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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5533_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •Acknowledgements
- •Chapter 3
- •Chapter 4
- •Chapter 5
- •Chapter 6
- •Chapter 7
- •Editor biographies
- •Vivek Kumar Chaturvedi
- •Anurag Kumar Singh
- •Jay Singh
- •Dawesh Prakash Yadav
- •Short description about chapters
- •Chapter 1
- •Chapter 2
- •Chapter 8
- •Chapter 9
- •Chapter 10
- •Chapter 11
- •Chapter 12
- •List of contributors
- •Introduction
- •1.1 Introduction
- •1.2 Nanotechnology in medical science
- •1.2.1 Nanomaterials in drug delivery
- •1.2.2 Use of nanomaterials in designing diagnostic nanosensors
- •1.2.3 Nanomaterials as theranostics
- •1.3 Artificial intelligence in medical science
- •1.3.1 Machine learning in diagnostics
- •1.3.2 Natural language processing in healthcare
- •1.3.3 Predictive analytics in patient care
- •1.4.1 Nanoscience in controlled drug release in the GI tract
- •1.4.3 Nanotechnology in gastrointestinal endoscopy
- •1.4.4 Nano-biotechnology in gastrointestinal cancer
- •1.5 Role of nanoparticles for the treatment of gastric cancer
- •1.6 Artificial intelligence in hepatitis and chronic liver disease
- •1.7 Artificial intelligence applications for clinical decisions support
- •1.9 Nanomedicines for liver fibrosis
- •1.10 Artificial intelligence-based colonoscopy
- •1.12 Summary and conclusions
- •Acknowledgments
- •References
- •2.1 Introduction
- •2.2 Causes
- •2.3 Mechanism
- •2.4 Diagnosis
- •2.5 Prognosis
- •2.6 Present methods of detection
- •2.7 Biosensors
- •2.7.1 Components of biosensors
- •2.7.2 Types of biosensors
- •2.7.3 Enzyme based biosensors
- •2.7.5 Immunosensors
- •2.7.6 Microbial biosensors
- •2.7.7 DNA-based biosensors
- •2.7.8 Phage sensors
- •2.7.9 Optical biosensors
- •2.7.10 Cantilever-based biosensors
- •2.7.11 Bio-MEMS
- •2.8 Physical biosensors
- •2.8.1 Thermometric biosensors
- •2.8.2 Acoustic biosensors
- •2.8.3 Magnetic biosensors
- •2.8.4 Wearable skins as biosensors
- •2.9 Electrochemical biosensors
- •2.9.1 Potentiometric
- •2.9.2 Coulometry methods
- •2.9.3 Conductometry methods
- •2.9.4 Potentiometric titration
- •2.10 Materials for biosensors
- •2.10.1 Nanomaterials for biosensors
- •2.10.2 Gastrointestinal diseases (GIDs) biosensor
- •2.11 Summary and future perspectives
- •3.1 Introduction
- •3.2 Challenges in drug delivery to the GI tract
- •3.2.1 Residence time
- •3.2.4 Metabolism in the GI tract
- •3.3 Role of nanoscience in drug delivery
- •3.3.2 Targeted drug delivery
- •3.3.3 Increased bioavailability
- •3.3.4 Reduced toxicity and side effects
- •3.3.5 Imaging and diagnostic capabilities
- •3.3.6 Drug designing
- •3.3.7 Delivery system
- •3.4 Methods of nanomedicine formulation
- •3.5 Drug release strategies
- •3.5.1 Active targeting strategies
- •3.5.2 Stimuli-based delivery strategy
- •3.5.3 pH-dependent drug release
- •3.5.4 ROS-dependent drug release
- •3.5.5 Time-dependent dosage forms
- •3.5.6 Gastro retentive strategies
- •3.5.7 Photothermal and photodynamic approach
- •3.6 Types of nanoparticles in drug delivery
- •3.6.1 Liposomes
- •3.8 Application of AI in GI disease
- •3.9 Future perspectives and challenges
- •3.6.2 Polymeric nanoparticles
- •3.6.3 Metallic nanoparticles
- •3.6.4 Quantum dots
- •3.7 Approved nanomedicines
- •3.9.1 Diagnostics
- •3.9.2 Individualized treatment
- •3.9.3 Proactive patient monitoring
- •3.9.4 Decision support systems
- •3.9.5 Biomarker discovery and therapeutic development
- •3.9.6 Patient outcomes and quality of life
- •3.9.7 Regulation and ethical issues
- •3.10 Conclusion
- •References
- •4.1 Introduction
- •4.2 Challenges and barriers in drug delivery
- •4.3 Drugs used in IBD
- •4.4 Novel drug delivery system for inflammatory bowel disease
- •4.4.1 Vesicular delivery system
- •4.4.2 Nanoparticle drug delivery system
- •4.5 pH-dependent nano-delivery systems
- •4.6 Inorganic nanoparticles
- •4.7 Prodrugs based
- •4.8 Hybrid drug delivery systems
- •4.9 Enteric coated formulations
- •4.10 RNA interference-based novel drug delivery
- •4.11 Toxicity profiling of IBD
