Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5435_Библиотеки_им_академика_М_И_Перельмана
.pdf
2.1.3.3 Rucaparib (Rubraca) for ovarian and prostate cancer
Rucaparib (approved in 2018), a PARP inhibitor approved for the treatment of ovarian
and prostate cancer, was discovered through FBDD (Figure 2.6). Scientists at Agouron
Pharmaceuticals employed FBDD to identify a small fragment that bound to the PARP
enzyme. Through iterative rounds of optimization and fragment linking, rucaparib
was developed. It has demonstrated clinical efficacy in patients with BRCA-mutated
ovarian and prostate cancer [31].
Cl
NH
NO
2
O
O
O
N
N
N
Cl
HN
S
O
N
N
N
NH
S
O=S=O
SO
2
CF
3
Structural
modifications in
ABT-263
improved the
potency against
BCL-2
Initial fragments
identified for
possessing binding
affinity towards
BCL-xL
ABT-263
K
i
< 0.0005
µ
M (BCL-xL)
Venetoclax
K
i
< 0.00001
µ
M (BCL-2)
HO
NH
O
O
H
N
K
i
= 1.4
µ
M (BCL-xL)
K
d
= 6000
µ
M
K
d
= 300
µ
M
F
OH
O
F
OH
O
Figure 2.5: Discovery of venetoclax through fragment-based lead discovery.
34 Gita Chawla and Tathagata Pradhan
https://t.me/med1917
2.1.3.4 Erdafitinib (Balversa) for bladder cancer
Erdafitinib (approved in 2019), a selective FGFR (fibroblast growth factor receptor) in-
hibitor, was discovered using FBDD (Figure 2.7). Researchers at Janssen Pharmaceuti-
cal applied FBDD to identify a fragment that bound to FGFR. Subsequent optimization
and fragment merging led to the development of erdafitinib, which has shown activity
in patients with FGFR-altered metastatic bladder cancer [24, 32].
2.1.3.5 Gefitinib (Iressa) for lung cancer
Gefitinib (approved in 2015) ( Figure 2.7) is an epidermal growth factor receptor
(EGFR) tyrosine kinase inhibitor used in the treatment of non-small cell lung cancer.
FBDD was employed to identify fragments that bound to the ATP-binding site of EGFR.
The subsequent optimization led to the development of gefitinib with enhanced po-
tency and selectivity against EGFR [24, 33].
These case studies exemplify the successful application of FBDD in hit-based lead
discovery for a range of therapeutic targets. FBDD allowed for the identification of
fragments that bound to the target proteins, which were then optimized and ex-
panded to generate lead compounds with improvedpotencyandselectivity. These
compounds have demonstrated clinical effectiveness and have been approved for the
treatment of various cancers, showcasing the value of FBDD in drug discovery.
N
N
NH
NH
O
HN
NH
O
O
O
O
F
O
O
O
F
N
N
O
OH
NU1025
Olaparib
Initial fragment
Rucaparib
CH
3
NH
2
N
N
H
Anti
N
N
H
H
IC
50
= 10
µ
M
IC
50
= 0.4
µ
M
PAR P 1, I C
50
= 0.8–3.2 nM
PARP1, IC
50
= 1–19 nM
Syn
Structural modifications in
the subsequent hits
improved the activity
against PARP1
Figure 2.6: Discovery of rucaparib through fragment-based lead discovery.
2 Lead-hit-based methods for drug design and ligand identification 35
https://t.me/med1917
2.1.4 Phenotypic screening
Phenotypic screening is an approach that involves the direct evaluation of a com-
pound’s effect on c ellular or organismal phenotypes. Unlike target-based screening,
phenotypic screening does not rely on prior knowledge of the target or specific molec-
ular mechanism [34]. Phenotypic screening involves the following steps:
– Identify the disease or biological process: Determine the specific disease or bi-
ological process you want to target. This could be a cellular pathway, a specific
phenotype, or a disease state.
