School of Pharmacy, Swami Ramanand Teerth Marathwada University- 431606 Nanded, Maharashtra, India.
Cancer continues to be a major global health concern, with cervical cancer ranking among the top malignancies affecting women, especially in low- and middle-income countries. The limitations of current treatments, including toxicity and drug resistance, highlight the need for novel, safer therapeutic alternatives. This study investigates the anticancer potential of phytoconstituents derived from Artocarpus heterophyllus (jackfruit) through a comprehensive in silico approach involving network pharmacology, molecular docking, ADME (absorption, distribution, metabolism, and excretion) profiling, and toxicity assessment. A total of 146 phytochemicals were screened, with eight showing significant relevance to cancer-related pathways. Network pharmacology analysis identified 97 common gene targets, and protein-protein interaction (PPI) network analysis highlighted hub genes such as MYC, EGFR, and IL6 as key targets. Molecular docking revealed strong binding affinities of compounds like ?-sitosterol, eriodictyol, and kaempferol with these oncogenic proteins, often outperforming the standard drug Erlotinib. ADME and toxicity profiling indicated favorable pharmacokinetic properties and low toxicity for several compounds, particularly artocarpin and kaempferol. These findings support the therapeutic potential of A. heterophyllus phytochemicals in cervical cancer management and warrant further in vitro and in vivo validation.
Cancer is a multifaceted disease encompassing over 100 distinct types, each characterized by the uncontrolled proliferation of abnormal cells that can invade local tissues and metastasize to distant organs. It is recognized as a genetic disease, often triggered by a combination of physical, chemical, or biological factors that induce mutations in the cellular genome. Despite substantial advances in treatment, cancer remains a leading global cause of mortality, accounting for nearly 10 million deaths in 2020 alone. Among the various types, cervical cancer stands out as one of the most prevalent malignancies affecting women, particularly in low- and middle-income countries where effective screening and vaccination programs are limited. Cervical cancer primarily arises from persistent infection with high-risk strains of human papillomavirus (HPV), especially types 16 and 18. These viral infections disrupt key regulatory proteins such as p53 and Rb through the expression of viral oncoproteins E6 and E7, facilitating malignant transformation. Despite the availability of screening methods like Pap smears and HPV testing, and therapeutic options including surgery, chemotherapy, radiotherapy, and targeted therapies, the disease burden remains significant. The emergence of resistance to conventional treatments and the associated adverse effects underscore the urgent need for safer and more effective therapeutic strategies. In this context, plant-derived compounds have gained considerable attention due to their broad-spectrum bioactivities, lower toxicity, and historical use in traditional medicine. Artocarpus heterophyllus (jackfruit), a widely cultivated tropical tree, is known for its rich phytochemical content including flavonoids, phenolics, and lectins. These constituents have demonstrated various pharmacological effects such as antioxidant, anti-inflammatory, antimicrobial, and anticancer properties. Recent advances in computational biology, particularly network pharmacology and molecular docking, provide powerful tools to explore the therapeutic potential of such natural compounds. Network pharmacology enables the systematic investigation of compound-disease-target interactions, while molecular docking predicts the binding affinity of bioactive compounds to key cancer-related proteins. Together, these methods facilitate the identification of promising phytoconstituents and their mechanisms of action at a molecular level. This study aims to evaluate the anticancer potential of phytoconstituents from Artocarpus heterophyllus using an integrated approach involving network pharmacology, molecular docking, ADME (absorption, distribution, metabolism, and excretion) profiling, and toxicity prediction. By identifying key molecular targets and assessing compound safety and efficacy, this research seeks to validate the ethnopharmacological relevance of jackfruit and contribute to the development of novel plant-based therapies for cancer management, with a particular focus on cervical cancer.
Review of Literature:
2.1 Cancer
Cancer consists of over 100 distinct diseases, all characterized by uncontrolled cell growth. invasion of local tissues, and the potential to metastasize to other parts of the body. (Dipiro 2017). It can be caused by various factors-physical, chemical, or biological-either individually or in combination. These factors predominantly affect the genetic material, which is why cancer is classified as a genetic disease. (Preethi 2008). It can be caused by various factors-physical, chemical, or biological-either individually or in combination. These factors predominantly affect the genetic material, which is why cancer is classified as a genetic disease. (Preethi 2008). When cancer cells spread to other parts of the body through the lymphatic system or bloodstream, this is known as metastasis. Not all tumors are malignant: many are benign and do not invade surrounding tissues or spread to other areas. There are over 200 identified types of cancer that can affect the human body (Ahmad M et al., 2013).
According to the tissue of origin, cancer is mainly categorized into four types as follows:
(a) Carcinoma: Originates from epithelial cells, which line the digestive tract and form organs such as the liver, kidneys, and pancreas. It is the most prevalent type of cancer.
(b) Sarcoma: Arises from cells in muscle, nerves, or blood vessels.
(c) Lymphoma: Develops from cells within a lymph gland.
(d) Leukemia: refers to cancers that affect the blood-forming tissue. (Preethi 2008).
2.2 Etiology of Cancer
Carcinogenesis
The exact causes of cancer are still not completely understood, but it is widely accepted that cancer, or neoplasm, begins when the normal regulatory mechanisms controlling cell growth and proliferation are disrupted. This disruption leads to a cascade of events in a process known as carcinogenesis, which is recognized as a multistage and genetically rguated phenomenon.
1. Initiation:
Normal cells are exposed to carcinogens (chemicals, radiation, biological agents), causing DNA mutations. If not repaired, these mutations become permanent, disrupting genes that control growth. Mutant cells gain a growth advantage and begin to proliferate.
2. Promotion:
Mutant cells are stimulated to grow by factors like hormones, inflammation, or continued carcinogen exposure. This stage does not involve new mutations but creates a favorable environment for the clonal expansion of initiated cells.
3. Progression:
Cells acquire additional genetic changes, leading to increased malignancy, invasiveness, and potential to metastasize. The tumor becomes more aggressive and dangerous over time.
Figure 2.1: Progression of cervical cancer (Bridge et. al, 2023)
Key aspects of progression include:
Tumor Invasion: Cancer cells gain the ability to invade neighbouring tissues. They often secrete enzymes that break down extracellular matrices, allowing them to penetrate surrounding tissues and establish themselves in new areas.
Metastasis: The process of metastasis involves cancer cells detaching from the primary tumour, entering the bloodstream or lymphatic system, and travelling to distant sites in the body. Once they reach these new locations, they can form secondary tumours, complicating treatment and increasing the severity of the disease. Throughout these stages, the interplay of genetic mutations and environmental factors ultimately drives the transformation of normal cells into a malignant tumor. Understanding these stages is crucial for developing effective prevention, carly detection. and treatment strategies for cancer. (Dipiro, 2017).
2.3 Epidemiology of cancer:
Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020.
The most common in 2020 (in terms of new cases of cancer) were:
The most common causes of cancer death in 2020 were:
2.4 Cervical cancer:
Cervical cancer is the fourth most common cancer in women, with around 660,000 new cases in 2022. In the same year, about 94% of the 350,000 deaths caused by cervical cancer occurred in low- and middle-income countries. (https://www.who.int/news-room/factsheets/detail/cervical-cancer) Cervical cancer is cancer that starts in the cells of the cervix. Cervical cancer usually develops slowly over time. Before cancer appears in the cervix, the cells of the cervix go through changes known as dysplasia, in which abnormal cells begin to appear in the cervical tissue. Over time, if not destroyed or removed, the abnormal cells may become cancer cells and start to grow and spread deeply more into (https://www.cancer.gov/types/cervical) the cervix and to surrounding areas. The cervix has two main parts: The ectocervix (also called exocervix) is the outer part of the cervix, and the endocervix is the inner part of the cervix. The squamocolumnar junction (also called the transformation zone) is the border where the endocervix and ectocervix meet. Most cervical cancers begin in this area.
