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20.1 Introduction
Cancer ranks as the second most common cause of mortality after cardiovascular disorders worldwide.
Its therapy is therefore quite challenging [1, 2]. Globally, the rising rate of cancer incidence and
death is among the most significant public health issues with a significant socioeconomic impact.
Decades of worldwide research efforts have yielded no cure for cancer since effective and targeted
medications are still lacking. This leads to high rates of recurrence and relapse, which in turn
perpetuates cancer’s leading role in global mortality. One of the primary obstacles to overcoming
the high incidence and prevalence of the disease in this environment is the design of fresh, effective
anticancer medicines that can lessen the unwanted effects of current treatments [3, 4].
A lot of ongoing research are being done on cancer therapy and prevention. Radiation, chemother-
apy, and surgery are the clinical methods of cancer treatment that are most frequently employed. As a
systemic treatment, chemotherapy can be beneficial for certain malignancies that have metastasized
to more advanced forms of the tumor or have a tendency to spread throughout the body [5, 6]. Creating
a new medication is difficult, costly, time consuming, and hazardous. The traditional drug discovery
process is thought to require up to 15 years and more than $1 billion USD before a new medication is
ready for the market. Fortunately, new techniques have recently emerged, altering this situation. To
enhance the effectiveness of the discovery stage, numerous innovative tools and approaches have
been created, and computational techniques are now an essential part of many drug discovery initia-
tives. Many discovery efforts employ strategies like ligand- or structure-based virtual screening (VS),
which range from hit identification to lead optimization. In the case of developing possible anticancer
medications and drug candidates, these computational methods have shown to be quite influential
throughout time and have yielded valuable insightful findings in the field of cancer research [7].
Utilizing disease-related screens for a wide range of chemical types, followed by systematic chemi-
cal alteration of active compounds, is the traditional method for finding new anticancer medications.
Conversely, identified specific macromolecular targets are becoming a more common starting point
for novel drug research. As the findings from research on the molecular basis of oncogenesis are used
to treat and prevent cancer, the significance of this trend will increase. Specifically, the involvement
of specific oncogenic proteins as targets for chemotherapeutic intervention is highlighted by their
participation in several malignancies, either as mutations or as overexpressed levels [8].
20
Rational Design of Anticancer Therapeutics
Debarupa Dutta Chakraborty and Prithviraj Chakraborty
Royal School of Pharmacy, The Assam Royal Global University, Guwahati, Assam, India
446
Rational drug design entails using molecular modeling tools like pharmacophore modeling,
molecular dynamics, VS, and molecular docking in order to describe molecular factors for drug
target contact, explain biomolecule function, and create more effective drug candidates [9]. The
Nobel Prizes given to George H. Hitchings and Gertrude Elion for their contributions to the study
of nucleic acid metabolism in both normal and malignant human cells can be used to trace
the origins of rational drug design. By introducing concepts from biochemistry and physiology to
the science of drug discovery, they transformed the field and made it easier to comprehend the
molecular basis of many disorders [10, 11].
Three components make up an excellent candidate for rational drug design: an imaging reporter,
a nanoparticle carrier, and a therapeutic DNA/RNA component. DNA/RNA-targeted methods
may play a crucial role in rational therapeutics. This crucial component utilizes the genome and
transcriptome “coded” nature. Therefore, DNA/RNA-targeted techniques provide the perfect
framework for a completely rational design of therapeutic and diagnostic agents based on the com-
plementarity phenomenon. An additional element of the rational treatment that is envisaged is
nanotechnology. More specifically, nanoparticles are carriers capable of incorporating all three of
the proposed therapeutic components. Without affecting the oligos’ ability to function, they are
readily functionalized with oligonucleotides. Standard synthetic chemistry, such as liposomes and
iron oxide nanoparticles, can be used to fine-tune their design. Finally, an imaging reporter, such
as a radionuclide, can be used to label them. The third component of the envisioned drug design is
image guidance. One can monitor the therapeutic agent’s transport to the target tissue by labeling
the drug with an imaging reporter. Delivery to target tissues can be evaluated and controlled with
the use of image-guided delivery. Imaging can assist in determining the best drug design, delivery
schedule, route, and therapeutic dose on an individual basis and suggest alternatives in the event
that a patient’s medication is unsuccessful [12].