- •4.11.1 Corticosteroids
- •4.11.2 Immuno modulators
- •4.11.3 Biologic therapies
- •4.11.4 JAK inhibitors
- •4.11.5 Immune dysregulation in IBD
- •4.11.6 Gastrointestinal effects
- •4.11.7 Antibiotics
- •4.11.8 Cyclosporine
- •4.11.10 Surgery-related complications
- •4.11.11 Increased risk of colorectal cancer
- •4.12 Current prospective of IBD
- •4.12.1 Personalized medicine and immunological therapies
- •4.12.2 Disease monitoring and surgical advances
- •4.12.3 Development of IL-6 signaling inhibitors
- •4.12.4 Genome-wide association studies (GWAS)
- •4.12.5 Rare variant analysis
- •4.12.6 Functional genomics and gene expression studies
- •4.12.7 Therapeutic targets
- •4.12.8 Gene-environment interactions
- •4.13 Future prospective of IBD
- •4.13.2 Microparticles-based delivery systems
- •4.13.3 Biological therapies
- •4.13.4 Combination therapies
- •4.14 Conclusion
- •References
- •5.1 Introduction
- •5.2 Nanotechnology
- •5.3 Nanoparticles
- •5.4 Classification of nanoparticles
- •5.4.1 Polymer-based nanoparticles
- •5.4.2 Solid nanoparticles
- •5.4.3 Carbon-based nanoparticles
- •5.4.4 Lipid-based nanoparticles
- •5.4.5 Nanoemulsions
- •5.4.6 Nanoparticles in biomedical applications
- •5.4.7 Characteristics of nanoparticles
- •5.4.8 Characterization of nanoparticles
- •5.5 Intestinal endoscopy
- •5.6 Medical nanotechnology
- •5.6.1 Diagnosis
- •5.6.2 Nanotechnology in the early diagnosis
- •5.6.3 Theragnostic
- •5.6.4 Tissue engineering
- •5.6.5 Targeted imaging and therapeutic in colorectal cancer
- •5.6.6 Gene therapy delivery
- •5.6.7 Colitis therapy
- •5.6.8 Oral delivery of vaccines
- •5.6.9 Mitigation
- •5.6.10 Role in targeted drug delivery
- •5.7 Role of nanotechnology in intestinal tract
- •5.8 Nanotechnological aids
- •5.8.1 Nanopowder
- •5.8.2 Plastic stents
- •5.8.3 Capsule endoscopy
- •5.9 Quality control of nanotechnology
- •5.10 Artificial intelligence in gastrointestinal endoscopy
- •5.11 Future perspectives
- •5.12 Limitations of nanotechnology
- •5.13 Conclusion
- •6.1 Introduction
- •6.2 Global burden of gastric cancer
- •6.3 Gastric cancer risk factors
- •6.3.1 Infection with Helicobacter pylori
- •6.3.2 Age and sex
- •6.3.3 Cigarette smoking
- •6.3.4 Obesity and metabolic dysfunction
- •6.3.5 Dietary factors
- •6.3.6 Alcohol use
- •6.3.7 Medications
- •6.3.8 Host genetics
- •6.4 Other risk factors
- •6.4.1 Epstein–Barr virus infection
- •6.4.2 Autoimmune disorders
- •6.4.3 Ménétrier’s disease
- •6.5 Nanotechnology in cancer diagnostic and therapeutics
- •6.6 Nanotechnology and gastric cancer diagnostic
- •6.6.1 Fluorescence imaging and gastric cancer detection
- •6.6.2 Photoacoustic imaging and gastric cancer detection
- •6.6.3 Computed tomography and gastric cancer detection
- •6.6.4 Magnetic resonance imaging and gastric cancer detection
- •6.6.5 Multimodal imaging and gastric cancer detection
- •6.7 Nanotechnology and gastric cancer management
- •6.7.1 Nanomaterial and chemotherapy
- •6.7.2 Nanomedicine and radiotherapy
- •6.7.3 Phototherapy and gastric cancer detection
- •6.7.4 Combination therapies and theranostics for gastric cancer detection
- •6.8 Challenges and prospectives
- •Acknowledgments
- •References
- •7.1 Introduction
- •7.2 Nanoparticles as drug delivery systems
- •7.2.1 Advantages of nanoparticles for drug delivery
- •7.2.2 Types of nanoparticles used in gastric cancer treatment
- •7.2.3 Targeted drug delivery to gastric cancer cells
- •7.3 Nanoparticles for imaging and diagnosis
- •7.3.1 Nanoparticles in gastric cancer imaging
- •7.3.2 Contrast agents and theranostic nanoparticles
- •7.3.3 Molecular imaging and targeting approaches
- •7.4 Therapeutic applications of nanoparticles in gastric cancer
- •7.4.1 Chemotherapy with nanoparticle formulations
- •7.4.2 Photothermal and photodynamic therapy
- •7.4.3 Immunotherapy and nanoparticles
- •7.4.4 RNA interference (RNAi) and gene therapy
- •7.5 Nanoparticles for combination therapy
- •7.5.1 Synergistic effects of nanoparticle-based combination therapies
- •7.5.2 Sequential and simultaneous delivery of therapeutics
- •7.6 Challenges and limitations of nanoparticle-based therapy
