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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5435_Библиотеки_им_академика_М_И_Перельмана
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– Target identification and validation: In this stage, researchers identify a specific
target molecule, such as a protein or enzyme, which is involved in a disease process
and can be targeted by a drug. The target should have a known association with
the disease and should be “druggable” in terms of its structural and functional
properties.
– Hit generation: It is a crucial early step in the process of developing new medica-
tions. It involves identifying and creating chemical compounds or molecules that
have the potential to interact with a specific biological target, such as a protein or
enzyme associated with a disease.
– Lead discovery: The goal of lead discovery is to identify potential compounds or
molecules that can interact with the target and modulate its activity. This stage in-
volves various approaches, such as high-throughput screening (HTS) of large com-
pound libraries, virtual screening using computational methods, or natural product
screening. The identified compounds are called “leads” and serve as starting points
for further optimization.
– Lead optimization: Once leads are identified, medicinal chemists work on opti-
mizing their properties to improve their potency, selectivity, pharmacokinetic
profile, and safety. This stage involves the synthesis and testing of numerous ana-
logs and derivatives of the lead compound, with the aim of finding a candidate
molecule that exhibits optimal drug-like properties.
– Preclinical testing: After lead optimization, the selected candidate undergoes
preclinical testing, which involves a series of laboratory and animal studies.
These tests evaluate the compound’s efficacy, safety, pharmacokinetics, and toxi-
cology. The data gener ated in this stage helps determine whether the candidate
molecule should proceed to clinical trials in humans.
– Clinical development: If a candidate molecule successfully passes preclinical test-
ing and is approved by regulatory authorities, it enters the clinical development
stage, which consists of three phases:
– Phase 1: This phase involves the initial administration of the drug to a small
number of healthy volunteers or individuals with the target disease. The
focus is on assessing the drug’s safety, dosage range, and pharmacokinetics.
– Phase 2: In this phase, the drug is administered to a larger group of patients
with the target disease. The primary objective is to evaluate the drug’seffi-
cacy, optimal dosage, and potential side effects.
– Phase 3: This phase involves a larger-scale study with an expanded patient
population to further evaluate the drug’s effectiveness, monitor side effects,
and compare it wi th existing treatment options. The data obtained in this
phase are submitted to regulatory authorities for approval.
– Regulatory approval: If the drug successfully completes Phase 3 trial and dem-
onstrates safety and efficacy, the pharmaceutical company can submit a new
24 Gita Chawla and Tathagata Pradhan
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drug application to regulatory agencies, such as the U.S. Food and Drug Adminis-
tration (FDA) or the European Medicines Agency. Regulatory agencies review the
data and make a decision regarding the approval of the drug for marketing and
commercial use.
– Post-marketing surveillance: After a drug is approved and reaches the market,
post-marketing surveillance begins. This involves ongoing monitoring of the
drug’s safety and effectiveness in a larger patient population. Adverse events and
long-term effects are carefully monitored, and additional studies may be con-
ducted to further investigate the drug’s benefits and risks.
A simple and conventional drug design strategy involves finding a chemical entity that
can fit both geometrically and chemically to an active catalytic site on a protein target.
Then the identified compound undergoes refinement using various techniques such as
structure-based design, fragment-based design, combinatorial chemistry, computational
techniques, etc. [2–4]. Following this, the obtained lead molecule undergoes lab and ani-
mal testing, risk assessment, safety profiling, etc. and subsequently becomes available
to the patients. The setback with this method remains its long cycle and high cost. To
overcome this, a modern and rational approach has been adapted to speed up the drug
discovery process in an efficient and economical manner [3].
Once a promising drug target is identified, researchers aim to identify molecules
that can interact with the target to produce desired biological effects.
Hit-based lead discovery is a specific approach within the broader field of drug
design that focuses on identifying and selecting promising compounds, known as
“hits,” that have the potential to become lead co mpounds for further optimization
Figure 2.1: Various stages of drug design workflows.
2 Lead-hit-based methods for drug design and ligand identification 25
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and development. It involves screening large compound libraries, either through ex-
perimental methods or virtual screening, to identify compounds that exhibit a desired
biological activity on a specific target [5].
