Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5563_Библиотеки_им_академика_М_И_Перельмана.pdf
126 The Dynamics of Tumor Microenvironment and Therapeutic Targets
Correia AS, Gärtner F, Vale N. Drug combination and repurposing for cancer therapy: The
example of breast cancer. Heliyon. 2021;7:e05948.2020.
da Silva Costa PM, Sales SLA, Pinheiro DP, Pontes LQ, Maranhão SS, Pessoa CdÓ, Furtado
GP, Furtado CLM. Epigenetic reprogramming in cancer: From diagnosis to treatment.
Front Cell Dev Biol. 2023 Feb 14:11:1116805. doi: 10.3389/fcell.2023.1116805.
eCollection 2023.
Dou QP. Current status and future perspectives on old drug repurposing for cancer treatment.
Recent Patents Anti-Cancer Drug Discov. 2021;16:120–121. https://doi.org/10.2174/15
7489281602210806102833.
Dudley ME, Rosenberg SA. Adoptive-cell-transfer therapy for the treatment of patients with
cancer. Nat Rev Cancer. 2003;3(9):666–675.
Fesnak AD, June CH, Levine BL. Engineered T cells: The promise and challenges of cancer
immunotherapy. Nat Rev Cancer. 2016;16(9):566–581.
Fraietta JA, Lacey SF, Orlando EJ, et al. Determinants of response and resistance to CD19
chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia. Nat
Med. 2018;24(5):563–571.
Gisselbrecht C, Van Den Neste E. How Imanage patients with relapsed/refractory diffuse large
B cell lymphoma. Br J Haematol. 2018;182(5):633–643.
Haberkorn U. What is cancer? Adv Nucl Oncol. 2007;62:1–16. https://doi.org/10.3109/
9781420091380-2.
Hanahan D, Coussens LM. Accessories to the crime: Functions of cells recruited to the tumor
microenvironment. Cancer Cell. 2012;21:309–322.
Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000 Jan 7;100(1):57–70. https://
doi.org/10.1016/s0092-8674(00)81683-9.
Hanahan D, Weinberg RA, Hallmarks of cancer: The next generation. Cell. 2011;144:646–674.
Hatem E, Azzi S, et al., Auranon/vitamin C: Anovel drug combination targeting triple-
negative breast cancer. J Natl Cancer Inst. 2018 Nov 20. doi: 10.1093/ije/djy149.
Hernández-Lemus E, Martínez-García M. Pathway-based drug-repurposing schemes in can-
cer: The role of translational bioinformatics. Front Oncol. 2020;10:605680. https://doi.
org/10.3389/fonc.2020.605680.
Hu Q, Sun W, Wang C, Gu Z. Recent advances of cocktail chemotherapy by combination
drug delivery systems. Adv Drug Deliv Rev. 2016;98:19–34. https://doi.org/10.1016/j.
addr.2015.10.022.
Ishida J, Konishi M, Ebner N, Springer J. Repurposing of approved cardiovascular drugs. J
Transl Med. 2016;14:1–15. https://doi.org/10.1186/s12967-016-1031-5.
W-L Jin, The updated landscape of tumor microenvironment and drug repurposing. Signal
Transduct Target Ther. 2020 Aug 25;5(1):166. doi: 10.1038/s41392-020-00280-x.
June CH, O’Connor RS, Kawalekar OU, Ghassemi S, Milone MC. CAR T cell immunother-
apy for human cancer. Science. 2018;359(6382):1361–1365.
Kalos M, June CH. Adoptive T cell transfer for cancer immunotherapy in the era of synthetic
biology. Immunity. 2013;39(1):49–60.
Kaushik I, Ramachandran S, Prasad S, Srivastava SK. Drug rechanneling: Anovel paradigm
for cancer treatment. Semin Cancer Biol. 2021;68:279–290. https://doi.org/10.1016/j.
semcancer.2020.03.011.
Kumari K, Keshari S, Sengupta D, Sabat SC, Mishra SK. Transcriptome analysis of genes
associated with breast cancer cell motility in response to Artemisinin treatment. BMC
Cancer. 2017;17:858. https://doi.org/10.1186/s12885-017-3863-7.
