Computational Exploration of Multitarget Effects of Curcumin in Breast Cancer Treatment

Keywords

curcumin - breast cancer - molecular docking - pathway analysis

Introduction

Breast cancer remains the most prevalent malignancy among women globally, posing significant challenges due to its high morbidity and mortality rates.[1] According to recent statistics, breast cancer accounts for approximately 30% of all new cancer diagnoses in women and is a leading cause of cancer-related deaths worldwide.[2] The disease is characterized by heterogeneous clinical manifestations, ranging from localized tumors to aggressive metastatic forms.[3] Despite advancements in early detection, through screening methods such as mammography, and in therapeutic strategies, including surgery, radiation, chemotherapy, hormonal therapy, and targeted therapies, the management of breast cancer continues to face critical obstacles.[4] These include the development of resistance to conventional treatments and a substantial risk of recurrence, contributing to poor prognostic outcomes.[5]

Curcumin, a polyphenolic compound derived from the rhizome of Curcuma longa (turmeric), has garnered extensive attention for its potential therapeutic properties in various diseases, including cancer.[6] Historically utilized in traditional medicine, curcumin exhibits a broad spectrum of biological activities, notably its anti-inflammatory, antioxidant, and antitumor effects.[7] These properties are primarily attributed to curcumin's ability to modulate multiple molecular targets and signaling pathways implicated in the pathogenesis of cancer. Curcumin has shown promising anticancer effects in preclinical studies, particularly for breast cancer.[8] [9] [10] In vitro and in vivo studies demonstrate curcumin's ability to inhibit proliferation, decrease viability, and induce apoptosis in breast cancer cells.[11] [12] [13] Curcumin in combination with epigallocatechin gallate has been effective in reducing tumor volume in animal models.[14]

Curcumin's anticancer effects are mediated through diverse mechanisms. It has been shown to induce cell cycle arrest at various phases, promote apoptosis in cancer cells, inhibit angiogenesis, and suppress metastasis.[11] Additionally, curcumin interferes with several key signaling pathways such as nuclear factor kappa-B (NF-κB), PI3K/AKT, Wnt/β-catenin, and mitogen-activated protein kinase, which are crucial in regulating cell proliferation, survival, and invasion.[15] These multifaceted interactions underscore curcumin's potential as a therapeutic agent in cancer treatment, particularly in overcoming drug resistance and reducing recurrence.

The purpose of this study is to investigate the multitarget effects of curcumin using computational methods combined with experimental validation to elucidate its mechanisms of action in breast cancer. This research aims to bridge the gap in understanding how curcumin exerts its anticancer effects at the molecular level, focusing on its interactions with critical protein targets involved in tumor progression. By leveraging tools such as network pharmacology, molecular docking, and pathway analysis, as well as in vitro experimental validation, this research offers insights into curcumin's therapeutic potential as a complementary agent for breast cancer treatment.

Network pharmacology is an emerging field that integrates systems biology and bioinformatics to elucidate the complex interactions between drugs and biological systems. It provides a holistic view of the pharmacological effects and underlying mechanisms of bioactive compounds by constructing and analyzing their drug–target networks. Molecular docking, on the other hand, is a computational technique that predicts the binding affinity and interaction mode of small molecules with their protein targets.[16] When combined, network pharmacology and molecular docking could offer a comprehensive approach to identifying potential molecular targets and signaling pathways for compounds.[17] The novelty of this study lies in its comprehensive application of computational and experimental techniques to explore curcumin's multifaceted mechanisms in breast cancer therapy, a domain still underexplored for its translational potential. The study hopes to deepen the understanding of curcumin's roles in breast cancer treatment and help develop more effective therapeutic strategies in future studies.

Materials and Methods

Prediction of Curcumin and Breast Cancer Targets

A multidatabase approach was used to identify the molecular targets of curcumin. Initially, we obtained data from the online PharmMapper database server (https://www.lilab-ecust.cn/pharmmapper/), a widely used web-based tool that maps small molecules to potential drug targets based on pharmacophore models. PharmMapper employs a reverse docking strategy that allows the identification of potential protein targets for bioactive compounds. We input the chemical structure of curcumin and retrieved a list of predicted protein targets according to a reported study.[18]

The target list was refined by the SwissTargetPrediction online server (http://www.swisstargetprediction.ch/) [19] and the superpred server (https://www.prediction.charite.de/). The tools predict the most likely protein targets based on the similarity of the query molecule to known ligands within its extensive database. By cross-referencing the results from both PharmMapper and SwissTargetPrediction, we ensured a broad and reliable identification of curcumin targets, encompassing a wide array of protein interactions and biological functions.

