Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research as well as computational approaches for drug discovery and repurposing for cancer therapy. The book also provides detailed descriptions about target molecules, pathways, and their inhibitors for easy understanding and applicability.
The book discusses tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers, and transcriptome; those are discussed in the context of different cancer types, such as colon, pancreatic, glioblastoma, endometrial, and retinoblastoma, among others.
This book is a valuable resource for researchers, students, and members of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor the treatment for cancer patients.
Discusses in silico remodeling of effective phytochemical compounds for discovering improved anticancer agents for substantial/significant cancer therapy
Covers potential tools of bioinformatics that are applied toward discovering new targets by drug repurposing and strategies to cure different types of cancers
Demonstrates the significance of computational and artificial intelligence approaches in anticancer drug discovery
Explores how these various advances can be integrated into a precision and personalized medicine approach that can eventually enhance patient care
Author(s): Ganji Purnachandra Nagaraju, Venkatesan Amouda, Ampasala Dinakara Rao
Publisher: Elsevier
Year: 2023
Language: English
Pages: 441
Cover
Computational Methods in Drug Discovery and Repurposing for Cancer Therapy
Copyright
Contributors
About the editors
Computational approaches for anticancer drug design
Abstract
Introduction
Current computational approaches for cancer drug designs
Structure-based methods
Ligand-based strategies
Applications of computational approaches in cancer drug designing
Challenges and future directions
Conclusion
References
Molecular modeling approach for cancer drug therapy
Abstract
Introduction
Drug designing
Molecular modeling
Methods of molecular modeling
Applications of molecular modeling
Applications in multidrug-resistant proteins
Conclusion
References
Discovery of anticancer therapeutics: Computational chemistry and Artificial Intelligence-assisted approach
Abstract
Introduction
Drug repurposing
Computational chemistry in drug designing
Structure-based drug designing
ADME/Tox screening and drug-likeness prediction
Molecular docking
Quantitative structure-activity relationship modeling
Molecular dynamics simulation
Artificial Intelligence in drug discovery
Conclusion
References
Artificial intelligence in oncological therapies
Abstract
Introduction
Importance of early diagnosis
How AI can improve accuracy and speed of cancer diagnoses
How AI can assess patient background information to determine risk of cancer
Diagnosis of cancer subtype and stage
AI in cancer drug discovery and development
De novo drug design
AI in recommending drug combinations and repurposing drugs
AI in identifying drug-target interactions
Deep learning, black boxes, and hidden layers
Future of AI in oncology
Conclusion
References
Approach of artificial intelligence in colorectal cancer and in precision medicine
Abstract
Introduction
History
Artificial intelligence
AI in colorectal cancer (CRC)
Precision medicine
Applications of AI in CRC
CRC screening and diagnosis
Colonoscopy
Virtual colonoscopy
Genomic characterization of polyps
Magnification endoscopy with NBI
Endoscopy
White light endoscopy (WLE)
Studies of polyp characterization
Drug discovery and repurposing
Treatment and prevention
Robotic-assisted surgery
Precision medicine in CRC
Benefits
Limitations
Current challenges and prospects
Conclusion
References
Artificial intelligence in breast cancer: An opportunity for early diagnosis
Abstract
Machine learning
Machine learning algorithms
Implementation of ML models
Assessment of ML models
ML in cancer prediction and diagnosis
Breast cancer
ML in breast cancer
ML in BC risk prediction
ML in breast cancer diagnosis
Conclusion
References
Quantitative structure-activity relationship and its application to cancer therapy
Abstract
Introduction
Function
Origin of QSAR