Some commonly used lead-hit-based methods for drug design includes HTS, virtual
screening, fragment-based drug design (FBDD), phenotypic screening, cheminformatics,
SAR, natural product-based, de novo design, machine learning (AI), and scaffold hop-
ping [6–9]. Figure 2.2 illustrates some important lead hit-based methods employed for
drug-design.
Overall, hit-based lead discovery and drug design are interconnected stages that work
together to identify and optimiz e compounds with the potential to become effective
drugs. Hit-based lead discovery provides a pool of initial hits, and drug design strate-
gies refine and optimize those hits into lead compounds with improved drug-like
properties. The iterative nature of the process allows for continual refinement and
optimization until a lead candidate with the desired efficacy, safety, and pharmacoki-
netic profile is identified.
High-Throughput
Screening (HTS)
Virtual Screening
Fragment-Based
Drug Design
(FBDD)
Phenotypic screening
Natural product
screening
Cheminformatics and
QSAR
Involves using computational methods to
screen large databases of chemical structures
for potential hits.
Involves screening a library of small and low
molecular weight fragments against a target
protein. These fragments can then be elaborated and
optimized to improve their binding affinity and
selectivity, ultimately leading to the development of
larger drug-like molecules.
Phenotypic screening involves screening
compounds or extracts for their ability to
produce a desired cellular or organismal
phenotype.
Natural product screening involves the
testing of crude extracts or purified
compounds from natural sources for their
activity against a specific target or disease.
Cheminformatics uses computational methods to
analyze and interpret large datasets of chemical
and biological information. Quantitative Structure-
Activity Relationship (QSAR) models, a subset of
cheminformatics, employ statistical and machine
learning techniques to predict the biological
activity of compounds based on their structural
properties.
Involves the rapid screening of
large compound libraries against
specific drug targets.
Some important lead hit-based methods
used for drug design
Figure 2.2: Lead-hit-based methods for drug design.
26 Gita Chawla and Tathagata Pradhan
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2.1.1 High-throughput screening (HTS)
HTS is a valuable technique in drug discovery that aids in hit-based lead discovery. It
involves rapid screening and assaying of a large number of chemical compounds or
biological modulators against a selected and specific target to identify potential hits,
which are compounds that show promising activity or affinity for the target [10].
For a successful HTS, several steps like target identification, reagent preparation,
compound management, assay development, and high-throughput library screening
must be carried out with utmost care and precision.
HTS can contribute to hit-based lead discovery through the following ways:
– Compound libraries: HTS allows researchers to screen large compound libraries
containing thousands to millions of diverse chemical entities. These libraries may
include s ynthetic compounds, natural products, or compounds from various
other sources. The vast number of compounds increases the chances of identify-
ing potential hits with desirable properties.
– Automation and miniaturization: HTS employs automation and miniaturization
techniques, enabling the screening of a large number of compounds in a short
period. Automated liquid handling systems, robotics , and microplate-based for-
mats facilitate the testing of multiple compounds simultaneously, enhancing effi-
ciency and throughput.
– Assay development and adaptation: HTS involves the development or adaptation
of assays that enable the detection of a specific activity or interaction relevant to
the target of interest. These assays may measure enzyme activity, receptor-ligand
binding, cellular response, or other relevant parameters. Assays need to be robust,
reliable, and amenable to high throughput screening conditions.
– Data analysis and informatics: HTS generates a massive amount of data, and
informatics tools are crucial for effective analysis and interpretation. Data man-
agement, image a nalysis, statistical analysis, and data mining techniques help
identify compounds that exhibit desired activity patterns, select the most promis-
ing hits, and prioritize them for further evaluation.
– Hit confirmation and optimization: HTS provides a starting point for hit identi-
fication, but it does not guarantee the quality or suitability of the hits for further
development. After identifying potential hits, additional assays and val idation
studies are performed to confirm the activity and assess the hit’s selectivity, po-
tency, and other properties. Hits may undergo further optimization through me-
dicinal chemistry or structure–activity relationship (SAR) studies to enhance their
drug-like properties.
– Accelerating lead discovery: HTS accelerates the lead discovery process by rap-
idly screening a large chemical space, increasing the chances of identifying hits.
This approach helps researchers explore a wide range of chemical diversity effi-
ciently and cost-effectively, providing a foundation for subsequent lead optimiza-
tion and development stages.