Figure 2.2: Anatomy of Cervix & Squamocolumnar Junction (cancer.gov 2023)
2.5 Types of cervical cancer:
Cervical cancers are named after the type of cell where the cancer started. The twomain types are:
2.6 Epidemiology of Cervical cancer:
Cervical cancer is the fourth most common cancer in women, with around 660,000 new cases in 2022. Cervical cancer is one of the leading causes of cancer death among women. Over the past 0 years, the increasing proportion of young women affected by cervical cancer has ranged om 10% to 40%. In 2018 worldwide, with an estimated 570.000 cases and 311,000 deaths, ervical cancer ranks as the fourth most frequently diagnosed cancer and the fourth leading cause of cancer death in women. However, approximately 85% of worldwide deaths from cervical cancer occur in underdeveloped or developing countries, and the death rate is 18 times higher in low- and middle-income countries compared with wealthier countries. Cervical cancer ranks second in incidence and mortality behind breast cancer in lower Human Development Index (HDI) settings. (Shaokai Zhang et al., 2019).
2.7 Risk factors for cervical cancer
Almost all cervical malignancies are caused by persistent, long-lasting infection with high risk strains of the human papillomavirus (HPV). Worldwide, 70% of cervical cancer cases are related to two high-risk strains of HPV: HPV 16 and HPV 18.
More than 75% of cervical cancer cases are attributed to high-risk HPV types 16 and 18. However, other HPV types can also lead to malignancy. Specifically, low-risk HPV types 6 and are known to cause anogenital warts, also known as condylomata acuminate. Despite over a million HPV cases being reported annaually, most are low grade infections that typically resolve on their own within 2 years. The development of high grade lesions and cancer usually involves additional carcinogenic risk factors, HPV DNA encodes oncoproteins E6 and E7, which disrupt the normal cell cycle. E6 targets the tumor suppressor protein p53, while E7 affects the retinoblastoma protein (PRB). Additionally, the ES protein may contribute to immune evasion. These factors are crucial in HPV-related neoplasia, including primary vaginal cancer. Oxidative stress and microRNAs are also thought to be involved in cervical carcinogenesis, and further research is needed to clarify their interactions.(https://www.ncbi.nlm.nih.gov/books/NBK431093/article-19222.53).
2.8 Cervical Cancer Screening:
There are three main ways to screen for cervical cancer: The human papillomavirus (HPV) test checks cells for infection with high-risk HPV types that can cause cervical cancer. The Pap test. also known as a Pap smear or cervical cytology, gathers cells from the cervix to check for changes caused by HPV that could potentially develop into cervical cancer if not treated. It can detect both precancerous and cancerous cells in the cervix and occasionally identifies non-cancerous conditions like infections or inflammation. The HPV/Pap cotest integrates an HPV test and a Pap test to concurrently detect high-risk HPV and any alterations in cervical cells.
Treatment of Cervical Cancer:
• Surgery
• Radiation therapy
• Chemotherapy
• Targeted therapy
• Immunotherapy
2.9 Surgery
Surgery (abso called an operation) is sometimes used to treat cervical cancer. The type of surgery depends on where the cancer is located.
The following surgical procedures may be used:
Cold knife conization
Cold knife conization uses a scalpel to remove a cone-shaped piece of tissue frons the cervis and cervical canal.
Hysterectomy
A hysterectomy is surgery to remove the uterus. As a treatment for cervical cancer, the cervis, and sometimes the surrounding structures, are removed.
Radical trachelectomy
Radical trachelectomy (also called radical cervicectomy) removes the eervis, nearby tissue, and the upper part of the vagina. Lymph nodes may also be removed.
Radiation therapy
Radiation therapy uses high-energy X-rays or other types of radiation to kill cancer cells or keep them from growing by damaging their DNA. The two main types of radiation therapy are external radiation therapy and internal radiation therapy (also called brachytherapy). Both external and internal radiation therapy are used to treat cervical cancer and may also be used as palliative therapy to relieve symptoms and improve quality of life in people with advanced cervical cancer,
External radiation therapy
External-beam radiation therapy uses a machine outside the body to send radiation toward the area of the body with cancer. Intensity-modulated radiation therapy (IMRT) is a way of giving external radiation therapy that can help keep radiation from damaging nearby healthy tissue.
Internal radiation therapy
Internal radiation therapy uses a radioactive substance sealed in needles, seeds, wires, or catheters that are placed directly into or near the cancer. Internal radiation therapy is also called brachytherapy.
2.10 Chemotherapy
Chemotherapy (also called chemo) uses drugs to stop the growth of cancer cells, either by killing the cells or by stopping them from dividing. Chemotherapy may be given alone or with other types of treatment.
Chemotherapy drugs used to treat cervical cancer include:
Combinations of these drugs may be used. Other chemotherapy drugs not listed here may also be used. Targeted therapy uses drugs or other substances to block the action of specific enzymes, proteins, or other molecules involved in the growth and spread of cancer cells.
Targeted therapies used to treat cervical cancer include
• Bevacizumab
• Tisotumab vedotin
• Immunotherapy
Immunotherapy helps a person's immune system fight cancer. Biomarker tests can be used to help predict your response to certain immunotherapy drugs. Pembrolizumab is an immunotherapy drug be used to treat certain patients whose cervical cancer has the biomarker PD-L1. (https://www.cancer.gov/types/cervical/treatment).
2.11 Network pharmacology
A method for explaining the complex relationships between medications, diseases, and biological systems is called network pharmacology. Through analysis of massive data sets, it also explains potential mechanisms of complicated bio-actives and establishes their synergistic effects. (Sakle et al. 2020), It uses computational power to systematically arrange the molecular interactions of a drug molecule in a living cell. Network pharmacology appeared as an important tool in understanding the underlying complex relationships between botanical formulas and the whole body. Drug discovery, the process by which new candidate medications are discovered, initially began with random searching of therapeutic agents from plants, animals, and naturally occurring minerals. Then classical pharmacology came into consideration, in which the therapeutic effects of molecules were tested on cells or whole organisms. Later, target-based drug discovery (also known as reverse pharmacology) started gaining response due to the human genome sequencing revolution. The protein that the drug binds to or interacts with is also called a target small molecules from a chemical library are screened for their effect on the target's known or predicted function. (Chandran et al., 2017) Network pharmacology research revolves around identifying compound and disease related genes, constructing a protein-protein interaction (PPI) network, and lastly, analyzing and visualizing the network. Identifying active compounds of medicinal plants and disease-related targets is the starting step in network pharmacology research. Mostly, a literature search is done to identify active compounds; however, various public databases provide an impressive interface to predict the active compounds of the medicinal plant. After obtaining active compounds, the canonical SMILES of active compounds are retrieved from available public databases. From these canonical smiles using online tools, we can obtain protein-related targets and similarly, we can obtain disease-related targets from various databases. Common targets (proteins) are taken using Venn diagram tools. Further protein-protein interaction (PPI) network construction is done. To determine which hub genes have the highest level of connectivity, a network analysis is subsequently carried out. Exclusive key target features are obtained at a functional level through the examination of linked pathways by GO enrichment analysis and KEGG pathway analysis.
2.12 ADME Study
An ADME study thoroughly examines how a drug travels through the body, from the momentit is administered to its final excretion. It assesses key factors including absorption (how the drug enters the bloodstream), distribution (how it disperses throughout the body), metabolism (how it is broken down and processed), and elimination (how it is removed from the body). It also evaluates potential toxicity (any adverse effects the drug may have). In drug development, it is vital to identify potential problems early and optimize drug candidates for both safety and effectiveness. With advancements in machine learning and molecular data, in silico models can now predict crucial ADME properties, enhancing the efficiency of the drug design process. A thorough understanding of ADME characteristics enables better optimization of drug candidates, boosting their effectiveness while minimizing risks. Tools like the 'ADME Predictor are invaluable for forecasting these properties, leading to a more streamlined and successful drug development process. Tools such as admetSAR, PreADMET, and SwissADME play a crucial role in assessing ADME (absorption, distribution, metabolism and excretion.) properties (Dong et al., 2018). ADME studies enable scientists to identify potential issues with medications before they are tested on humans. These studies examine the drug's physical and chemical properties, its dissolution and movement within the body, interactions with other medications, and its toxicity. Gaining insights into these aspects is crucial for improving the drug's effectiveness, enhancing its performance, and reducing the risk of adverse side effects for patients. As research progresses and advances in computational and laboratory techniques continue, ADME profiling remains essential for developing safe and effective new treatments.
