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• The toxicity to native species is predicted by the QSAR model. It directs the
choice of mixes, whether synthetic or naturally occurring, with the best pharma-
cokinetic qualities.
• Predicting a range of physical and chemical properties of molecules, whether
they are medicament, pesticides, consumer products, or specialty chemicals
(Bastikar etal. 2022).
16.4.9 Commonly Used Databases/Software inVirtual Screening
The most commonly used databases/software in virtual screening are
explained below:
16.4.9.1 Available Software forDocking Studies
Molecular docking uses computers to forecast how molecules t together. It has two
main stages: the rst one is sampling the ligand and the second is scoring function.
Sampling contains algorithms which assist in recognizing the most energetically
advantageous arrangements of the ligand inside the protein’s active site, considering
how they bind together, and then those positions are ranked based on how well they
t on the basis of scoring functions (Kitchen etal. 2004; Das etal. 2020). An over-
view of various molecular docking software options highlights the most commonly
used software package.
16.4.9.1.1 AutoDock
The widely used AutoDock programme from Scripps Research Institute allows the
general public to freely and conveniently use both rigid and exible docking. It
provides several scoring systems for determining afnity and maximizes ligand
employment within receptor binding sites through the use of a Lamarckian genetic
algorithm. AutoDock (2024) can be accessed via http://autodock.scripps.edu and
supports a variety of input le formats, including PDB, MOL2, and SDF.
16.4.9.1.2 Chimera
The University of California, San Francisco (UCSF), created Chimaera, a exible
programme for modelling, analysis, and visualization of molecular structures. It
provides resources for exhibiting tiny molecules, proteins, and nucleic acids in three
dimensions. The “Dock Prep” module adds hydrogens, charges, and creates molec-
ular surfaces to direct the location of ligands in target proteins. Additionally,
Chimera makes it easier to analyse docking results by visualizing binding postures
and calculating binding energies (UCSF Chimera 2024). At https://www.cgl.ucsf.
edu/chimera/, it is accessible.
16.4.9.1.3 Discovery Studio
Discovery Studio, established by Dassault Systèmes BIOVIA, is a comprehensive
molecular modelling and simulation software. It offers tools for molecular docking,
VS, protein modelling, and molecular dynamics examination. Its docking
R. Sharma et al.
415
component predicts ligand binding modes and interaction strength with target pro-
teins using various algorithms like CDOCKER, GOLD, and LibDock. The software
enables visualization, analysis, and comparison of docking results (Dassault
Systèmes 2023). It is available at https://discover.3ds.com/discovery-studio-
visualizer- download.
16.4.9.1.4 Dock
Dock is created by the UCSF Chimera team, and it’s a user-friendly molecular
docking software for positioning small molecules within receptor-binding sites. It
utilizes a grid-based approach to assess ligand-receptor binding afnity and offers
scoring functions for pose ranking. Supporting various input le formats such as
PDB, MOL2, and SDF, Dock (UCSF Dock 2024) can be accessed at http://dock.
compbio.ucsf.edu/.
16.4.9.1.5 MolDock
MolDock, created by MolSoft LLC, is a rapid and effective molecular docking soft-
ware for positioning small molecules within receptor-binding sites. Utilizing a fast
Fourier transform (FFT) algorithm, it assesses ligand-receptor binding afnity
(Fig.16.3). The software incorporates a scoring function. Supporting multiple input
le formats such as PDB, MOL2, and SDF, MolDock (Molsoft L.L.C. 2024) is
accessible at https://www.molsoft.com/about.html.
16.4.9.1.6 Argus Lab
Argus Lab, created by Mark Thomson at Pacic Northwest National Laboratory for
the Department of Energy, simulates solvent effects using a combination of quan-
tum mechanics and classical mechanics algorithms. It manages jobs including
visual design, medicinal design, and molecular modelling (ArgusLab 2024). You
can visit Argus Lab at http://www.arguslab.com.
The steps which need to perform in molecular docking study are as follows
(Fig. 16.3): identifying essential target proteins and ligands is an essential
Fig. 16.3 Flowchart for molecular docking
16 Drug Repurposing andVirtual Screening
416
component for conducting docking. Ensure the target protein is accessible through
Swiss UniProt or PDB databases. If not available, utilize Swiss model repository or
modeller programs.