FGFR3, IC
50
= 0.003
µ
M
FGFR3, IC
50
= 0.012
µ
M
FGFR3, IC
50
= 0.33
µ
M
FGFR3, IC
50
= 6.5
µ
M
FGFR3, IC
50
= 120
µ
M
Wild-type EGFR, IC
50
= 0.02
µ
M
L858R/T790M EGFR, IC
50
= 0.92
µ
M
O
O
Structural modifications in
Erdafitinib resulted in the improved
selectivity of Gefitinib
towards EGFR.
O
O
N
N
N
N
N
N
N
N
N
HN
N
H
O
O
N
N
Cl
F
Cl
O
O
O
N
HN
N
N
N
N
N
H
N
N
N
N
N
NH
2
N
Initial fragment
Gefitinib
Erdafitinib
Figure 2.7: Discovery of gefitinib and erdafitinib through fragment-based lead discovery.
36 Gita Chawla and Tathagata Pradhan
https://t.me/med1917
– Select an appropriate model system: Choose a suitable model organism or cell
culture system that recapitulates the disease or process of interest. This could be
a cell line, primary cells, animal models, or even 3D organoids.
– Develop an assay: Design and develop an assay that can measure the desired
phenotype or response. This could involve imaging, high-content screening, bio-
chemical assays, or functional assays.
– Create a compound library: Assemble a library of compounds to screen. This
can include natural prod ucts, synthetic compounds, FDA-approved d rugs, or
chemical libraries.
– Perform the screening: Treat the model system with each compound from the
library and monitor the resulting phenotypic changes. This may involve the use
of robotic systems to handle large numbers of samples.
– Data analysis: Analyze the screening data to identify compounds that exhibit the
desired phenotypic effect. This could involve statistical analysis, data mining, and
visualization techniques.
– Hit validation: Perform secondary and tertiary assays to validate the initial hits
obtained from the primary screening. This helps confirm the activity of the com-
pounds and rule out false positives.
– Mechanism of action studies: Investigate the mode of action of the validated
hits to understand the underlying biological pathways or targets involved. This
may involve genetic or molecular techniques to dissect the mechanism.
– Lead optimization: Once a lead compound or compounds have been identified,
further optimize their properties to improve potency, selectivity, pharmacokinet-
ics, and safety.
– Preclinical and clinical development: Conduct preclinical studies to evaluate
the lead compound’s efficacy, safety, and pharmacokinetics in animal models. If
successful, proceed to clinical trials to test the compound in humans.
Phenotypic screening has been successful in identifying hits and lead compounds for
various therapeutic areas, including cancer, infectious diseases, and neurodegenera-
tive disorders. By evaluating the overall phenotype, this approach offers a holistic
view of compound activity and can lead to the discovery of novel targets and mecha-
nisms, enabling the development of innovative and effective therapeutics.
Phenotypic screening contributes to hit-based lead discovery in the following ways:
– Broad exploration of biological activity: Phenotypic screening allows for the
unbiased exploration of the effects of compounds on complex biological systems.
Instead of focusing on a specific target, it evaluates the overall phenotype or be-
havior of cells or organisms. This approach can uncover unexpected biological
activities, such as modulation of signaling pathways, cellular processes, or dis-
ease-related phenotypes.
– Identification of novel targets and mechanisms: Phenotypic screening can re-
veal new therapeutic targets and mechanisms of action. By assessing the overall
2 Lead-hit-based methods for drug design and ligand identification 37
https://t.me/med1917
phenotypic response, it may identify compounds that modulate multiple targets
or engage in complex biological interactions. This can lead to the discovery of
new pathways or targets relevant to the disease or condition of interest.
– Detection of polypharmacology: Phenotypic screening can identify compounds
with polypharmacological effects, that is, they interact with multiple targets si-
multaneously. This can be advantageo us, especially in complex diseases where
multiple pathways or targets are involved. Polypharmacological compounds may
have synergistic effects or provide broader therapeutic benefits.
– Complex disease modeling: Phenotypic screening allows for the evaluation of
compounds in relevant disease models, including cell-based assays, animal models,
or even ex vivo tissue samples. This enables the assessment of compound efficacy
and safety within the context of disease biology, providing a more comprehensive
understanding of their potential therapeutic effects.
