspired by ligand-based and target-based approaches, have been proposed.
Signicant people have attempted in past few years to integrate the vast
quantity of diverse information in an attempt to develop predictive multitarget designs by integrating the information.
2.3.6 EXAMPLES OF SUCCESSFUL DRUG REPOSITIONING: CASE
STUDIES
Research into drug repositioning often involves integrating existing
knowledge about diseases, pathways, targets, and ligands into new
studies. This endeavor seems to be very difficult, given the wide range
of terms and circumstances under which experimental and clinical data
may be collected in various formats. Because of this, substantial and
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coordinated community efforts are required for effective use of existing
biological and clinical information as well as extraction of knowledge
from this information, which may aid in the better repositioning of already
available medicines in the marketplace. A large number of research are
presently ongoing that are aimed at the annotation, curation, and integration of information regarding chemical–biological interactions and their
mechanisms.
129, 130
In computational drug repositioning is gaining popularity across the
world these days, owing to the availability of a huge quantity of information on protein structures, pharmacophores, illness data, clinical studies,
and gene expression proles of pharmaceutical compounds. Furthermore,
the proliferation of public social networking technologies, as well as the
availability of computational access to genetic information, has signicantly helped the computer methods in their attempts to anticipate novel
indicators. As a result, current bioinformatics or computational tools are
being used by the majority of pharmaceutical rms to reposition drugs
from a variety of chemical regions. Any pharmaceutical business wishes
to prot from the improved speed and lower costs offered by strong
in silico technology. This is the ultimate goal of every pharmaceutical
company. New computational techniques for focused proling of small
compounds have been created in response to the rise in drug-related data.
These methods have higher levels of recall and accuracy than previous
methods.
8
These techniques enhance the repositioning process by using chemoinformatics, bioinformatics, network biology, systems biology, or genomic
information to uncover previously undiscovered targets or processes of
authorized medicines in a shorter amount of time than traditional methods.
As a result, computational drug repositioning techniques may be generally
classied into the following categories: target-based, knowledge-based,
signature-based, network-based, and targeted-mechanism-based. Based on
the computational ndings, a selection of compounds may be selected for
additional experimental testing to ensure that the computational insights
are correct. A hybrid method that incorporates both computational and
experimental tests is thus required to repurpose medicines for new illnesses,
and the majority of pharmaceutical companies have already embraced this
strategy to assess the therapeutic effectiveness of new indications.
131,132
Table 2.6 lists some successful drug repositioning.
46
TABLE 2.6 List of Some Successful Drug Repositioning.
Drug Initial References
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
Suggested
Parkinson’s disease [133]
Attention deficit hyperactivity
disorder
Depression [134,144]
Obesity
Depression [135]
Diabetic-neuropathy
Analgesia and [136]
depression
Premature ejaculation
Immunosuppressant [137]
Pancreatic neuroendocrine
tumors
Drug Initial References
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Suggested
Depression [138]
Gastrointestinal stromal tumor
Chronic myeloid [139]
leukemia
Pregnancy termination [140]
Cushing’s syndrome
47 Computational Drug Discovery and Drug Repurposing
Depression [141]
Fibromyalgia syndrome
Cancer [142]
Restenosis
48
Drug Initial References
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
Suggested
Epilepsy [143]
Migraine
Opioid addiction [144]
Obesity
DISCOVERY
Despite availability of a wide range of tools and resources in computational
drug discovery, developing a robust computational model is a complex
process and associated with many challenges. Besides, complexity of
mapping these theoretical approaches to predict behavior of living beings,
inaccurate, missing, or biased data are some of the major challenges in
putting these approaches into action. For example, it may be quite difficult
to define a reliable gene expression signature profile because of variations
in experimental conditions such as patient’s age, environment factors across
different experiments; this may lead to discrepancies in gene expression data.
When such inaccurate or biased data are utilized to build models it may lead
to unreliable results. Further, unavailability of high-resolution structural data
of drug targets makes it difficult to identify interactions between drug and
target. Lack of gold-standard datasets to evaluate models performance is yet
another challenge in computational drug discovery. Therefore, researchers
have to either split their dataset into test, training, and validation sets and the
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use k-fold cross validation and evaluation metrics or build their own dataset
and use prevalent metrics such as specificity, sensitivity, recall, F1 score,
accuracy, etc., to evaluate the model and to avoid over fitted model.
145
Despite all these challenges, integration of multisource data related to
disease, drugs, and how these drug and diseases affect the body is important
aspect in computational drug discovery models. Availability and integration
of these datasets could improve the performance of the models. However,
there are several diseases that still lack treatments and inspire the researchers
all over the world to develop novel and leading candidates for treatment of
unknown and rare diseases.
146
In summary, computational drug discovery and repurposing can be of
immense benefit for speeding up the drug development process as well as
increasing the plausibility for the failed or withdrawn drug to get a second
chance to reach the market. While extra supports toward computational drugs
repurposing are the need of the hour for the researchers and scientists to make
further efforts to come up with new finding in this area. With this in mind,
availability of open-source databases/tools, workflow systems, pipelines in
the form of codes, software tools that are more robust, reproducible, and easy
access to validate the analysis could be helpful for the computational drug
discovery process.
KEYWORDS
• bioinformatics
• chemoinformatics
• drug discovery
• drug repurposing
• network biology
REFERENCES
1. Dudley, J. T.; Desphande, T.; Butte, A. J. Exploiting Drug–Disease Relationships for
Computational Drug Repositioning. Brief Bioinform. 2011, 12 (4), 303–311.
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