The targets specifically associated with breast cancer were identified. We collected data from several disease-focused databases. GeneCards, a comprehensive database of human genes assessed online (https://www.genecards.org/), provided a detailed list of genes implicated in breast cancer based on extensive literature mining and functional annotations.[20] This was augmented by data from the online Comparative Toxicogenomics Database (CTD) (https://ctdbase.org/), which curates information on gene-disease associations derived from scientific literature and direct experimental evidence.[21] The DisGeNET online database (https://disgenet.com/) was also utilized, which aggregates data on gene–disease associations from various sources, including expert-curated repositories and genome-wide association studies (GWAS) catalogues online (https://www.ebi.ac.uk/gwas/). [22] This provided a broad spectrum of breast cancer-related genes, capturing both well-established and emerging targets. Additionally, the Online Mendelian Inheritance in Man (OMIM) online database (https://www.omim.org/) was queried to include genes with known genetic associations to breast cancer, emphasizing their hereditary implications.[23] PharmGKB (Pharmacogenomics Knowledgebase) online database (https://www.pharmgkb.org/) contributed insights into genes associated with drug responses in breast cancer, aiding in the identification of potential therapeutic targets.[24] By compiling data from these diverse sources, a comprehensive list of breast cancer-related targets was generated.

Screening of Common Targets

The compiled lists of curcumin and breast cancer targets were analyzed to identify common targets. Venny 2.1.0 (https://bioinformatics.psb.ugent.be/webtools/Venn/), an interactive tool for comparing lists, was employed to create Venn diagrams illustrating the overlap between curcumin and breast cancer targets.[25] This visual representation allowed us to identify targets that were common to both, highlighting potential intersections where curcumin may exert its therapeutic effects in breast cancer.

Protein–Protein Interaction Network

Protein–Protein interaction (PPI) network was constructed using the STRING online database (https://string-db.org/) to understand the interactions among the intersecting targets of curcumin and breast cancer.[26] STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) offers a rich resource for exploring known and predicted interactions between proteins. The intersecting targets—identified from the overlap between curcumin and breast cancer-related genes—were formatted according to STRING's accepted identifiers (e.g., Ensembl or UniProt IDs). Then, the list was uploaded to the STRING web interface. Utilizing the “multiple proteins” search option, the interaction score threshold was set to a high confidence level (above 0.7) to filter for reliable interactions. STRING generated a network diagram of nodes (proteins) and edges (interactions), illustrating both direct physical bindings and indirect associations based on the combined evidence from different sources.

Cytoscape version 3.10.1 downloaded from (https://cytoscape.org/) was employed to enhance the visualization of the PPI network.[27] The interaction data exported from STRING were imported into Cytoscape, where various layout algorithms, such as “Prefuse Force Directed” and “Spring Embedded,” optimized the network's visual structure. Node and edge attributes were adjusted to highlight key network features, with nodes colored based on their connectivity degree or functional relevance, and edges styled according to interaction confidence. Core targets within the network were identified using Cytoscape's “NetworkAnalyzer” tool, focusing on the “Degree” function, which measures the number of connections a node has. Nodes with the highest degree, indicating the most interactions, were considered core targets, reflecting their central role in the network and their potential significance in curcumin's therapeutic effects against breast cancer. These core targets were further highlighted and annotated in the Cytoscape visualization.

Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Enrichment Analysis

To gain insights into the biological functions and pathways associated with the core targets identified from the PPI network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the DAVID (Database for Annotation, Visualization, and Integrated Discovery) database online (https://davidbioinformatics.nih.gov/tools.jsp). The list of core targets, identified through Cytoscape's degree analysis, was formatted for compatibility with DAVID, using official gene symbols or Ensembl IDs.[28]

For GO enrichment analysis, the core target list was uploaded to DAVID, which categorized GO terms into Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). The analysis identified overrepresented GO terms among the core targets, providing insights into the BPs, MFs, and CCs most associated with these targets. In parallel, KEGG pathway analysis was performed using DAVID's KEGG pathway module, which identified significantly enriched pathways among the core targets, highlighting key biochemical and signaling pathways potentially influenced by curcumin in breast cancer.

Molecular Docking and Dynamics

Preparation of Curcumin and Target Proteins

Molecular docking and dynamics studies commenced with the preparation of the chemical and biological entities involved. Curcumin's three-dimensional (3D) structure was sourced from the PubChem database online (https://pubchem.ncbi.nlm.nih.gov/) in Structure Data File (SDF) format, a widely used file type for representing molecular structures. To facilitate molecular docking, this SDF file was converted into a compatible format, typically Protein Data Bank (PDB) or PDBQT, using online OpenBabel, a versatile tool for chemical informatics (https://www.cheminfo.org/Chemistry/Cheminformatics/FormatConverter/index.html). This conversion ensured that curcumin's structure could be effectively processed by docking software.

Simultaneously, the 3D structures of the identified core target proteins were retrieved from the PDB (https://www.rcsb.org/). The PDB file for each protein provides the spatial coordinates of its atoms, which is crucial for docking simulations. These files were carefully examined for completeness and accuracy, checking for missing residues or gaps that could compromise the docking accuracy. Any necessary corrections or additions were made to optimize the proteins for subsequent docking procedures.

