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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_6035_Библиотеки_им_академика_М_И_Перельмана.pdf
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and NP extracts. Several online SEC–HPLC–DAD and SEC–HPLC–MS procedures
have been developed with the goal of screening chemical components from NPs (Sadilek
et al., 2007). The NP extracts and target molecules are rst incubated together, and the
resulting mixture is then applied to an SEC column. The target–NP component complexes
are then eluted as waste after the unbound components are loaded onto the HPLC systems,
where they are separated and identied. Variations in chromatograms between the extract
and the target are recorded before and after incubation in order to determine the ligand that
specically binds to the target. Due to their hydrophobic interactions with the adsorbing
materials, small molecules are retained in the SEC column for a longer period of time,
whereas large molecules are rapidly eluted when access to the inner surface of the SEC
column is restricted (Sadilek et al., 2007).
8.7.6 LIGAND FISHING
In numerous domains, such as cancer treatment research, drug–protein investigations, and
electrochemical biosensing, protein immobilization has a wide range of applications. The
enhanced stability and longer lives that follow from protein immobilization have found
numerous uses in NP screening techniques (Tao et al., 2013). The technique of ligand
fishing based on target protein immobilization has been widely employed to find bioactive
NPs. The role of magnetic nanoparticles has been observed for protein immobilization in
active compound screening from NP extracts because of their good suspension stability,
high surface area, simplicity of surface modification, and ease of solid–liquid separation
(Cao et al., 2016). The solution or suspension is supplemented with Bovine Serum Albumin
or Human Serum Albumin to increase affinity and facilitate pharmacological interactions
with the target molecules. The resulting labeled complex is then pulled out by magnetic
particles with the right affinity after the solution has been filtered of any excess unattached
compounds (Tao et al., 2013). Various biological screening techniques are represented in
Figure 8.6.
8.8 LIMITATIONS
Although biological screening methods have shown to be effective for quickly identifying
and assessing potential bioactive candidates in NPs, there are still significant drawbacks
to these methods. The fact that NPs are complex and frequently exert their therapeutic
effects through a variety of components functioning on a variety of targets and pathways
is widely accepted. The unincluded targets risk being lost. Only one type of target was
used for each assay in the biological screening models discussed above, which led to some
possible activities. Biological fishing methods are less successful and less efficient since the
proteins, polymers, or cells used as targets often have low stability , limiting their reuse. The
resultant product is first screened before being subjected to chromatography or MS analysis
for many of the systems covered here. Screening and analysis of certain models are still
done separately as discrete procedures. When compared to online models, the offline mode
174
may use up more samples and transfer time while having considerably lower screening
and analysis efficiency (Song et al., 2014). Online approaches that combine separation and
analysis are far more effective, although they have not always been effective in all cases.
Moreover, nonspecific binding poses a significant issue for biological screening methods.
The interactions of the nonactive chemicals with the carriers and targets are what mostly
lead to nonspecific absorption. To ensure that the screened compounds are precisely bound
to the target’s active site, several researchers have incorporated known competing ligands
or target active site blocking ligands during the incubation procedure. By comparing the
chromatographic profiles of the compounds that bonded to the target before and after the
active site competitive testing, the selective ligands may be distinguished from the nonselective binders (Yang et al., 2012).
FIGURE 8.6 Schematic representation of biological screening techniques.
⏎
8.9 MOLECULAR MODELLING AND NP DATABASE
Molecular modeling is done to understand the chemistry of a product by using the
three-dimensional structures of a molecule while resolving all the issues related to time
and money. It illustrates the creation, manipulation, or depiction of three-dimensional
175
structures and related physicochemical characteristics of a molecule. Once bioactive
NPs have been identified, they can be exploited as lead compounds for structural feature
optimization to create new and more effective analogs using modern medicinal chemistry techniques like molecular modeling and combinatorial chemistry. These NPs are
a class of structurally related molecules that coexist with various chemical compounds.
