Open Access is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new Open Access publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers.
We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too.
To purchase hard copies of this book, please contact the representative in India:
CBS Publishers & Distributors Pvt. Ltd.
www.cbspd.com
|
customercare@cbspd.com
Natural products and medicinal plants have been extremely important contributors to the field of drug development due to their ability to bind to and change cellular targets that have been linked to cancer. On the other hand, when it comes to the quest for alternative treatments for cancer, bioinformatics and databases are of critical importance to the field of cancer research. The knowledge of drug-target interactions, the prediction of therapeutic efficacy and side effects, the identification of novel drug targets and the repurposing of current medications are all made easier by computer-aided drug design and network pharmacology. Through the use of bioinformatics, researchers are able to get a more in-depth understanding of the biology behind cancer and speed up the process of developing plant-based therapy options that are effective, safe, affordable and available. In this chapter, we provide a comprehensive review of computer-aided drug design and network pharmacology together with their importance in plant-based drug discovery in the era of cancer.
Department of Molecular Biology and Biotechnology, Pan African University for Basic Sciences, Technology, and Innovation (PAUSTI, Kenya
Stephen Wilson Kpordze
Department of Molecular Biology and Biotechnology, Pan African University for Basic Sciences, Technology, and Innovation (PAUSTI, Kenya
Hadil Suliman Hussein
Department of Molecular Biology and Biotechnology, Pan African University for Basic Sciences, Technology, and Innovation (PAUSTI, Kenya
Oumarou Soro
Department of Molecular Biology and Biotechnology, Pan African University for Basic Sciences, Technology, and Innovation (PAUSTI, Kenya
Samuel Mulondo
National Livestock Resource Research Institute, Uganda
Jonas Nshimirimana
Department of Molecular Biology and Biotechnology, Pan African University for Basic Sciences, Technology, and Innovation (PAUSTI, Kenya
Tekeba Sisay Melese
Department of Molecular Biology and Biotechnology, Pan African University for Basic Sciences, Technology, and Innovation (PAUSTI, Kenya
*Address all correspondence to: elbasyouni.amel@hotmail.com
1. Introduction
Worldwide, cancer is a major contributor to mortality rates and a major bottleneck to extending the human lifespan. Due to population ageing and growth, changes in the prevalence and distribution of cancer risk factors and socioeconomic development, the global burden of cancer incidence and mortality is increasing with 19.3 million new cases and 10 million deaths [1]. According to GLOBOCAN 2020, female breast cancer remains the most frequently occurring with 2,261,419 new cases (11.7%), followed by lung, prostate, nonmelanoma of the skin, colon stomach, liver, rectum and cervical [1]. Tobacco, alcohol consumption, high body mass index, low physical activity, endocrine disruptors, diet, socioeconomic status, hygiene and infections are the major extrinsic risk factors for cancerogenesis, whereases genomic signatures and cancer-specific mutations predispose to malign transformation and tumour development and progression [2, 3, 4, 5, 6, 7, 8].
Cancer development and progression are multistep processes. Fourteen hallmarks have been identified as cancer signatures, and have been relevant to understand its complexity and mechanism. These hallmarks include maintenance of proliferative signalling and an uncontrolled capacity of replication and proliferation, evading growth regulators, impaired regulation at the genomic level, evading the immune system, immortality, resistance to apoptosis and cell death mechanisms, maintained inflammation, polymorphic microbiome, senescence, genome instability and mutations in genes of predisposition, impaired metabolomics and hypoxia, angiogenesis, plasticity and invasion and metastasis [9].
Cancer genesis and its development mechanisms are linked to impaired signalling pathways due to driver and/or passenger mutations [10]. The Ras-ERK and PI3K-Akt signalling pathways are the major oncogenic pathways leading to uncontrolled proliferation, cell death resistance, metabolism alteration and migration, if consecutively active. Other pathways promoting the hallmarks of abnormal cells include Notch, NF-κB, Wnt/β-catenin and Hedgehog.
Conventional treatment regimens include radiotherapy, surgery, hormone-based therapy, chemotherapy and targeted therapy [11]. However, they are associated with many side effects, treatment resistance and failure, tumour relapse and metastasis. Therefore, there is an immediate need to develop and promote safe anti-cancer drug alternatives and integrate them into the routine clinical medicine. Plant-based traditional medicine is used worldwide as a first-stop therapy for the treatment and prevention of many diseases, including malaria, asthma and cancer [12, 13, 14]. Thus, there is a growing demand for serious scientific inquiry into the phyto-chemicals responsible for the activities of these plants. Currently, there are over 3000 plant species with confirmed anticancer properties [15], including Abrus precatorius, Albizzia lebbeck, Alstonia scholaries, Anacardium occidentale, Asparagus racemose, Boswellia serrata, Erthyrina suberosa, Euphorbia hirta, Gynandropis pentaphylla, Nigella sativa, Peaderia foetida, Catharanthus roseus, Picrorrhiza kurroa and Withania somnifera against various tumours. Interestingly, plant-derived secondary metabolites and their analogues have been already approved and commercialised due to their anti-cancer effectiveness and less toxicity (vincristine, paclitaxel, docetaxel…) compared to synthetic therapeutics associated with side effects, non-specificity and drug resistance [12, 15, 16, 17, 18, 19].
