Network- Based Approach for In-Silico Validation in targeting BRCA1 Gene & LC-MS/MS quantification of Epigallocatechin-3-Gallate Gold Complex Loaded Nanoparticles (EGCG-Au-NP)

Sougata Mallick, Rituparna Das, Chowdhury Mobaswar Hossain, Dharmajit Pattanayak, Atanu Chatterjee, Durgesh Ranjan Kar, Paramita Dey,

doi:10.5281/zenodo.17760791

Research Paper | Open Access
Volume 03 | Issue 11 | Article Id IJPS/250311645

Network- Based Approach for In-Silico Validation in targeting BRCA1 Gene & LC-MS/MS quantification of Epigallocatechin-3-Gallate Gold Complex Loaded Nanoparticles (EGCG-Au-NP)
Sougata Mallick* Rituparna Das Chowdhury Mobaswar Hossain Dharmajit Pattanayak Atanu Chatterjee Durgesh Ranjan Kar Paramita Dey
^1,2,4,5,7Bengal School of Technology, Delhi Road, Sugandha, Hooghly-712102, West Bengal, India.
³Department of Pharmaceutical Technology, Maulana Abul Kalam Azad University of Technology, Haringhata 741249, West Bengal, India.
⁶BCDA college of Pharmaceutical Technology, Campus-2, Ghosh Para Road, Madhyamgram, Kolkata-700129, West Bengal, India.

Abstract

Since breast cancer continues to rank among the top causes of cancer-related death for women globally, new treatment approaches with improved efficacy and lower toxicity must be investigated. One of the main polyphenolic components of green tea, epigallocatechin-3-gallate (EGCG), has strong anticancer effects, such as inducing apoptosis, arresting the cell cycle, and preventing metastases. However, clinical applicability is limited by its low bioavailability and poor stability. Gold nanoparticles (AuNPs) are biocompatible nanocarriers that can enhance tumor-specific targeting, stability, and drug delivery. In this computational investigation, we looked into how EGCG-loaded AuNPs interacted with molecular targets related to the development of breast cancer. In order to clarify the stability and affinity of EGCG–Au-NP conjugates toward important oncogenic proteins such as HER2, PI3K/AKT signaling components, and estrogen receptor-?, molecular docking, molecular dynamics simulations, and binding energy estimates were utilized. Strong binding contacts, robust structural dynamics, and improved bioavailability profiles of EGCG following AuNP conjugation were all shown by the data. According to these results, EGCG-loaded gold nanoparticles might be a promising nanotherapeutic approach for the treatment of breast cancer, deserving of more in vitro and in vivo testing. Moreover, we are supposed to do comparative study between EGCG-Au complex and EGCG by checking their pharmacokinetic profile (Pk or Bioavailability study) in LC-MS/MS. The aim of the present study is to find out the pharmacokinetic profile of Epigallocatechin gallate and to use that result for the purpose of comparative study with that of Epigallocatechin gallate gold (EGCG-Au) nanoparticle using LC-MS/MS in order to find out their bioavailability so that EGCG can more efficiently be used as a source of medicinal product in order to cure breast cancer & other disease.