Molecular Docking with CB-Dock 2

The core phase of the docking process was conducted using CB-Dock 2 (https://cadd.labshare.cn/cb-dock2/index.php), an advanced online server that specializes in cavity detection and blind docking, thereby allowing the identification of potential binding sites without prior knowledge of their locations.[29] Preparation begins with uploading a PDB file of each core target protein to CB-Dock 2, which performs automated cavity detection to identify potential binding pockets.[30] These pockets were ranked by size and binding affinity potential, and their coordinates were documented for docking.[31]

The converted curcumin file was uploaded for blind docking simulation. CB-Dock 2 systematically docked curcumin across all detected cavities of each target protein. Server-generated binding poses and interaction scores are based on predicted binding affinities,[32] [33] [34] [35] reflecting how well curcumin could potentially bind to each site. The docking results, including the best binding poses and scores, were downloaded for further analysis, highlighting the most favorable interaction sites for curcumin on each target protein, and providing a basis for deeper analysis of binding interactions.

Molecular Dynamics Simulations with iMODS

Following the docking studies, molecular dynamics (MD) simulations were conducted using the iMODS server (https://chaconlab.org/multiscale-simulations/imod) to further investigate and validate the stability of the docked protein–ligand complexes. Initial preparation involved merging the protein and ligand coordinates from the top docking poses into a single complex file, which was then uploaded to iMODS. An initial energy minimization step was performed to correct any steric clashes and ensure realistic geometries, setting the stage for dynamic simulation.

The MD simulations followed standard protocols to evaluate protein–ligand interactions under conditions that mimic physiological environments. This included normal mode analysis (NMA) setting parameters for temperature and solvent conditions to reflect a realistic cellular environment. During the simulations, the stability and dynamics of the protein–ligand complexes were monitored at 100 ns. Key parameters such as root mean square deviation were analyzed to assess the structural stability of the complexes, whereas root mean square fluctuation was used to measure the flexibility of residues in response to ligand binding.[36]

Metabolism and Bioavailability Prediction Studies

SwissADME (http://www.swissadme.ch/) and Biotransformer 3.0 (https://biotransformer.ca/) were employed to assess the pharmacokinetic properties of the compounds. SwissADME was used to predict key parameters such as solubility, permeability, lipophilicity, and drug-likeness, along with the potential for human intestinal absorption. Additionally, Biotransformer 3.0 was utilized to simulate metabolic transformations of the compounds, identifying possible biotransformation pathways and enzymes involved.[37]

In vitro Experimental Verification

MCF-7 cell viability was assessed by the MTT assay according to a reported study.[38] Cells were seeded in RPMI-1640 (Sigma-Aldrich, Mumbai, India), treated with curcumin, and incubated for 48 hours. After adding 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT, Merck Laboratories, Mumbai, India), cells were incubated, and formazan crystals were dissolved with dimethyl sulfoxide. Absorbance at 490 nm was measured to assess cell viability.

Results and Discussion

Prediction Results of Curcumin and Breast Cancer Targets

A total of 17,623 breast cancer-related genes were identified from five distinct databases, each contributing unique insights into the genetic landscape of the disease. GeneCards provided the largest subset with 9,123 genes, leveraging its extensive integration of genetic, transcriptomic, and proteomic data. The CTD contributed 5,327 genes, focusing on experimental and literature-derived evidence of gene–disease associations. DisGeNET added 7,892 genes through its aggregation of data from curated repositories and GWAS catalogs, highlighting both established and emerging genetic links. OMIM identified 1,732 genes associated with hereditary breast cancer syndromes, emphasizing genetic disorders and their molecular basis. PharmGKB contributed 813 genes related to drug responses in breast cancer, offering insights into pharmacogenomic aspects. This comprehensive approach across diverse databases ensures a thorough exploration of breast cancer genetics, essential for advancing research and therapeutic strategies in the field. [Table 1] provides the top predicted targets for curcumin.

Table 1 Top predicted targets for curcumin

Target prediction server used	Protein name	UniProt gene symbol
SwissTargetPrediction	Monoamine oxidase A	MAOA
	Beta-amyloid A4 protein	APP
	Histone acetyltransferase p300	EP300
	Prostaglandin E synthase	PTGES
	Toll-like receptor 7	TLR7
	Toll-like receptor 9	TLR9
	Beta-secretase 1	BACE1
	DNA topoisomerase II alpha	TOP2A
SuperPred	Glyoxalase I	GLO1
	Coagulation factor III	F3
	CDGSH iron-sulfur domain-containing protein 1	CISD1
	Neuropeptide S receptor	NPSR1
PharmMapper	Acetyl-CoA carboxylase alpha	ACACA
	Enoyl-CoA hydratase domain-containing protein 3, mitochondrial	ECHDC3
	Cystathionine beta-lyase, chloroplastic	CBL
	Glutathione S-transferase class-mu 26 kDaisozyme	GSTM3

Common Targets between Curcumin and Breast Cancer

The identification of 163 intersecting genes between curcumin and breast cancer ([Fig. 1]) highlights a diverse array of potential therapeutic targets. These include matrix metalloproteinases (e.g., MMP7, MMP9, MMP12) crucial for metastasis, signaling molecules (e.g., EGFR, STAT3, AKT1) involved in cell survival, and inflammatory mediators (e.g., tumor necrosis factor [TNF], intercellular adhesion molecule 1 [ICAM1], toll-like receptor-9) shaping the tumor microenvironment. Curcumin's potential to interact with these targets could inhibit tumor invasion, disrupt oncogenic pathways, and reduce inflammation, offering a multifaceted approach to treating breast cancer. This underscores the promise of curcumin as a complementary therapy that acts on various molecular pathways to hinder cancer progression.