Unfortunately, research and development of new anti-cancer therapeutics is highly expensive (2 to 10 billion USD) [20]. In addition to the high cost, therapeutics are failing when brought to the clinical trials due to the high complexity and heterogeneity of cancer biology, plasticity and drug resistance [21]. Despite the sluggish pace of anticancer medication development, a number of approaches are worth exploring. New anticancer medications and the development of clinical paradigms around the world may 1 day benefit from an improved understanding of tumour heterogeneity and complexity.
Fortunately, the conventional drug research and development pipeline has been significantly speeded up, and the cost is reduced via integrating computational approaches and bioinformatics to provide more specific, cheaper, more effective and safer plant-based alternatives to cancer therapeutics [17, 22, 23, 24, 25, 26, 27]. In this chapter, we aim to provide an explanation of the main computational approaches enhancing the area of plant-based anti-cancer drug research, development and discovery. We will discuss the pipeline of computer-aided drug design, and network pharmacology in order to promote the integration of medicinal plants and phytotherapy in the clinical oncology for a better management of cancer.
Structure-based strategy relies on the known three-dimensional structure of the proteins to define the interaction effect between bioactive compounds and the corresponding receptors to trigger therapeutic effects [28]. Biomolecular spectroscopic technologies such as X-ray crystallography and nuclear magnetic resonance spectroscopy (NMR) have improved our structural understanding of drug targets [29, 30, 31, 32]. Hence, structure-based design can provide critical insights into new drug design, development and discovering and optimising initial lead compounds [33]. Novel and powerful, this computational strategy promotes medication discovery that is quicker, cheaper and more effective [34]. The following diagram provides an insight into the steps involved in structure-based drug design (Figure 1).
Figure 1.
Structure-based drug design flowchart.
The first step is to choose a protein target with a known three-dimensional structure and involvement in a disease pathway [35]. A small molecule drug requires a binding site on the target. Getting the protein’s three-dimensional structure is the next move. X-ray crystallography, NMR and other methods can be used for this purpose. The protein’s structure can be utilised to locate locations where small-molecule medicines might bind. Molecular docking software, which estimates how tiny molecules might attach to the protein, can be used to examine these locations. In order to find small molecule compounds that bind to a target protein with high affinity and specificity after the binding site has been determined, a library of compounds can be screened [36]. This can be achieved through the use of either high-throughput screening techniques or virtual screening, or both. Compounds that fare well in the first round of screening go on to have their potency, selectivity and other drug-like qualities enhanced in subsequent rounds of testing [37]. Lead optimisation is the process of improving a compound’s performance by altering the compound’s chemical make-up. Molecular docking and dynamic simulation can be used to guide further optimisation [38]. The compounds that show the most promise go through in vitro and in vivo testing to determine their safety and effectiveness. This requires evaluating the drugs’ pharmacokinetic and pharmacodynamic properties through testing in animal models. At last, the most promising chemicals are put through human clinical trials to determine whether or not they are safe and effective for use.
Available databases used for each step of the structure-based strategy are mentioned in the following table (Table 1).
Major databases used in structure-based drug design.
In contrast to structure-based drug design, ligand-based drug design relies on the discovery of novel compounds with similar or enhanced biological activity by analysing the structural and physicochemical features of a ligand or a library of small molecules. Figure 2 provides the steps involved in ligand-based drug design approach. Finding a target protein that is essential in cancer progression or development is the first step in ligand-based drug design. Followed by molecular docking process, where it is necessary to have either the 3D structure of the target protein or a homology model that might serve as a starting point [39]. After that, a library of ligands is compiled that have been shown to have biological activity against the target protein. The information in the database can come from either direct experimentation or from existing public sources in databases [39]. The binding mechanisms and affinities of the ligands to the target protein are predicted using a molecular docking programme. Drug discovery often uses molecular docking as it enables to visually analyse binding modes before making decisions [40]. After the ligands have been docked into the protein’s binding site, the resultant complexes are evaluated according to their expected binding energies and interactions with the protein [38]. Predicted binding affinity, specificity and other physicochemical parameters are used to rank and filter the docked ligands. The best ligands are chosen for more study [41]. Computational chemistry techniques are then used to fine-tune the chosen ligands in order to increase their binding affinity, specificity and drug-like characteristics. Synthesis and experimental testing of the optimised ligands’ biological activities follow. Preclinical and clinical investigations are conducted on the optimised ligands to determine their efficacy and safety. The findings are used to hone the process for ligand-based drug creation and boost the precision and dependability of computational approaches.