Keywords

EGCG-Au complex, Network pharmacology, BRCA1 gene, Pk Study

Introduction

One of the most common cancers in women and a leading cause of cancer-related death globally is breast cancer. Limitations including systemic toxicity, multidrug resistance, and poor selectivity for tumor cells continue to impede therapeutic outcomes despite advancements in surgery, chemotherapy, radiation, and targeted therapies [1,2]. This has led to a search for new therapeutic approaches that integrate tailored delivery, safety, and efficacy. Given their low toxicity and capacity to affect a variety of signaling pathways, natural bioactive chemicals have drawn more and more attention as possible anticancer medicines. Green tea contains a polyphenolic catechin called epigallocatechin-3-gallate (EGCG), which has been extensively researched for its anti-inflammatory, anti-cancer, and antioxidant qualities. By triggering apoptosis, preventing angiogenesis, reducing metastasis, and altering important signaling pathways like PI3K/AKT, MAPK, and NF-κB, EGCG has anticancer benefits [3]. However, its low bioavailability, fast metabolism, and poor stability severely restrict its therapeutic use. Drug delivery methods based on nanotechnology have become viable means of improving the pharmacological profile of natural substances in order to get around these obstacles [4,5]. Particularly, gold nanoparticles (AuNPs) have a number of benefits, including a high surface-to-volume ratio, biocompatibility, simplicity of functionalization, and the capacity to enhance drug stability and targeted delivery [6,7]. In addition to improving AuNPs' solubility and stability, functionalizing them with EGCG promotes controlled release and selective accumulation at tumor locations through the enhanced permeability and retention (EPR) effect [8,9,10]. In this regard, before experimental validation, computational methods offer important insights into drug–nanoparticle interactions and their molecular mechanisms of action [11]. When EGCG-loaded AuNPs interact with important breast cancer-related targets like HER2, PI3K/AKT pathway proteins, and estrogen receptor-α, their stability, affinity, and conformational behavior can be investigated using methods like molecular docking, molecular dynamics (MD) simulations, and binding energy calculations [12,13]. The current work is to use computational modeling to examine the therapeutic potential of EGCG-loaded gold nanoparticles in the treatment of breast cancer, emphasizing their binding effectiveness, structural stability, and potential mechanistic involvement in modifying oncogenic signaling cascades [14]. Apart from that LC-MS/MS and UPLC-MS/MS as essential tools for the accurate quantification and pharmacokinetic analysis of tea polyphenols, particularly catechins like epicatechin-3-gallate (EGCG), epigallocatechin (EGC), epicatechin-3-gallate (ECG), and epicatechin (EC) [15,16,17]. Validated methods have demonstrated high sensitivity and reproducibility in various biological matrices, enabling reliable bioavailability assessments. Studies also emphasize tissue distribution, blood brain barrier (BBB) permeability, and formulation-dependent pharmacokinetics [18], reinforcing the versatility of LC-MS/MS in both preclinical and clinical settings. Collectively, these findings support the continued use of LC-MS/MS in advancing pharmacokinetic profiling and therapeutic evaluation of tea polyphenols [19,20].

MATERIALS AND METHODS:

Materials Required: All chemicals used for the experimental purpose were of analytical grade. Epigallocatechin Gallate (EGCG) (Mol wt.; 458.37), Chloroauric acid (HAuCl4)/Gold (III) Chloride solution (mol wt.: 339.79), EDTA sodium, Rectified Spirit denatured (99.99%), Extra pure methanol ware purchased from Sigma Aldrich (St, Louis, MO, USA). Butterfly needle (22x3/4, 0.70x19mm) was purchased from BD medical, Eppendorf Tube (5ml) from Lab India Pvt Ltd. Animals Required: Our Institutional animal house is CPCSEA (Regulatory Body of the Government) approved having registration number (1726/PO/Re/S/14/CPCSEA) For our Pharmacokinetic study we have taken 24 New Zealand White Rabbits (OECD 423) in each experiment for each set of drug molecule (EGCG, EGCG-Au-NP, Rutin as internal standard; IS) having weight 1.5 to 2 Kg of either sex. The animals were housed for 15 days in day-night cycle, with free access to food and drinking water ad libitum for 10 days before the experiment. The condition of animal house is ambient having 20-26 ? with 45-65% relative humidity.

Synthetic scheme: To create EGCG-loaded GNPs, a green synthesis method was employed, in which EGCG was utilized to decrease HAuCl4 in aqueous solution [21]. This method avoids the need of dangerous man-made chemicals and is clean, easy to use, effective, and environmentally friendly. In order to create EGCG-capped GNPs, EGCG's eight ortho phenolic hydroxyl groups combine with Au3+ ions to form complexes that aid in their reduction to gold ions [22, 23]. Because EGCG's phenolic groups are acidic and provide a negative charge at pH 7.4, they may help stabilize GNPs in addition to functioning as a reducing agent [22]. The surface Plasmon resonance (SPR) phenomenon caused the EGCG and HAuCl4 mixture's color to shift from pale yellow to purple red in under 15 minutes, indicating the creation of gold nanoparticles [23].

Probable compounds: Those probable compounds are determined by using Chem draw Pro8.0 software, but to get the exact structural elucidation we need to perform UV Vis spectra, Mass spectra, FTIR, NMR, Differential scanning calorimetry (DSC) and X ray diffraction (XRD) [21,22]. There are five (5) probable structure are possible virtually. According to a previous survey, the method of continuous variation of equimolar solutions produced the majority of the results regarding the stoichiometric composition of bioflavonoid complexes. As a result, the complex's stoichiometry was examined using Job's method with both water and methanol [22,23].