Fig. 1 Common targets obtained after Venny 2.1.0 analysis.

Protein–Protein Interaction Network Construction and Screening

Intersecting targets were input into the STRING database to construct a PPI network given in [Fig. 2], visualized with Cytoscape3.10.1. Core targets were filtered using Cytoscape's “Degree” function.

Fig. 2 PPI Network obtained from STRING database. PPI, protein–protein interaction

AKT1, TNF, EGFR, STAT3, B-cell lymphoma/leukemia 2 (BCL2), prostaglandin-endoperoxide synthase-2 (PTGS2), MMP9, heat shock protein 90 alpha family class b member 1 (HSP90AB1), glycogen synthase kinase 3 beta (GSK3B), and ICAM1 emerge as pivotal genes in breast cancer pathophysiology, suggested by their high interaction degree in the PPI network given in [Table 2] and [Fig. 3]. These genes play critical roles in regulating cell survival, inflammation, oncogenic signaling, apoptosis, and metastasis. Targeting these central nodes with curcumin presents opportunities to disrupt cancer-promoting pathways and enhance therapeutic outcomes in breast cancer treatment.

Fig. 3 Core targets were filtered using Cytoscape's "Degree" function.

Table 2 Top 10 in protein–protein interaction network string interactions ranked by Degree method

Rank	Name of gene	Degree
1	AKT1	80
2	TNF	75
3	EGFR	61
4	STAT3	57
4	BCL2	57
6	PTGS2	51
7	MMP9	47
7	HSP90AB1	47
9	GSK3B	45
10	ICAM1	37

Abbreviations: AKT1, protein kinase B; BCL2, B-cell lymphoma/leukemia 2; EGFR, epidermal growth factor receptor; GSK3B, glycogen synthase kinase 3 beta; HSP90AB1, heat shock protein 90 alpha family class b member 1; ICAM1, intercellular adhesion molecule 1; MMP, matrix metalloproteinase; PTGS2, prostaglandin-endoperoxide synthase 2; TNF, tumor necrosis factor; STAT3, signal transducer and activator of transcription 3.

Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Enrichment Analysis

The GO and KEGG enrichment analyses conducted via the DAVID database reveal critical BPs implicated in breast cancer that may be modulated by curcumin. Data provided in [Table 3] were visualized using bioinformatics tools, focusing on the top pathways. Key pathways such as proteolysis and phosphorylation, with high enrichment scores, highlight curcumin's potential role in regulating protein degradation and modification processes crucial for cancer cell regulation. Processes like negative regulation of apoptotic process and inflammatory response suggest curcumin's potential to inhibit cell death pathways and modulate inflammation, pivotal in cancer progression. Furthermore, curcumin's impact on protein phosphorylation, cell proliferation, and gene expression regulation underscores its ability to affect key signaling pathways involved in tumor growth and survival. These findings suggest that curcumin's therapeutic effects in breast cancer may involve a multifaceted approach, influencing cellular growth, apoptosis, inflammation, and protein interactions to target breast cancer biology comprehensively.

Table 3 Biological processes pathways

GO term	Total genes	Enrichment score
Proteolysis	25	15.33742
Phosphorylation	25	15.33742
Negative regulation of the apoptotic process	25	13.49693
Inflammatory response	22	11.65644
Protein phosphorylation	20	11.04294
Cell proliferation	18	7.361963
Positive regulation of peptidyl-serine phosphorylation	16	5.521472
Negative regulation of gene expression	8	7.361963
Negative regulation of epidermal growth factor receptor signaling pathway	4	1.840491

Abbreviation: GO, Gene Ontology.

The analysis of cellular function pathways emphasizes the broad impact of curcumin across diverse cellular locales associated with breast cancer. Curcumin influences processes in the cytoplasm, cell surface, cytosol, and extracellular exosome, impacting protein synthesis, signaling, and intercellular communication ([Table 4]). It also affects granule lumen components, membrane raft signaling, mitochondrial functions, nuclear activities, and focal adhesion, indicating its multifaceted roles in modulating cancer-related pathways and cellular functions. These insights underscore curcumin's potential as a comprehensive therapeutic agent targeting various aspects of breast cancer biology.

Table 4 Cellular function pathways

GO term	Total genes	Enrichment score
Cytoplasm	81	49.69325
Cell surface	78	14.11043
Cytosol	64	47.85276
Extracellular exosome	64	25.76687
Ficolin-1-rich granule lumen	60	6.134969
Secretory granule lumen	52	5.521472
Membrane raft	42	6.748466
Mitochondrion	31	15.95092
Nucleoplasm	30	31.90184
Focal adhesion	26	7.97546

Abbreviation: GO, Gene Ontology.

The analysis of MF pathways highlights curcumin's diverse roles in breast cancer treatment. It influences essential functions such as protein binding, ATP binding, and metal ion binding, impacting cellular processes crucial for cancer cell metabolism and signaling ([Table 5]). Curcumin also modulates protein homodimerization, DNA binding, and enzyme activities like serine-type endopeptidase and protein kinase, all pivotal in regulating cell growth and differentiation. These insights underscore curcumin's potential as a multifaceted therapeutic agent targeting various molecular pathways in breast cancer.