Fig 2.3: "Integrated Framework of Network Pharmacology and ADME Study in Drug Discovery"
2.13 Molecular Docking:
Two important sub networks were chosen, and the most important gene was chosen for a molecular docking study. The Uniport database (https:/www.uniprot.org) was searched for the receptor protein that the chosen gene codes for. We obtained the protein's three-dimensional structure from the RCSB (PDB) library (https://www.rosh.org/). I retrieved the two dimensional structure of the ligand molecules from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The 3D structure was calculated and exported using ChemBio 3D software, which minimizes energy. The receptor protein was dehydrated wing PyMOL. 2.4.0 software, and proteins hydrogenation and charge calculations were done using Autodock software. The active pocket locations where small molecule ligands attach were added to the receptor protein docking site's parameters as shown in (Figure 2.12). To dock the receptor protein with the small molecule ligands of the active ingredients in LQC, Autodock Vina was ultimately utilized (Xia et al., 2020).
Application of molecular modelling in modern drug development:
1. Drug development: Docking is most commonly used in drug discovery because most pharmaceuticals are made up of small chemical molecules.
2. Hit identification: When used with a scoring function, docking allows for the quick screening of enormous databases of potential drugs in silico to identify molecules that can bind to a certain target of interest.
3. Lead optimization: The location and relative position of a ligand's interaction with a protein-also known as the binding mode or pose can be predicted using docking. Analogs with greater potency and selectivity can be created with the help of this data.
4. Remediation: it is possible to predict which contaminants are amenable to enzymatic degradation using protein-ligand docking. It can be used to choose the ideal site and gather the best medicine. Enzymes and their mechanisms of action can be determined by molecular docking. It can also be used to ascertain the connections between various proteins. The remediation process virtually screens molecules (Raval and Ganatra, 2022).
Fig.: "Workflow of Molecular Docking Study for Drug-Target Interaction"
The medicinal use of plants has been documented for centuries, with early evidence indicating that humans have long relied on botanical remedies to treat various health issues such as infections, inflammation, and respiratory illnesses. This traditional knowledge, passed down through generations, has played a crucial role in guiding scientific exploration and drug discovery. Between 1940 and 2010, the U.S. Food and Drug Administration (FDA) approved 84 of 173 small-molecule drugs for cancer treatment that were either directly derived from or inspired by natural products. Advancements in organic chemistry and analytical methods have greatly improved the extraction, isolation, and identification of plant-based bioactive compounds. Significant examples include vincristine and vinblastine from Catharanthus roseus, codeine and morphine from Papaver somniferum, artemisinin from Artemisia annua, quinine from Cinchona officinalis, cocaine from Erythroxylum coca, paclitaxel from Taxus brevifolia, and digitoxin from Digitalis purpurea and Digitalis lanata. It is estimated that approximately 25% of currently available pharmaceuticals originate from plant sources. These compounds, initially produced by plants as defense mechanisms or for pollination and adaptation, have become essential in modern drug development, particularly in the treatment of cancer (Adetunji, T. L. et al., 2024). Plant-based therapeutics are increasingly important, particularly for underprivileged populations, due to their availability, cost-effectiveness, and lower toxicity. These phytocompounds—including alkaloids, flavonoids, tannins, terpenoids, and pigments—are present in various plant parts such as leaves, bark, stems, roots, seeds, fruits, and flowers. They exhibit a wide range of pharmacological activities, including cardioprotective, anti-inflammatory, antimicrobial, antiangiogenic, and anticancer effects. Several phytocompounds are known to modulate key signaling pathways involved in cancer progression. These include enzymes such as cyclin-dependent kinases (CDK2, CDK4), mammalian target of rapamycin (mTOR), matrix metalloproteinases (MMP), MAPK/ERK, Akt, PI3K, Bcl-2, and COX-2. Additionally, they play roles in DNA repair mechanisms involving proteins like p53, p21, and p27, and stimulate the activity of apoptosis-related proteins such as caspases (3, 7, 8, 9, 10, and 12), and antioxidant enzymes like glutathione peroxidase (GPx), GST, and GSH [5] (Priya Chaudhary et al., 2024). Over the past 25 years, there has been a significant increase in fruit processing, paralleling the rise in demand for prepackaged and ready-to-eat foods. This surge has led to the generation of substantial agro-industrial waste, including peels, cores, seeds, and leaves, contributing to environmental challenges. Interestingly, these by-products are rich in bioactive phytoconstituents with potential health benefits, including cancer prevention. For example, jackfruit seeds—typically discarded as waste—contain valuable lectins such as jacalin, jacalin-related lectin (AHL), and ArtinM (Artocarpin/KM+), a mannose-binding lectin. Preclinical studies suggest these compounds can inhibit cancer cell proliferation and induce apoptosis (Ghosh, Puja et al., 2023). Artocarpus heterophyllus (jackfruit) is known for its wide-ranging pharmacological properties, yet its potential chemopreventive effects remain underappreciated. Recent reviews have focused on evaluating the role of jackfruit and its derivatives in cancer prevention and suppression (Nurul Syamilah Abdul Razak, 2022). Moreover, a recent study employed network pharmacology and molecular modeling to assess the therapeutic potential of whole green jackfruit flour methanol extract (JME) against obesity-linked diabetes. Through drug-likeness profiling, docking simulations, and gene ontology (GO) analyses, the research identified the mechanisms by which JME phytocompounds may contribute to metabolic disease amelioration (Maradesha T., Martiz R. M. et al., 2023). These findings underscore the broad spectrum of applications for phytocompounds, particularly from jackfruit, in addressing chronic diseases including cancer and diabetes.
2.14 .Plant Profile:
Fig 2.4: Artocarpus heterophyllus: Whole Jackfruit with Arils and Seeds
Taxonomical classification of Artocarpus heterophyllus.
|
Kingdom |
Plantae…Plant,Plantes,Plants,vegetal |
|
Subkingdom |
Tracheobionta…Vascular plants |
|
Division |
Magnoliopsida..angiosperms, flowering plants, phanerogames |
|
Class |
Magnoliopsida.. dicots,dicotyledones,dicotyledons |
|
Subclass |
Hamamelidae |
|
Order |
Urticales |
|
Family |
Moraceae..mulberries |
|
Genus |
Artocarpus_breadfruit |
|
Species |
Artocarpus heterophyllus Lam. |
Geographical source: Throughout indian tropical low land. Artocarpus heterophyllus grows as an evergreen tree that has a relatively short trunk and dense tree top. The leaves are alternate and spirally arranged. They are gummy and thick and are divided into petiole and leaf blade. The bark of jackfruit tree is reddish brown and smooth. In the event of injury to the bark, a milky sap is released. The fruits grow on a long and thick stem on the trunk. They vary in size and ripen from an initially yellowish green to yellow, and then at maturity to yellowish brown. They possess a hard , gummy shell with small Pimples.
Common names : Jackfruit Nangka, Jack, Jak, Jake tree.
Vernacular names
• Hindi: Kathal, Katal
• Sanskrit: Panasam
• Marathi: Phanas
• Assamese: Kothal
• Tamil: Pila
• Malayam: Kottachakka
Parts Used: Leaves, Root, Root Bark, Latex, Stem Bark, Flowers, Seeds.
Reported activites of Artocarpus heterophyllus : Studies have showed that jackfruit contains many classes of compounds such as flavonoids, volatile acids , sterols , and tannins, coloring matters, carotenoid. (Arung et.al., 2007).
Antimicrobial activity:
(Manuel et al., 2012) The antibacterial activity of the organic extracts (n-hexane, acetone and methanol) from the fruit of Artocarpus heterophyllus Lam was studied against Staphylococcus mireus, (methicillin-susceptible Staphylococcus aureus [MSSA]). The extract that demonstrated the greatest activity was that of acetone, with a minimum inhibitory concentration (MIC) value of 0.375 mg/ml against S. aureus MSSA.
Antimutagenic and antiproliferative activity:
(Ruiz-Montanez et al., 2015) The study of the antimutagenic and antiproliferative potential of pulp Jackfruit (Artocarpus heterophyllus Lam) extract, using Salmonella typhimurium tester strains TA98 and TA100 with metabolic activation (S9) and a cancer cell line M12.C3.F6 (murine B-cell lymphoma), respectively. Jackfruit pulp extract was sequentially fractionated by chromatography (RP-HPLC) and each fraction was tested for antimutagenic and antiproliferative activities.
