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19.3 Rational Strategies for Antiviral Therapeutics 433
Table 19.2 Antiviral therapeutics based on various drug discovery pipelines.
Based drug
discovery Drug name Mechanism Reference
Structure-based
drug discovery
Remdesivir By entering human cells through the ACE2 receptor,
SARS-CoV-2 transforms remdesivir into triphosphate
and forms a complex with ATP, viral RNA, and
RNA-dependent RNA polymerase that prevents the
synthesis of new viral RNA
[67]
Lopinavir The HIV-1 protease, which is necessary for the
development of a mature, infectious virus, is inhibited
by lopinavir. It functions by inhibiting HIV-1’s
development, which prevents the virus from spreading
[53]
Ligand-based
drug discovery
Maraviroc Maraviroc (MVC) is an entrance inhibitor that inhibits
gp120 from binding to the chemokine receptor CCR5
(CCR5), preventing HIV from infecting human cells
[68]
Imiquimod The immune response modifier imiquimod activates
Toll-like receptor 7, which in turn causes the release of
cytokines such as tumor necrosis factor-alpha and
interferons. The release of interferon-alpha and a Th1
response are two examples of the innate and adaptive
immune responses that are triggered by this. In
addition to its antiviral properties, imiquimod also
promotes apoptosis in skin cancer cells and
demonstrates antitumoral effect
[38]
Pharmacophore
drug discovery
Sofosbuvir Sofosbuvir is a nucleotide prodrug targeted at the liver,
which functions as a chain terminator in HCV
genotypes 1–4 by blocking the RNA-dependent NS5B
RNA polymerase
[69]
Oseltamivir
(Tamiflu)
Oseltamivir blocks the neuraminidase enzyme of
influenza virus, which is essential for viral growth. It
limits the spread of infection by stopping the release
of fresh virus particles from infected cells. The
prodrug oseltamivir phosphate is converted into the
active form oseltamivir carboxylate in the body
[37]
QSAR-based
drug discovery
Ritonovir Indinavir is an antiretroviral protease inhibitor that
works by binding to the HIV protease’s catalytic site
and blocking the cleavage of viral polyprotein
precursors into mature, functional proteins that are
required for viral replication
[70]
Lamivudine
(3TC)
Lamivudine functions as a cytosine analog to prevent
the synthesis of viral DNA. After entering the cell, it
undergoes phosphorylation to its active forms
lamivudine monophosphate (L-MP) and lamivudine
triphosphate (L-TP), which block the replication of
viral DNA. Since lamivudine is not effectively
recognized by human polymerases, unlike natural
cytidine triphosphate, it can compete and inhibit the
synthesis of reverse transcriptase DNA in HIV-1 and
HBV infections. It can, however, weakly inhibit
mitochondrial DNA polymerase and mammalian
DNA polymerases
[71]
(Continued)
19 Rational Design of Antiviral Therapeutics434
One of the important ligand-based modeling approaches is QSAR, which models and highlights
2D and 3D descriptors of the ligand that correlate with biological activity like IC50, GL50, and
LD50 [37, 70]. The diverse set of descriptors like topological, electronic, constitutional, and
geometrical can be calculated and applied for constructing the models. Programming languages
like R (Rcpi) [80] and Python (PyL3dMD) [81] possess simulation packages that underpin
Table 19.2 (Continued)
Based drug
discovery Drug name Mechanism Reference
AL and
ML-based drug
discovery
Acyclovir
By operating as an analog of deoxyguanosine
triphosphate (dGTP), acyclovir triphosphate
competitively inhibits viral DNA polymerase. As
acyclovir triphosphate does not have a 3′ hydroxyl
group, it does not allow other nucleosides to bond to
DNA, which causes chain termination
[72]
Zidovudine
(AZT)
Zidovudine is categorized as a nucleoside reverse
transcriptase inhibitor (NRTI) and is a synthetic
analog of the nucleoside thymidine. By replacing
thymidine in freshly synthesized viral DNA and
functioning as a viral DNA chain terminator,
zidovudine serves as an antiviral drug. This disrupts
the HIV-1 life cycle by preventing HIV-1 reverse
transcriptase from creating viral DNA from the RNA
template
[73]
Systems biology
or network-
based drug
discovery
Baloxavir
marboxil
(Xofluza)
This prodrug is hydrolyzed to produce Baloxavir, the
active form. To prevent the influenza virus from
replicating, it blocks the endonuclease activity of the
polymerase acidic (PA) protein
[74]
Favipiravir
(Avigan)
Favipiravir inhibits viral RNA polymerase, which
blocks viral protein synthesis, once it has been
metabolized to its active form, favipiravir-RTP. To
prevent future viral replication, it can also be
integrated into the RNA of the virus. Furthermore, it
is uncertain if it is active against SARS-CoV-2 because
it causes deadly mutagenesis in some viruses
[75]
Reverse
vaccinology-
based drug
discovery
REGN-EB3 The vaccine neutralizes the virus by hindering its
entry and recruiting additional immune cells to seek
out and eliminate contaminated cells from the body
[76]
Gardasil 9
(9vHPV)
The exact mechanism of the 9-valent human
papillomavirus (9vHPV) vaccine is still unclear; it is
thought to function by triggering the humoral
immune response. According to studies, the
vaccination stimulates the development of
neutralizing antibodies against different HPV strains,
resulting in a potent immune response that protects
against diseases linked to HPV and dysplastic lesions.