Advanced techniques of QSAR
Application in drug design
Application in cancer therapy
Concerns
Conclusion
References
Structure-based virtual screening for the identification of novel Greatwall kinase inhibitors
Abstract
Introduction
Computational methods
Software and hardware used in this investigation
Protein preparation and receptor grid generation
Database retrieval and ligand preparation
High-throughput structure-based virtual screening
Protein-ligand free energy binding calculations
Results and discussion
High-throughput virtual screening and molecular docking studies of GWL kinase
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Estimation of the binding free energy for the top-scoring compounds
Conclusion
References
Strategies for drug repurposing
Abstract
Introduction
Computational drug repurposing
Profile-based or signature mapping
Transcriptome mapping
Chemical structure mapping
Mapping of adverse effects profile
Genome-wide association studies
Network or pathway mapping
Data based
Challenges and limitations of computational drug discovery strategies
Experimental drug repurposing
Phenotypic screening
Target-based screening
Conclusions and perspectives
References
Principles of computational drug designing and drug repurposing-An algorithmic approach
Abstract
Introduction
Overview of basic thermodynamic principles involved in computational algorithms
Fundamentals of computational algorithms
Searching the conformational space
Analysis of protein flexibility
Drug repurposing [161-167]
Conclusion
References
Drug discovery and repositioning for glioblastoma multiforme and low-grade astrocytic tumors
Abstract
Introduction
Approved therapeutics for astrocytic tumors
Alkylating agents
Mechanistic inhibitors
Drug discovery approaches against astrocytic tumors
Pharmacogenomic analysis
Network analysis
Quantitative structure-activity relationship
Meta-analysis
Drug discovery for astrocytic tumors by virtual screening
IDH-mutant targeting therapies
Tumor vasculature normalization therapies
Unfolded protein response targeting therapies
Hedgehog/glioma-associated oncogene homolog targeting therapeutics
Octamer-binding transcription factor-4 targeting therapeutics
Apoptosis-inducing therapeutics
Autophagy targeting compounds
Glucose transporter targeting compounds
Drug repositioning in astrocytic tumor therapy
Repositioning anticancer drugs to astrocytic tumor therapy
Nonanticancer drug repurposing
Conclusion
References
Repurposing of phytocompounds-derived novel bioactive compounds possessing promising anticancer and cancer th ...
Abstract
Drug repurposing
Introduction
Strategies in drug repurposing
Pros and cons of drug repurposing
Computational advancements in oncology research
Structure-based and target-based virtual screening
Systems biology integrated approach in drug repositioning
In silico databases and web-based tools for drug repurposing
Phytochemicals repurposed in cancer therapy
Metformin
Artemisinin
Curcumin
Genistein
Berberine
Levofloxacin
Aspirin
Tanshinone
Digoxin
Ginkgolide
Hypericin
Paclitaxel
Vinblastine
Antidiabetic phytocompounds repurposed for cancer therapy
Conclusion
References
Old drugs and new opportunities-Drug repurposing in colon cancer prevention
Abstract
Introduction
Principles and tools used in drug repurposing
Categories of repurposed drugs against human cancers
Drugs used in the treatment of colon cancer
Drug repurposing in the prevention of colon cancer
Drug repurposing pitfalls
Computational approaches in drug repurposing for colorectal cancer
Conclusions and perspectives
References
Repurposing cardiac glycosides as the hallmark of immunogenic modulators in cancer therapy
Abstract
Introduction
Repurposing cardiac glycosides in cancer treatment
Structural peculiarities of cardiac glycosides augmenting anticancer property
Anticancer effects of CGs in cancer cells
CGs hamper Na+/K+-ATPase signaling complex in cancer
Role of the immune system in cancer
CGs as immunomodulators in cancer
Conclusions
References
Systems biology tools for the identification of potential drug targets and biological markers effective for c ...