Antitumor activity:
(Rajendran et al., 2010) Artocarpus heterophyllus are rich sources of the isoprenylated phenolic compounds, including flavonoids. In this study, crude extracts from the tegmen of A. heterophyllus were tested in vitro for their antitumor activity. Total polyphenol content of the extracts ranged from 97.33 to 117.75mg gallic acid equivalent (GAE)/g extract depending on the solvent used and extraction time applied. Among the three solvent extracts, methanol extract showed maximum poly phenol content at 2hr extraction time followed by ethanol and butanol respectively. Methanolic extract showed maximum cytotoxicity on HEp2 cells up to 1:4 dilution. Cytotoxic changes observed was cell aggregation, cell rounding & cell death. The overall results indicate promising baseline information for the potential uses of from the tegmen of A. heterophyllus as an antitumor agent.
Anti inflammatory activity:
(Fang et al., 2008) Artocarpus heterophyllus Lam studied for in vitro anti-inflammatory effects using three phenolic compounds were characterized as artocarpesin [5,7,2,4-tetrahydroxy-6-(3-methylbut-3-enyl) flavone] (1), norartocarpetin (5,7,2,4-tetrahydroxyflavone) (2), and oxyresveratrol [trans-2.4.3',5'-tetrahydroxystilbene (3) by spectroscopic methods and through comparison with data reported in the literatures. The antiinflammatory effects of the isolated compounds (1-3) were evaluated by determining their inhibitory effects on the production of proinflammatory mediators in lipopolysaccharide (LPS)-activated RAW 264,7 murine macrophage cells. These three compounds exhibited potent anti-inflammatory activity. The results indicated that artocarpesin (1) suppressed the LPS-induced production of nitric oxide (NO) and prostaglandin E2 (PGE2) through the down-regulation of inducible nitric oxide synthase (INOS) and cyclooxygenase 2 (COX-2) protein expressions. Thus, artocarpesin (1) may provide a potential therapeutic approach for inflammation-associated disorders.
Antioxidant Effect:
(Ko et al,1998) The antioxidant properties of prenylflavones, isolated from Artocarpus heterophyllus Lam were evaluated in this study. Among them, artocarpine, artocarpetin. artocarpetin A, and cycloheterophyllin diacetate and peracetate had no effect on iron-induced lipid peroxidation in rat brain homogenate. In contrast, cycloheterophyllin and artonins A and B inhibited iron-induced lipid peroxidation in rat brain homogenate and scavenged 1. 1-diphenyl-2-picrylhydrazyl. They also scavenged peroxyl radicals and hydroxyl radicals that were generated by 2, 2'-azobis (2- amidinopropane) dihydrochloride and the Fe3+-ascorbate-EDTA-H202 system, respectively. Moreover, cycloheterophyllin and artonins A and B inhibited copper-catalyzed oxidation of human low-density lipoprotein, as measured by fluorescence intensity. thiobarbituric acid- reactive substance and conjugated-diene formations and electrophoretic mobility. It is concluded that eycloheterophyllin and artonins A and B serve as powerliul antioxidants against lipid peroxidation when biomembranes are exposed to oxygen radicals.
Antifungal Effect:
(Trindale et al., 2006) Two novel chitin-binding lectins from seeds of the Artocarpus gemis were described, one from jackfruit and one from breadfruit. Both are 14 kDa proteins, made up of 3 chains linked by disulfide bonds. The partial amino acid sequences of the two lectins showed they are homologous to each other but not to other plant chitin-binding proteins. Both lectins inhibited the growth of Fusarium moniliforme and saccharomyces cerevisiae,and presented activity against human and rabbit erythrocytes.
MATERIAL & METHODS
3.1 Network Pharmacology:
1.Botanical Identification and Recovery of Active Ingredients:
The phytoconstituents of the various botanicals were identified through literature review and IMPPAT (Indian Medicinal Plants, Phytochemistry and Therapeutics) mochimic.res.in/imppat), as well as Dr. Duke's Phytochemical and Ethnobotanical Database (http://phytochem.nal.usda.gov/ The botanicals were identified through previous literature. Next, the Structure data file (SDF) format of the active chemicals was retrieved from PubChem database (Chakkittukandivil et al., 2023)
2.Examination of Phytochemicals for Potential Drug-Like Qualities:
The collected phytochemicals were assessed using Lipinski's rule of five and the ADMET Characteristics (absorption, distribution, metabolism, excretion, and toxicity). Software like (Prop was used to help with the ADMET study. For additional research, phytoconstituents exhibiting the designated range for several parameters, such as molecular weight, water partition coefficient, blood partition coefficient, etc., were choosen (Khanna et al., 2024).
3.Phytotarget and Disease Target Identification:
Use databases such as Swiss Target Prediction (https://www.swisstargetprediction.ch/), which predicts targets based on structural characteristics of compounds and experimental data on the compounds binding properties to different proteins, the therapeutic targets for each active phytoconstituents were found. These databases used the canonical SMILES of the phytoconstituents as input to obtain the appropriate targets. DisGeNET database (https://www.disg enct.org/) helped identify the treatment targets of disease and provides a comprehensive and evidence-based view of the relationship between diseases and genes (Khanna et al., 2024).
4.Common Targets Identification:
The online Venny 2.1.0 tool was used to identify common targets by analyzing the relationships between different datasets. By adding the therapeutic targets of disease after the targets of the chosen phytochemicals, the common targets were found. The shared targets are the output of the Venny tool's analysis of the two sets of data (Zhang et al., 2021).
5.Target Network Construction and Topological Analysis:
A PPI network was established by selecting "Homo sapiens" as the species and adding the common targets found using the Venny 2.1.0 tool to the STRING database. This comprehensive database provides a comprehensive overview of PPIs and functional links, facilitating an understanding of the interactions between various proteins. Then, with the aid of the topological analysis, different biological networks could be visualized and analyzed wing Cytoscape software. Degree Centrality (DC) and Betweenness Centrality (BC), two topological parameters, were evaluated using the CytoNCA plugin for Cyscape, On the basis of these topological criteria, the top proteins were identified (Zhang et al., 2021).
6. Gene Ontology (GO) Enrichment Analysis:
The FunRich software, a helpful tool for visualizing and analyzing functional enrichment in genes and proteins, was used to assist with the Gene Ontology (GO) enrichment study. It's a useful tool for analyzing the proteome and genetic data's functional significance. For molecular function (MF), cellular process (CP), and biological process (BP), an enrichment analysis was performed (Liu et al., 2023).
7.Pathway Enrichment Analysis:
The Reactome software, a comprehensive database of biological pathways and activities, was used to assist with the pathway enrichment study. It is widely utilized in many scientific contexts, such as drug development, disease research, and systems biology for pathway analysis and visualization (Liu et al., 2023).
3.2. Molecular Docking:
Three-dimensional structures are used in molecular docking, an inexpensive, safe, and user-friendly technique that facilitates the interpretation, identification, and study of molecular properties. The structural interactions between two or more molecules of a chemical are predicted using a method known as docking. The method is applicable to computational chemistry, computer-aided biology, and molecular systems ranging in size from tiny molecules to large biomolecules and material assemblies. Currently, the primary focus of docking research is the interaction between a flexible ligand and a physiological receptor (Raval and Ganatra, 2022).
1) Target Protein Determination and Preparation:
The process of docking begins with obtaining the protein's 3D structure, preferably bound by a ligand, from the PDB. High-resolution or high-affinity ligand structures are recommended: however, this may not apply to all proteins. Molecular docking requires the specification of certain parameters. Hydrogens must be added, water must be removed, charges must be assigned and energy minimization must be done during protein preparation. Several reparation modules are available to address common issues with PDB files. The software-specific parameterization techniques used differ. AutoDock and SwissDock employ an program force field (Muhammed and Aki-Yalcin, 2024).
2.Preparation of Ligand:
The ligand structures obtained from chemical libraries or databases such as PubChem Energy minimization has to be done before using these structures in docking. It is advised to visually inspect the target and ligand preparations' outcomes. Because erroneous connections, missing bonds, and aberrant geometries can result from certain preparation techniques in molecular descriptions. These mistakes frequently happen while converting molecules between different formats. It therefore spreads quickly. Once the ligand and target are ready, the binding site needs to be identified and restricted. This phase can be completed using the coordinates of a ligand that is coupled to the protein, or by manually specifying the coordinates. The docking calculations' center, the binding area, is mapped using the grid. The probe atoms in the grid represent the outline of a potential interaction, and the grid itself can be compared to a box with known dimensions that has been divided into tiny squares. Docking outcomes are impacted by grid size and resolution (Trott and Olson, 2010).