Lamb J. The connectivity map: A new tool for biomedical research. Nat Rev Cancer.
2007;7:54–60. https://doi.org/10.1038/nrc2044.
Landsberg J, Kohlmeyer J, Renn M, et al. Melanomas resist T-cell therapy through
inammation-induced reversible dedifferentiation. Nature. 2012;490(7420):412–416.
Drug Repurposing and Tumor Microenvironment in Cancer 127
Langley RE, Burdett S, Tierney JF, Cafferty F, Parmar MKB, Venning G. Aspirin and cancer:
Has aspirin been overlooked as an adjuvant therapy? Br J Cancer. 2011;105:1107–1111.
Law LW. Effects of combinations of antileukemic agents on an acute lymphocytic leukemia of
mice. Cancer Res. 1952;12:871–878.
Lee JJ, Saiful Yazan L, Che Abdullah CA. A review on current nanomaterials and their drug
conjugate for targeted breast cancer treatment. Int J Nanomed. 2017;12:2373–2384.
https://doi.org/10.2147/IJN.S127329.
Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, Kuang W, Peng J, Chen L, Zeng J. A network integra-
tion approach for drug-target interaction prediction and computational drug reposition-
ing from heterogeneous information. Nat Commun. 2017;8:573. https://doi.org/10.1038/
S41467-017-00680-8.
Mahon K, Martins MVA, Geraldes MC. Chemical composition of garnet and ilmenite from
stream sediments and soil applied to diamond fertility investigation: the kimberlite intru-
sion of Ariquemes, Rondonia (Brazil), Journal of Sedimentary Environments, 2022.
DOI:10.1007/s43217-022-00095-6.
Malani PN. Harrison’s principles of internal medicine. J Am Med Assoc. 2012;308:
1813–1814.
Malik JA, Ahmed S, Jan B, Bender O, Al Hagbani T, Alqarni A, Anwar S. Drugs repurposed: An
advanced step towards the treatment of breast cancer and associated challenges. Biomed
Pharmacother. 2022;145:112375. https://doi.org/10.1016/j.biopha.2021.112375.
Meseure D, Drak Alsibai K, Nicolas A. Pivotal role of pervasive neoplastic and stromal cells
reprogramming in circulating tumor cells dissemination and metastatic colonization.
Cancer Microenviron. 2014;7(3):95–115. https://doi.org/10.1007/s12307-014-0158-2.
Nagaraj AB, Wang QQ, Joseph P, Zheng C, Chen Y, Kovalenko O, Singh S, Armstrong A,
Resnick K, Zanotti K, Waggoner S, Xu R, DiFeo A. Using a novel computational
drug-repositioning approach (DrugPredict) to rapidly identify potent drug candi-
dates for cancer treatment. Oncogene. 2018;37:403–414, https://doi.org/10.1038/
ONC.2017.328.
National Cancer Institute, NCI dictionary of cancer terms. https://www.cancer. gov/publications/
dictionaries/cancer-terms. (Accessed 23 December 2020).
Olgen S, Kotra LP. Drug Repurposing in the development of anticancer agents. Curr Med
Chem. 2019;26:5410–5427. https://doi.org/10.2174/0929867325666180713155702.
Palmer AC, Sorger PK. Combination cancer therapy can confer benet via patient-to-patient
variability without drug additivity or synergy. Cell. 2017;171:1678–1691.e13. https://
doi.org/10.1016/j.cell.2017.11.009.
Palve V, Liao Y, Remsing Rix LL, Rix U. Turning liabilities into opportunities: Off-target
based drug repurposing in cancer. Semin Cancer Biol. 2021;68:209–229. https://doi.
org/10.1016/J.SEMCANCER.2020.02.003.
Pantziarka P, Bouche G, Meheus L, Sukhatme V, Sukhatme VP, Vikas P. The Repurposing
Drugs in Oncology (ReDO) project. Ecancermedicalscience. 2014;8:442. https://doi.
org/10.3332/ecancer.2014.442.
Pantziarka P, Verbaanderd C, Sukhatme V, et al. ReDO_DB: The repurposing drugs in oncology
database. Ecancermedicalscience. 2018;12:886. https://doi.org/10.3332/ecancer.2018.