Figure 2.
Ligand-based drug design flowchart.
The ligand-based drug design strategy is a useful method for discovering novel compounds that are both highly potent and selective against a target protein. To guarantee the anticipated ligands have the requisite pharmacological properties and can be converted into successful therapies, thorough validation and optimisation are necessary.
Rapidly dividing cancer cells are the primary targets of unspecific chemotherapeutic medications. However, certain normal cells are also affected, leading to the adverse side effects of major chemotherapeutics. Targeted cancer therapies inhibit the development and the progress of cancer by targeting certain proteins/molecular abnormalities that are unique to each tumour. Currently, two types of targeted medicines are developed: small molecule inhibitors and monoclonal antibodies. The small molecules attenuate the impaired kinases and abnormally overactivated signalling pathways, whereas monoclonal antibodies block extracellular proteins. The resistance to monoclonal antibodies-based targeted therapy is common and rises due to the occurrence of mutations in the targeted proteins [42]. This is traced back to the heterogeneity of tumours and genomic instability.
Fortunately, a number of phytochemicals isolated from medicinal plants have been shown to decrease the capacity of tumour growth and cell proliferation, arrest the cell cycle, induce apoptosis, regulate the tumour micro-environment and inhibit metastasis and angiogenesis [43, 44, 45]. Curcumin was shown to inhibit CD44 and CD166 and metalloproteinase-2, and downregulate Gli-1, Notch-1 and cyclin D1 in various types of cancer, including colon, gastric, breast, head and neck and lung cancers. The anti-cancer activity of curcumin has been studied in silico and in vitro through the analysis of its capacity to specifically target EGFR, metalloproteinase-2 and NF-κB, cancer-specific proteins [46, 47, 48]. Similarly, molecular docking approach also confirmed the anticancer potential of curcumin and its analogues by showing binding interaction with the GSK-3β, EGFR and Bcr-Abl proteins. Using chemical synthesis and molecular docking, Naqvi et al. [49] have identified the inhibitory effects on oestrogen receptors of a new chemically modified bioactive curcumin. Based on the results of the computational investigation, the chemical in question is an effective oestrogen blocker due to its low binding energy and high drug-likeness ratings, where its antiproliferative effect has been proved in vitro. Curcumin and its derivates exert a xanthine oxidase inhibitory effect according to the in-silico study conducted by Malik et al. [50]. Using molecular docking analysis and molecular dynamic simulation, Rasul et al. [51] suggested a further lead optimisation of eugenol and its derivatives as effective anti-cancer therapeutics against breast cancer. The compounds have shown potent inhibitory effects on oestrogen receptors, progesterone receptors, EGFR, CDK2, mTOR, ERBB2, c-Src, HSP90 and chemokines receptors, breast cancer-related proteins. The compounds have displayed drug-likeness properties and stable protein-ligand complexes.
Pharmacophore-oriented molecular design using natural templates is the last resort for natural lead optimisation and helps accelerate optimisation of natural product core structures to enhance their anti-tumour activities [52, 53, 54].
When applied to the study of medications, targets and disorders, network analysis, systems biology and pharmacology all come together to form the developing area of network pharmacology [55]. Potential therapeutic targets can be found, drug efficacy can be predicted, drug mechanisms of action can be understood and drug repurposing can be investigated when network pharmacology is performed [56]. Drug databases, protein-protein interaction databases, gene expression data, disease-related databases and other pertinent sources of biological information are the foundation of this approach [57].
Identifying the drug’s target proteins or genes is the first step [58, 59, 60, 61, 62]. Search for the medicines and their known targets in drug databases like DrugBank and PubChem [63, 64, 65, 66]. Second, model the relationships among medications, targets and diseases using biological networks. Several programs and applications exist specifically for this purpose of network analysis. For instance, you can build a network that shows how drugs and diseases interact with one another [57]. Network topology analysis, cluster analysis, functional enrichment analysis and pathway analysis are all part of the analysis of the resulting network. The purpose is to discover key nodes or modules in the network that relate medications, targets and diseases [67]. The procedures of network pharmacology are depicted in Figure 3.
Figure 3.
Network pharmacology flowchart.
Besides Drugbank, BindingDB and Pubchem, Therapeutic Target Database (TTD) enables the identification of therapeutic targets (proteins), their functions and respective drugs [68]. In addition, Online Mendelian Inheritance in Man (OMIM) database provides information about deregulated/mutated genes implicated in various genetic diseases. Uniprot, a comprehensive protein database, provides disease-related proteins, their structure, function, variations and their involvement in diseases. Furthermore, Comparative Toxicogenomics Database (CTD) focuses on the interactions between chemicals, genes and diseases via the integration of data from toxicological, genomic and pharmacological aspects. Search Tool for Interactions of Chemicals (STITCH) database provides information on the chemical-protein interactions and drug-target associations. It allows the construction of interaction networks, and analyse their functional implications. Moreover, Drug Gene Interaction Database (DGIdb) and DisGeNET database provide information on the potential therapeutic implication of associated genes and drugs. They explore both drug-disease and gene-disease associations. ChEMBL database explores the interaction between bioactive compounds and the target proteins. ChEMBL provides additional information on the potency, selectivity and the binding affinity of drugs towards their disease-associated targets [69].