3. Selection of the Best Docking Scoring Function:
The stability of the ligand-protein combination determines whether docking scoring function is optimal. Selecting an appropriate scoring function that provides an accurate binding pattern and a potential ligand is challenging. According to theory, a protein-ligand combination is more stable the lower its binding free energy (AG). Numerous programs calculate the docking score in order to quickly detect and rank multiple ligand poses. Binders and nonbinders should be easily distinguished using scoring functions. Furthermore, it must be able to quickly and accurately distinguish between the proper and improper binding modalities of a ligand. There are three primary classifications for scoring functions: knowledge-based, force field, and empirical (Muhammed and Aki-Yalcin, 2024)
4. Docking Validation:
Docking process validation is just as important as any other technique. Verifying the docking results involves redocking reference ligands with targets and contrasting the estimated bindings' coverage, RMSD (root mean square deviation), binding posture, and binding affinity with previously obtained data. Molecular dynamics studies should be performed if the structures of the ligand and the target are complicated. Calculating the binding free energy including the solvent effect, fixing the complex after docking, providing flexibility, and ensuring the precise sequence of putative ligands can all be accomplished via molecular dynamics simulations. The ligands' binding positions, binding residues, and binding energies are disclosed at the conclusion of the process (Counia et al., 2017).
RESULT
4.1. Network Pharmacology:
1) Active components in Artocarpus Heterophyllus:
A literature review, along with data from the IMPPAT (Indian Medicinal Plants, Phytochemistry and Therapeutics) database, revealed that Artocarpus heterophyllus contains 146 active components. The selected components were further subjected to an evaluation of the pharmacokinetic parameters. The ADME criteria for optimum bioavailability is defined as having a value of 30% or greater, while the drug-likeness greater than 0.18. These thresholds are essential for evaluating the pharmacokinetic properties and potential efficacy of a drug candidate.
2) Screening of Components of Artocarpus heterophyllus For Cancer Treatment :
Among the 146 components, only 8 components were found to relate with Cancer treatment. The genes of each active component were fetched from Swiss target prediction genes of the disease i.e, Cervical Cancer were retrieved from GeneCards man database specific to Homo sapiens were selected. Using a Venn diagram as shown in (Figure 4.1), 97 common targets were identified between the compound targets 317 and cancer -related targets 932. These common targets were as shown in following (Table 4.1)
Fig 4.1: Venn diagram of common gene from Target Gene and Disease Gene through Network Pharmacology
Table 4.1: Common gene of Cancer
|
Gene Symbols of Common gene (97) |
|
MMP2 PLAU NOS2 FGFR1 MET FASN SMO PTPN11 PLK1 PLA2G2A ALK CDK1 PTK2 BCL2 CYP1A1 MAPK10 PPARG RARB AURKA SRD5A2 ABCB1 AHR AURKB CYP2A6 KDM1A CYP19A1 MAPK14 CDK6 TNF MPO KIT SHH LDHA GLI1 CA9 PTGS2 ESR1 CTSD VEGFA GSK3B MCL1 WEE1 HSP90AA1 PARP1 VDR KDR IDO1 ABCG2 RARA MMP3 AXL MAPK1 MDM2 EGFR FLT3 ERBB2 TOP2A MAPK8 IGF1R BCL2L1 MAPK3 CHEK2 TERT RELA HIF1A CFTR SRC CDK2 PGR ODC1 CYP17A1 SERPINE1 INSR AKT1 PIK3R1 PRKACA IDH1 MAP2K1 NFKB1 ABCC1 IGFBP3 PTGS1 AR FLT4 ESR2 DAPK1 PRKCA EPHB4 TYMP NEK2 CYP1B1 PRSS1 MDM4 RPS6KB1 MMP9 TOP1 CDC25A |
3) Construction and Analysis of Target PPI Network:
To enhance the visualization and interpretation of the underlying molecular mechanisms, an in-depth analysis of protein-protein interactions (PPI) was conducted for the identified target genes. These genes, associated with specific bioactive compounds, were analyzed using STRING v11, a widely recognized database and tool for constructing and visualizing PPI networks. The analysis provided an integrated perspective on the functional interrelationships among the target proteins and their involvement in biological pathways pertinent to Cancer. To ensure the reliability of the interactions, the network was filtered to retain only high-confidence interactions with a combined score > 0.9, thereby emphasizing the most robust and biologically relevant associations. The resulting PPI network, illustrated in Figure 6.2, highlights the connectivity and interaction dynamics among the identified targets. The local clustering coefficient of the network was calculated to be 0.69, indicating a relatively high degree of interconnectedness among the nodes, which reflects the potential for cooperative or co-regulated functional roles within the disease context. To identify the most central and functionally important proteins within the network, the CytoHubba plugin in Cytoscape v3.10.0 was employed. Hub genes were prioritized using topological analysis methods such as Density of Maximum Neighbourhood Component (DMNC) and Maximum Neigh bourhood Component (MNC). This analysis revealed a set of core targets implicated in Cancer pathogenesis, including MYC, EGFR, IL6, STAT3, ALB, EP300, BCL2, CD4, CASP3, and BRCA1. These hub proteins may serve as critical nodes in the regulatory network and represent potential therapeutic targets for intervention.
Fig 4.2: Protein-Protein interactions analyzed using STRING v11.
4) Top Hub Gene:
Genes have many interaction with other genes that are hub genes, more interaction to other genes (more degree) and less interaction to other gene are (less degree) considered. Top ten were selected for the Network Pharmacology as shown in (Figure 4.3)
Fig 4.3: Top 10 gene obtained from Cytoscape ranked by Degree method
5) Construction of Compound-Target Network:
A compound-target network was constructed using Cytoscape to explore the relationships between the identified compounds and their associated target genes as shown in (Figure: 4.4). This analysis allowed for a deeper understanding of the signaling pathways and potential therapeutic uses of the compounds identified in the extract. In compound-target-disease interaction network to explore how drugs work in treating Cancer. Our analysis showed that several targets were impacted by multiple compounds, suggesting that these active biochemical substances might exert a synergistic effect on these targets. This observation also implies that the compounds could potentially offer therapeutic benefits for other diseases and disorders beyond just Cancer. The network analysis reveals several important proteins with high degrees that are critical targets in Cancer and some compounds may have significant pharmacological effects on other diseases.
Fig 4.4: Cytoscape visualization target pathway network
6) GO and KEGG Pathway Enrichment Analysis:
On conducting the Gene Ontology (GO) annotation and KEGG pathway enrichment analysis using a set of potential Cancer-related target genes uploaded to the DAVID 2021 The threshold for identifying significant pathways or gene functions was set P0.05. Fr analysis focused on pathways or gene functions with the highest occurrence count. The GO annotation analysis offered insights into the functional categorization of the genes under study, based on three primary aspects, cellular component (CC), molecular function (MF), and biological process (BP). For cellular components, the analysis is useful to identify the subcellular locations or structures where the genes are predominantly active or present. This information is crucial for understanding the spatial organization and localization of the gene products within the cell Regarding molecular function, the analysis highlighted the specific biochemical activities or roles performed by the gene products, such as enzyme activity, receptor binding, or response functions. This provided valuable information about the functional properties associated with these genes. Biological processes or series of events carried out by one or more organized assemblies of molecular functions.
Top enriched processes include:
This bar chart represents the Gene Ontology (GO) enrichment analysis for genes associated with a cancer-related dataset, categorized into three GO domains: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). Each bar reflects the enrichment score, indicating the significance of overrepresented gene sets in cancer biology. In the Biological Process category (green), the most enriched terms are related to transcriptional regulation, such as "positive regulation of transcription by RNA polymerase II" and "positive regulation of gene expression", highlighting the critical role of altered transcriptional control in cancer progression. Other enriched processes like "signal transduction" and "cell population proliferation" suggest disruptions in cellular signaling and growth control, which are hallmarks of cancer. The Cellular Component category (orange) shows enrichment in compartments like the "nucleus", "cytoplasm", "extracellular region", and "chromatin". This distribution implies that cancer-associated proteins are localized in key cellular structures involved in gene regulation and intercellular communication—particularly emphasizing the role of the nucleus and chromatin in controlling gene expression changes during tumor development. In the Molecular Function category (purple), significant terms include "DNA binding", "RNA polymerase II transcription factor activity", and "sequence-specific DNA binding". These functions underscore the importance of transcription factors and regulatory proteins that bind DNA to control the expression of oncogenes and tumor suppressor genes in cancer. Overall, this GO enrichment analysis illustrates how dysregulated transcription, altered protein localization, and specific molecular functions contribute to the mechanisms driving cancer pathogenesis.