2 Lead-hit-based methods for drug design and ligand identification 27
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The following are a few case studies discussing the use of HTS in lead discovery
(Figure 2.3):
2.1.1.1 Glivec (imatinib) for chronic myeloid leukemia (CML)
HTS played a pivotal role in the discovery of Glivec (approved in 2001), a break-
through drug for chronic myeloid leukemia (CML). Researchers at Novartis used HTS
to screen a large library of compounds against the Bcr-Abl kinase, a target associated
with CML. The HTS campaign identified Imatinib as a potent inhibitor of the kinase.
Further optimization and clinical trials led to the development of Glivec, which revo-
lutionized the treatment of CML [11].
2.1.1.2 Tamiflu (oseltamivir) for influenza
Tamiflu (approved in 2000), an antiviral medication used to treat and prevent influ-
enza, was discovered through HTS. Researchers at Gilead Sciences and Roche utilized
HTS to screen a library of compounds against the influenza virus neuraminidase en-
zyme. This screening identified oseltamivir as a potent inhibitor. Subsequent optimiza-
tion and clinical studies resulted in the approval of Tamiflu as an effective treatment
for influenza [12].
2.1.1.3 Xarelto (rivaroxaban) for stroke prevention
Xarelto (approved in 2011), an oral anticoagulant used for stroke prevention and treat-
ment of blood clots, was discovered using HTS. Scientists at Bayer and Johnson & John-
son performed an HTS campaign to screen a diverse compound library against Factor
Xa, a target involved in blood clotting. Rivaroxaban emerged as a lead compound,
showing potent and selective inhibition of Factor Xa. Extensive optimization and clini-
cal trials led to the approval of Xarelto as an anticoagulant medication [13, 14].
2.1.1.4 Vemurafenib (Zelboraf) for melanoma
Vemurafenib (approved in 2011), a targeted therapy for metastatic melanoma, was iden-
tified through HTS. Researchers at Plexxikon used HTS to screen a library of com-
pounds against mutated BRAF kinase, a common driver of melanoma. Vemurafenib
demonstrated remarkable selectivity and potency against the mutant kinase. This dis-
covery paved the way for the development of vemurafenib (marketed as Zelboraf),
which significantly improved outcomes for patients with BRAF-mutated melanoma [15].
28 Gita Chawla and Tathagata Pradhan
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These case studies highlight how HTS has been instrumental in identifying hits th at
have led to the development of successful drugs for various diseases. HTS enables the
screening of large compound libraries, accelerates the discovery process, and pro-
vides valuable starting points for subsequent lead optimization and development
stages.
2.1.2 Virtual screening
Virtual screening is a computational approach widely used in hit-based lead discovery
to identify potential active compounds from large libraries of chemical structures. It
involves the use of computer algorithms and models to predict the binding affinity or
activity of compounds against a specific target [16, 17]. Here is how virtual screening
contributes to hit-based lead discovery:
– Compound library filtering: Virtual screening enables the rapid filtering and
prioritization of compound libraries. Instead of physically screening all com-
pounds, virtual screening methods analyze the chemical structures and proper-
ties of the compounds to identify those most likely to interact with the target of
interest. This reduces the number of compounds to be experimentally tested, sav-
ing time and resources.
– Ligand-based virtual screening: In this approach, virtual screening methods an-
alyze the chemical features and properties of known active compounds and de-
velop predictive models or fingerprints based on their structural characteristics.
These models are then used to search large compound databases and rank poten-
tial hits according to their similarity to the known active compounds. This ap-
proach is useful when the target’s structure is unknown or difficult to obtain.
Cl
N
N
N
N
NH
2
N
H
N
Cl
S
N
N
H
N
H
N
H
F
Rivaroxaban
(Xarelto)
Imatinib
(Glivec)
Oseltamivir
(Tamiflu)
Zelboraf
(Vemurafenib)
F
O
O
O
O
O
O
O
O
O
O
O
N
N
HN
O
O
S
Figure 2.3: Different drugs discovered through HTS-based lead discovery.
2 Lead-hit-based methods for drug design and ligand identification 29
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– Structure-based virtual screening: This approach relies on the 3D structure of
the target protein to perform virtual screening. Using computational docking al-
gorithms, compounds from a library are docked into the binding site of the target
protein, and their binding affinity or interaction energy is calculated. Compounds
with favorable bindi ng characteristics are considered potential hits. Structure-
based virtual screening requires knowledge of the target’s structure or a reliable
homology model.