Regarding the network construction, various tools are available to analyse the interactions between the drug candidates, the genes and the respective diseases identified in the previous step. These tools include Cytoscape and cytoscapse.js, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), GeneMANIA, Ingenuity Pathway Analysis (IPA) and NetworkX. It is noteworthy that two types of networks exist: drug-target interaction network and drug-disease interaction network. First, drug-target interaction network represents the direct association between the identified drugs and their selected targets/proteins that they interact with [70, 71]. The molecular mechanism of the drugs on their protein/gene targets could be depicted by capturing the direct interactions between the two entities [72, 73]. However, repurposing medications, discovering new treatment opportunities and novel drug candidates and learning more about the connections between pharmaceuticals and disorders are all possible with the use of a drug-disease interaction network [74].
Different approaches can be used to explore and analyse the constructed network, including pathway analysis, functional enrichment analysis, topology analysis and clustering analysis (Figure 4). The topology analysis examines a network’s structure and characteristics. It aids in understanding network organisation, connectivity patterns and key nodes and edges. Network topology analysis methods include the study of the network’s nodes’ degree distribution (number of connections of nodes) [57]. It helps detect a power-law distribution or normal distribution. Measuring the number of connections of each node presents the degree centrality analysis where central or prominent nodes have higher degrees. In addition, the betweenness centrality, how many shortest pathways a node is on, affects the interaction flow, whereas nodes with higher betweenness centrality have a greater influence on the interactions. Another topological parameter is the closeness centrality, which measures a node’s network proximity. It calculates the average shortest path between nodes. High-closeness centrality nodes are more accessible and can swiftly transduce the signals in the network. The coefficient of clustering measures nodes’ neighbours’ connectivity, where high clustering coefficients suggest strongly connected network units. Another parameter of topology aspect is the network motifs, which are repeated interconnection patterns among a small collection of nodes revealing local connection and functional units. Moreover, modularity analysis detects coherent groups or communities of nodes in a network by comparing the density of connections between nodes in the same community to those in different communities. Finally, path length analysis finds the average shortest path between all pairs of network nodes. It shows the efficiency of signal transduction.
Figure 4.
Approaches used for biological network analysis.
From another aspect, analysing the network from clustering outlook uses connection patterns to find node clusters. It seeks meaningful structural and functional network units and reveals network organisation, community structure and interactions. Common clustering analysis approaches are modularity- and density-based, hierarchical, K-Means and spectral clustering. The modularity-based clustering measures the strength of network communities (modules). It optimises the modularity score to locate groups of nodes with dense internal connections and sparse external connections. It is worth noting that the communities are located based on network structural features: the community detection method. The density-based clustering clusters dense node regions based on the density of connections within a region of the network. K-means clustering divides the network nodes into K-related clusters. In addition, overlapping clustering finds nodes that belong to numerous clusters or communities.
The analysis of a network from pathway and functional aspect aims to investigate the molecular signalling pathways that are significantly enriched among the identified genes/proteins in the constructed network. Briefly, a set of nodes is identified based on differentially expressed genes, topological features or network clusters or modules. Genes/proteins could be, then, annotated and mapped to the corresponding signalling pathways in databases like KEGG, Reactome and WikiPathways using corresponding identifiers. To annotate the nodes with relevant biological information, such as gene symbols, protein identifiers or other functional annotations, Gene Ontology (GO), KEGG and other functional databases could be used.
Interestingly, network pharmacology enable the identification of particular molecular targets and signalling pathways involved in tumorigenesis and other cancer characteristics. In addition, network pharmacology helps predict the efficacy and potential adverse effects of novel/alternative anticancer therapeutic candidates by conducting protein-protein interaction networks, drug-target networks and drug-disease networks, and by integrating gene expression data. Network pharmacology has been shown to be an effective method for investigating the breadth and depth of mechanism of action exerted by medicinal plants. Furthermore, it provides opportunity for drug repurposing in the treatment of cancer. Researchers can identify existing/novel drugs and biomolecules with the potential to modulate specific cancer-related pathways or targets. In addition, this holistic approach facilitates the identification of novel biomarkers and therapeutic targets, allows for a more comprehensive understanding of cancer biology through integration of multi-omics data and contributes to the development of precision and personalised medicine,, therefore, enhancing treatment efficacy. Potential therapeutic targets and cancer-related signalling pathways affected by drugs and plant-based chemicals can be predicted with the aid of network pharmacology. A recent study conducted by Sakle et al. [73] is one of the successful stories. The study uncovered the exact molecular mechanism of Caesalpinia pulcherima against breast cancer. The approach enabled the precise identification of four active compounds and 150 target genes involved in the anti-cancer activity of the plant. Zhang et al. [75] have been able to elucidate the potential of Rheum palmatum L. to induce apoptosis, and the exact molecular mechanism of the plant involving 16 active phytocompounds, targeting 563 lung cancer-related genes. In Lee et al. [76] have been able to elucidate the molecular mechanism exerted by three plants: Cordyceps militaris, Lonicera japonica Thunberg and Artemisia capillaris Thunberg, combined, against breast cancer. The study has demonstrated 18 phytocompounds are responsible for the anti-cancer effect by targeting 140 breast cancer-related genes. Jin et al. [77] studied the pharmacological effect of Xiao-Chai-Hu-Tang against colorectal cancer. The study revealed, precisely, the synergism of quercetin, kaempferol, stigmasterol, acacetin and baicalein to target COX-2, cyclin B1, NR3C2, CA2 and MMP1. Taken together, network pharmacology enhances the plant-based drug research, discovery and development [78].