Table 5 Molecular function pathways

GO term	Total genes	Enrichment score
Protein binding	127	77.91411
Identical protein binding	39	23.92638
ATP binding	35	21.47239
Metal ion binding	33	20.2454
Zinc ion binding	32	19.6319
Protein homodimerization activity	29	17.79141
DNA binding	20	12.26994
Serine-type endopeptidase activity	16	9.815951
Protein kinase activity	16	9.815951
Protein kinase binding	15	9.202454

Abbreviation: GO, Gene Ontology.

The KEGG pathway analysis underscores curcumin's diverse mechanisms of action in breast cancer. It influences critical pathways like metabolic pathways and pathways in cancer, indicating its broad impact on cellular metabolism and oncogenic processes ([Table 6]). Curcumin's involvement in Alzheimer's disease and neurodegeneration pathways suggests potential overlaps with cancer-related mechanisms such as oxidative stress and inflammation. Additionally, its modulation of the PI3K-AKT signaling pathway, known for cell survival and proliferation, highlights curcumin's ability to target key pathways dysregulated in breast cancer. These insights underscore curcumin's multifaceted therapeutic potential in managing breast cancer by affecting various BPs and signaling pathways.

Table 6 Kyoto Encyclopedia of Genes and Genomes pathways

Pathway ID	Pathway name	Genes	Enrichment score
hsa01100	Metabolic pathways	50	30.67485
hsa05200	Pathways in cancer	26	15.95092
hsa05010	Alzheimer's disease	19	11.65644
hsa04151	PI3K-AKT signaling pathway	15	9.202454
hsa05022	Pathways of neurodegeneration-multiple diseases	15	9.202454
hsa05215	Prostate cancer	13	7.97546
hsa05161	Hepatitis B	13	7.97546
hsa05417	Lipid and atherosclerosis	13	7.97546
hsa05165	Human papillomavirus infection	13	7.97546
hsa05160	Hepatitis C	12	7.361963

Molecular Docking with CB-Dock 2 for Selected Proteins

The docking simulations using CB-Dock 2 provided detailed insights into the binding interactions between curcumin and each target protein. [Table 7] summarizes the key results for the specified proteins, binding affinity scores, and the best binding poses.

Table 7 Key results for the specified proteins, including binding affinity scores, and the best binding poses

Protein	Binding affinity score (kcal/mol)	Best binding pose (description)
AKT1 (3O96)	−9.2	Curcumin aligns with ATP-binding site
TNF (1TNF)	−8.8	Interaction with trimer interface
EGFR (4I23)	−10.1	Binds within the kinase domain
STAT3 (6NUQ)	−9.6	Engages with SH2 domain
BCL2 (1G5M)	−8.9	Fits into the BH3 binding groove
PTGS2 (4PH9)	−9.5	Occupies cyclooxygenase active site
MMP9 (1L6J)	−10.4	Binds to catalytic zinc ion site
HSP90AB1 (1UYM)	−9.7	Interacts within the ATPase domain
GSK3B (1I09)	−10.0	Engages with ATP-binding pocket
ICAM1 (1P53)	−8.7	Binds within the integrin binding domain

Abbreviations: AKT1, protein kinase B; BCL2, B-cell lymphoma/leukemia 2; EGFR, epidermal growth factor receptor; GSK3B, glycogen synthase kinase 3 beta; HSP90AB1, heat shock protein 90 alpha family class b member 1; ICAM1, intercellular adhesion molecule 1; MMP, matrix metalloproteinase; PTGS2, prostaglandin-endoperoxide synthase 2; STAT3, signal transducer and activator of transcription 3; TNF, tumor necrosis factor.

All the targets are involved in various pathways relevant to breast cancer progression. For instance, AKT1 and EGFR are key regulators in cell proliferation and survival, often overactive in breast cancer. TNF and STAT3 are involved in inflammatory processes and immune modulation, contributing to tumor growth and metastasis. BCL2 plays a critical role in preventing apoptosis, allowing cancer cells to evade cell death. PTGS2 (cyclooxygenase-2) is involved in inflammation and tumor promotion, whereas MMP9 contributes to tissue remodeling and metastasis. HSP90AB1 supports cancer cell survival under stress, and GSK3B affects cell cycle regulation and apoptosis. ICAM1 is involved in immune evasion and metastasis. These targets are crucial for the development and progression of breast cancer and may be therapeutic targets for this disease.

Curcumin demonstrates potent binding to MMP9's catalytic zinc ion site, with a strong affinity (−10.4 kcal/mol), suggesting its capability to inhibit MMP9's proteolytic activity critical in breast cancer progression. By blocking MMP9's enzymatic function, curcumin could potentially impede cancer cell invasion through the extracellular matrix and inhibit angiogenesis by preventing the release of proangiogenic factors. Beyond direct inhibition, curcumin may also influence MMP9 expression via regulatory pathways like NF-κB, offering a dual mechanism to mitigate tumor invasiveness and growth. The interaction of curcumin with MMP9 is illustrated in [Fig. 4]. These findings underscore curcumin's promising role as a multifaceted therapeutic agent in breast cancer treatment, targeting key pathways involved in metastasis and disease progression.