Abstract
Introduction
Current problems in cancer therapies
Need for alternative approaches in cancer
GIN: A systems biology approach
Types of biological networks
Protein-protein interaction (PPI) networks
GCN
GRNs
Metabolic networks
Signal transduction networks (STNs)
Drug-gene interaction network (DGI)
Cancer databases
Comprehensive tumor database
The Cancer genome atlas (TCGA)
cBioPortal
Catalog of somatic mutation in cancer (COSMIC)
International cancer genome consortium (ICGC)
Cancer genome project (CGP)
DriverDBv3
Human cancer proteome variation database (CanProVar 2.0)
Network of cancer genes (NCG)
Databases for specific cancer
HColonDB
Pancreatic cancer database (PCD)
Pancreatic expression database (PED)
Cervical cancer database (CCDB)
Gene-to-systems breast cancer database (G2SBC)
Human lung cancer database (HLungDB)
Renal cancer gene database (RCDB)
Microbial databases for host-pathogen interaction
Host-pathogen interaction database (HPIDB 3.0)
VirHostNet
VirusMentha
Hepatitis C virus protein interaction database (HCVpro)
Pathogen-host interaction search tool (PHISTO)
Databases for interaction data curation
Gene interaction databases
BioGRID
GeneMANIA
Protein-protein interaction databases
Search tool for the retrieval of interacting genes (STRING)
Molecular interaction database (MINT)
IntAct
Network construction and visualization
Cytoscape
Gephi
Medusa
BioLayout Express3D
GeNeCK
Network analysis
Clustering analysis
clusterMaker
MCODE
ClusterViz
Functional enrichment analysis (FEA)
The database for annotation, visualization, and integrated discovery (DAVID)
FunRich
BiNGO
GeneWeaver
GeneCodis3
Topological parameter analysis (TPA) & hub gene identification
NEtwork analysis tools (NEAT)
NetworkAnalyzer
CytoNCA
The network analysis profiler (NAP)
CentiScaPe
cytoHubba
CHAT contextual hub analysis tool
CyNetSVM
How can the identified targets be used for cancer therapy?
Conclusion
References
Role of human body fluid biomarkers in liver cancer: A systematic review
Abstract
Introduction
Methods
Results
Discussion
CP
WFA
MC-LR
DCP
Glycoprotein
IGF-I
TALDO
miRNAs
CA1
ENO1
MVs
ERBB3
ANXA2
B2M
Metabolites
MDSCs
Conclusions
References
Study on biomarkers in endometrial cancer using transcriptome data: A machine learning approach
Abstract
Introduction
Materials and methods
Dataset and preprocessing
Identification of DEGs
Enrichment analysis
Protein-protein interaction network
Optimal diagnostic biomarkers
Survival analysis of hub genes
Results
Data processing and DEGs identification
Functional and pathway enrichment
Protein-protein interaction network
Identification of optimal diagnostic biomarkers
Survival analysis
Discussion
Conclusion
References
Drug targeting PIWI like protein-piRNA complex, a novel paradigm in the therapeutic framework of retinoblastoma
Abstract
Introduction
Biological functions of PIWI/piRNA in physiological conditions
Transposon silencing and gene repressions
Epigenetic regulations
Transgenerational memory and silencing
PIWI in DNA repair, chromosome dynamics, cell cycle progression, and apoptosis
PIWI in neurons and other somatic tissues
Emerging significance of PIWI/piRNA in various cancers
An insight of piRNAs in different cancers
piRNA in breast cancer
piRNA in lung cancer
piRNA in colorectal cancer
piRNA in liver cancer
piRNA in gastric cancer
piRNA in ovarian cancer
piRNA in kidney/renal cell cancer
piRNA in glioblastoma cancer
piRNA multiple myeloma
PIWI-like proteins in cancer
PIWIL1 (HIWI)
PIWIL2 (HILI)
PIWIL3
PIWIL4 (HIWI2)
Retina and its structure
Retinoblastoma
Pathologies of retinoblastoma
Cell of origin
The physiological role of retinoblastoma protein
Novel regulators of RB
Potential role of PIWI and piRNA in RB
PIWI/piRNA as future biomarkers in cancer
piRNAs as biomarkers
PIWI as biomarkers for various cancers
Conclusion
References
Further reading
Emerging role of biosimilars: Focus on Bevacizumab and hepatocellular carcinoma
Abstract
Introduction
Biologics and biosimilars
FDA approved biosimilars to date
Role of Bevacizumab and its biosimilar in hepatocellular carcinoma
Clinical trials with Bevacizumab and its biosimilar in HCC
Atezolizumab plus Bevacizumab in unresectable HCC
Sintilimab plus Bevacizumab biosimilar for advanced HCC
Conclusions and future perspectives
References
Integrated computational approaches to aid precision medicine for cancer therapy: Present scenario and future ...
Abstract
Introduction
Precision cancer medicine: Prospects and hurdles
Next generation sequencing and computational genomics in PCM
Drug repositioning using translational bioinformatics
Future perspectives
Conclusion
References
Index
Preface