– Hit validation and target deconvolution: Phenotypic screening can identify hits
that elicit a desired phenotype but without knowing the underlying molecular tar-
get. Subsequent target deconvolution approaches, such as genetic or biochemical
studies, are employed to elucidate the target and mechanism of action of the hit
compounds. This process helps establish the link between the compound’s effect
on the phenotype and its interaction with specific molecular targets.
– Lead optimization and mechanism-based drug design: Once hits are identified
from phenotypic screening, they serve as starting points for lead optimization.
The elucidation of the compound’s target and mechanism of action allows for the
rational design and optimization of compounds with improved potency, selectiv-
ity, and drug-like properties.
Here are a few notable case studies that demonstrate the successful use of phenotypic
screening in hit-based lead discovery:
2.1.4.1 Imatinib (Gleevec) for chronic myeloid leukemia (CML)
Phenotypic screening played a critical role in the discovery of imatinib, a targeted
therapy for CML. Researchers at Novartis employed a phenotypic screening approach
using a CML cell line to identify compounds that selectively inhibited the proliferation
of cancer cells. This led to the discovery of imatinib, which targets the BCR-ABL fusion
protein, a hallmark of CML. Imatinib revolutionized the treatment of CML and served
as a paradigm for targeted cancer therapies [35].
38 Gita Chawla and Tathagata Pradhan
https://t.me/med1917
2.1.4.2 Vorinostat (Zolinza) for cutaneous T-cell lymphoma (CTCL)
Phenotypic screening was instrumental in the identification of vorinostat (approved
in 2006), a histone deacetylase (HDAC) inhibitor approved for the treatment of CTCL.
Scientists at Merck utilized a phenotypic screening approach using CTCL cell lines to
identify compounds that induced differentiation and apoptosis in cancer cells. Vorino-
stat emerged as a lead compound and demonstrated clinical efficacy in CTCL patients,
leading to its approval as a treatment option [36].
2.1.4.3 Osimertinib (Tagrisso) for EGFR-mutated non-small cell lung cancer (NSCLC)
Phenotypic screening played a significant role in the discovery o f osimertinib (ap-
proved in 2018), a targe ted therapy for NSCLC with EGFR mutations. Researchers at
AstraZen eca employed a phenotypi c screening strategy using NSCLC cell lines with
EGFR mutations to identify compounds that selectively inhibited the growth of mutant
EGFR-driven cancer cells. Osimertinib emerged as a potent and selective inhibitor, tar-
geting EGFR with mutations resistant to other EGFR inhibitors. Osimertinib has
shown remarkable clinical efficacy in patients with EGFR-mutated NSCLC [37].
2.1.4.4 Selinexor (Xpovio) for multiple myeloma
Phenotypic screening contributed to the discovery of selinexor (approved in 2020), a
selective inhibitor of nuclear export, for the treatment of multiple myeloma. Re-
searchers at Karyopharm Therapeutics employed a phenotypic screening approach
using multiple myeloma cell lines to identify compounds that induced cell death in
cancer cells. Selinexor, which specific ally inhibits the nuclear export protein XPO1,
demonstrated efficacy in preclinical models; it subsequently entered clinical trials as
a potential treatment for multiple myeloma [38].
These case studies highlight the successful application of phenotypic screening in
hit-based lead discovery for various diseases, including leukemia, lymphoma, lung
cancer, and multiple myeloma. Phenotypic screening allowed for the identification of
compounds with selective activity against disease-relevant phenotypes, leading to the
discovery of targeted therapies and the development of effective treatment options.
2.1.5 Natural product screening
Natural product screening is a valuable approach in hit-based lead discovery that in-
volves the identification and evaluation of compounds derived from natural sources,
such as plants, marine organisms, or microorganisms [39]. Natural products have
2 Lead-hit-based methods for drug design and ligand identification 39
https://t.me/med1917
served as a rich source of bioactive compounds and have contributed to the develop-
ment of numerous drugs [40]. Here’s how natural product screening contributes to
hit-based lead discovery:
– Chemical diversity and bioactivity: Natural products offer a vast array of chem-
ical structures and exhibit diverse biological activities. Screening natural product
libraries allows for the exploration of unique chemical space and the identifica-
tion of compounds with various mechanisms of action. These compounds may
target specific disease-related pathways, receptors, or enzymes, providing promis-
ing leads for drug development.