With the advent of precision medicine, researchers have discovered that changes in key intracellular
biomolecules – which are often found at the subcellular level – play a crucial role in carcinogenesis
and the progression of cancer. The use of molecular-level pathogenesis to design therapeutic candi-
dates has emerged as a new paradigm in drug discovery. In addition to delivering therapeutic com-
pounds to specific tissues and cells, efficient nanoparticle-based drug delivery systems (NDDSs)
should precisely target organelles as discrete subcellular locations. They are thought to be among the
most promising methods for treating cancer. Through targeted modifications and proper design,
tumor cells are enriched in subcellular-targeting nanoformulations. They are internalized by endocy-
tosis across subcellular barriers and target-specific subcellular structures. Subsequently, the therapeu-
tic compounds are released under controlled conditions at the desired locations, thereby improving
their anticancer efficacy, decreasing their deleterious effects, and surmounting the most significant
obstacle to intracellular drug delivery-multidrug resistance (MDR) [13–18].
This study describes the rational design of nanomedicine, the drug delivery mechanism required
for cancer treatment, and focuses on the essential components based on the most current research
advancements over the previous few years. Furthermore, various methods of rational drug design
in anticancer theranostics will be summarized.
20.2 Rational Design of Nanomedicine for Cancer Treatment
Cancer nanomedicines have been designed to overcome the pharmacokinetic restrictions con-
nected with conventional drugs. The biggest benefit is their capacity to accumulate preferentially
in tumor tissues by taking advantage of the enhanced permeability and retention (EPR) effect that
tumors exhibit due to their inadequate lymphatic drainage and pathologically leaky vasculature.
447
Clinical data has accumulated evidence demonstrating the nanoparticle localization in solid
human tumors following systemic delivery to cancer patients [19–28].
These nanomedicines have demonstrated numerous benefits over traditional chemotherapeu-
tics, including increased drug solubility, extended circulation in the blood compartments, improved
bioavailability, and significantly decreased adverse effects from the parent drugs, all of which have
significantly improved patients’ quality of life. However, current cancer nanomedicines have failed
to improve the therapeutic benefits – these nanomedicines cannot or only modestly improve
patients’ overall survival [19, 29–33].
Although these nanomedicines were able to accumulate more in tumor tissues than free
pharmaceuticals with less adverse effects, detailed clinical assessments revealed that larger drug
concentrations did not improve therapeutic efficacy. Therefore, the secret to creating next-
generation nanomedicines with great therapeutic efficacy lies in understanding how to construct
them so that they can both effectively target tumor tissues and exert the drugs’ therapy [19, 34].
20.3 The CAPIR Cascade: A Nanomedicine Strategy for
Administering Cancer Medications
Delivering medications into cancer cells as free molecules so they can have their therapeutic effects
is the ultimate aim of cancer drug delivery. The process of administering a nanomedicine intrave-
nously usually involves five steps: the drug circulates through the blood compartments, accumu-
lates in the tumor due to its poorly constructed and leaky vasculature, penetrates deep into the
tumor tissue to reach the tumor cells, internalizes within those cells, and finally releases the drug
intracellularly: CAPIR cascade, to put it briefly (Figure 20.1). Thus, the overall delivery efficiency,
the percentage of the drug as free molecules inside targeted cells over the injected, of a system is a
product of efficiencies of the five steps [19, 35–37].
20.4 Rational Regulation of Nanoparticle’s
Physicochemical Characteristics
Particle size, shape, surface decoration, and intelligent change under particular conditions are
examples of physicochemical features that can be the main determinants of nanoparticle tumor
accumulation and deep penetration capabilities [38].
20.4.1 Particle Size
One of the most crucial aspects of nanoparticles is their size, as it has a direct bearing on the in vivo
circulation, distribution, tumor accumulation, and elimination. Compelling evidence from multi-
ple groups has led to an agreement that improved nanoparticle penetration into the tumor mass
requires smaller particle sizes.