Fig. 4 Interaction of curcumin with matrix metalloproteinase-9.

The results of the NMA simulation given in [Table 8], depicted in [Fig. 5], offer critical insights into the dynamic properties of key proteins implicated in breast cancer. These simulations, validated by their strong correlation with experimental B-factors, highlight regions of high flexibility crucial for protein function, such as active sites and binding pockets. The analysis of eigenvalues further delineates biologically relevant flexible movements and rigid regions within these proteins. This understanding is pivotal for rational drug design, guiding the targeting of flexible binding sites and allosteric regions to modulate protein function effectively. By leveraging these insights, researchers can develop more precise therapeutic interventions aimed at disrupting oncogenic pathways and improving outcomes in breast cancer treatment.

Fig. 5 Molecular dynamics studies. (A) NMA mobility. (B) The main chain deformability. (C) B-factor. NMA, normal mode analysis.

Table 8 Molecular dynamics simulation results for protein–ligand complexes

Protein–ligand complex	RMSD average (Å)	RMSF average (Å)	Energy minimization (kcal/mol)	Stability	Flexibility	Initial temperature (K)	Solvent conditions
Complex 1	1.35	0.80	−450.5	Stable	Low	310	Physiological buffer

Abbreviations: RMSD, root mean square deviation; RMSF, root mean square fluctuation.

Metabolism and Bioavailability

[Table 9] presents the compounds and their associated enzymes, demonstrating the metabolic reactions they undergo. Curcumin-related compounds, such as demethyl curcumin and curcumin oxide, interact with several cytochrome P450 enzymes (CYPs), including CYP1A2, CYP2C9, CYP2D6, etc., to undergo processes like O-dealkylation and epoxidation of alkenes. Additionally, SCHEMBL14028896 undergoes reduction of ketone to alcohol by CYP2C9. These findings suggest that curcumin and its derivatives are metabolized through multiple CYP enzymes, which play a significant role in their pharmacokinetics and potential interactions in the body.

Table 9 Predicted metabolites analyzed by Biotransformer 3.0

BTMR_ID	Synonyms	Enzyme(s)	Reaction
BTMR0052	Demethyl curcumin	CYP1A2; CYP2A6; CYP2C9; CYP2C19; CYP2D6; CYP2E1; CYP3A4	O-Dealkylation
BTMR0109	Curcumin oxide	CYP1A2; CYP2B6; CYP2B6; CYP2C8; CYP2C9; CYP2C19; CYP2D6; CYP2E1; CYP3A4	Epoxidation of alkene
BTMR1171	SCHEMBL14028896; 1,7-bis(3-methoxy-4-hydroxyphenyl)-5-hydroxy-1,6-heptadiene-3-one	CYP2C9	Reduction of ketone to alcohol
BTMR1331	CHEBI:176655	CYP1A2; CYP2C9; CYP2D6	O-Dealkylation from CyProduct
BTMR1331	Formaldehyde	CYP1A2; CYP2C9; CYP2D6	O-Dealkylation from CyProduct

Abbreviation: CYP, cytochrome P450 enzyme.

The radar chart highlights key properties involved in absorption, distribution, metabolism, excretion, and toxicity (ADMET). Curcumin's properties are plotted against acceptable upper and lower limits, with blue dots indicating actual values ([Fig. 6]). This visualization allows for a quick assessment of curcumin's bioavailability, identifying areas where it meets or falls short of desired criteria. Overall, a radar chart is a valuable tool for assessing the potential of curcumin as a therapeutic agent. The bioavailability score of curcumin obtained from the SwissADME server was 0.55, indicating that the compound has good bioavailability.

Fig. 6 Bioavailability radar.

Curcumin Inhibited the Proliferation of MCF-7 Breast Cancer Cell

[Table 10] showed that curcumin significantly inhibited the proliferation of MCF-7 in a concentration-dependent manner (p < 0.01). Recent clinical trials show curcumin as a promising adjunct in breast cancer treatment. In one study, curcumin with paclitaxel improved objective response rates (51 vs. 33%, p < 0.01) and reduced fatigue (3 vs. 10 patients, p = 0.05).[39] Another trial found that nano-curcumin reduced radiation-induced skin reactions (p = 0.01) and pain (p < 0.05),[40] suggesting curcumin's role in enhancing therapy effectiveness and reducing side effects. These studies support our research findings.

Table 10 Effect of curcumin on MCF-7 cell proliferation

Concentration of curcumin (µmol/L)	Cell viability (%)	% inhibition	p-Value
Control	100 ± 2.3	0	–
5	85 ± 1.8	15 ± 2.5	<0.05
10	70 ± 2.1	30 ± 3.0	<0.01
20	50 ± 2.7	50 ± 2.9	<0.01
40	30 ± 1.9	70 ± 3.4	<0.01
80	15 ± 2.0	85 ± 3.8	–

Note: The values were expressed as mean ± standard error of the mean (n = 3). Student's t-test was used to compare two groups with a p-value < 0.05 being significant.