In conclusion, because of their potential to bind and modify cellular targets implicated in cancer, natural products and medicinal plants have played a crucial role in drug discovery. Their promise in traditional medicines is highlighted by their low toxicity, low cost and convenience of availability. On the other hand, bioinformatics and databases are of vital significance to cancer research when it comes to finding therapeutic alternatives. Computer-aided drug design and network pharmacology facilitate the comprehension of drug-target interactions, the prediction of drug efficacy and adverse effects, the identification of novel drug targets and the repurposing of existing drugs. These applications enable researchers to obtain a deeper understanding of the biology of cancer and expedite the creation of effective plant-based therapeutic strategies.
1.Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a Cancer Journal for Clinicians. 2021;71(3):209-249
2.Danaei G, Vander Hoorn S, Lopez AD, Murray CJ, Ezzati M. Causes of cancer in the world: Comparative risk assessment of nine behavioural and environmental risk factors. The Lancet. 2005;366(9499):1784-1793
3.Thomas F, Roche B, Ujvari B. Intrinsic versus extrinsic cancer risks: The debate continues. Trends Cancer. 2016;2(2):68-69
4.Tran KB, Lang JJ, Compton K, Xu R, Acheson AR, Henrikson HJ, et al. The global burden of cancer attributable to risk factors, 2010-19: A systematic analysis for the global burden of disease study 2019. The Lancet. 2022;400(10352):563-591
5.Warnakulasuriya S. Causes of oral cancer – An appraisal of controversies. British Dental Journal. 2009;207(10):471-475
6.Wu S, Zhu W, Thompson P, Hannun YA. Evaluating intrinsic and non-intrinsic cancer risk factors. Nature Communications. 2018;9(1):3490
7.Wu S, Hannun Y. The importance of extrinsic factors in the development of cancers. Molecular & Cellular Oncology. 2016;3(3):e1143079
8.Zhu W, Wu S, Hannun YA. Contributions of the intrinsic mutation process to cancer mutation and risk burdens. eBioMedicine. 2017;24:5-6
9.Hanahan D. Hallmarks of cancer: New dimensions. Cancer Discovery. 2022;12(1):31-46
10.Sever R, Brugge JS. Signal transduction in cancer. Cold Spring Harbor Perspectives in Medicine. 2015;5(4):a006098-a006098
11.Abbas Z, Rehman S. An overview of cancer treatment modalities. In: Shahzad HN, editor. Neoplasm [Internet]. London: InTech; 2018 [cited 2023 May 3]. Available from: http://www.intechopen.com/books/neoplasm/an-overview-of-cancer-treatment-modalities
12.Licciardi PV, Underwood JR. Plant-derived medicines: A novel class of immunological adjuvants. International Immunopharmacology. 2011;11(3):390-398
13.Mohan L. Plant-based drugs as an adjuvant to cancer chemotherapy. In: Akram M, editor. Alternative Medicine - Update [Internet]. London, UK: IntechOpen; 2021 [cited 2023 Feb 1] Available from: https://www.intechopen.com/books/alternative-medicine-update/plant-based-drugs-as-an-adjuvant-to-cancer-chemotherapy
14.Siddiqui AJ, Jahan S, Singh R, Saxena J, Ashraf SA, Khan A, et al. Plants in anticancer drug discovery: From molecular mechanism to chemoprevention. Kabir Y, editor. BioMed Research International. 2022;2022:1-18
15.Desai A, Qazi G, Ganju R, El-Tamer M, Singh J, Saxena A, et al. Medicinal plants and cancer chemoprevention. Current Drug Metabolism. 2008;9(7):581-591
16.Cragg GM, Pezzuto JM. Natural products as a vital source for the discovery of cancer chemotherapeutic and Chemopreventive agents. Medical Principles and Practice. 2016;25(Suppl. 2):41-59
17.Dehelean CA, Marcovici I, Soica C, Mioc M, Coricovac D, Iurciuc S, et al. Plant-derived anticancer compounds as new perspectives in drug discovery and alternative therapy. Molecules. 2021;26(4):1109
18.Iqbal J, Abbasi BA, Mahmood T, Kanwal S, Ali B, Shah SA, et al. Plant-derived anticancer agents: A green anticancer approach. Asian Pacific Journal of Tropical Biomedicine. 2017;7(12):1129-1150
19.Tuorkey MJ. Cancer therapy with phytochemicals: Present and future perspectives. Biomedical and Environmental Sciences. 2015;28(11):808-819