20.4.2 Shape
Another crucial aspect of nanoscale vehicles is particle shape, which plays a key role in tumor
penetration. Elongated particles have demonstrated deeper penetration in multiple tumor models
compared to spherical particles.
448
20.4.3 Surface Modification
Another essential component of nanoparticles that influences their in vivo destiny and penetration
effectiveness in primary tumors and metastases is surface modification. Most significantly, active
targeting moieties added to the surface of nanoparticles are widely used to enhance selective accu-
mulation and therapeutic effects.
A wide range of biological, organic, and inorganic materials can be used to create nanoscale parti-
cles, which can then be designed with a variety of qualities to aid with tumour penetration and medi-
cation administration. Smaller nanoparticles have superior penetration effects in primary tumours or
lymph node metastases, according to all of the above data from a range of materials and structures,
especially when the diameter is less than 50 nm. To treat metastatic tumours in the lung and brain, for
example, nonspherical nanoparticles can gather preferentially in those organs. However, the shape of
these particles needs to be carefully regulated. Surface modification is essential for improving detec-
tion and penetration in primary or metastatic tumours, in addition to optimised particle size or shape.
20.5 Some Approaches of Rational Drug Design
in Anticancer Theranostics
In contemporary molecular pharmacology, the discovery of novel, pharmacologically active small
molecules is a significant and quickly developing field. Since there are just a few proteins that can
be drugged, it is critical to find as many chemical effectors as possible in order to determine the
The CAPIR cascade of cancer drug delivery
Circulation
Accumulation
Internalization
Q = Q
C
× Q
A
× Q
P
× Q
I
× Q
R
Penetration
5-step CAPIR cascade
Drug Release
✓ To be there
✓ To be free
Figure 20.1 A 5-step CAPIR cascade of nanomedicines. Source: Sun et al. (19)/with permission of John
Wiley & Sons.
449
most effective anticancer treatment plan for each unique situation. An interesting target for
small-molecule inhibitors is Mdm2, an E3 ubiquitin ligase that regulates ubiquitin-dependent
degradation of the critical tumor suppressor p53. Numerous novel inhibitors of the p53-Mdm2
interaction were found by employing a hybrid technique that combines high-content screening
utilizing cancer cell lines with the rational creation of small compounds picked from the virtual
library. At rates greater than the well-known Mdm2 inhibitor Nutlin-3, these substances were
able to activate and stabilize the p53 protein, resulting in extensive apoptosis, preferably in
p53-positive cells [39].
A product of glutamic acid, glutamine is a necessary growth factor for tumor cells that prolifer-
ate quickly. Studies have shown that tumors significantly alter the host’s glutamine metabolism,
causing the nitrogen metabolism to adjust to the increased glutamine requirements of the tumor.
Overall, glutamine promotes the rapid growth and proliferation of cancer cells, pointing to a poten-
tial positive correlation between glutamine and glutamic acid in cancer. Moreover, the reintroduc-
tion of thalidomide, a synthetic derivative of glutamic acid, in clinical studies for the management
of different malignant tumors provides strong evidence linking glutamine and glutamic acid to
cancer. Since thalidomide is a synthetic derivative of glutamic acid with a chiral stereocenter, it
experiences in vivo racemization resulting in the production of R and S enantiomers. It is well
known that thalidomide has antiangiogenic, anticytokine, anti-integrin, and immunomodulatory
properties. Additionally, it inhibits the synthesis of proinflammatory cytokine TNF-α and initiates
the synthesis of IL-2 and IFN-γ. Furthermore, it is commonly recognized that thalidomide has
proapoptotic and antiproliferative effects on tumor cells. It was thought that thalidomide would be
extremely important in the treatment of multiple myeloma and other cancers since it has positive
effects on the immune system and inhibits the angiogenesis process. Owing to thalidomide’s low
solubility in water and its propensity for spontaneous hydrolytic cleavage, a number of chemically
altered thalidomide analogs were created, produced, and studied for use in treating different can-
cer types. In comparison to thalidomide, Lenalidomide (CC-5013), a structural thalidomide analog,
exhibited immunomodulatory effects and significantly greater potency in the proliferation
and tube formation assays of human umbilical vein endothelial cells. It has been noted that
Pomalidomide (CC-4047) triggers protective, long-lasting, and tumor-specific in vivo Th1-type
responses. It is significantly more effective than thalidomide in vitro and in vivo at inhibiting the
angiogenesis and proliferation of myeloma malignancies. Thalidomide’s small-molecule analog is
ENMD-0995. It is interesting to note that there was no evidence of hazardous side effects and that
this thalidomide analog exhibited improved angiogenesis-inhibiting activity. ENMD-0995 was des-
ignated as an orphan drug by the FDA in 2002 in order to treat multiple myeloma patients. Once
outlawed and removed from the market, thalidomide is now being used as a model for the design
of new anticancer medications and as the preferred anticancer medication for treating a number
of solid tumors [1, 40–52].