– Traditional medicine and ethnobotanical knowledge: Natural product screen-
ing often involves the utilization of traditional medicine and ethnobotanical
knowledge. Traditional medicinal practices have been used for centuries, and
screening natural products derived from medicinal plants or traditional remedies
can unveil bioactive compounds with therapeutic potential. This approach com-
bines ancient wisdom with modern scientific methods to identify leads for drug
discovery.
– Hit identification and lead optimization: Natural product screening can lead to
the identification of hits, which are compounds showing desired biological activ-
ity. Hits derived from natural products can be further optimized through chemi-
cal modifications, analog synthesis, or structural optimization to improve
potency, selectivity, pharmacokinetics, and other drug-like properties. This pro-
cess enables the development of lead compounds for further preclinical and clini-
cal studies.
– Biodiversity exploration: Natural product screening contributes to the explora-
tion of biodiversity by examining various ecosystems and their associated organ-
isms. This approach allows for the discovery of unique natural products from
different habitats, including rainforests, marine environments, or extreme envi-
ronments like deep-sea or polar regions. By exploring biodiversity, new chemical
scaffolds and novel bioactive compounds can be discovered, expanding the poten-
tial for hit-based lead discovery.
– Synergistic effects and complex mixtures: Natural products often occur as
complex mixtures of compounds. Screening natural product extracts or fractions
can uncover synergistic effects among the comp onents, where the combined ac-
tivity is greater than that of individual compounds. This can lead to the discovery
of synergistic combinations or multicomponent leads that exhibit enhanced ther-
apeutic effects or improved target selectivity.
Several successful drugs have been derived from natural product screening (depicted
in Table 2.1 [41]. These examples demonstrate the potential of natural product screen-
ing in hit-based lead discovery and the translation of natural product-derived com-
pounds into clinically useful therapeutics [42, 43].
40 Gita Chawla and Tathagata Pradhan
https://t.me/med1917
Table 2.1: Examples of drug discovered from natural product screening [44– 49].
S. no. Drug name Source organism Uses
. Artemisinin Artemisia annua, sweet wormwood
plant
Treatment of fever and malaria-like
symptoms
. Paclitaxel Taxus brevifolia, bark of the pacific yew
tree
Treatment of various cancers, including
breast, ovarian, and lung cancers
. Etoposide Derivative of podophyllotoxin found in
the American mandrake plant,
Podophyllum peltatum
Treatment of testicular cancer, small-cell
lung cancer, and certain types of
leukemia
. Vinblastine
and
vincristine
Alkaloids isolated from the Madagascar
periwinkle plant, Catharanthus roseus
Treatment of various cancers,
particularly Hodgkin’s lymphoma, non-
Hodgkin’s lymphoma, and childhood
leukemia
. Quinine Obtained from the bark of the cinchona
tree
Anti-malarial properties
. Tacrolimus Isolated from the soil bacterium
Streptomyces tsukubaensis
Immunosuppressant, treatment of
autoimmune disorders like rheumatoid
arthritis and psoriasis
. Rapamycin Byproduct of soil bacteria Streptomyces
hygroscopicus
Immunosuppressant, prevent restenosis
after angioplasty
. Digoxin Derived from the foxglove plant,
Digitalis purpurea
Treat heart failure and arrhythmias
. Aspirin Derived from salicylic acid found in
willow bark
Analgesic, anti-inflammatory, and
antiplatelet agent
. Morphine Derived from opium poppy, Papaver
somniferum
Painkiller
. Lovastatin Derived from fungal fermentation Lower cholesterol levels
. Cyclosporine Isolated from soil fungus Immunosuppressant
. Bleomycin Derived from Streptomyces bacteria Treat cancers like Hodgkin’s lymphoma
. Camptothecin Camptotheca acuminata Anticancer agent
. Pilocarpine Jaborandi plant Treat glaucoma
. Capsaicin Chili peppers Pain relief
. Galantamine snowdrops and daffodils Treat mild to moderate Alzheimer’s
disease
. Colchicine Colchicum autumnale Relieve gout attacks
. Ergotamine Claviceps purpurea fungus Treat severe migraines
2 Lead-hit-based methods for drug design and ligand identification 41
https://t.me/med1917
It is important to note that natural product screening faces challenges related to com-
pound availability, scalability, and optimization of bioactivity. However, advancements
in extraction techniques, compound isolation, and synthetic biology approaches have
enabled better access to natural products and facilitated their utilization in hit-based
lead discovery.