Ribeiro E, Costa B, Vasques-Nóvoa F, Vale N, In vitro drug repurposing: Focus on vasodila-
tors. Cells. 2023 Feb;12(4):671. https://doi.org/10.3390/cells12040671.
Roberts S, Peyman S, Speirs V. Current and emerging 3D models to study breast cancer. Adv
Exp Med Biol. 2019;1152:413–427. https://doi.org/10.1007/978-3-030-20301-6_22.
Rodrigues R, Duarte D. Vale N. Drug repurposing in cancer therapy: Inuence of patient’s
genetic background in breast cancer treatment. Int J Mol Sci. 2022 Apr;23(8):4280.
Rosenberg SA, Restifo NP. Adoptive cell transfer as personalized immunotherapy for human
cancer. Science. 2015;348(6230):62–68.
128 The Dynamics of Tumor Microenvironment and Therapeutic Targets
Rudrapal M, Khairnar SJ, Jadhav AG. Drug Repurposing (DR): An emerging approach in drug
discovery. IntechOpen. 2020. DOI: 10.5772/intechopen.93193.
Sathishkumar K, Chaturvedi M, Das P, Stephen S, Mathur P. Cancer incidence estimates for
2022 & projection for 2025: Result from National Cancer Registry Programme, India.
Indian J Med Res. 2022 Oct–Nov;156(4&5):598–607. https://doi.org/10.4103/ijmr.
ijmr_1821_22.
Schuster SJ, Svoboda J, Chong EA, et al. Chimeric antigen receptor T cells in refractory B-cell
lymphomas. N Engl J Med. 2017;377(26):2545–2554.
Sleire L, Førde H, Netland IA, Leiss L, Skeie B, Enger P. Drug repurposing in cancer.
Pharmacol Res. 2017. https://doi.org/10.1016/j.phrs.2017.07.013.
Sobti RC, Lal SK, Goyal RK (editors). Drug Repurposing for Emerging Infectious Diseases
and Cancer. Springer, 2023. ISBN: 978-981-19-5399-6.
Sodani K, Patel A, Kathawala RJ, Chen ZS. Multidrug resistance associated proteins in multin-
drung resistance. Chin J Cancer. 2012;31:58–72. https://doi.org/10.5732/cjc.011.10329.
Stanta G, Jahn SW, Bonin S, Hoeer G. Tumour heterogeneity: Principles and practical conse-
quences. Virchows Archiv. 2016;469:371–384
Webster RM. Combination therapies in oncology. Nat Rev Drug Discov. 2016;15:81–82.
https://doi.org/10.1038/nrd.2016.3.
Yuan Y, Jiang Y-C, Sun C-K, Chen Q-M. Role of the tumor microenvironment in tumor pro-
gression and the clinical applications (review). Oncol Rep. 2016 May;35(5):2499–2515.
Zhang Z, Zhou L, Xie N, Nice EC, Zhang T, Cui Y, Huang C. Overcoming cancer therapeutic
bottleneck by drug repurposing. Signal Transduct Targeted Ther. 2020;5:113.
129
7
Tumor Microenvironment
and Cancer Growth,
Detection Using
AI, and Computer
Vision Techniques
Rathi Dasgupta
7.1 THE DYNAMIC ECOSYSTEM OF THE
TUMOR MICROENVIRONMENT
Cancer was once viewed primarily as a disease of transformed cells operating in
isolation. However, modern understanding recognizes tumors as complex ecosys-
tems where malignant cells exist within and interact with a specialized niche known
as the tumor microenvironment (TME). This microenvironment profoundly inu-
ences virtually every aspect of cancer biology, from initial transformation to meta-
static spread. The TME comprises diverse cellular and noncellular components that
collectively create a dynamic framework supporting tumor growth (Hanahan and
Weinberg 2011):
7.2 CELLULAR COMPONENTS
Cancer-associated broblasts (CAFs): Activated broblasts that differ phe-
notypically and functionally from normal broblasts. CAFs secrete growth
factors, cytokines, and extracellular matrix proteins that promote tumor
growth and invasion.