Another novel class of potential cancer treatment is represented by membranolytic anticancer
peptides (ACPs). Their receptor-independent mechanism of action might prevent the development
of cellular resistance. In the 1990s, “Simulated Molecular Evolution (SME)” – a stochastic optimi-
zation method – was created for computational peptide design. SME is a member of the evolution-
ary algorithm class, which also includes genetic algorithms, and can be used to optimize peptide
properties encoded in a theoretical fitness function or in conjunction with an experimental fitness
evaluation in situations where structure–activity relationships cannot be beforehand. The compu-
tational approach established by Gabernet et al. [53] is a step toward the rational design of effective
and targeted ACPs. By merging a machine learning model with the SME algorithm, ACPs with
low-micromolar efficacy against various cancer cells and selectivity against nontransformed cells
(HDMEC, human dermal microvascular endothelial cells) and human erythrocytes were generated in
450
this study. The machine-learning classifier by itself was able to recognize active peptides, but it was
not able to recognize peptides that were specific to cancer cells. Four new ACPs were found using
VS of computationally built peptide libraries using an integrated machine-learning classifier.
These ACPs will provide as the foundation for selectivity optimization by SME. SME is applicable
to all kinds of experimental readouts and avoids using more traditional peptide optimization tech-
niques like alanine scanning. Simultaneously, the findings imply that further enhanced cancer cell
selectivity of membranolytic ACPs might come at the price of reduced peptide potency. This working
hypothesis serves as a foundation for future research [53–59].
20.6 Artificial Intelligence’s Progress in Anticancer Drug Development
The simulation of human intelligence in machines that are designed to act and think like humans
is known as artificial intelligence, or AI. These days, it is applied to a broad variety of cancer
research, including the prediction of drug–protein interactions, the prediction of target protein
structures, and the image classification of aberrant cancer cells. These findings show how the
application of AI methods can significantly alter the development of anticancer medications.
Figure 20.2 shows some applications where AI is used in the design of anticancer drugs [60–65].
• Drug target interaction
• Druggability
• Data integration
• Structure-based
• Ligand-based
• Fragment-based
new
new
• Sensitivity
• Toxicity
• Drug–drug interactions
• Caco-2 permeability
• Carcinogenicity
indications
AI
• Target-center
• Disease-center
• Variational auto-encoder
• Recurrent neural network
• Generative adversarial network
• Reinforcement learning
Sample
Act
Update
Render
Reward
Render
Iterative process
C C O
N
C
C
C
C C
C
C
C
C C
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C
CCC
new graph
Continue
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Figure 20.2 Application of AI in anticancer drug design. Source: Xuan et al. [60]/MDPI/CC BY 4.0.
451
20.6.1 Identification of Anticancer Drug Targets Using Artificial Intelligence
The first stage in designing an anticancer drug is to identify drug–target interactions (DTIs).
Binding affinity constants, which include indicators like a dissociation constant (K
d
), an inhibition
constant (K
i
), and a half-maximal inhibitory concentration (IC50), are frequently used to character-
ize the strength of drug–target binding. The computational prediction of DTIs is quite interesting
because experimentally determining them is expensive and time-consuming. Precise and effective
DTI forecasts can greatly aid in the development of new drugs and hasten up the search for lead or
hit compounds [64, 66].