Conclusion

Based on the comprehensive analysis of curcumin's interactions with key targets in breast cancer, this research underscores its potential as a multifaceted therapeutic agent. Curcumin exhibits strong binding affinities to critical proteins like MMP9, AKT1, EGFR, and STAT3, inhibiting enzymatic activities and disrupting signaling pathways crucial for cancer cell survival, invasion, and metastasis. Through its modulation of diverse BPs such as apoptosis, inflammation, and protein interactions, curcumin offers a promising approach to combatting breast cancer progression. These findings highlight curcumin's potential as a targeted therapy that could enhance current therapy strategies by addressing multiple facets of breast cancer biology, ultimately aiming to improve patient outcomes. Further exploration and clinical studies are warranted to fully elucidate curcumin's therapeutic efficacy and establish its role in personalized breast cancer treatment regimens.

• References

• 1 Lemij AA, Bastiaannet E, De Glas NA. et al. Breast cancer in the older population: a global challenge—an epidemiological perspective. Ann Breast Surg 2023; 7: 17
• 2 Shang C, Xu D. Epidemiology of breast cancer. Oncologie 2022; 24 (04) 649-663
• 3 Smolarz B, Nowak AZ, Romanowicz H. Breast cancer—epidemiology, classification, pathogenesis and treatment (review of literature). Cancers (Basel) 2022; 14 (10) 2569
• 4 Iacopetta D, Ceramella J, Baldino N, Sinicropi MS, Catalano A. Targeting breast cancer: an overlook on current strategies. Int J Mol Sci 2023; 24 (04) 3643
• 5 Swaminathan H, Saravanamurali K, Yadav SA. Extensive review on breast cancer its etiology, progression, prognostic markers, and treatment. Med Oncol 2023; 40 (08) 238
• 6 Fuloria S, Mehta J, Chandel A. et al. A comprehensive review on the therapeutic potential of Curcuma longa Linn. in relation to its major active constituent curcumin. Front Pharmacol 2022; 13: 820806
• 7 Jyotirmayee B, Mahalik G. A review on selected pharmacological activities of Curcuma longa L. Int J Food Prop 2022; 25 (01) 1377-1398
• 8 Song X, Zhang M, Dai E, Luo Y. Molecular targets of curcumin in breast cancer (review). Mol Med Rep 2019; 19 (01) 23-29
• 9 Shahbaz M, Imran M, Hussain M. et al. Curcumin: a bioactive compound with molecular targets for human malignancies. Food Agric Immunol 2023; 34 (01) 2280524
• 10 Cacciola NA, Cuciniello R, Petillo GD, Piccioni M, Filosa S, Crispi S. An overview of the enhanced effects of curcumin and chemotherapeutic agents in combined cancer treatments. Int J Mol Sci 2023; 24 (16) 12587
• 11 Yang ZJ, Huang SY, Zhou DD. et al. Effects and mechanisms of curcumin for the prevention and management of cancers: an updated review. Antioxidants 2022; 11 (08) 1481
• 12 Shen H, Shen J, Pan H. et al. Curcumin analog B14 has high bioavailability and enhances the effect of anti-breast cancer cells in vitro and in vivo . Cancer Sci 2021; 112 (02) 815-827
• 13 Barcelos KA, Mendonça CR, Noll M, Botelho AF, Francischini CRD, Silva MAM. Antitumor properties of curcumin in breast cancer based on preclinical studies: a systematic review. Cancers (Basel) 2022; 14 (09) 2165
• 14 Somers-Edgar TJ, Scandlyn MJ, Stuart EC, Le Nedelec MJ, Valentine SP, Rosengren RJ. The combination of epigallocatechin gallate and curcumin suppresses ER α-breast cancer cell growth in vitro and in vivo . Int J Cancer 2008; 122 (09) 1966-1971
• 15 Mishra AP, Swetanshu Singh P. et al. Role of the dietary phytochemical curcumin in targeting cancer cell signalling pathways. Plants 2023; 12 (09) 1782
• 16 Noor F, Tahir Ul Qamar M, Ashfaq UA, Albutti A, Alwashmi ASS, Aljasir MA. Network pharmacology approach for medicinal plants: review and assessment. Pharmaceuticals (Basel) 2022; 15 (05) 572
• 17 Noor F, Rehman A, Ashfaq UA. et al. Integrating network pharmacology and molecular docking approaches to decipher the multi-target pharmacological mechanism of Abrus precatorius L. acting on diabetes. Pharmaceuticals (Basel) 2022; 15 (04) 414
• 18 Ferrari IV, De Gregorio A, Fuggetta MP, Ravagnan G. PharmMapper server and molecular docking study focusing on polydatin to identify potential targets. Eur J Appl Sci 2023; 11 (05) 163-167
• 19 Yan L, Zhang Z, Liu Y. et al. Anticancer activity of erianin: cancer-specific target prediction based on network pharmacology. Front Mol Biosci 2022; 9: 862932