- •7.6.1 Biocompatibility and toxicity concerns
- •7.6.2 Nanoparticle clearance and stability
- •7.6.3 Regulatory aspects and clinical translation
- •7.7.1 Preclinical studies and animal models
- •7.7.2 Clinical trials and human studies
- •7.7.3 Promising results and future directions
- •7.8 Nanoparticles in personalized medicine for gastric cancer
- •7.8.1 Biomarker-driven nanoparticle therapies
- •7.8.2 Individualized treatment approaches
- •7.9 Nanoparticle-based theranostics for gastric cancer
- •7.9.1 Diagnostic and therapeutic integration
- •7.9.2 Multifunctional nanoparticle platforms
- •7.10 Future perspectives and concluding remarks
- •Acknowledgments
- •References
- •8.1 Introduction
- •8.2 Artificial intelligence role in hepatitis disease
- •8.3 Artificial intelligence role in non-alcoholic fatty liver disease
- •8.4 Artificial intelligence role in hepatocellular carcinoma
- •8.5 Conclusion
- •References
- •9.1 Introduction
- •9.2 Overview of clinical decision support
- •9.2.2 Medical imaging and diagnostic services
- •9.2.3 Virtual patient care
- •9.2.4 Patient safety
- •9.2.5 Diagnostic support
- •9.2.6 Medical research and drug discovery
- •9.2.7 Rehabilitation
- •9.2.8 Administrative applications
- •9.3 Types of AI algorithms in CDS
- •9.3.1 Machine learning algorithms
- •9.3.2 Bayesian Gaussian regression
- •9.4 Supervised learning
- •9.4.1 Diagnosis and treatment prediction
- •9.5 Unsupervised learning
- •9.6 Deep learning and neural networks
- •9.7 Natural language processing (NLP) techniques
- •9.7.1 Convolutional neural networks (CNNs) for medical image analysis
- •9.7.2 Recurrent neural networks (RNNs) for signal processing
- •9.8 Current AI-based clinical data support system
- •9.9 Challenges and considerations
- •9.9.1 Current AI-based CDS systems
- •9.10 Regulatory and ethical issues (HIPAA, GDPR, etc)
- •9.11 Challenges for clinical translation
- •References
- •9.12 Obstacles, restrictions, and missing knowledge
- •9.13 Future trends
- •9.14 Future trends and developments
- •9.14.1 Advancements in AI algorithms
- •9.15 Expansion to point-of-care devices
- •9.16 AI-driven drug discovery
- •9.17 AI in public health and epidemiology
- •9.18 Conclusion
- •10.1 Introduction
- •10.2 Developing history of AI
- •10.3 AI’s role in the early detection of GC
- •10.3.1 Screening of GC by AI
- •10.3.2 Accuracy of sampling from early endoscopic diagnosis
- •10.3.3 Digital pathological diagnosis
- •10.4 Role of AI from endoscopic diagnosis to treatment
- •10.5 Artificial intelligence in surgery
- •10.6 Molecules and genes
- •10.7 AI models’ function in prognosis prediction
- •10.7.1 Metastasis and staging prediction
- •10.7.2 AI aided treatment decisions
- •10.7.3 Clinical massive data analysis and prognostic prediction
- •10.8 Survival analysis
- •10.9 Conclusion and future prospects
- •References
- •11.1 Introduction
- •11.2 Stages of liver fibrosis
- •11.3 Etiology of liver fibrosis
- •11.3.1 Chronic viral hepatitis
- •11.3.2 Alcohol-related liver disease (ALD)
- •11.4 Pathogenesis
- •11.5 Symptoms
- •11.6 Diagnosis
- •11.7 Invasive approach
- •11.7.1 Liver biopsy
- •11.7.2 Limitations of liver biopsy
- •11.8 Non-invasive approach
- •11.8.1 Ultrasonographic based
- •11.9 Non-surgical tests
- •11.9.1 Serum biomarkers
- •11.10 Treatment
- •11.11 Limitations of antifibrotic therapy
- •11.12 Role of nanomedicines in the treatment of hepatic fibrosis
- •11.13 Type of nanoparticles currently in use for LF
- •11.13.1 Phytochemical compound for LF
- •11.13.3 siRNA derived NPs
- •11.14 HSC targeted nanoparticle delivery
- •11.15 Advantage of nanomedicine for LF
- •11.15.2 Enhanced drug delivery
- •11.15.4 Reduced adverse effects
- •11.15.5 Improved pharmacokinetic properties
- •11.16 Challenges of nm for LF
- •11.17 Future of nm in the treatment of LF
- •References
- •12.1 Introduction
- •12.2 Medical requirement for colonoscopy
- •12.3 Limitation of colonoscopy
- •12.4 Advancement of colonoscopy
- •12.5 High-definition and ultra-high-definition imaging technology
- •12.6 Computed tomography
- •12.7 Artificial intelligence and machine learning