2.1.6 Cheminformatics and QSAR (quantitative structure–activity
relationship)
They involve the use of computational methods to analyze and interpret chemical and
biological data, allowing for the prediction of compound properties and activities
based on their structural characteristics.
Cheminformatics:
Cheminformatics, also known as chemical informatics or cheminformatics, is the ap-
plication of computational methods and techniques to gather, analyze, and interpret
chemical data. It combines principles from chemistry, computer science, and informa-
Table 2.1 (continued)
S. no. Drug name Source organism Uses
. Pregabalin Synthetic derivative of gamma-
aminobutyric acid (GABA)
Used for neuropathic pain and epilepsy
. Fingolimod Derived from a fungal natural product
called myriocin
Treatment of multiple sclerosis
. Eribulin Synthetic derivative of a natural
product called halichondrin B which
was originally isolated from marine
sponges
Treatment of metastatic breast cancer
and liposarcoma
. Ibrutinib Derived from a natural compound
found in a traditional Chinese medicinal
plant
Treatment of certain types of blood
cancers, such as chronic lymphocytic
leukemia and mantle cell lymphoma
. Belinostat Synthetic derivative of a natural
product found in soil bacteria
Used as a histone deacetylase inhibitor
for the treatment of peripheral T-cell
lymphoma
. Pomalidomide Derivative of thalidomide, a natural
product originally developed as a
sedative
Treatment for multiple myeloma, a type
of blood cancer
. Ziconotide Synthetic version of a peptide originally
found in the venom of a cone snail
Used as analgesic in severe chronic pain
42 Gita Chawla and Tathagata Pradhan
https://t.me/med1917
tion science to extract meaningful insights from large datasets of chemical informa-
tion [50].
Cheminformatics methods are used to handle and analyze diverse chemical data,
such as compound structures, molecular properties, biological activities, and experi-
mental measu rements. By employing various algorithms and statistical techniques,
cheminformatics allows researchers to extract valuable knowledge and make predic-
tions about the properties and behavior of chemical compounds [51].
QSAR (quantitative structure–activity relationship):
QSAR is a subset of cheminformatics that focuses on establishing quantitative rela-
tionships between the structural features of compounds and their biological activities
or properties. QSAR models are developed based on the assumption that there is a
correlation between the physicochemical properties of compounds and their observed
activities or properties [50].
The process of developing a QSAR model involves the following steps:
1. Dataset compilation: Gathering a dataset of compounds with known activities or
properties.
2. Descriptor calculation: Calculating numerical descriptors that represent the struc-
tural and physicochemical properties of the compounds.
3. Model development: Applying statistical or machine learning methods to build a
predictive model that relates the descriptors to the observed activities or properties.
4. Validation: Evaluating the performance and reliability of the QSAR model using
validation techniques and external test sets.
5. Prediction: Using the validated QSAR model to predict the activities or properties
of new compounds.
QSAR models can be utilized in various stages of drug design and discovery, such as
virtual screening, lead optimization, and toxicity prediction. They enable researchers
to prioritize compounds, estimate biological activities, optimize chemical structures,
and guide decision-making processes.
Cheminformatics and QSAR are powerful tools in the field of drug discovery and
lead optimization. They are widely used to accelerate the process of identifying and
optimizing potential drug candidates. These computational approaches play a valu-
able role in na rrowing down the vast chemical space and identifying potential lead
candidates for drug development [51].
Here are some key applications of cheminformatics and QSAR in lead discovery:
– Compound database mining: Cheminformatics enables efficient searching and
mining of large compound databases to identify potential lead molecules. By analyz-
ing and comparing chemical structures, properties, and activities, cheminformatics
tools can prioritize compounds with desired features for further investigation.
2 Lead-hit-based methods for drug design and ligand identification 43
https://t.me/med1917
Соседние файлы в папке Библиотека им академика М.И. Перельмана