• 20 Luo Y, Fu Y, Tan T. et al. Screening of lncRNA-miRNA-mRNA coexpression regulatory networks involved in acute traumatic coagulation dysfunction based on CTD, GeneCards, and PharmGKB Databases. Oxid Med Cell Longev 2022; 2022 (01) 7280312
• 21 Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly CJ. Comparative toxicogenomics database (CTD): update 2023. Nucleic Acids Res 2023; 51 (D1): D1257-D1262
• 22 Wang Y, Lao Y, Li R. et al. Network pharmacological analysis and experimental study of melatonin in chronic prostatitis/chronic pelvic pain syndrome. Naunyn Schmiedebergs Arch Pharmacol 2024; 397 (11) 8691-8706
• 23 Zu G, Sun K, Li L, Zu X, Han T, Huang H. Mechanism of quercetin therapeutic targets for Alzheimer disease and type 2 diabetes mellitus. Sci Rep 2021; 11 (01) 22959
• 24 Gong L, Whirl-Carrillo M, Klein TE. PharmGKB, an integrated resource of pharmacogenomic knowledge. Curr Protoc 2021; 1 (08) e226
• 25 He Q, Liu C, Wang X. et al. Exploring the mechanism of curcumin in the treatment of colon cancer based on network pharmacology and molecular docking. Front Pharmacol 2023; 14: 1102581
• 26 Wimalagunasekara SS, Weeraman JWJK, Tirimanne S, Fernando PC. Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice (Oryza sativa). J Genet Eng Biotechnol 2023; 21 (01) 69
• 27 Wu Z, Yang T, Ma H. Molecular mechanism of modified Huanglian Wendan decoction in the treatment of polycystic ovary syndrome. Medicine (Baltimore) 2023; 102 (15) e33212
• 28 Sherman BT, Hao M, Qiu J. et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res 2022; 50 (W1): W216-W221
• 29 Wakale V, Kachave R, Gholap P, Mahajan K, Tare H. Design and discovery of genistein-based drugs as a potential tyrosine kinase inhibitor for lung adenocarcinoma through hybrid in silico methods. International Journal of Drug Delivery Technology 2023; 13 (04) 1422-1427
• 30 Deore S, Kachave R, Gholap P, Mahajan K, Tare H. Computational identification of methionyl-tRNA-synthetase inhibitors for Brucella melitensis: a hybrid of ligand-based classic 3-point pharmacophore screening and structure cavity guided blind docking approach. Int J Pharm Qual Assurance 2023; 14 (04) 1151-1157
• 31 Tajane P, Kayande N, Bhosale A, Deore S, Tare H. Design and discovery of silmitasertib-based drugs as a potential casein kinase II inhibitor for cholangiocarcinoma through hybrid in-silico ligand-based virtual screening with molecular docking method. Int J Drug Deliv Technol 2023; 13 (04) 1514-1519
• 32 Tare H, Vaidya V, Fulmali S, Jadhao S, Wankhade M, Bhise M. Transcriptomic insight and structural integration: repositioning FDA-approved methotrexate derivative for precision therapy in lung cancer through drug-drug similarity analysis and cavity-guided blind docking. Int Res J Multidisciplinary Scope 2024; 5 (01) 631-639
• 33 Mujawar T, Tare H, Deshmukh N, Udugade B, Thube U. Repurposing FDA-approved anastrozole-based drugs for breast cancer through drug-drug transcriptomic similarity and cavity detection guided blind docking. Int J Drug Deliv Technol 2023; 13 (04) 1172-1177
• 34 Gaikwad A, Kayande N, Tare H, Udugade B, Kachave R. In silico design and development of multi-target agents targeting glycogen synthase kinase-3 beta and vascular endothelial growth factor receptor 2 for acute myeloid leukemia. Int J Drug Deliv Technol 2023; 13 (04) 1428-1434
• 35 Tare H, Thube U, Kachave R, Wagh V, Udugade B. Catechins as catalase modulators: a comprehensive in silico analysis unveiling their potential antioxidant effects. Int J Drug Deliv Technol 2023; 13 (04) 1156-1160
• 36 Raman APS, Kumari K, Jain P. et al. In Silico evaluation of binding of 2-deoxy-D-glucose with Mpro of nCoV to combat COVID-19. Pharmaceutics 2022; 14 (01) 135
• 37 Wishart DS, Tian S, Allen D. et al. BioTransformer 3.0-a web server for accurately predicting metabolic transformation products. Nucleic Acids Res 2022; 50 (W1): W115-W123
• 38 Senthilraja P, Kathiresan KJ. In vitro cytotoxicity MTT assay in Vero, HepG2 and MCF-7 cell lines study of Marine Yeast. J Appl Pharm Sci 2015; 5 (03) 80-84
• 39 Tan T, Li S, Hu W. et al. Efficacy and safety of nab-paclitaxel plus platinum in non-small cell lung cancer: a meta-analysis. Front Med (Lausanne) 2023; 10: 1139248
• 40 Talakesh T, Tabatabaee N, Atoof F. et al. Effect of nano-curcumin on radiotherapy-induced skin reaction in breast cancer patients: a randomized, triple-blind, placebo-controlled trial. Curr Radiopharm 2022; 15 (04) 332-340