- •12.8 Advancement in patient experience
- •12.9 Capsule endoscopy
- •12.10 Simulated detection systems
- •12.11 Improved training and workshop programs
- •12.12 Future of colonoscopy
- •12.13 Multi-spectral imaging
- •12.14 Machine learning algorithms integration
- •12.15 Robotic-assisted colonoscopy
- •12.16 Virtual colonoscopy
- •12.17 Tailoring colonoscopy screening
- •12.18 Patient-compatible techniques
- •12.19 Remote monitoring and consultations
- •12.20 Alternative bowel preparation methods
- •12.21 Preventive measures enhancement
- •12.22 Conclusion
- •References

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
includes Turicibacter, Escherichia,andClostridium and is involved in the control of
metabolism. More research is needed to determine how the small intestinal bacteria
affects oral dose forms and drug absorption [13].
3.3 Role of nanoscience in drug delivery
3.3.1 Importance of nanotechnology-based techniques in controlled drug release in the
GI tract
A controlled delivery system ensures that an optimum concentration of a drug is
maintained in the blood so as to protect the body from adverse effects [14]. Drugs
can be built into nanoparticles for controlled release in order to maintain a sustained
response. By altering nanoparticle properties including size, surface charge, and
composition, the rate of drug release can be precisely controlled to achieve the
desired therapeutic effects [15]. Nanotechnology-based approaches have drawn a lot
of interest and demonstrated considerable promise as a significant therapeutic
option for GI diseases as nanotechnology provides protection of sensitive drugs
and enhanced drug stability [16]. Certain drugs are susceptible to deterioration, but
by being enclosed within a nanoparticle, these drugs can withstand the tough GI
environment which includes bile salts, enzymes, and HCl exposure. This extends the
biological half-life of the drug [3] and enhances its stability [15, 17].
3.3.2 Targeted drug delivery
By adding site-specific ligands or antibodies to the surface of the nanoparticles, which
enable them to recognize and bind to particular receptors, one can target particular
parts of the GI tract, such as the colon, small intestine, or stomach [15, 18].
3.3.3 Increased bioavailability
By enhancing the solubility as well as the rate of dissolution of less-soluble drugs,
nanoparticles can increase their oral bioavailability. The drug can hence be
distributed and absorbed more effectively throughout the GI tract, improving [3]
the effectiveness of therapy [15, 17].
3.3.4 Reduced toxicity and side effects
Excessive amounts of drug in normal tissues can have adverse consequences which
can be decreased by controlled nanotechnology-based drug release systems. These
delivery systems improve the therapeutic index by confining drug release at the
target site while minimizing exposure to normal cells [15, 17].
3.3.5 Imaging and diagnostic capabilities
Imaging agents or contrast agents can be incorporated into nanoparticles to provide
non-invasive imaging of the GI tract. As a result, personalized medicine and
treatment optimization are made possible by the real-time monitoring of drug
release, biodistribution, and therapeutic response [15].
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
3.3.6 Drug designing
While designing a nano drug delivery system, the surface charge is a crucial factor to
be taken into account since all the interactions with the cells, receptors, or other
biomolecules are significantly dependent on the surface charge. A suitable surface
charge aids the nanoparticles to maintain colloidal stability by preventing their
aggregation or sedimentation leading to increased shelf life of nanomedicines during
storage and retention of therapeutic properties of nanomedicine during administration [14].
3.3.7 Delivery system
Another significant factor to be taken into account is surface hydrophobicity as it
also influences the behaviour of a nanoparticle in a biological environment by
affecting the cellular uptake, protein adsorption, and stability of the nanomedicine.