Several homologs may be formed from a single source, which may reveal information
about structure–activity relationship (SAR). New leads from sources that are natural will
continue to be discovered and made available to undergo biological screening in order to
locate new therapeutic compounds, as only a small percentage of the available plants have
been studied for biological activities thus far. Modern drug detection techniques for NPs
employ newly discovered compounds with acceptable bioactivity that have been isolated
to SAR analyses and molecular modeling procedures in order to create analogs that are
more potent, have less harmful side effects, and have better pharmacokinetic profiles.
The inquiry may also reveal how a compound’s biological activity may be impacted by
interactions with specific enzymes. In vitro and in vivo biological studies can be used to
design and evaluate the best druggable analogs (Kitchen et al., 2004). An overview of
the procedures involved in creating variants from a naturally extracted lead molecule is
provided below:
• Construction and Preparation of In Silico Ligand
Procedures for molecular modeling demand Protein Data Bank (PDB)-formatted,
optimized 3D models of the ligands. NP databases as well as other databases
like PUBCHEM and ZINC can be used to access the structures of known natural
compounds in a number of useful formats, including SDF, mol, mol2, PDB, and
others (Sorokina and Steinbeck, 2020). The geometry of the structures must be
optimized to have the least amount of energy. Prior to docking, energy minimization can be done using various structure building and optimization software such
as Avogadro, Chimera, and Chem 3D Ultra (Liao et al., 2011) or docking software
such as AutoDockVina and Discovery Studio.
• Identification and Preparation of Target
Finding a druggable target that is relevant to the desired ailment is the first step in
the drug development process. When these targets are presented, an effort is made
to find prospective chemicals that could alter the target pathway and eventually
result in a phenotypic response. A 3D structure of the target molecule, such as a
protein (human serum), an enzyme (kinase), or a receptor (peroxisome proliferatoractivated receptor), is constructed or downloaded from the PDB during this process,
and it is then optimized for energy and geometry. The location of the binding site
and any natural ligands that are present must have their standard scores estimated.
In silico methods like homology modeling, molecular docking, and molecular
dynamics (MD) models have become essential to support in vitro studies for target
validation such as site-directed mutagenesis, radio–ligand binding, protein structure
elucidation (e.g., X-ray diffraction), and alanine scanning.
176
• Docking
It is an in silico technique of docking a ligand (flexible tiny molecule) to an appro-
priate binding site (protein, DNA, RNA, or peptide) by using an energy-efficient
pathway. The 3D structures of natural compounds are positioned against the target
structure using docking software, and the binding energy is rated. The optimal pose
for that conformation is thought to be the complex with the lowest binding energy.
Common docking solutions include AutoDock, AutoDockVina, FlexX, Discovery
Studio, and MDock (Liao et al., 2011). A prerequisite for docking is that both the
targets and ligands have optimized 3D structures in PDB format. Docking makes
use of a variety of search methods, such as the Lamarckian genetic algorithm, to
find the ligand’s ideal binding shape. To support the docking results, postdocking
studies that examine intermolecular interactions are important.
• Identification of Hits
The best interactions are chosen when the docking simulation is complete, and
they are then further subjected to MD simulation studies based on the energy
ratings. For this, MD simulation may be applied to two systems, namely, (1) an apo
(uncomplexed) protein and (2) a protein or receptor complexed with an interacting
substrate. The hit molecules that have the highest sensitivity for the target have been
chosen based on the ligands’ relative rankings and how they interact with the tar get.
• Hits Optimization
In a drug discovery program, it is very difficult to find the appropriate hit compound
that can regulate a single target. As an alternative to the extraction and biological
assay of NPs, modern drug development includes virtual screening or in silico
research to investigate a vast range of compounds created from natural sources that
have a diversity of chemical structures. The optimization process for hits is carried
out by creating many analogues of hits and analyzing them on the basis of higher
affinity toward the target. Hits with the highest affinity are then produced and can
be studied by using QSAR software (Devillers, 2013) for various drug-like features
such as pharmacokinetics, pharmacodynamic, and stability components.