The vaccine’s humoral response mechanisms have
demonstrated efficacy, as seen by the much higher
antibody levels created by the vaccine compared to
those produced by natural infection
[65]
19.3 Rational Strategies for Antiviral Therapeutics 435
molecular informatics by merging bioinformatics and chemoinformatics knowledge of a molecule
or a protein. The prediction of the biological activity (log 1/c) is used to correlate against the
pharmacological properties of descriptors [37]. The best-correlated QSAR model reflects R
2
, nearer
to 0.8 or 0.9 and the quality of the QSAR model can be further checked by using the cross-validation
(Q
2
) based on the LOO (leave one compound out) approach [81]. However, the error rate of the
QSAR model can be observed as root mean square error (RMSE) [11]. Multidimensional QSAR can
be used in 4D, 5D, and 6D and are a few popular computational methods for antiviral drug design
apart from the traditional QSAR modeling. These are some emerging tools for incorporating
different ligands such as nanoparticles and small molecules [34, 82].
19.3.2 AI and ML
Compared to the conventional methods, the integration of AI and Omics has crossed the barriers
associated with time, cost, precision, and accuracy. Systems can be trained with the preexisting
data of viral targets (e.g., HIV and COVID-19) to screen the primary hits (antiviral targets) to be
further docked using algorithms like protein–ligand scoring functions. A cloud-based platform VS
integrated with multiple databases to generate datasets of potential antiviral compounds that can
be tested against viruses using in vitro and in vivo methods for validation [83]. Algorithms of ANN
and deep learning (DL) train the dataset from different repositories, where the neural network can
identify and classify antiviral peptides for their antiactivity. The particle swarm optimization (PSO)
algorithm optimally classifies the viral peptides that are critical for the innate immune system with
an ability to inhibit viral growth [84]. Deep-AVPpred [85], AVPIden [86], D3AI-CoV [87], Meta-
iAVP [88], and iAVPs-ResBi [89] are a few of the state-of-the-art classifiers that adopt DL and ML
algorithms to model based on the multiple descriptors and feature selections specific to the antiviral
mechanism. These interfaces classify and predict novel antiviral compounds with drug-like
properties that can be chemically synthesized, optimized, and scientifically verified. Emami et al.
(2022) have discussed some of the optimization algorithms that use heuristic approaches
integrating evolutionary theory to model the best features that can mimic the viral behavior leading
to novel drug discovery [84]. Accuracy of various scoring functions is implemented in VS and
prediction tools to attain the properties or descriptors that show similar biological activity [34, 81].
Geometric DL and the knowledge of graph theory use SMILES notations of drug and target to fed
in a form node features to train the neural network for predicting PPI networks to identify highly
potential antiviral drug targets [10].
19.3.3 Systems Biology and Network Pharmacology
The SB approach also known as “Network Pharmacology” integrates networks of proteins, genes, and
host–pathogen interactions. Large datasets with different file formats retrieved from databases such as
GEO, Uniprot, GeneBank, and STRING are used to construct networks to identify viral interactions
with host proteins [90]. Tools like CytoScape have many plugins to cluster the nodes on topological
parameters, which are calculated based on qualitative data, quantitative data, and numerical modeling
of biological systems. Kyoto Encyclopedia of Genes and Genomics (KEGG), gene ontology (GO), and
enrichment analysis determine potential pathways and genes that can alter biomolecular processes [26].