Hydrophobic nanoparticles possess enhanced cellular uptake as they can favorably
interact with lipids by a layer of cell membrane that facilitates the endocytosis
process. Hydrophobic surfaces have a higher tendency to adsorb proteins that can
decide the distribution, immune response, and biological journey of the nanoparticles (figure 3.1)[19].
The third factor to consider is the optimization of the drug release profile which
directly impacts the performance and efficacy of a nanomedicine. The surface
properties of the shell in which the drug is contained greatly influence the rate at
which the drug is released. For example, hydrophobic nanoparticles interact
strongly with hydrophobic drugs, leading to drug release, whereas the hydrophilic
drug in the same shell will be released faster since it has weaker interactions with the
Figure 3.1. Significant elements to be considered while designing a nanoparticle drug.
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
shell. The controlled drug release strategies should also be selected according to
the nature of the disease, the location of the target site, and the surface properties of
the encapsulated drug (figure 3.1)[19, 20].
3.4 Methods of nanomedicine formulation
Conventional techniques used for nanomedicine formulation are the solvent
evaporation method (figure 3.2) and ionic gelation method. In the solvent evaporation method, a polymer and a drug are dissolved in a volatile solvent which is then
allowed to evaporate to precipitate out the nanoparticles. Ionic gelation on the other
hand depends on the interaction of ions with opposite charges to form crosslinked
mesh-like networks (figure 3.3)[20].
Supercritical fluid technology is one of the recent and widely utilized approaches
in nanomedicine design. Supercritical fluids are employed as solvents for dissolution
of polymers. Owing to its inflammability, less toxicity and ability to exist as both
liquid and gas above mild critical conditions, carbon dioxide is a widely used
supercritical solvent. A supercritical fluid can either aid in nanoparticle formation
while it rapidly expands via a small pointed tube causing separation of solutes (rapid
expansion of supercritical solutions) or when a drug is first dissolved quickly in
supercritical fluid and then precipitated out by antisolvent addition. Supercritical
fluids are reported to be used for coating nanoparticles making them more porous
and facilitating effective drug penetration. They can also extract contaminants or
unwanted remanent solvents from nanoformulations supporting the purification
process [15, 21].
Uniform particle size and consistent shape are critical to the effectiveness of a
drug delivery process and the particle replication technique provides precisely
Figure 3.2. Steps explaining solvent evaporation method for nanomedicine formulation.
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Figure 3.3. Flowchart explaining ionic gelation method for nanomedicine formulation.
controlled nanoparticle synthesis assuring homogeneity and batch-to-batch reproducibility. In this technique, numerous copies of nanoparticles are prepared by using
a master replica or mold. Techniques like soft lithography in which a soft
elastomeric mold is created by using PDMS to be further used as a template for
nanoparticle production or nanoimprint lithography that exploits a stiff mold to
imprint the required pattern onto a precursor material for nanoparticle synthesis,
also come into play [15, 20, 21].
3.5 Drug release strategies
3.5.1 Active targeting strategies
In order to optimize therapeutic outcomes and minimize harm to healthy cells, active
targeting strategies in nanoparticle-based drug delivery utilize the modification of
nanoparticle surfaces with targeted ligands. Nanoparticles can specifically bind to
receptors or biomarkers that are overexpressed specifically on the diseased cells
differentiating them from normal cells, by interacting with targeted ligands. This
enhances the accuracy of the therapy by promoting the attaching and absorption of
nanoparticles by the target cells [22]. Active targeting methods involve targeting via
cell membranes, targeting via antibodies, and targeting via receptors. Active targeting
frequently employs receptor-mediated targeting. If the target cells have unique
receptors that are either absent or barely expressed in normal cells, this strategy is
especially helpful. Another successful strategy is antibody-mediated targeting, in
which antibodies designed for disease-associated antigens are coupled to the surface of
the nanoparticle. This strategy is widely used in cancer therapy because it allows for
precision tumor targeting by creating antibodies that specifically target tumor
antigens. While in a cell membrane-mediated strategy, nanoparticles can successfully
migrate to the appropriate target tissues or cells by utilizing the cell membranes of cells
that have specific targeting capabilities, such as immune cells or cancer cells [8].
Precise and better therapeutic results for conditions affecting the colon such as
IBD and colon cancer have been achieved by active targeting. In an inflamed colon,
drug delivery to macrophages by aiming at receptors like mannose receptors or
macrophage galactose-type lectin enhances site-specific drug deposition and
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
therapeutic efficacy [23, 24]. Inflamed colon tissues show surface ICAM-1 and CD98
overexpression which aids in the selective targeting and aggregation of nanoparticles
at desired locations by conjugating these nanoparticles to ICAM-1[25] or CD98 [26]
specific ligands, respectively. Overexpressed transferrin receptors on the cancer cell
surface aid in selective nanoparticle binding and uptake of these nanoparticles by
receptor-mediated endocytosis when they have ligands customized to transferrin
receptors attached to them, in the treatment of colon cancer [8, 23, 26].