As an alternative to the extraction and biological assay of NPs, virtual screening, or
in silico research, is employed in the current drug discovery process to investigate a huge
number of compounds derived from natural sources that have a diversity of chemical
structures. Millions of these structurally varied chemicals are stored in a number of such
libraries that are owned and operated by academic institutions and research facilities.
Virtual screening can be used to nd potential hits for a specic biological activity, and
lead optimization can produce a favorable SAR. The number of chemicals needed for the
actual test can be decreased in this way using bioassay-based virtual screening (Stockwell, 2004). Since the NP database could only provide the structural details of the test
compounds, the hit molecules must be physically available for the necessary bioassay to
verify the expected unique biological activity. If they are accessible, these substances can
either be acquired from a commercial company or synthesized in a lab. Some popular NP
177
libraries and databases include Phytopure, ChemSpider, Natural Product Alert, and Tim
Tech Natural Products. (Gray et al., 2012).
8.10 FUTURE THRUST
Future drug development will often follow a molecular/genetic target-based lead optimization strategy that employs chemical techniques to boost potency, lessen toxicity, and
overcome drug resistance. Medicinal chemists examine the features of drug candidates’
absorption, distribution, metabolism, and elimination/excretion in order to ascertain the
gradual chemical changes in a drug molecule. Utilizing the data pool that is presently
available and technological innovation is crucial. Artificial intelligence, machine learning,
plant metabolite databases, chemical libraries, and clinical trial data analysis have successfully processed large volumes of data, opening up new research directions for medicines
that have previously received approval. In order to acquire bigger amounts, new bioactive
chemicals may easily be generated in bacteria or yeasts using molecular biology techniques.
By increasing the use of genomes and metabolomics in research activities, the process of
discovering new medications from microbial NPs may be sped up (Sukumarini, 2021).
Future descriptions of novel and re-evaluated drugs/leads should focus on high-quality
structures, as well as solubility, stability, and embedded cell membrane permeability data,
as well as biochemical and biological data, including information on cellular and molecular
targets, spectrum, and safety/toxicity/potency-related information (minimal inhibitory
concentration, minimum bactericidal concentration, half inhibitory concentration, therapeutic induction, etc.). Furthermore, more attention should be paid to the finding of natural
resource-based leads for the treatment of several understudied and unusual diseases.
8.11 CONCLUSION
Using plants for medicinal purposes to treat a variety of communicable and noncommunicable
diseases is an age-old practice, and even today they remain a rich source of new lead
compounds and significant therapeutic agents. Many plant-derived herbal compounds have
been tested clinically for a variety of therapeutic effects. It is plainly clear from research
patterns and the resurging scientific interest that this topic has promise as a potential source
of innovative therapeutic molecules in the future. The metabolites of plants are being
optimized in the context of plant-based medication development and discovery research in
order to produce prospective analogues that may exhibit the requisite safety and efficacy . As
a result of the medicinal chemists’ increased interest in the field of NP drug development,
a number of novel approaches are available to select, identify, isolate, and characterize,
and screening of biological activity of NPs is created along with technical advancement.
These cutting-edge techniques could eliminate the technical constraints associated with
creating new NPs and resolve the challenges associated with discovering and creating
new NPs because of their complex behavior. The development of technology enabled the
178
exploration of complicated phytoconstituent profiles, which resulted in the isolation or
synthesis of numerous potent therapeutic drugs as well as novel lead compounds that can
act as the basic building blocks for upcoming pharmaceuticals. It is essential to employ an
interdisciplinary approach that combines ethnopharmacological and traditional knowledge,
analytical chemistry, phytochemistry, botany, modern drug development technology, and
pertinent biological screening techniques in order to be successful in this field. Future drug
development processes will use more new compounds with plant origins. New approaches
to medication development for NPs will make the process easier and increase success rates.
It could be useful in finding solutions to issues with global health and in the development
of new drugs.
KEYWORDS
• natural products
• drug development
• characterization
• pharmaceutical
• novel technologies
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