– Pharmacophore-based virtual screening: Pharmacophore models represent the
essential features and spatial arrangements required for a compound to interact
with a target. Virtual screening methods utilize these models to search compound
databases and identify molecules that match the pharmacophore requirements.
This approach is particularly useful when the target’s structure is unavailable or
challenging to determine.
– Machine learning and data mining: Virtual screening techniques often employ
machine learni ng algorithms to learn patterns and relationships between com-
pound structures and target activity. By training on known act ive and inactive
compounds, these models can predict the activity of new compounds. Data min-
ing methods can also be used to extract valuable information from large chemical
databases, facilitating hit discovery and optimization.
– Hit validation and optimization: Hits obtained through virtual screening still
require experimental validation to confirm their activity and suitability as lead
compounds. Experimental assays, such as biochemical and cell-based assays are
performed to validate the predicted activity. Subsequent lead optimization ef-
forts, such as medicinal chemistry, further refine and improve the properties of
the hits to enhance their potency, selectivity, and drug-like properties.
Virtual screening in hit-based lead discovery offers an efficient and cost-effective ap-
proach to explore vast chemical spaces and prioritize compounds for experimental
evaluation. It complements exper imental high throughput screening and provides
valuable insights and starting points for the discovery and development of novel lead
compounds.
Few notable case studies that demonstrate the successful use of virtual screening
in hit-based lead discovery:
2.1.2.1 Falcipain inhibitors for malaria treatment
Virtual screening played a crucial role in the discovery of falcipain inhibitors, a poten-
tial class of antimalarial drugs. Researchers at the Swiss Tropical and Public Health
Institute employed a structure-based virtual screening approach to identify small mol-
ecule inhibitors of falcipain, a key enzyme involved in the survival of the malaria par-
asite. The virtual screening campaign helped identify several lead compounds, which
30 Gita Chawla and Tathagata Pradhan
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were subsequently validated and optimized through experimental studies, leading to
the development of potential drug candidates for malaria treatment [18].
2.1.2.2 Inhibitors of HIV integrase
Virtual screening has been instrumental in identifying inhibitors of HIV integrase, an
essential enzyme for the replication of the HIV virus. In a study published in the Jour-
nal of Medicinal Chemistry, researchers used a combination of ligand- and structure-
based virtual screening to identify potential inhibitors from a large compound library.
The identified hits were experimentally validated, leading to the discovery of novel
HIV integrase inhibitors with promising antiviral activity [19, 20].
2.1.2.3 Tubulin inhibitors for cancer therapy
Virtual screening has been applied in the discovery of tubulin inhibitors, which are
potential anticancer agents targeting microtubules. In a study published in the Jour-
nal of Chemical Information and Modeling, researchers performed a ligand-based vir-
tual screening using a large compound database. The virtual screening approach
identified several compounds with predicted tubulin inhibitory activity; these com-
pounds were subsequently tested experimentally. The study resulted in the discovery
of new lead compounds with anticancer potential [21].
2.1.2.4 SARS-CoV-2 main protease inhibitors
Virtual screening has been extensively used in the search for potential inhibitors of
the main protease of the SARS-CoV-2 virus, a target for antiviral drug development. In
a study published in Science Advances, researchers conducted a structure-based vir-
tual screening campaign using a library of approved drugs and drug-like compounds.
The virtual screening approach identified several compounds with potential inhibi-
tory activity against the SARS-CoV-2 main protease. Subsequent experimental valida-
tion confirmed the activity of these hits, paving the way for further development as
COVID-19 therapeutics [22].
These case studies highlight the successful application of virtual screening in hit-
based lead discovery across various therapeutic areas, including malaria, HIV, cancer,
and viral infections. Virtual screening techniques have proven valuable in rapidly
screening large compound libraries, identifying potential hits, and guiding subse-
quent experimental validation and optimization efforts.