Find the ligand using PubChem, Zinc, or ChmBl databases, or synthesize it using
ChemDraw or ChemSketch if unavailable. Protein preparation is essential for pre-
cise docking simulations. It involves rening the protein structure, either from a
databank like PDB or by utilization of tools, i.e. SWISS-MODEL, and adding any
necessary atoms or residues. The protein undergoes energy minimization to relax its
structure and remove any crowding. Ionizable residues’ protonation states are
adjusted for correct electrostatic interactions. Water molecules and unnecessary
ligands are eliminated from the protein structure in order to further simplify it (Agu
etal. 2023). In molecular docking, initially the binding sites can be determined. The
ligand undergoes docking with the protein, and the resulting interactions are exam-
ined. A counting function assigns a score to the most favourable docking complex
identied (The Institute of Molecular and Translational Medicine, Czech Republic
2023; Torres etal. 2019). After the docking of ligands to the protein, an analysis is
conducted to pinpoint the most viable candidates for subsequent investigation. Each
ligand’s binding afnity is determined by predicting interaction energy, leading to
the ranking of ligands based on their afnity scores. Furthermore, the docked ligand
and protein complex structures are examined in detail to analyse important interac-
tions between the ligands and the protein, such as electrostatic, hydrophobic, and
hydrogen bonding interactions. These interactions give insights into the mechanism
of the ligands and pointers for further structural optimization (The Institute of
Molecular and Translational Medicine, Czech Republic 2023; Pinzi and
Rastelli 2019).
Few examples of virtual screening are the following: interleukin-1 receptor-
associated kinase 4 (IRAK-4) is an attractive target for treatment because of its criti-
cal role in immunological and inammatory disease pathways, as demonstrated by
a study conducted in 2016 by Zhong etal. Research has utilized ligand-based phar-
macophore modelling and three-dimensional QSAR analysis to investigate ATP
competitive inhibitors of IRAK-4, leading to the discovery of 12 strong and unique
inhibitors. These results were further conrmed by virtual screening and molecular
dynamics (MD) simulation analyses, which showed encouraging IRAK-4 inhibi-
tory potential, especially in anthraquinones ZINC09047206 and ZINC09477176
(Zhong etal. 2016).
Jawarkar etal. (2023) reported a study in which they used QSAR modelling to a
diverse dataset of 657 compounds to uncover structural features essential for ACE2
inhibitory activity, aiming to identify novel hit molecules. High predictivity
(R2tr=0.84, R2ex=0.79) was demonstrated by the created QSAR model, which
unveiled previously undiscovered characteristics and novel mechanistic insights.
This model was used to estimate the ACE2 inhibitory activity (PIC50) of 1615
ZINC FDA compound. This allowed for the identication of a hit molecule
(ZINC000027990463) with a docking score of −9.67kcal/mol (RMSD 1.4) and a
R. Sharma et al.
417
PIC50 of 8.604M.Molecular docking highlighted 25 connections with the residue
ASP40, while MD simulation afrmed the stability of the hit compound-ACE2
receptor complex over 400ns, supporting its potential as a viable ACE2 inhibitor
(Jawarkar etal. 2024).
16.4.9.2 Available Software forMolecular Dynamics Studies
The most commonly used is GROMACS which is freely available; however, graphi-
cal processing unit with good conguration is a must to run the simulations for
longer duration. Desmond, AMBER, CHARMM, Discovery Studio, etc. are also
available. The steps need to follow for molecular dynamics are as follows: step 1,
preparation of molecules; step 2, dene the box; step 3, solvation (implicit, GBMB,
PBSA, etc.; explicit, SPC, TIP3P, etc.); step 4, ionization, neutralization, and mini-
mization; step 5, equilibration (NVT, NPT); step 6, production and simulation; step
7, analysis (Jacob etal. 2017).
16.4.9.3 Databases/Software forNetwork
Various databases are available to perform the network pharmacological studies to
identify the potential repurposing drugs. The databases available for the prediction
of targets of drugs are SwissTargetPrediction (http://swisstargetprediction.ch/),
SEA database (https://sea.bkslab.org/), and so on. The targets involved in the par-
ticular diseases can be extracted from GeneCard (https://www.genecards.org/) and
DisGeNET (https://www.disgenet.org/). Venny tool (https://csbg.cnb.csic.es/
BioinfoGP/venny.html) is helpful to identify the common targets between the drugs
and diseases. The interaction among the common targets is usually checked by
STRING server. Most of the researchers have used Cytoscape for the creation and
analysis of network. Briey the steps need to follow for network pharmacology are
as follows: identication of targets of drugs, identication of targets involved in
disease, identication of common targets, interactions among common targets, net-
work construction and analysis, and enrichment analysis (Saima etal. 2024; Bhati
etal. 2024).