3.5.2 Stimuli-based delivery strategy
To overcome issues like the premature release of drugs and desired drug concentration not reaching the target location, the focus is shifting toward stimulusresponsive delivery strategies. Stimuli-based drugs are capable of reacting to a
particular stimulus at the desired location by changing their physiochemical
characteristics and disassembling or breaking down specific linkers. To enable
precise and regulated drug release, these stimuli frequently depend on variations
between the surrounding conditions of healthy and diseased tissues or cells.
Major alterations in the GI tract can be observed during a chronic disease like
Crohn’s disease or ulcerative colitis (UC), including penetration by macrophages
and lymphocytes, enhanced production of mucus, compromised intestinal barrier as
a consequence of inflammation, ulcers, and crypt distortion that causes altered GI
motility affecting the pH, intestinal volume as well as epithelial permeability [8]. In
another instance, the tumor microenvironment differs from normal tissues in a
number of ways, and these variations can be used as activation-inducing endogenous
cues. Acidic pH, elevated ROS levels, overexpressed enzymes, increased ATP
concentrations, high redox potential and are some of the prevalent triggers present
in the tumor microenvironment (table 3.1)[23].
3.5.3 pH-dependent drug release
The difference in the pH of diseased tissue as well as normal tissues can be utilized to
design a pH-responsive drug delivery system that can undergo changes like swelling
Table 3.1. Polymeric nanoparticle coatings along with their optimum pH [15, 8].
pH range 5.0 5.5 6.0 7.0
Polymer
coatings
Polyvinyl acetate
phthalate
Hydroxypropyl
methylcellulose
phthalate 50
Hydroxypropyl
methylcellulose
phthalate 55
Eudragit®
L 30D-55
Eudragit®
L 100 55
Cellulose acetate
trimellitate
Cellulose acetate
phthalate
Eudragit®
L-100
Eudragit®
S-100
Eudragit®
FS 30D
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
or degradation at a specific pH leading to the release of the encapsulated drug under
regulated conditions, releasing at the precise moment and location. Polymers that
are pH sensitive might show an altered solubility or structure as a consequence of
fluctuations in pH. One can either employ pH sensitive matrix or coatings while
designing nanoparticles that allow them to swell and rupture at a specific pH,
leading to site-specific regulated drug release or pH-responsive linkers can be
employed, which can be made stable at neutral pH but cleave or degrade at acidic
pH levels leading to release of the medicine (table 3.1)[27, 28].
3.5.4 ROS-dependent drug release
These methods provide specific therapy for GI diseases by exploiting the reactive
oxygen species (ROS) gradient as a drug release trigger. In contrast to healthy tissues,
enhanced ROS production, including superoxide radicals and H
, has been
2O2
reported in diseased or inflamed GI tissues. ROS-sensitive linkers, moieties, and
matrix materials are employed which cleave or undergo structural alterations in the
presence of an oxidizing environment, i.e., when ROS are present. The drug is hence
released due to the disassembly of the encapsulating nanoparticle at the desired release
site lowering systemic toxicity and off-target effects to the bare minimum [15, 8].
Studies reveal that the biopsies collected from inflamed mucosa of UC patients
show 10–100-fold greater ROS levels than normal conditions. These increased
amounts are often restricted to the diseased area and keep on increasing with the
development of the disease. Activated ROS generation by phagocytes is a significant
factor contributing to inflammation during UC that can in turn directly harm the
biological constituents of the cells [29, 30].
Additionally, specific ligands or surface alterations can be added to ROSdependent delivery systems to ensure target delivery by increased specificity, i.e.,
coupling of active targeting and ROS responsiveness methods. Ligands that show
specific binding toward overexpressed biomarkers on diseased GI tissues might be
functionalized onto the surface of ROS-dependent drug-delivering nanoparticles so
that the drug can be released at the intended place.