20.Prasad V, Mailankody S. Research and Development spending to bring a single cancer drug to market and revenues after approval. JAMA Internal Medicine. 2017;177(11):1569
21.Lu DY, Lu TR. Anticancer drug development, challenge and dilemma. Nursing & Care Open Access Journal. 2020;7(3):72-75
22.Dangi-Garimella S. The role of bioinformatics in diabetes drug development--and precision medicine. The American Journal of Managed Care. May 2014;20(8 Spec No.):E1. PMID: 25617937
23.Efferth T, Saeed MEM, Mirghani E, Alim A, Yassin Z, Saeed E, et al. Integration of phytochemicals and phytotherapy into cancer precision medicine. Oncotarget. 2017;8:50284-50304. Retrieved from https://www.oncotarget.com/article/17466/text/
24.Fridlender M, Kapulnik Y, Koltai H. Plant derived substances with anti-cancer activity: From folklore to practice. Frontiers in Plant Science [Internet]. 2015 [cited 2023 May 9];6. Available from: http://journal.frontiersin.org/article/10.3389/fpls.2015.00799
25.Li K, Du Y, Li L, Wei DQ. Bioinformatics approaches for anti-cancer drug discovery. Current Drug Targets. 2019;21(1):3-17
26.Sharma V, Sarkar IN. Bioinformatics opportunities for identification and study of medicinal plants. Briefings in Bioinformatics. 2013;14(2):238-250
27.The International Natural Product Sciences Taskforce, Atanasov AG, Zotchev SB, Dirsch VM, Supuran CT. Natural products in drug discovery: Advances and opportunities. Nature Reviews. Drug Discovery. 2021;20(3):200-216
28.Batool M, Ahmad B, Choi S. A structure-based drug discovery paradigm. International Journal of Molecular Sciences. 2019;20(11):2783
29.Faraggi E, Dunker AK, Sussman JL, Kloczkowski A. Comparing NMR and X-ray protein structure: Lindemann-like parameters and NMR disorder. Journal of Biomolecular Structure & Dynamics. 2018;36(9):2331-2341
30.Keiffer S, Carneiro MG, Hollander J, Kobayashi M, Pogoryelev D, Ab E, et al. NMR in target driven drug discovery: Why not? Journal of Biomolecular NMR. 2020;74(10-11):521-529
31.Maveyraud L, Mourey L. Protein X-ray crystallography and drug discovery. Molecules. 2020;25(5):1030
32.Yee AA, Savchenko A, Ignachenko A, Lukin J, Xu X, Skarina T, et al. NMR and X-ray crystallography, complementary tools in structural proteomics of small proteins. Journal of the American Chemical Society. 2005;127(47):16512-16517
33.Anderson AC. Structure-based functional Design of Drugs: From target to Lead compound. In: Espina V, Liotta LA, editors. Molecular Profiling [Internet]. Totowa, NJ: Humana Press; 2012 [cited 2023 May 21]. pp. 359-366. (Methods in Molecular Biology; Vol. 823) Available from: https://link.springer.com/10.1007/978-1-60327-216-2_23
34.Iglesias J, Saen-oon S, Soliva R, Guallar V. Computational structure-based drug design: Predicting target flexibility. WIREs Computational Molecular Science [Internet]. 2018 Sep [cited 2023 May 21];8(5) Available from: https://onlinelibrary.wiley.com/doi/10.1002/wcms.1367
35.Bajad NG, Rayala S, Gutti G, Sharma A, Singh M, Kumar A, et al. Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2. Current Research in Pharmacology and Drug Discovery. 2021;2:100026
36.Hardianto A, Yusuf M, Liu F, Ranganathan S. Structure-based drug design workflow. In: Encyclopedia of Bioinformatics and Computational Biology [Internet]. Elsevier; 2019 [cited 2023 May 21]. pp. 273-282 Available from: https://linkinghub.elsevier.com/retrieve/pii/B9780128096338201040
37.Anderson AC. The process of structure-based drug design. Chemistry & Biology. 2003;10(9):787-797
38.Śledź P, Caflisch A. Protein structure-based drug design: From docking to molecular dynamics. Current Opinion in Structural Biology. 2018;48:93-102
39.Molla M, Aljahdali M, Sumon M, Asseri A, Altayb H, Islam M, et al. Integrative ligand-based pharmacophore Modeling, virtual screening, and molecular docking simulation approaches identified potential Lead compounds against pancreatic cancer by targeting FAK1. Pharmaceuticals. 2023;16(1):120
40.Fischer A, Smieško M, Sellner M, Lill MA. Decision making in structure-based drug discovery: Visual inspection of docking results. Journal of Medicinal Chemistry. 2021;64(5):2489-2500