16.5 Conclusion
Virtual methods are playing a signicant role in the identication of repurposed
candidates against various diseases. There are number of methods to identify poten-
tial repurposing drugs; however, each method has its own merits and demerits. The
access to considerable volumes of data, including compound libraries, patent data,
pharmacological data, and published scientic literature, is one of the biggest chal-
lenges in the eld of drug repurposing. Further, experimental studies are required to
conrm the potential of identied drug in a particular disease. The extensive safety
studies are required if repurposed dose is found higher than the approved dose.
16 Drug Repurposing andVirtual Screening
418
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17
Pre-clinical andClinical Studies,
Pharmacovigilance, Pharmacogenomics,
andCommercialization
ofPharmaceutical Products
MitJoshi andBhoomikaM.Patel
Abstract
Even with the development of new technologies and understanding of patho-
physiology of diseases, drug discovery is still considered as a long and tedious
process comprised of different stages which eventually leads to the availability
of new drugs to the entire population. Development of new drug starts with
research on target identication which generally takes place during academic
research. Target identication and validation lead to the synthesis of new mole-
cules through different in silico techniques such as high-throughput screening
and other molecular modeling. New molecules are then examined for possible
interaction with the target molecule which leads to identication and validation
of new molecule. New molecules then undergo pre-clinical and clinical studies.
Pre-clinical evaluations mainly involve the assessment of the safety and efcacy
of novel molecules invitro and invivo setup before going into the clinical phase.
In the clinical phase, new drug molecules are scrutinized for their safety and
efcacy in human patients. After successful clinical trials, the new drug is
allowed for commercialization in the market. Clinical trials assess the safety and
efcacy of new drugs in a relatively small population compared to the entire
population. When a drug enters the market, a pharmacovigilance program is ini-
tiated to track and report any adverse drug reaction associated with the drug.
Sometimes, certain drug does not follow the expected ADME due to variation in
the genetic build of a particular population. Due to that, pharmacogenomic
assessment of each patient is important to personalized medication to certain
M. Joshi
Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, India
B. M. Patel (
*)
National Forensic Sciences University, Gandhinagar, Gujarat, India
e-mail: bhoomika.patel@nfsu.ac.in
424
patient populations. Drug discovery is a vast process and each step has its advan-
tages and disadvantages. Here we discuss pre-clinical and clinical studies, along
with pharmacovigilance and pharmacogenomic programs. In the end, we discuss
the commercialization of pharmaceutical products which is different from any
other product available in the market.
Keywords
Clinical trials · Pre-clinical · Pharmacovigilance · Pharmacogenomics · Toxicity
· Efcacy
17.1 Introduction
The need for new drug candidates arises in two scenarios: First is a medical condi-
tion or disease or the emergence of a new disease such as COVID-19 where no
suitable treatment or drug is available. Second, there is an unmet medical need, or
currently available intervention is not enough to mitigate the disease (Hughes
etal. 2011).
The primary or basic research, most carried out in academia, provides hypothe-
ses regarding the potential pathophysiology and identifying particular targets. The
result of this research further requires proper validation before beginning the next
phase of lead discovery to support the drug discovery effort (Mohs and Greig 2017).
Despite signicant achievements made in unraveling the pathophysiology of bio-
logical systems at the molecular level and substantial progress in the innovation of
novel technologies, the journey of drug discovery remains extensive and resource-
intensive. The process of drug development is characterized by prolonged duration,
substantial expenses, and a notable rate of setbacks. To address these challenges,
innovative methodologies, such as articial intelligence (Paul et al. 2021) and
groundbreaking in vitro technologies, are being harnessed to accelerate research
and development efforts, aiming to deliver novel medications to patients in a more
efcient manner.
Developing a new drug is time-consuming and tedious and divided into several
segments. Drug development starts with genomic and proteomic studies with target
identication and validation (Schenone etal. 2013). This data leads to the synthesis
of a drug or a molecule and its optimization (Bruno etal. 2019). The discovery of
the molecule was further investigated through pre-clinical (in silico studies, toxicity
studies, efcacy studies) and clinical trials (Steinmetz and Spack 2009). When the
drug is approved for commercial use, it is studied and tracked in a large population
through a pharmacovigilance program (Kalaiselvan etal. 2019). The entire process
takes around 10–15years (Fig.17.1).
Every category is further divided into many subcategories like drug discovery
divided into basic research, target identication and validation, discovery and iden-
tication of hit and lead, optimization of lead, pre-clinical studies, and clinical
studies.
M. Joshi and B. M. Patel