Fig 4.5: (A) Biological Processes [BP], (B)Molecular Functions [MF], (C) Cellular Component [CC] of target genes through the GO enrichment analyses, (D) The KEGG pathway analysis of the target genes: Graphical representation of Gene Ontology classification, highlighting functional annotations and visualization of KEGG pathway interactions, mapping molecular networks and processes.
This dot plot visualizes KEGG pathway enrichment analysis for genes likely implicated in cancer, displaying the most significantly enriched biological pathways. The x-axis represents the enrichment score, indicating the degree to which each pathway is overrepresented, while the color gradient reflects statistical significance (−log??(p-value)), with red indicating highly significant pathways. Dot size corresponds to the number of genes involved in each pathway. At the top, "Pathways in cancer" stands out with the highest enrichment score, largest gene count, and strongest significance, emphasizing its central role in tumor development. "MicroRNAs in cancer" also appears highly enriched, highlighting the growing recognition of miRNA-mediated gene regulation in oncogenesis. Key oncogenic signaling routes such as the PI3K–Akt and MAPK pathways are prominently featured, both well-established as crucial for tumor cell survival, proliferation, and metastasis. Interestingly, "Human papillomavirus (HPV) infection" is enriched, underscoring the link between viral oncogenes and cancers like cervical cancer. Pathways like "Proteoglycans in cancer" suggest roles in modifying the tumor microenvironment and promoting invasion. Additionally, non-cancer pathways such as "Alzheimer disease" and "Pathways of neurodegeneration" might share overlapping molecular mechanisms with cancer, particularly in cell death and repair pathways. Overall, the analysis illustrates a network of interconnected signaling and regulatory mechanisms frequently altered in cancer, reinforcing the complex biology underlying tumorigenesis and identifying potential targets for cancer diagnostics or therapy.
4.2. Molecular Docking:
To find the active phytoconstituents in Artocarpus heterophyllus that bind to the MYC, EGFR and IL6 receptor, a molecular docking studies was conducted. Additionally, to identify compounds that exhibit promising outcomes as possible therapeutic candidates based on their pharmacokinetics and toxicity characteristics. AutoDock vina (1.2.0) was used to determine the interaction of phytoconstituents from the Artocarpus heterophyllus plant and receptors. All selected phytoconstituents were docked, and their binding energies were shown in following (Table 4.2), (Table 4.3), (Table 4.4).
Table 6.2: Binding affinity of different phytoconstituents from Artocarpus heterophyllus with MYC.
|
Sr.no. |
Ligand + Receptor |
Binding Affinity (kcal/mol) |
|
1.
|
beta-Sitosterol + MYC |
-5.466 |
|
2. |
Eriodictyol + MYC |
-5.3666 |
|
3. |
Kaempferol + MYC |
-5.244 |
|
4. |
Artocarpin + MYC |
-5.22 |
|
5. |
(-)-Butyrospermol + MYC |
-5.133 |
|
6. |
Cycloartenone + MYC |
-4.977 |
|
7. |
beta-Carotene + MYC |
-4.622 |
|
8. |
2-Hexenal + MYC |
-2.833 |
Table 6.3: Binding affinity of different phytoconstituents from Artocarpus heterophyllus with EGFR
|
Sr.no. |
Ligand + Receptor |
Binding Affinity (kcal/mol) |
|
1. |
beta-Carotene + EGFR |
-7.933 |
|
2. |
(-)-Butyrospermol + EGFR |
-7.866 |
|
3. |
beta-Sitosterol + EGFR |
-7.57 |
|
4. |
Eriodictyol + EGFR |
-7.322 |
|
5. |
Cycloartenone + EGFR |
-7.08 |
|
6. |
Artocarpin + EGFR |
-7.044 |
|
7. |
Kaempferol + EGFR |
-7.01 |
|
8. |
2-Hexenal + EGFR |
-3.466 |
|
9. |
Erlotinib + EGFR |
-6.244 |
Table 6.4: Binding affinity of different phytoconstituents from Artocarpus heterophyllus with IL6
|
Sr.no. |
Ligand + Receptor |
Binding Affinity (kcal/mol) |
|
1. |
Eriodictyol + IL6 |
-7.8 |
|
2. |
(-)-Butyrospermol + IL6 |
-7.677 |
|
3. |
beta-Carotene + IL6 |
-7.66 |
|
4. |
Kaempferol + IL6 |
-7.655 |
|
5. |
Artocarpin + IL6 |
-7.433 |
|
6. |
beta-Sitosterol + IL6 |
-7.322 |
|
7. |
Cycloartenone + IL6 |
-7.077 |
|
8. |
2-Hexenal + IL6 |
-3.877 |
|
9. |
Reveratrol + IL6 |
-6.822 |
4.3. 3D and 2D structure after interaction between ligand and target protein.
Table 4.5: Docking structure after interaction between ligand and target protein. (MYC)
|
Phytoconstituents |
2D Structure |
3D structure |
|
1.Beta-Sitosterol
|
|
|
|
Eriodictyol
|
|
|
|
Kaempferol
|
|
|
|
Artocarpin
|
|
|
|
(-)-Butyrospermol
|
|
|
|
Beta-Carotene
|
|
|
|
2-Hexenal
|
|
|
Table 4.6: Docking structure after interaction between ligand and target protein. (EGFR)
|
Phytoconstituents |
2D Structure |
3D structure |
|
beta-Carotene
|
|
|
|
(-)-Butyrospermol
|
|
|
|
beta-Sitosterol
|
|
|
|
Eriodictyol
|
|
|
|
Cycloartenone
|
|
|
|
Artocarpin
|
|
|
|
Kaempferol |
|
|
|
2-Hexenal
|
|
|
|
Erlotinib |
|
|
Table 4.7: Docking structure after interaction between ligand and target protein. (IL6)
|
Phytoconstituents |
2D Structure |
3D Structure |
|
Eriodictyol
|
|
|
|
(-)-Butyrospermol
|
|
|
|
beta-Carotene
|
|
|
|
Kaempferol
|
|
|
|
Artocarpin
|
|
|
|
beta-Sitosterol
|
|
|
|
Cycloartenone
|
|
|
|
2-Hexenal
|
|
|
|
Reveratrol |
|
|
Fig 4.6: The bioavailability radar of best-docked compounds. The pink area represents oral bioavailability, considering factors such as polarity, lipophilicity, saturation, size, flexibility, and solubility.
Table 4.8: Virtual screening of Artocarpus heterophyllus phytoconstituents against MYC
|
Sr.no. |
Chemical constituent |
Binding Affinity with MYC |
Bonding |
Amino acid with numbering |
Bond distance |
|
1.
|
beta-Sitosterol
|
-5.466 |
1.Alkyl |
LYS B:21 ALA B:25 |
4.76 4.08 |
|
2. |
Eriodictyol |
-5.3666 |
1.Amide-Pi stacked 2.Pi -Alkyl |
ALA B:25 ASN B:24 |
3.90 4.24 |
|
3. |
Kaempferol
|
-5.244 |
1.Carbon- Hydrogen bond 2.Pi-Alkyl |
LYS B:21 |
5.22 3.43 |
|
4. |
Artocarpin
|
-5.22 |
1.Carbon –Hydrogen bond 2.Pi-Alkyl |
LYS B:21 |
5.29 3.34 |
|
5. |
(-)-Butyrospermol
|
-5.133 |
1.Alkyl |
LYS B:21 ALA B:25 |
4.93 3.71 |
|
6. |
Cycloartenone |
-4.977 |
- |
- |
- |
|
7. |
beta-Carotene
|
-4.622 |
1.Alkyl |
LEU A:13 |
5.27 4.81 |
|
8. |
2-Hexenal
|
-2.833 |
1.Alkyl |
LEU A:13 |
4.27 4.06 |
The table titled "Virtual screening of Artocarpus heterophyllus phytoconstituents against MYC" presents the molecular docking results of eight phytochemicals from Artocarpus heterophyllus evaluated for their binding affinity to the MYC protein, a well-known oncogenic transcription factor. Among the compounds tested, β-Sitosterol exhibited the strongest binding affinity with a docking score of -5.466 kcal/mol, followed closely by Eriodictyol (-5.3666 kcal/mol), Kaempferol (-5.244 kcal/mol), and Artocarpin (-5.22 kcal/mol). These interactions primarily involved alkyl, π-alkyl, and hydrogen bonding types, with residues such as LYS B:21, ALA B:25, and ASN B:24 being frequently involved. Notably, β-Sitosterol, (-)-Butyrospermol, and Artocarpin shared common binding sites with LYS B:21 and ALA B:25, suggesting a potential binding hotspot on the MYC protein. Cycloartenone showed a relatively weaker binding affinity (-4.977 kcal/mol) and no specific bonding interactions were identified. On the other end of the spectrum, 2-Hexenal demonstrated the weakest binding affinity at -2.833 kcal/mol. Overall, the findings highlight β-Sitosterol and Eriodictyol as promising MYC-binding phytoconstituents that could be explored further for potential anticancer properties.