In addition, this approach can identify several host variables that are common between any two
infectious pathways [91]. For example, Zhuang et al. [92] investigated bioactive compounds and target
genes of Shuanghuanglian, a traditional Chinese medicine (TCM). These findings indicated that four
key hub genes have been enriched in pathways of HCMV and papillomavirus with potential
19 Rational Design of Antiviral Therapeutics436
pharmacological responses of the TCM against these viruses [92]. Farooq et al. curated protein datasets
of the influenza A virus (IAV) and identified potent therapeutic targets that are linked not only with
the IAV but also with hepatitis B and C viruses, measles, and cancer-triggering viruses [26]. The SB
models can hypothesize based on the comparative analysis of molecular mechanisms to unravel the
insights of pathogenesis and replication of the viruses [93]. Recently, Liu et al. [94] study screened
target genes of Mulberry leaves for antiviral therapeutics against EV71 virus (Enterovirus 71). The
network identified Quercetin to possess drug-likeness to inhibit NF-κB signaling pathway and other
associated pathways eventually targeting the effectiveness of EV71 [94].
19.3.4 CRISPR Systems
CRISPR-Cas systems can be engineered to target and cleave essential viral genes involved in the
replication of the viral cycle. The CRISPR-based antiviral therapy has the potential for selective
inhibition of viral replication. Antiviral therapeutics targeting the replication machinery face
challenges such as viral mutations causing drug resistance, delivery methods, and potential off-
target effects [95]. The combination of more than one inhibitory strategy, such as using a
combination of small molecules and CRISPR-Cas systems, may be a promising approach to
overcome these challenges [96]. CRISPR technology has evolved beyond SpCas9, introducing
innovations like SaCas9, FnCas9, C2c1/2/3, and Cas13 in the antiviral realm [95]. SaCas9, a
compact alternative to SpCas9, facilitates versatile genome editing using adeno-associated virus
(AAV) delivery [97], overcoming limitations associated with the size and immunogenicity of other
vectors. SaCas9 variants, such as SaCas9 (KKH), broaden target recognition sites, offering flexibility
in biomedical applications across various organisms [95, 97]. Cpf1, a type V CRISPR system,
recognizes 5′-TTN-3′ or 5′-CTA-3′ motifs and processed crRNA design, which has high efficiency
in inhibiting SAR-COV-2 and influenza viruses [98, 99]. Notably, Cpf1 also exhibits RNase activity,
extending its potential to target these viral RNA genomes through genome editing provoking
antimechanism [100]. FnCas9, derived from F. novicida, uniquely modulates bacterial virulence by
degrading endogenous antibacterial lipoprotein mRNA [95]. It demonstrates its versatility by
targeting RNA viruses, for example, in the hepatitis C virus (HCV), the system weakens the
phosphorylation of p38, an essential factor for cellular functions (like cell entry, replication, and
virion assembly) [15]. Eventually showcasing its potential as an antiviral defense tool by suppressing
the replication and translation process of the virus.
Class 2 candidate (1/2/3) and Cas13 [95] cleave the RNA restricting the replication and disrupt-
ing the virus [100]. The targeted host factors and restriction mechanisms employed by CRISPR-
Cas systems present a dynamic approach to antiviral research. The alteration of the genetic makeup
of viruses such as HIV, hepatitis (B and C) SARS, MERS, and influenza, through these systems, has
shed some light on innovative strategies for antiviral targets by minimizing on off-target activity
and drug resistance.
19.3.5 Nanotechnology-Based Design and Delivery Systems
Nonviral vectors, such as lipid-based or polymer-based nanocarriers, show promising alternatives
to viral vectors, addressing integration-related issues and immune responses [101]. Nanoparticle-
based inhibition employs functionalized nanoparticles to block viral entry by binding to viral
particles, disrupting receptor interactions, or interfering with fusion processes [102]. The use of
nanocarriers for targeted drug delivery represents a cutting-edge approach to antiviral drug
development. The nanotechnology-driven systems circumvent biological barriers by potentially
minimizing side effects; enhancing drug delivery specificity, drug stability, and circulating
19.4 Conclusion 437
time [101, 103]. Nanoparticles (NPs) such as gold, silver, and certain metallic oxides of zinc, copper,
titanium, silicon, and ceric fused with antiviral peptides modify the surface proteins of a broad
spectrum of viruses such as dengue, papilloma, influenza, hepatitis, and HIV [103]. Virus-like
particles (VLPs) and virosomes directly target the infectious sites by attaching to the envelope
proteins, which boosts the bioavailability by modulating the burst release of the drug [104]. siRNAs
conjugated with gold NPs reduced viral duplication of dengue [105] and SARS-CoV-2 virus [82]. In
order to prevent and control viral infections, research on improving immune response and the use
of nanomaterials as an adjuvant to antiviral vaccinations have shown promising outcomes.