2 Lead-hit-based methods for drug design and ligand identification 31
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2.1.3 Fragment-based drug design
FBDD is an approach used in hit-based lead discovery that focuses on screening small,
low molecular weight fragments rather than larger compounds [23]. FBDD involves
identifying and optimizing fragments that bind to a target of interest, and these frag-
ments serve as starting points for further lead optimization [24, 25]. Here is how
FBDD contributes to hit-based lead discovery:
– Fragment library screening: FBDD begins with the screening of a diverse li-
brary of small fragments against the target of interest. These fragments typically
consist of low molecular weight compounds (100–300 Da) that cover a wide range
of chemical space. Screening smaller fragments allows for a higher hit rate, as
they are more likely to bind to the target in different ways.
– Biophysical techniques: FBDD relies on biophysical techniques, such as nuclear
magnetic resonance spectroscopy, X-ray crystallography, surface plasmon reso-
nance, or differential scanning fluorimetry, to detect and characterize fragment-
target interactions. These techniques provide structural and binding information,
helping to guide fragment optimization and prioritize hits.
– Fragment linking and growing: Once initial fragment hits are identified, they
can be chemically linked or grown to generate larger compounds with improved
binding affinity and potency. This process involves connecting or expanding the
fragments while maintaining or enhancing their binding interactions with the
target. This step helps transform the initial fragment hits into more potent lead-
like compounds.
– Scaffold hopping and merging: FBDD allows for scaffold hopping, wherein dif-
ferent fragments with distinct structural scaffolds are combined to generate new
compounds. This approach increases chemical diversity and can lead to the dis-
coveryofnovelchemicalentitiesthatexhibit improved potency or selectivity.
Scaffold merging involves combini ng two or more fragments to create a single,
more complex compound.
– Iterative optimization: FBDD involves iterative cycles of fragment screening, hit
optimization, and structural characterization to gradually improve the affinity,
selectivity, and drug-like properties of the lead compounds. The process involves
synthesizing and testing analogs, optimizing the fragment binding mode, and iter-
atively refining the lead compound based on the obtained structural information.
– Lead generation and optimization: Once a lead compound with desired potency
and selectivity is obtained, it serves as the starting point for further optimization
using traditional medicinal chemistry approaches. Lead optimization involves
modifying the chemical structure of the compound to improve properties such as
pharmacokinetics, toxicity, and metabolic stability, ultimately leading to the de-
velopment of a drug candidate.
32 Gita Chawla and Tathagata Pradhan
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FBDD has shown success in hit-based lead discovery, particularly in identifying leads
against challenging targets, protein-protein interactions, or allosteric binding sites. It
offers advantages in terms of efficiency, chemical diversity, and reducing compound
library sizes, enabling the identification of high-quality lead compounds with im-
proved chances of successful development into drugs [26].
Few notable case studies that demonstrate the successful application of FBDD in
hit-based lead discovery:
2.1.3.1 Vemurafenib (Zelboraf) for BRAF-mutant melanoma
FBDD played a significant role in the discovery of vemurafenib (approved in 2011)
(Figure 2.4), a targeted therapy for metastatic melanoma with BRAF mutations. Re-
searchers at Plexxikon employed FBDD to identify a fragment, 7-azaindole that shows
affinity towards BRAF kinase. Through subsequent optimization and structure-based
design, the fragment was expanded and linked with other fragments, leading to the
development of vemurafenib. This drug has shown remarkable clinical efficacy and
improved outcomes for patients with BRAF-mutant melanoma [24, 27, 28].
2.1.3.2 Venetoclax (Venclexta) for chronic lymphocytic leukemia (CLL)
Venetoclax (approved in 2020) (Figure 2.5), a BCL-2 inhibitor used for the treatment of
CLL, was discovered using FBDD. Researchers at AbbVie and Genentech utilized FBDD
to identify a small fragment that bound to the BCL-2 protein. The fragment was fur-
ther optimized through structure-guided design and medicinal chemistry to develop
venetoclax. This drug has shown significant clinical activity, particularly in patients
with CLL harboring the 17p deletion [29, 30].
O
O
O
O
O
S
Structural modifications
led to improved activity
7-Azaindole
A significant pharmacophore
showing activity against BRAF
kinase
PLX4720
BRAF IC
50
=0.013
µ
M
S
F
F
F
F
N
H
O
Cl
Cl
N
N
N
N
H
N
H
N
H
Zelboraf
(Vemurafenib)
BRAF IC
50
=0.031
µ
M
Figure 2.4: Discovery of vemurafenib through fragment-based lead discovery.
2 Lead-hit-based methods for drug design and ligand identification 33
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