Immune cells: Aheterogeneous population including tumor-associated mac-
rophages (TAMs), T cells, B cells, natural killer cells, neutrophils, and
myeloid-derived suppressor cells. These cells can either inhibit or promote
tumor progression, with immunosuppressive phenotypes often dominating
established tumors (Binnewies et al., 2018; Chen and Song 2019; Mantovani
et al., 2008; Hanahan and Weinberg 2011; Kalluri 2016).
Endothelial cells: Form the tumor vasculature, which is typically disorga-
nized, leaky, and functionally abnormal compared to normal blood vessels.
DOI: 10.1201/9781003405023-7
130 The Dynamics of Tumor Microenvironment and Therapeutic Targets
Pericytes: Supporting cells that wrap around blood vessels and regulate vascu-
lar function, often displaying altered characteristics in tumors.
Adipocytes: Fat cells that can provide energy-rich lipids to cancer cells and
secrete adipokines that inuence tumor behavior.
7.3 NONCELLULAR COMPONENTS
• Extracellular matrix (ECM): Acomplex network of proteins, glycopro-
teins, and proteoglycans that provides structural support and biochemical
cues. Tumor ECM is typically stiffer and more abundant than normal ECM,
a phenomenon known as desmoplasia (Mouw et al., 2014).
• Soluble factors: Growth factors, cytokines, chemokines, and metabolites
that mediate cell–cell communication and regulate cellular behaviors.
• Exosomes and microvesicles: Small membrane-bound vesicles that trans-
fer proteins, lipids, and nucleic acids between cells, enabling intercellular
communication across the TME.
• Reciprocal communication in the TME: Communication between cancer
cells and stromal components is bidirectional and occurs through multiple
mechanisms:
• Paracrine signaling: Cancer cells and stromal cells secrete factors that
inuence each other’s behavior. For example, cancer cells produce factors
like TGF-β that activate broblasts, while CAFs secrete growth factors like
HGF that stimulate cancer cell proliferation and migration.
• Direct cell–cell contact: Physical interactions between cancer cells and
stromal cells through adhesion molecules and gap junctions facilitate signal
transduction and material exchange.
• ECM modication: Both cancer and stromal cells remodel the ECM
through production of matrix proteins and matrix-degrading enzymes,
altering mechanical properties and liberating matrix-bound growth factors.
• Metabolic cooperation: Cancer cells and stromal cells engage in metabolic
symbiosis, where metabolic byproducts from one cell type serve as fuel for
another.
7.4 TME-DRIVEN SUPPORT OF CANCER HALLMARKS
The TME critically inuences all hallmarks of cancer as described by Hanahan
and Weinberg and provides a rich source of growth factors that activate as well as
proliferative signaling in cancer cells. CAFs secrete factors, such as EGF, FGF, and
HGF, that bind to receptors on cancer cells, activating MAPK and PI3K/AKT path-
ways. Additionally, inammatory cells release cytokines and chemokines that further
stimulate proliferation, creating a self-reinforcing loop of growth-promoting signals.
Additionally, stromal cells in the TME can help cancer cells evade growth suppression
by secreting TGF-β that paradoxically promotes cancer progression despite its growth-
suppressive effects in normal cells. Stromal cells release matrix metalloproteinases
that cleave and inactivate growth suppressors. Creation of physical barriers that prevent
access of growth suppressors to cancer cells also another activity of TME components.
131Tumor Microenvironment and Cancer Growth
7.4.1 the tme CoNfers resistaNCe to apoptosis
through multiple meChaNisms
a. Hypoxia-induced upregulation of anti-apoptotic proteins like Bcl-2 and
Bcl-xL.
b. CAF-derived growth factors that activate survival pathways: ECM-mediated
integrin signaling that suppresses anoikis (detachment-induced cell death).