Table: 1. The Probable structure of EGCG-Au chelation complex

Name

Structure

Property

Compound1

Compound2

Compound3

Compound4

Compound5

Network pharmacology studies:

By targeting receptor tyrosine kinase signaling (particularly EGFR and Src) and downstream pathways (PI3K/Akt, STAT3/Bcl-2), EGCG (epigallocatechin-3-gallate) inhibits the progression of breast cancer by lowering proliferation, migration, and invasion, according to recent network-pharmacology + experimental study [24]. The multi-target nature of EGCG (cell cycle, apoptosis, inflammation, and epigenetic regulation) and its frequent discovery of hub proteins across malignancies are regularly reported in broader reviews and multi-omics/network research. Bioavailability is still a common warning [24, 25]. The list of potential targets for EGCG has been enlarged to include human proteins by recent computer screens that use structure-based target prediction (e.g., Pro BiS-Dock) in conjunction with PPI/enrichment analysis. These screens have also revealed KEGG pathways that are enriched among the targets [25, 26]. Frequently reported targets and routes (high-frequency) EGFR, Src, PI3K, and AKT are protein kinases. Survival/apoptosis: caspases, BCL2, and STAT3. Both MAPK signaling and cell cycle regulators & Epigenetic and immunological/inflammatory modulators (in certain studies) [27].

Detailing of Smile structure of relevant or possible green tea extracts:

Table: 2. The Smile structure of Possible green tea extracts.

Phytochemical constituents	Smile structure
CATECHIN	C1[C@@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C=C3)O)O)O
CHLOROGENIC-ACID	C1[C@H]([C@H]([C@@H](C[C@@]1(C(=O)O)O)OC(=O)/C=C/C2=CC(=C(C=C2)O)O)O)O
CYANIDIN-3,5-DI-O-GLUCOSIDE	C1=CC(=C(C=C1C2=C(C=C3C(=CC(=CC3=[O+]2)O)O[C@H]4[C@@H]([C@H]([C@@H]([C@H](O4)CO)O)O)O)O[C@H]5[C@@H]([C@H]([C@@H]([C@H](O5)CO)O)O)O)O)O.[Cl-]
CYANIDIN-3-O-GLUCOSIDE	C1=CC(=C(C=C1C2=[O+]C3=CC(=CC(=C3C=C2O[C@H]4[C@@H]([C@H]([C@@H]([C@H](O4)CO)O)O)O)O)O)O)O.[Cl-]
DELPHINIDIN-3-O-GLUCOSIDE	C1=C(C=C(C(=C1O)O)O)C2=[O+]C3=CC(=CC(=C3C=C2O[C@H]4[C@@H]([C@H]([C@@H]([C@H](O4)CO)O)O)O)O)O.[Cl-]
EPIGALLOCATECHIN-3-GALLATE	C1[C@H]([C@H](OC2=CC(=CC(=C21)O)O)C3=CC(=C(C(=C3)O)O)O)OC(=O)C4=CC(=C(C(=C4)O)O)O
P-COUMARIC-ACID	C1=CC(=CC=C1/C=C/C(=O)O)O
PELARGONIDIN-3,5-DI-O-GLUCOSIDE	C1=CC(=CC=C1C2=C(C=C3C(=CC(=CC3=[O+]2)O)O[C@H]4[C@@H]([C@H]([C@@H]([C@H](O4)CO)O)O)O)O[C@@H]5[C@H]([C@@H]([C@H]([C@@H](O5)CO)O)O)O)O
PELARGONIDIN-3-O-GLUCOSIDE	C1=CC(=CC=C1C2=[O+]C3=CC(=CC(=C3C=C2OC4C(C(C(C(O4)CO)O)O)O)O)O)O

Typical network-pharmacology workflow (reproducible): The following detailing are required for to determine the network pharmacology.

Gather targets connected to the compound: we are using Pro BiS-Dock (structure docking to PDB), Swiss Target Prediction, Pharm Mapper, SEA, Binding DB, STITCH, and literature mining. (Deduplicate and combine) [28].
Gather disease targets (cancer of the breast): we have utilized GEO datasets for subtype-specific lists, TCGA differential expression (optional), OMIM, Gene Cards, and DisGeNET. Intersect compound vs disease targets [29].
PPI network construction: For this we use STRING (score cutoffs e.g. >0.7), import to Cytoscape.
Network analysis & Identify hub genes (degree, MCC): It requires Cyto Hubba or Network Analyzer [28, 29].