20.6.2 Artificial Intelligence for Hit Compound Screening of Anticancer Drugs
The next step after determining the therapeutic targets for anticancer drugs is to search for anti-
cancer drug hit compounds, which are molecules with initial activity against a particular target or
mechanism of action. The primary method for finding computer-assisted hit compounds is high-
throughput screening (HTS). HTS can be accomplished using two different techniques: ligand-
based screening and structure-based screening. Structure-based VS uses docking and scoring to
find compounds with high binding affinities for target proteins. This technique is an important
tool for the discovery of anticancer drugs, despite the fact that many of the existing docking
approaches are time-consuming and pose challenges for large-scale VS. Ligand-based screening
works by taking small compounds with known activity and using a compound library to find
structures as candidates that have comparable chemical or physical properties [64, 67–69].
20.6.3 Artificial Intelligence-Based De Novo Anticancer Drug Design
The estimated number of drug-like compounds in the chemical space is 10
23
~10
60
. Consequently,
mining the full chemical space with computational approaches is almost impossible. In this set-
ting, identifying particular lead molecules within the large chemical space is a significant issue.
HTS and VS technologies are able to efficiently evaluate compounds in huge compound libraries
using a broad variety of filters due to the fast advancements of computational power and experi-
mental procedures. De novo drug design is a molecular generation technique that uses AI to create
and optimize a molecule [64, 70–73].
20.6.4 Artificial Intelligence for Repurposing Anticancer Drugs
A key component of drug development is the efficient identification of novel indications from
approved or well-established clinical medications. One term for this procedure is “drug reposition-
ing.” Theoretically, repurposing is less expensive, quicker, safer, and easier than the established obsta-
cles to the creation of novel molecules. Possibilities for repurposing drugs are frequently determined
by incidental findings or time-consuming, nonhypothesis-driven preclinical drug screening [64].
20.6.5 Reactions to Anticancer Drugs Accurately Predicted with Artificial
Intelligence Support
Drug sensitivity, drug toxicity, and drug–drug interactions are all influenced by the ADMET
characteristics of the drugs. Predicting drug reactions accurately may enhance patient outcomes
and boost clinical trial success rates. A rising number of relevant research employing AI
approaches during the drug design stage are being proposed as a result of the AI technologies’ quick
development [64, 74, 75].
452
The incorporation of AI techniques in various stages of anticancer drug development offers
promising solutions to obstacles like identifying DTIs, screening for hit compounds, designing
novel drugs, repurposing old ones, and predicting drug reactions accurately. AI-powered
approaches hold the potential to significantly expedite the drug discovery process, reduce costs,
and improve patient outcomes in cancer treatment. As AI technologies continue to evolve, their
application in drug development is anticipated to further advance, leading to more effective and
targeted therapies for cancer patients.
20.7 Conclusion
In conclusion, the fight against cancer continues to be one of the biggest obstacles in modern medi-
cine, given its devastating impact on global health and the lack of a definitive cure. Despite decades
of research and significant advancements in therapeutic modalities including chemotherapy, radia-
tion, and surgery, the high incidence and prevalence of cancer persist, necessitating the creation of
new and more potent anticancer treatments. The amalgamation of rational drug design principles,
nanomedicine approaches, and AI technologies offers promising avenues for revolutionizing cancer
therapy. Rational drug design strategies leverage molecular insights into cancer pathogenesis to
develop targeted therapies with enhanced efficacy and reduced side effects. Nanomedicine plat-
forms enhance drug delivery to tumor tissues while minimizing systemic toxicity, potentially
improving patient outcomes. Moreover, AI-driven approaches enable the efficient identification of
drug targets, screening of potential hit compounds, designing new drugs, repurposing of existing
medications, and forecasting drug reactions, thereby expediting the drug discovery process. By com-
bining these innovative approaches, researchers are poised to usher in a new era of precision medi-
cine, offering personalized and more effective treatment options for cancer patients worldwide.
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