19.3.6 Reverse Vaccinology
In the relentless pursuit of effective antiviral therapeutics amidst the perpetual threat of viral
infections, the conventional landscape of drug discovery has encountered formidable challenges in
keeping stride with the rapid mutational dynamics of viruses and the emergence of resilient drug-
resistant strains. However, amid this, an approach emerges in the form of reverse vaccinology,
heralding a transformative paradigm in the quest for novel antiviral interventions. During the
prescreening phase, primary information on membrane proteins and their localized and secreted
virulence factors is retrieved from various databases that are associated with the pathogen of
interest [60]. Vaccine candidates are selected based on the PPI networks, molecular parameters of
proteins like molecular weight, associated antigens, and various B-cell and T-cell epitopes
conjugated with their adjuvants [61]. The rigid interaction of the vaccine with its receptor is
simulated and predicted through docking and MD results further predict the immunological
response. Overall, the computational methods discussed in the above sections are used for antiviral
vaccines depending on the structural properties of the pathogens [64].
The development of vaccines is hampered by the diversity of viruses because each one interacts
differently with a standardized medication because of its structure and replication cycle. For
example, Arboviruses represent a diverse group of viruses belonging to different families, including
Flaviviridae, Togaviridae, Bunyaviridae, and Reoviridae [106]. Developing vaccines against
individual arboviruses may not provide sufficient protection, especially in regions where multiple
arboviruses cocirculate. A multivalent vaccine targeting several diverse viruses simultaneously
offers the potential for cross-protection and broad-spectrum immunity, enhancing its efficacy in
diverse epidemiological settings [61, 106]. Hence, instead of designing a specific drug, designing a
multivalent vaccine against Herpes [107], Arboviruses [106], Monkeypox [108], and Ebola
virus [76] through the innovative approach of reverse vaccinology is more viable. This is done by
employing bioinformatics tools and methodologies to potential vaccine targets and epitopes across
various viruses to identify. Advances in high-throughput sequencing technologies have enabled
researchers to rapidly sequence the genomes of a wide range of viruses, providing valuable insights
into their genetic composition, structure, and evolution.
19.4 Conclusion
Rational drug designing are multifaceted strategies that offer diverse approaches for inhibiting
viral entry, tailored to the unique mechanisms employed by different viruses, and hold significant
promise for antiviral development. Blocking attachment sites involves compounds that directly
bind to these sites and competitively hinder the virus’s attachment to host cells. Bioactive molecules
that interfere with these evasion mechanisms can be valuable in enhancing the immune response
against such pathogens. These approaches involve assigning confidence scores to candidate
19 Rational Design of Antiviral Therapeutics438
compounds, with higher scores indicating a greater likelihood of efficacy. Additionally,
nanoparticle-based inhibition employs functionalized nanoparticles to block viral entry by binding
to viral particles, disrupting receptor interactions, or interfering with fusion processes. By
leveraging the number of drug targets associated with each candidate, researchers can effectively
gauge the potential therapeutic utility of these compounds across multiple biological pathways or
molecular targets. The “drug–target–drug” computational approach showcases its robustness.
However, as computational techniques evolve, integrating diverse approaches such as molecular
simulations, RNAseq, and drug network analyses could provide a more comprehensive
understanding of drug-repurposing strategies.
Conversely, the removal of pharmaceuticals from categories that include prohibited, discontinued,
or nutraceuticals helps mitigate potential safety or regulatory concerns, ensuring that repurposed
candidates meet stringent regulatory standards and can proceed smoothly through the drug develop-
ment pipeline. The retention of approved drugs in the candidate pool is pivotal, as it ensures that
repurposed candidates have already undergone rigorous preclinical and clinical evaluation, possess
a proven safety profile, and exhibit known pharmacokinetics. This accelerates the drug development
process by bypassing the need for extensive safety and toxicity studies, thereby expediting the transla-
tion of promising candidates into clinical trials. This approach not only enhances the efficiency of
drug-repurposing efforts but also contributes to the rapid identification and development of novel
treatments for a wide range of medical conditions. As drug repurposing continues to gain traction as
a viable strategy for therapeutic innovation, the utilization of such innovative approaches will be
instrumental in addressing unmet medical needs and advancing patient care on a global scale.
In the future, precision medicine and personalized immunotherapy approaches may become
more prominent, tailoring bioactive interventions to an individual’s unique immune profile.
Additionally, ongoing research in immunology and bioactive molecule design will likely uncover
new targets and strategies for optimizing the host immune response against various diseases,
ultimately improving healthcare outcomes for patients. In summary, the process of prioritizing
repurposed drug candidates through the calculation of confidence scores which is based on the
number of drug targets represents a crucial aspect of drug-repurposing endeavors, particularly in
the context of addressing emerging infectious diseases and pandemics. Advances in bioactive
molecule design are contributing to the emergence of personalized antiviral therapies. By taking
into account the individual’s genetic makeup and viral strain characteristics, researchers can tailor
antiviral treatments to maximize efficacy and minimize side effects.
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