Table 4.9: Virtual screening of Artocarpus heterophyllus phytoconstituents against EGFR
|
Sr.no. |
Chemical constituent |
Binding Affinity with EGFR |
Bonding |
Amino acid with numbering |
Bond distance |
|
1.
|
beta-Carotene
|
-7.933 |
1.Alkyl |
PRO C:45 VAL B:5 |
4.01 4.27 4.04 |
|
2. |
(-)-Butyrospermol
|
-7.866 |
1. Alkyl |
- |
5.03 5.25 5.37 |
|
3. |
beta-Sitosterol
|
-7.57 |
1. Alkyl |
LYS B:23 VAL B:5 ALA C:85 |
4.51 4.20 5.04 4.90 4.30 |
|
4. |
Eriodictyol
|
-7.322 |
1.Pi - Alkyl |
PRO C:45 LYS C:44 VAL B:5 |
5.36 4.21 4.35 |
|
5. |
Cycloartenone
|
-7.08 |
1. Alkyl |
VAL B:28 |
5.24 5.23 |
|
6. |
Artocarpin
|
-7.044 |
1. Van der waals 2. Amide-Pi-stacked 3.Pi-Alkyl |
LYS A:23 VAL D:88 ALA D:85 VAL A:5 GLU D:86 |
4.85 4.37 4.98 4.92 5.27 5.6 4.03 |
|
7. |
Kaempferol
|
-7.01 |
1.Conventional Hydrogen Bond 2.Pi-Alkyl 3.Pi-Sigma |
THR C:102 PRO C:100 TRP A:47 |
3.25 3.49 3.82 5.23 |
|
8. |
2-Hexenal
|
-3.466 |
1.Pi-Alkyl |
PHE B:104 TRP B:33 HIS B:35 |
3.78 4.15 4.73 5.09 4.81 4.24 |
|
9. |
Erlotinib |
-6.244 |
1.Alkyl 2.Pi-Alkyl |
VAL B:5 PRO C:45 ALA C:85 |
3.72 4.58 3.61 |
The table titled "Virtual screening of Artocarpus heterophyllus phytoconstituents against EGFR" presents the docking analysis of various phytochemicals from Artocarpus heterophyllus evaluated for their interaction with the Epidermal Growth Factor Receptor (EGFR). Among the compounds tested, beta-Carotene exhibited the strongest binding affinity with EGFR at -7.933 kcal/mol, forming alkyl interactions with residues PRO C:45 and VAL B:5. It was closely followed by (-)-Butyrospermol (-7.866 kcal/mol), which also engaged in alkyl interactions but without specific residue annotation. Beta-Sitosterol showed a binding energy of -7.57 kcal/mol and interacted with multiple residues, including LYS B:23, VAL B:5, and ALA C:85. Other notable compounds include Eriodictyol (-7.322 kcal/mol), which formed π-alkyl bonds with PRO C:45, LYS C:44, and VAL B:5, and Cycloartenone (-7.08 kcal/mol), which interacted with VAL B:28. Artocarpin demonstrated diverse interaction types—including van der Waals forces, amide-π stacking, and π-alkyl interactions—with several residues such as LYS A:23 and VAL D:88, yielding a binding score of -7.044 kcal/mol. Kaempferol (-7.01 kcal/mol) formed conventional hydrogen bonds along with π-alkyl and π-sigma interactions. On the lower end, 2-Hexenal showed the weakest binding affinity at -3.466 kcal/mol, and the standard EGFR inhibitor Erlotinib showed a binding score of -6.244 kcal/mol, with alkyl and π-alkyl interactions involving VAL B:5, PRO C:45, and ALA C:85. These results suggest that several Artocarpus heterophyllus phytoconstituents—particularly beta-Carotene, Butyrospermol, and Beta-Sitosterol—may have stronger binding potential to EGFR than the reference drug Erlotinib.
Table 4.10: Virtual screening of Artocarpus heterophyllus phytoconstituents against IL-6
|
Sr.no. |
Chemical constituent |
Binding Affinity with IL-6 |
Bonding |
Amino acid with numbering |
Bond distance |
|
1.
|
Eriodictyol
|
-7.8 |
1.Van der waals 2.Amide-Pi-Stacked |
PRO L:80 GLU L:81 |
4.09 |
|
2. |
(-)-Butyrospermol
|
-7.677 |
1.Alkyl |
ALA A:63 |
4.61 |
|
3. |
beta-Carotene
|
-7.66 |
1.Alkyl |
PRO H:41 VAL H:95 |
4.26 5.08 4.64 |
|
4. |
Kaempferol
|
-7.655 |
1.Carbon Hydrogen Bond 2.Pi-Donor Hydrogen Bond 3.Pi-Sigma |
THR A:190 PRO H:192 |
3.64 |
|
5. |
Artocarpin
|
-7.433 |
1.Van der waals 2.Amide –Pi-Stacked |
GLY A:42 LYS A:43 |
4.33 3.98
|
|
6. |
beta-Sitosterol
|
-7.322 |
1.Alkyl |
LYS L:45 ALA L:43 |
5.23 5.13 |
|
7. |
Cycloartenone
|
-7.077 |
1.Conventional Hydrogen Bond 2. Alkyl |
LYS B:45 TAM A:1221 |
3.00 5.38 |
|
8. |
2-Hexenal |
-3.877 |
1.Alkyl |
ALA :63 |
4.28 |
|
9. |
Reveratrol |
-6.822 |
1.Pi-Anion 2.Pi-Alkyl |
PRO L:105 |
4.92 5.41 |
The table titled "Virtual screening of Artocarpus heterophyllus phytoconstituents against IL-6" summarizes the docking interactions of various phytochemicals with the interleukin-6 (IL-6) protein, which plays a key role in inflammatory processes and disease progression. Among the screened compounds, Eriodictyol displayed the highest binding affinity at -7.8 kcal/mol, forming van der Waals and amide–π-stacked interactions with residues PRO L:80 and GLU L:81. This was followed closely by (-)-Butyrospermol (-7.677 kcal/mol) and beta-Carotene (-7.66 kcal/mol), which both engaged in alkyl interactions, particularly with residues like ALA A:63 and VAL H:95. Kaempferol (-7.655 kcal/mol) formed multiple types of bonds—carbon hydrogen, π-donor hydrogen, and π-sigma—with residues including THR A:190 and PRO H:192, indicating a versatile binding profile. Artocarpin (-7.433 kcal/mol) also formed van der Waals and amide–π-stacked interactions with GLY A:42 and LYS A:43. Beta-Sitosterol and Cycloartenone showed binding affinities of -7.322 and -7.077 kcal/mol, respectively, with alkyl and hydrogen bond interactions involving residues such as LYS L:45 and TAM A:1221. At the lower end of the spectrum, 2-Hexenal and Resveratrol showed weaker binding affinities of -3.877 and -6.822 kcal/mol, respectively, with limited bonding interactions. These results suggest that Eriodictyol, Butyrospermol, and Beta-Carotene are the most promising IL-6 inhibitors among the tested phytoconstituents, potentially offering anti-inflammatory benefits.