3.5.5 Time-dependent dosage forms
The objective of time-dependent dosage forms is to create a delivery system that
releases the drug into the colon after a certain amount of time in the stomach and
small intestine. Ethyl cellulose and hydroxypropyl methylcellulose (HPMC), two
hydrophilic polymers, are frequently used in time-dependent formulations. These
polymers can gradually enlarge over a period of time and are integrated into the
coating or matrix of the dosage form. The hydrophilic polymers expand and swell up
as they gradually take in the water while traveling through the stomach and small
intestine, causing a lag phase that delays the drug’s release until it arrives at the
appropriate site in the colon. The enlarged hydrophilic polymers start to further
hydrate and disintegrate in the colon, where the transit time is greater and the
environment is better suited to drug absorption. Ultimately, this procedure results in
the drug being released in a continuous and regulated manner, maximizing drug
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Table 3.2. Functional groups that can be employed as linkers for different stimuli-responsive strategies [32].
pH responsive Redox responsive Light responsive
Silyl ether Thioketal Anthracene
Orthoester Disulphide Arylmethyl
Cis-aconityl PBA/PBE O-nitrobenzyl
Acetal Oxalate ester Azobenzene
Hydrazone Diselenium Coumarinyl ester
Imine Vinyldithioether Spiropyan
delivery to the colon and other specified areas of the GI tract and reducing drug
wastage (table 3.2) [15, 31].
3.5.6 Gastro retentive strategies
Another challenge faced during drug delivery in GI diseases is the rapid elimination
of drugs from the stomach, so to retain the drug in the stomach for longer durations,
researchers have been exploring gastroretentive strategies that prevent drug loss and
ensure localized drug action [33]. Mucoadhesive systems and high-density systems
are two frequently used techniques. Utilizing certain polymeric materials or coats
with adhesive capabilities, mucoadhesive methods enable the adhesion of nanoparticles to the mucus membrane covering the stomach wall. These adhesive
interactions tend to increase the residence period of the nanoparticles in the stomach
and slow down their quick evacuation ensuring controlled drug release and
improved drug absorption, whereas the goal of high-density systems is to make
the nanoformulation denser than gastric fluids in order to avoid buoyant characteristics and promote retention. Substances like chitosan, Carbopol, polycarbophil, and
lectins are the extensively used coatings in these drug delivery systems [33, 34].
Mucoadhesive systems are generally cationic since they have to interact with the
negative charges on the inflamed mucus. Colonic mucus is rich in negative charge
due to highly substituted sialic acid and sulfate carbohydrate residues making
cationic mucoadhesive nanoparticle delivery systems, a colon-specific delivery
strategy. On the other hand, anionic delivery methods are bioadhesive as they
interact and stick to the inflamed tissue rich in positively charged proteins, by
electrostatic interactions. High-density polymers or heavy metals can be added so
that the nanoformulations become denser and the longer the nanoparticles stay in
the stomach, the longer the drug is released ensuring better bioavailability and less
toxicity [33, 34].
The development and optimization of these gastroretentive systems necessitate a
thorough evaluation of a number of elements including choice of a
suitable biocompatible polymer, size of nanoparticle, modifications or alterations
to be made onto the surface of nanoparticle and formulation techniques. These
elements in turn affect how well the gastroretentive nanoparticulate composition
works including its density and adhesive tendencies. [8, 35]. These strategies have
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
proven to be quite beneficial against local digestive disorders like bacterial
infections, gastric ulcers and even gastritis.
3.5.7 Photothermal and photodynamic approach
Nanoparticles with photothermal characteristics including gold nanorods or carbon
nanotubes, are employed in photothermal therapy (PTT). To promote their preferential deposition in the diseased or cancer cells within the GI tract, the nanoparticles
might be functionalized with certain ligands or targeting moieties. These nanoparticles
can take in certain light wavelengths and transform this absorbed light into heat
energy. Near infrared has the ability to activate these nanoparticles once they have
reached the intended location. The light energy is subsequently transformed into heat
which specifically kills cancer cells providing therapeutic consequences. This photothermal reaction can also aid in speeding up the drug release from the nanoparticles
[15]. When used in photodynamic treatment (PDT), photosensitizers that are very often
organic compounds, can produce ROS when activated by light. These photosensitizers
deposit within the cancer cells or diseased areas and upon exposure to a particular light
wavelength, high ROS production is initiated which can cause cell death or slow down
the tumor growth. Different photosensitizers can be employed for the PDT like firstgeneration photosensitizers which include hematoporphyrins. Under this category,
performer sodium is widely used as a therapeutic agent against esophageal cancer but
first-generation photosensitizers are generally non-specific and absorb less light which
leads to less ROS generation resulting in poor therapeutic efficacy. To eradicate these
shortcomings, second-generation photosensitizers came into existence, including chlorins and phthalocyanine which show high tumour specificity and penetration, fewer side
effects and light absorption at a particular wavelength (table 3.3)[36].