41.Pinzi L, Rastelli G. Molecular docking: Shifting paradigms in drug discovery. International Journal of Molecular Sciences. 2019;20(18):4331
42.Baudino T. Targeted cancer therapy: The next generation of cancer treatment. Current Drug Discovery Technologies. 2015;12(1):3-20
43.Hosseini A, Ghorbani A. Cancer therapy with phytochemicals: evidence from clinical studies. Avicenna Journal of Phytomedicine. Mar-Apr 2015;5(2):84-97. PMID: 25949949; PMCID: PMC4418057
44.Sadeghnia HR, Ghorbani Hesari T, Mortazavian SM, Mousavi SH, Tayarani-Najaran Z, Ghorbani A. Viola tricolor induces apoptosis in cancer cells and exhibits antiangiogenic activity on chicken Chorioallantoic membrane. BioMed Research International. 2014;2014:1-8
45.Tan W, Lu J, Huang M, Li Y, Chen M, Wu G, et al. Anti-cancer natural products isolated from chinese medicinal herbs. Chinese Medicine. 2011;6(1):27
46.Praseetha NG, Divya UK, Nair S. Identifying the potential role of curcumin analogues as anti-breast cancer agents; an in silico approach. Egyptian Journal of Medical Human Genetics. 2022;23(1):100
47.Saeed MEM, Yücer R, Dawood M, Hegazy MEF, Drif A, Ooko E, et al. In silico and In vitro screening of 50 curcumin compounds as EGFR and NF-κB inhibitors. International Journal of Molecular Sciences. 2022;23(7):3966
48.Shefrin S, Manakadan AA, Saranya TS. A computational study of anticancer activity of curcumin derivatives using in silico drug designing and molecular docking tools. Asian Journal of Chemistry. 2018;30(6):1335-1339
49.Naqvi A, Malasoni R, Gupta S, Srivastava A, Pandey R, Dwivedi A. In silico and In vitro anticancer activity of isolated novel marker compound from chemically modified bioactive fraction from Curcuma longa (NCCL). Pharmacognosy Magazine. 2017;13(51):640
50.Malik N, Dhiman P, Khatkar A. In Silico Design and Synthesis of Targeted Curcumin Derivatives as Xanthine Oxidase Inhibitors. Current Drug Targets. 2019;20(5):593-603
51.Rasul HO, Aziz BK, Ghafour DD, Kivrak A. In silico molecular docking and dynamic simulation of eugenol compounds against breast cancer. Journal of Molecular Modeling. 28 Dec 2021;28(1):17. doi: 10.1007/s00894-021-05010-w. Erratum in: Journal of Molecular Modeling. 2022 Mar 5;28(4):78. PMID: 34962586
52.Wu S, Ding M, Wang X, Li W, Zhao Y. 25-Methoxyl-Dammarane-3β, 12β, 20-Triol, a ginseng Saponin derivative and an anticancer agent: In vitro and In vivo activities, molecular mechanism of action, pharmacokinetics and structural modification. Medicinal Chemistry [Internet]. 2017 [cited 2023 May 21];07(03) Available from: https://www.omicsonline.org/open-access/25methoxyldammarane3-12-20triol-a-ginseng-saponinderivative-and-an-anticancer-agent-in-vitro-and-in-vivo-activitiesmolecular-mecha-2161-0444-1000437.php?aid=87824
53.Xiao Z, Morris-Natschke SL, Lee KH. Strategies for the optimization of natural leads to anticancer drugs or drug candidates: Strategies for the optimization of natural leads. Medicinal Research Reviews. 2016;36(1):32-91
54.Xu Z, Eichler B, Klausner EA, Duffy-Matzner J, Zheng W. Lead/drug discovery from natural resources. Molecules. 2022;27(23):8280
55.Zheng S, Xue T, Wang B, Guo H, Liu Q. Application of network pharmacology in the study of the mechanism of action of traditional chinese medicine in the treatment of COVID-19. Frontiers in Pharmacology. 2022;13:926901
56.Zhou Z, Chen B, Chen S, Lin M, Chen Y, Jin S, et al. Applications of network pharmacology in traditional Chinese medicine research. Evidence-based Complementary and Alternative Medicine. 2020;2020:1-7
57.Luo T, ting, Lu Y, Yan S kai, Xiao X, Rong X lu, Guo J. Network pharmacology in research of Chinese medicine formula: Methodology, application and prospective. Chinese Journal of Integrative Medicine. 2020;26(1):72-80
58.Alegría-Arcos M, Barbosa T, Sepúlveda F, Combariza G, González J, Gil C, et al. Network pharmacology reveals multitarget mechanism of action of drugs to be repurposed for COVID-19. Frontiers in Pharmacology. 2022;13:952192