Table 4.11: ADME profile of Artocarpus heterophyllus phytoconstituents.
|
Sr. No. |
Phyto- constituent |
Lipinski rule of five |
BBB |
Skin permeability |
GI absorption |
Log p |
|
|
H-bond acceptor |
H-bond donar |
||||||
|
1. |
Artocarpin |
6 |
3 |
No |
-4.90 |
High |
4.78 |
|
2. |
beta-Carotene |
0 |
0 |
No |
-4.90 |
High |
4.78 |
|
3. |
beta-Sitosterol |
1 |
1 |
No |
-2.20 |
Low |
7.24 |
|
4. |
(-)-Butyrospermol |
1 |
1 |
No |
-2.36 |
Low |
7.35 |
|
5. |
Cycloartenone |
1 |
0 |
No |
-2.17 |
Low |
7.55 |
|
6. |
Eriodictyol |
6 |
4 |
No |
-6.62 |
High |
1.45 |
|
7. |
2-Hexenal |
1 |
0 |
Yes |
-5.82 |
High |
1.50 |
|
8. |
Kaempferol |
6 |
4 |
No |
-6.70 |
High |
1.58 |
|
9. |
Erlotinib |
6 |
1 |
Yes |
-6.35 |
High |
3.20 |
|
10. |
Reveratrol |
3 |
3 |
Yes |
-5.47 |
High |
2.48 |
The table titled “ADME profile of Artocarpus heterophyllus phytoconstituents” presents pharmacokinetic properties of ten phytoconstituents. It includes key parameters such as Lipinski’s rule of five, blood-brain barrier (BBB) penetration, plasma protein binding, skin permeability, gastrointestinal (GI) absorption, and Log P (lipophilicity). Most compounds, such as Artocarpin and Eriodictyol, violate multiple Lipinski rules—specifically in hydrogen bond acceptors and donors—suggesting potential issues with drug-likeness. Compounds like 2-Hexenal, Erlotinib, and Resveratrol show the ability to cross the BBB, while others do not. GI absorption is high for the majority, though compounds like beta-Sitosterol and Cycloartenone exhibit low absorption. Skin permeability values are negative throughout, with the lowest being for Kaempferol (-6.70), suggesting limited dermal penetration. Log P values range from 1.45 (Eriodictyol) to 7.55 (Cycloartenone), indicating varying degrees of lipophilicity, with some exceeding the ideal range. Overall, this profile provides insights into the drug-likeness and pharmacokinetic potential of these natural compounds.
Table 4.12: Toxicity profile of Artocarpus heterophyllus phytoconstituents.
|
Sr. No.. |
Phyto-Constituent |
Hepatoto Xicity |
Neurotoxici Ty |
Carcino Genecit Y |
Mutage Necity |
Cytotoxi City |
|
1. |
Artocarpin |
Inactive |
Inactive |
Inactive |
Inactive |
Inactive |
|
2. |
beta-Carotene |
Inactive |
Active |
Inactive |
Active |
Inactive |
|
3. |
beta-Sitosterol |
Inactive |
Active |
Inactive |
Inactive |
Inactive |
|
4. |
(-)-Butyrospermol |
Inactive |
Active |
Inactive |
Inactive |
Inactive |
|
5. |
Cycloartenone |
Inactive |
Active |
Inactive |
Inactive |
Inactive |
|
6. |
Eriodictyol |
Inactive |
Inactive |
Active |
Inactive |
Inactive |
|
7. |
2-Hexenal |
Inactive |
Inactive |
Active |
Active |
Inactive |
|
8. |
Kaempferol |
Inactive |
Inactive |
Inactive |
Inactive |
Inactive |
|
9. |
Erlotinib |
Active |
Inactive |
Inactive |
Active |
Active |
|
10. |
Reveratrol |
Active |
Active |
Inactive |
Inactive |
Inactive |
The Table presents the toxicity profile of various phytoconstituents derived from Artocarpus heterophyllus. The data covers five types of toxicological effects: hepatotoxicity, neurotoxicity, carcinogenicity, mutagenicity, and cytotoxicity. Among the ten compounds analyzed, Artocarpin and Kaempferol show no activity in any toxicity category, indicating a potentially safe profile. Beta-Carotene exhibits neurotoxicity and mutagenicity, while beta-Sitosterol, (-)-Butyrospermol, and Cycloartenone show only neurotoxic activity. Eriodictyol and 2-Hexenal are associated with carcinogenicity, with 2-Hexenal also showing mutagenicity. Erlotinib, a known anticancer agent, displays hepatotoxicity, mutagenicity, and cytotoxicity. Lastly, Resveratrol demonstrates both hepatotoxic and neurotoxic effects. Overall, while some constituents appear non-toxic, others like Erlotinib and Resveratrol exhibit multiple toxic effects, emphasizing the need for careful evaluation in therapeutic use.
DISCUSSION:
The study employed a multi-pronged computational approach combining network pharmacology, molecular docking, ADME profiling, and toxicity assessment to evaluate the anticancer potential of phytoconstituents from Artocarpus heterophyllus. This integrative strategy elucidated the bioactive components, their pharmacological targets, and mechanistic pathways, especially in relation to cervical cancer. An initial screen identified 146 active compounds from Artocarpus heterophyllus, of which only 8 were significantly associated with cancer-related targets. Using a comparative gene intersection analysis, 97 common genes were found between the compound targets and cervical cancer-associated genes. These common targets suggest the plant’s phytoconstituents potentially interact with key oncogenic pathways. The PPI network constructed via STRING revealed high confidence interactions (score > 0.9), with a clustering coefficient of 0.69, underscoring a well-integrated network. Using CytoHubba analysis, the top hub genes—including MYC, EGFR, IL6, STAT3, and BCL2—were identified, reflecting central nodes in cancer signaling. These genes are known to play pivotal roles in proliferation, apoptosis, immune evasion, and metastasis, reinforcing their relevance as therapeutic targets. Compound-target interaction networks visualized in Cytoscape showed that several phytochemicals simultaneously affect multiple targets, indicating possible polypharmacological or synergistic effects. GO and KEGG pathway enrichment further validated the involvement of target genes in cancer-related pathways. Enrichment of "Pathways in cancer," "MicroRNAs in cancer," and "HPV infection" is especially relevant to cervical cancer. The roles of PI3K-Akt and MAPK signaling, alongside apoptosis and transcription regulation processes, support the compounds' potential to modulate tumorigenic mechanisms at multiple levels.
Molecular docking confirmed the strong interactions of specific phytoconstituents with oncogenic targets:
The overlapping residues across ligands and targets (e.g., LYS B:21, VAL B:5) indicate conserved binding hotspots, critical for drug design optimization. Most compounds demonstrated high GI absorption, essential for oral bioavailability. However, some phytochemicals (e.g., Eriodictyol, Kaempferol) violated multiple Lipinski rules due to high numbers of H-bond donors/acceptors, which may affect drug-likeness. Compounds like β-Sitosterol and Cycloartenone, despite favorable binding, exhibited poor GI absorption. Additionally, lipophilicity values (Log P) varied widely, with some compounds (Log P > 5) suggesting poor solubility and limited systemic distribution. Toxicity evaluation revealed Artocarpin and Kaempferol as the safest candidates, showing no activity in hepatotoxicity, neurotoxicity, carcinogenicity, mutagenicity, or cytotoxicity. Conversely, Erlotinib, though a validated anticancer drug, exhibited multiple toxicities, including hepatotoxicity and mutagenicity, highlighting the relative safety of certain phytoconstituents. The mutagenic and carcinogenic potential of 2-Hexenal and Eriodictyol underscores the need for cautious preclinical validation before clinical translation.
CONCLUSION:
This comprehensive investigation reveals that phytochemicals from Artocarpus heterophyllus, especially β-Sitosterol, Eriodictyol, Artocarpin, and Kaempferol, exhibit promising anticancer properties by targeting central oncogenes like MYC, EGFR, and IL6. Their ability to engage in multi-target interactions, modulate key cancer-related pathways, and exhibit relatively safe ADME and toxicity profiles positions them as potential candidates for future development into plant-based anticancer therapeutics. However, further in vitro and in vivo validation is crucial to confirm efficacy and safety.
REFERENCES
Tejaswini Usrate*, Dr. R. S. Moon, Pranita Yengunde, Abhinav Bhadange, Sushil Kshirsagar, Exploring Jackfruit Phytoconstituents for Cervical Cancer Intervention: An Integrated in Silico Drug Discovery Study, Int. J. of Pharm. Sci., 2025, Vol 3, Issue 7, 218-260. https://doi.org/10.5281/zenodo.15788733
10.5281/zenodo.15788733