c. Immune-cell-derived cytokines that activate NF-κB signaling, promoting
survival, enabling replicative immortality. Although telomerase activation
is primarily an intrinsic property of cancer cells, the TME can inuence
telomere maintenance through inammatory signals that upregulate telom-
erase expression. Oxidative stress often accelerates telomere shortening,
selecting for cells with telomere maintenance mechanisms
7.4.2 iNduCiNg aNgiogeNesis, formatioN of New blood
vessels is a CritiCal tme-depeNdeNt proCess
a. Hypoxia within the tumor core triggers release of pro-angiogenic factors
like VEGF, CAFs, and TAMs and leads to the secretion of additional angio-
genic factors, including FGF, PDGF, and angiopoietins, thereby resulted in
formation of dense tumor vasculature. This tumor vasculature is abnormal,
with irregular branching, leakiness, and intermittent blood ow
b. The TME actively facilitates invasion and metastasis through CAF-mediated
ECM remodeling that creates tracks for cancer cell migration. Moreover,
TAM-derived proteases that degrade basement membranes. Inammatory
cytokines that induce epithelial-mesenchymal transition (EMT) also linked
with pre-metastatic niche formation in distant organs through systemic
effects of primary tumor-derived factors (Quail and Joyce 2013; DeNardo
and Ruffell 2019).
7.4.3 immuNomodulatioN iN the tme
The complex interplay between cancer cells and immune components creates an
immunosuppressive environment that enables tumor evasion of immune surveillance:
Immunosuppressive cell recruitment: Cancer cells secrete chemokines that
attract regulatory T cells, M2-polarized macrophages, and myeloid-derived suppres-
sor cells, all of which have immunosuppressive functions (Quail and Joyce 2013;
Kalluri 2008; DeNardo and Ruffell 2019; Fridman et al., 2012; De Palma et al.,
2017; Nieman et al., 2013).
i. Checkpoint inhibition: Expression of immune checkpoint molecules like
PD-L1 by cancer and stromal cells engages inhibitory receptors on T cells,
dampening their activity (Sharma and Allison 2015).
ii. Metabolic immunosuppression: Metabolic competition for glucose and amino
acids, combined with accumulation of immunosuppressive metabolites like
adenosine and kynurenine, impairs T cell function (Munn and Bronte 2016).
132 The Dynamics of Tumor Microenvironment and Therapeutic Targets
iii. Cytokine milieu: An abundance of immunosuppressive cytokines (IL-10,
TGF-β) and a paucity of immunostimulatory cytokines (IL-2, IFN-γ) cre-
ate an environment hostile to effective antitumor immunity.
iv. Physical barriers: Dense ECM and abnormal vasculature impede immune
cell inltration and function within the tumor core.
7.4.4 the tme iN therapeutiC respoNse aNd resistaNCe
The TME signicantly impacts therapeutic outcomes across treatment modalities
such as chemotherapy (Anderson et al., 2017; Schmid et al., 2017; Fukumura et al.,
2018; Chauhan et al., 2011; Martin et al., 2020). Multiple mechanisms have been
reported that confer resistance to therapy.
i. CAFs secrete factors that activate pro-survival pathways in cancer cells.
ii. ECM density impedes drug delivery by increasing interstitial uid pressure.
iii. Hypoxia induces drug-efux pumps and DNA repair mechanisms.
iv. Inammatory cytokines activate NF-κB signaling, promoting treatment
resistance.
v. Hypoxic regions are inherently radioresistant due to reduced oxygen-
dependent DNA damage.
vi. Radiation-induced cell death triggers inammatory responses that can pro-
mote tumor regrowth.
vii. Irradiated stroma may paradoxically support cancer stem cell survival
7.5 METABOLIC REPROGRAMMING IN THE TME
The metabolic landscape of tumors is characterized by regions of hypoxia, acidosis,
and nutrient deprivation. These conditions drive metabolic adaptations in both can-
cer and stromal cells:
i. Warburg effect: Cancer cells often exhibit aerobic glycolysis, consuming
glucose at high rates and producing lactate even in the presence of oxygen.
This metabolic pattern provides rapid ATP generation and intermediates
for biosynthetic pathways.
ii. Metabolic symbiosis: Cancer cells located in different microenvironmen-
tal niches develop complementary metabolic proles. For example, hypoxic
cancer cells may export lactate that is used as fuel by better-oxygenated
cells (Vande Voorde et al., 2019).
iii. Reverse Warburg effect: In some tumors, cancer cells induce aerobic gly-
colysis in adjacent CAFs, which then export lactate and other metabolites
that fuel oxidative phosphorylation in cancer cells (Fu et al., 2017).
iv. Glutamine addiction: Many tumors display enhanced glutamine metabo-
lism, using this amino acid as a nitrogen source for nucleotide synthesis
and as an anaplerotic substrate for the TCA cycle.
v. Lipid metabolism: Tumors often upregulate fatty acid synthesis and may
obtain additional lipids from adjacent adipocytes.