59.Chen S, Jiang H, Cao Y, Wang Y, Hu Z, Zhu Z, et al. Drug target identification using network analysis: Taking active components in Sini decoction as an example. Scientific Reports. 2016;6(1):24245
60.Feng Y, Wang Q , Wang T. Drug target protein-protein interaction networks: A systematic perspective. BioMed Research International. 2017;2017:1-13
61.Zhao S, Iyengar R. Systems pharmacology: Network analysis to identify multiscale mechanisms of drug action. Annual Review of Pharmacology and Toxicology. 2012;52(1):505-521
62.Zhao S, Li S. Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One; 2010;5(7):e11764
63.Li Q , Cheng T, Wang Y, Bryant SH. PubChem as a public resource for drug discovery. Drug Discovery Today. 2010;15(23-24):1052-1057
64.Lomenick B, Olsen RW, Huang J. Identification of direct protein targets of small molecules. ACS Chemical Biology. 2011;6(1):34-46
65.Schenone M, Dančík V, Wagner BK, Clemons PA. Target identification and mechanism of action in chemical biology and drug discovery. Nature Chemical Biology. 2013;9(4):232-240
66.Wishart DS. DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research. 2006;34(90001):D668-D672
67.Kibble M, Saarinen N, Tang J, Wennerberg K, Mäkelä S, Aittokallio T. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Natural Product Reports. 2015;32(8):1249-1266
68.Wang Y, Zhang S, Li F, Zhou Y, Zhang Y, Wang Z, et al. Therapeutic target database 2020: Enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Research. 2019;48:D1031-D1041
69.Zhang R, Zhu X, Bai H, Ning K. Network pharmacology databases for traditional Chinese medicine: Review and assessment. Frontiers in Pharmacology. 2019;10:123
70.Li L, Lu Y, Liu Y, Wang D, Duan L, Cheng S, et al. Network pharmacology analysis of Huangqi Jianzhong Tang targets in gastric cancer. Frontiers in Pharmacology. 2022;13:882147
71.Wang L, Wang P, Chen Y, Li C, Wang X, Li M, et al. To explore the network pharmacology and molecular docking mechanism of Chaihu Shugan powder with the “same treatment for different diseases” for insomnia and depression based on the COVID-19 pandemic [internet]. In Review. 2022 [cited 2023 May 23]. Available from: https://www.researchsquare.com/article/rs-1332355/v2
72.Lv L, Huang X, Li X, Fang R, Wang X, Zhong K, et al. Study on the anti-inflammatory mechanism of volatile oil of Amydrium sinense based on network pharmacology. Journal of Physics Conference Series. 2020;1549(3):032024
73.Sakle NS, More SA, Mokale SN. A network pharmacology-based approach to explore potential targets of Caesalpinia pulcherima: An updated prototype in drug discovery. Scientific Reports. 2020;10(1):17217
74.Batool S, Javed MR, Aslam S, Noor F, Javed HMF, Seemab R, et al. Network pharmacology and bioinformatics approach reveals the multi-target pharmacological mechanism of Fumaria indica in the treatment of liver cancer. Pharmaceuticals. 2022;15(6):654
75.Zhang Q , Liu J, Li R, Zhao R, Zhang M, Wei S, et al. A network pharmacology approach to investigate the anticancer mechanism and potential active ingredients of Rheum palmatum L against lung cancer via induction of apoptosis. Frontiers in Pharmacology. 2020;11:528308
76.Lee HS, Lee IH, Kang K, Park SI, Moon SJ, Lee CH, et al. A network pharmacology study on the molecular mechanisms of FDY003 for breast cancer treatment. Evidence-based Complementary and Alternative Medicine : eCAM. 2021;2021:1-18
77.Jin J, Chen B, Zhan X, Zhou Z, Liu H, Dong Y. Network pharmacology and molecular docking study on the mechanism of colorectal cancer treatment using Xiao-chai-Hu-Tang. PLoS One. 2021;16(6):e0252508
78.Lin L, Chen W, Yao C, Wu L, Yan Q , Cai X, et al. Exploring the target and molecular mechanism of Astragalus membranaceus in the treatment of vascular cognitive impairment based on network pharmacology and molecular docking. Medicine (Baltimore). 2023;102(12):e33063
Written By
Amel Elbasyouni, Stephen Wilson Kpordze, Hadil Suliman Hussein, Oumarou Soro, Samuel Mulondo, Jonas Nshimirimana and Tekeba Sisay Melese
Submitted: 01 June 2023Reviewed: 02 June 2023Published: 30 June 2023
Open access peer-reviewed chapter