3.6 Types of nanoparticles in drug delivery
3.6.1 Liposomes
Dr Alec D Bangham made their discovery in 1964 at the Babraham Institute at the
University of Cambridge [41]. The Greek words ‘Lipos’ (fat) and ‘Soma’ (body) are
where the name ‘liposome’ originates [42]. Aqueous artificial vesicles with a
spherical form and a diameter of 30 nm to several micrometers called liposomes
have one or more circumferential lipid bilayers around them [43]. Numerous factors,
such as magnitude, lipid composition, production technique, and surface charge
have an impact on liposome properties. Liposomes are superior drug delivery
devices because they protect the constituents they contain from physiological
deterioration [44], increasing the drug’s half-life, limiting the release of therapeutic
molecules [45], and offering enhanced safety and safety. Cholesterol, sphingomyelin,
and glycerolphospholipid are the main ingredients utilized in commercially available
products. Furthermore, by passively or actively directing their payload to the
location, the maximum tolerated dose, systemic side effects, and therapeutic
outcomes can all be improved using liposomes [46].
Based on compartment structure and lamellarity, liposomes can be classified as
unilamellar vesicles (ULVs), oligolamellar vesicles (OLVs), multilamellar vesicles
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
.
[37]
Cancer cells coated with nanoparticles are
subjected to x-ray irradiation that
nanoparticles
destroys them leaving healthy cells
[36]
unharmed.
Application of a photosensitizer on the
• Porfimer sodium
cancer cells that can be activated at a
• Aminolevulinic acid
certain wavelength to produce ROS that
• Foscan
[37]
damages the cells.
• Phthalocyanine
of Paclitaxel nanoparticles and produces
Tentradine is used to boost up the stability
incorporated with tentradine
carbohydrates which results in stronger
and specific binding.
(Continued)
[26]
ROS while paclitaxel simultaneously
eliminates antioxidants leading to cell
death.
receptors expressed on inflamed
epithelial cells.
pectinase assuring controlled release of
Accurately bind to amplified CD44
nanoparticles with tripeptide
coating
Mesalazine in the colon.
Pectin is dissolved in the colon by enzyme
silica-based nanoparticles
Mesalazine incorporated pectin-
[15, 8]
expressed at the inflammatory sites.
Polymer layers protect the drug from
Target amplified Folate receptors
nanoparticles
Mannosylated PGLA-PEG
upper GI degradation and trigger
(N, N-dimethylamino ethyl
release only at a specific pH creating a
methylacrylate) nanoparticles
delayed as well as extended drug release
profile.
The mucus released by inflamed colon is
loaded PLGA nanoparticles
Eudragit-coated budesonide-
abundant in negatively charged
nanoparticles
Table 3.3. This table depicts the different nanoformulation utilized in the treatment different GI diseases along with the underlying mechanism involved.
Disease Drug delivery approach Nanoformulation Mechanism of action References
Esophageal cancer Photothermal ablation therapy Chitosan-coated gold-gold sulphide
Photodynamic therapy
Gastric cancer Stimuli-responsive strategy Paclitaxel loaded nanoparticles
Active targeting Hyaluronic acid loaded polymeric
Crohon’s disease and
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ulcerative colitis
pH-dependent dosage Resveratrol incorporated into poly
Charge-mediated Targeting Positively charged chitosan

Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
[38]
The pH-dependent polymer covering
dissolves at an ideal pH of 7, ensuring
S100 coated
®
Eudragit
regulated medication release in the
nanoparticles
[39]
colon.
The nanoparticles target amplified EGFR
EGFR conjugated Fucoidan/
on cancer cells and deliver the apoptotic
alginate loaded hydrogel
agent fucoidan while the photosensitizer
utilizing chlorin e6
coats the cells and can produce ROS
when stimulated.
photosensitizer
[40]
Destruction of cancer cells using
irradiations without sabotaging the
nanoparticles
healthy cells.
[40]
Drugs delivered to colon specifically by
providing resistance to drug release in
the upper GI tract withstanding the
incorporated with fluorescein
microparticles
[40]
gastric pH and enzymatic degradation.
The environment of colon is rich in
Pectin-Aminothiophenol coated
pectinolytic enzymes that ensure the
Metronidazole loaded
[37]
specific drug release in colon.
Reduction of tumor metastasis once IL-12
microparticles
is provided to the cancer cells.
nanoparticles (utilizing TPP as
crosslinking agent)
[37]
receptors overexpressed on the colon
Specific delivery of drug to the folate
acid conjugated guar gum
cancer cells ensuring no drug pre-release
in the upper GI tract.
nanoparticles
therapy
Active targeting and photodynamic
Table 3.3. (Continued )
Disease Drug delivery approach Nanoformulation Mechanism of action References
Colon cancer pH-responsive dosage forms Quercetin dihydrate loaded
Photothermal therapy IR780 loaded chitosan
Colon-specific drug delivery Resistant starch film coated
Active targeting Methotrexate incorporated folic
Colorectal cancer Target delivery IL-12 incorporated chitosan
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