133Tumor Microenvironment and Cancer Growth
7.6 THERAPEUTIC TARGETING OF THE TME
i. Anti-angiogenic therapy: Drugs targeting VEGF or its receptors disrupt
tumor vasculature, though effects are often transient due to activation of
alternative angiogenic pathways.
ii. CAF-directed approaches: Strategies to normalize or deplete CAFs
include inhibition of FAP (broblast activation protein) and targeting of
CAF-derived factors like TGF-β.
iii. ECM modication: Enzymatic degradation of hyaluronan and inhibition
of LOX (lysyl oxidase) can decrease ECM density, improving drug delivery
and T cell inltration.
iv. Immunomodulation: Beyond checkpoint inhibitors, approaches include
depletion of immunosuppressive cells, blockade of immunosuppressive
cytokines, and activation of innate immune pathways.
v. Metabolic intervention: Inhibitors of glycolysis, glutaminolysis, and fatty
acid metabolism can disrupt TME-dependent metabolic adaptations.
vi. Normalization strategies: Rather than ablating TME components, some
approaches aim to normalize their phenotype and function, restoring tissue
homeostasis.
7.7 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
A. Articial Intelligence and Machine Learning in Drug Repurposing:
Articial intelligence (AI) and machine learning (ML) technologies have
become increasingly crucial, especially with the rise of sophisticated deep
learning and neural network algorithms. ML algorithms can learn from
data and replicate cognitive human behavior. In contemporary times, bio-
logical researchers have incorporated machine learning into the processes
of drug repurposing to enhance overall outcomes. ML helps drug design
in two ways. First, a statistical survey of the effectiveness of a group of
existing drugs is done in a very focused way. Second, the design of repur-
posed drugs takes suitable feedback (Kather et al., 2018; Zhang et al.,
2023; Abels et al., 2019; Saltz et al., 2018; Sun et al., 2018; Stuart and
Satija 2019; Rodriques et al., 2019; Bocci et al., 2020; Lim et al., 2020;
Kather et al., 2018).
B. Support Vector Machine (SVM): An SVM is a machine learning algorithm
that has found application in various domains, including drug repurposing.
The data points closer to the hyperplane, inuencing its position and orien-
tation, are termed support vectors. Utilizing these vectors, the largest mar-
gin is established, dividing the dataset into two groups and determining the
category to which new data belongs. The SVM kernel transforms the input
space into higher dimensions, showcasing the distinction between the new
data and the support vectors. The key element of SVM lies in identifying
the optimal hyperplane to effectively partition the data into distinct clus-
ters. The performance of SVM in drug repurposing depends on the quality
and representativeness of the training data, the choice of features, and the
specic characteristics of the problem at hand.
134 The Dynamics of Tumor Microenvironment and Therapeutic Targets
C. Random Forest (RF): RF is a frequently employed algorithm specically
crafted for extensive datasets with numerous features. It streamlines tasks
by eliminating outliers, classifying data, and assigning datasets based on
relative features tailored for the specic algorithm. Typically trained for
substantial inputs and variables, it gains accessibility through data collec-
tion from various databases. RF proves advantageous in various scenar-
ios, including handling missing data, addressing outliers, and estimating
characteristics for classication. In the realms of drug discovery and drug
repurposing, RFs nd primary applications in feature selection, classica-
tion, or regression.
D. K-Nearest Neighbor (K-NN): The K-NN algorithm involves storing all
data points during the training phase, and the classication of new data
points is determined by their similarity to existing data. As new instances
are introduced to the established categories, the K-NN algorithm assesses
these new cases against previous instances and assigns them to the same
category as the new data [67]. In the K-NN algorithm, the Minkowski
parameter (p) is a congurable parameter that inuences the choice of dis-
tance metric. Choosing different values of p can impact the performance of
the algorithm based on the characteristics of the dataset. Common choices
include p= 1 for Manhattan distance and p= 2 for Euclidean distance.
K-NN offers a straightforward yet powerful approach in drug repurposing
by leveraging the concept of similarity. Its capacity to identify drugs with
analogous characteristics provides a valuable tool for predicting potential
novel therapeutic uses for existing medications.
E. Logistic Regression (LR): Logistic regression nds application in drug
repurposing as a predictive modeling tool. It is primarily employed for
categorizing observations into discrete classes. This technique adjusts its
output utilizing the logistic sigmoid function, converting probability values
into at least two distinct classes.
F. Ensemble Model: An ensemble strategy merges two or more base classi-
ers, elevating the outcomes of machine learning. It delivers superior pre-
dictive performance compared to a single model.
G. Homogeneous Model: Ahomogeneous model integrates similar types of
base classiers, resulting in improved performance. Random forest and
AdaBoost are two distinct homogeneous models utilized for training the
dataset. The random forest method employs numerous decision trees as
base classiers in a parallel manner, whereas the AdaBoost classier amal-
gamates multiple weak classiers to generate a robust classier with a high
level of accuracy.
H. Heterogeneous Model: This diverse model integrates various base classi-
ers to achieve enhanced accuracy. The heterogeneous ensemble comprises
ve distinct types of base classiers: K-nearest neighbor, logistic regres-
sion, random forest, support vector machine, and AdaBoost. Hard voting
and soft voting techniques are applied to aggregate the results. The voting
classier receives training data from multiple machine learning models
135Tumor Microenvironment and Cancer Growth
and predicts an output based on the class with the highest probability, com-
bining the results from each base estimator.
I. Deep Learning in Drug Repurposing: Deep learning, a subeld of
machine learning, has revolutionized various elds, including drug discov-
ery and repurposing. Its ability to extract meaningful patterns from large
datasets makes it well-suited for identifying novel therapeutic applications
for existing drugs, potentially reducing the time and costs associated with
traditional drug discovery processes.
One of the primary applications of deep learning in drug repurposing lies in predict-
ing drug–target interactions (DTIs). Deep learning models can analyze vast biologi-
cal data, including protein sequences, gene expression proles, and drug structures,
to uncover potential interactions between known drugs and novel targets. This capa-
bility enables researchers to explore new therapeutic opportunities for existing drugs
that may have been overlooked using traditional methods [69]. Three different algo-
rithms widely used in deep learning, namely, feedforward neural network (FNN),
convolutional neural network (CNN), and recurrent neural network (RNN). The
FNN algorithm is utilized to create vector representations of samples in the dataset.
It connects articial neurons layer by layer, starting from input variables and ending
with output targets. Techniques such as CNNs are employed to analyze biological
images, such as those from high-throughput screenings or medical imaging. This
facilitates the identication of patterns indicative of drug efcacy or safety, contrib-
uting to the discovery of new therapeutic applications. The RNN’s primary applica-
tion lies in investigating features for drug repurposing within biological sequences.
These models play a key role in producing extensive libraries of molecules for drug
development, where each molecule is represented as a sequence using straightfor-
ward computational codes for molecular input line entry.
Deep learning also plays a crucial role in identifying drug combinations, where
multiple drugs are used together to achieve enhanced therapeutic effects. By analyz-
ing patterns in drug response data and patient outcomes, deep-learning models can
predict synergistic drug interactions, leading to the development of more effective
treatment regimens.
7.8 DIRECTION OF FUTURE RESEARCH IN AI
The availability of comprehensive databases and powerful articial intelligence
techniques has signicantly enhanced the drug repurposing process by enabling
researchers to identify new uses for existing drugs, predict drug–target interac-
tions, and optimize drug combinations. Databases such as DrugBank, BindingDB,
PubChem, etc., provide valuable information on drug properties, targets, and bio-
logical activities, facilitating the exploration of potential repurposing opportunities.
Additionally, tools like DrugMap, DrugQuest, etc., offer computational methods for
analyzing and interpreting drug-related data, further aiding in the identication of
promising repurposing candidates. Articial intelligence techniques, particularly
deep learning, have revolutionized drug repurposing by enabling the extraction of