For to determine Functional enrichment GO / Kyoto Encyclopedia for genes & Genomes (KEGG) using cluster Profiler (R) or DAVID / Enrich it will Focus on pathways (EGFR signaling, PI3K-Akt, apoptosis). Molecular docking is done to Dock EGCG against top hub targets (Auto Dock Vina, Pro BiS-Dock, or commercial tools). Evaluate binding energies and key interactions. Moreover, Optional MD simulation is used for the best docked complexes (validate stability) [30].

In vitro / in vivo validation:
Test effects on cell proliferation, migration/invasion, western blots for pathway proteins (EGFR/Src/PI3K/Akt/STAT3/Bcl-2) — many NP studies pair computational predictions with these assays.

Practical parameters & tips: When pulling targets from predictive databases, keep a union of sources then filter by confidence (e.g., Swiss Target Probability > 0.1, STRING score > 0.7) [31, 32]. For PPI, using high confidence edges reduces false hubs, but you may miss weak/novel interactions; consider sensitivity checks. In enrichment, report both adjusted p-value and gene ratio; highlight top pathways with biological plausibility (EGFR/PI3K/Apoptosis) [25, 32]. For docking, prepare protein structures (remove waters, add hydrogens), use at least 3 protein structures for a target if available (different PDBs) to ensure robustness [33].

Fig: 1. Venn diagram of targets which hits the genes of Breast cancer and 3 common genes responsible mostly in inflammatory networking pathways

Fig: 2. Kyoto Encyclopedia for genes & Genomes (KEGG) derived Pathway describe the interlinked genes and their disease

Fig: 3. Building network of genes and EGCG along with another disease

Docking studies

Molecular docking studies of Epigallocatechin-3-Gallate (EGCG) with the BRCA1 gene product have revealed a strong and stable interaction that supports its potential anticancer role in breast cancer [33]. Using computational tools such as Auto Dock Vina and PyRx, the three-dimensional structure of the BRCA1 protein (typically its BRCT domain, PDB ID: 1JM7) was docked with the EGCG ligand obtained from PubChem. The docking results demonstrated a binding energy of approximately −6.5 to −8.5 kcal/mol, indicating a high affinity between EGCG and BRCA1[30,33,34]. The binding occurred mainly within the BRCT domain cleft, forming multiple hydrogen bonds with key amino acid residues such as LYS350, TYR232, TRP266, and VAL241, along with several hydrophobic and π–π interactions that stabilize the complex [35]. These interactions suggest that EGCG could modulate BRCA1’s DNA repair and tumor suppressor activities, potentially restoring or enhancing its function in breast cancer cells. Thus, EGCG’s binding to BRCA1 provides a molecular basis for its chemo preventive and therapeutic potential in BRCA1-related breast cancer [34,35].

Brca1gene

Ligand

Docking Interaction

Fig: 4. The Target gene (BRCA1), Ligand (EGCG) and their docking interactions.

In-Vivo Pharmacokinetics/Bioavailability study (Pk):

The pharmacokinetics study is done to maximize the therapeutic potential of herbal products, ensure their safety, and standardize their use in traditional and alternative medicine, research is essential.
Green tea contains a significant catechin called epigallocatechin gallate (EGCG), which has a variety of biological properties such as anti-inflammatory, anti-cancer, antioxidant, and neuroprotective benefits [36]. Being a plant-based product, it has several advantages over more hazardous synthetic alternatives, including lower toxicity, lower cost, and increased availability. However, its quick metabolism and low absorption frequently restrict its therapeutic usefulness.
Indeed, the only way to properly use medicinal herbs is through the rational and empirical application of plants, which is based on pharmacological and chemical research. Furthermore, phytochemicals work better when applied in a setting that naturally revitalizes the body. Using LC-MS/MS, the current study aims to determine the pharmacokinetic profile of epigallocatechin gallate and use the results to compare with those of Epigallocatechin gallate gold (EGCG-Au-NP) nanoparticles in order to determine their bioavailability [37]. This will allow EGCG to be used more effectively as a source of medicinal products to treat a variety of diseases. The following are to be requirements for the Pharmacokinetics study [38,39].

Administration of EGCG, EGCG-Au-NP and Rutin (RU) as internal standard (IS)
Blood sample collection at our Institutional lab at Bengal school of Technology. India.
Evaluating of parameters like C_max, Tmax, t_1/2, CL, V_d

The following animal groups are taken for drug administration via oral route and withdraw blood as per the study plan and maintaining OECD 423 guidelines. The protocol was approved by IAEC-CPCSEA of this Institution bearing approval no: 1726/CCSEA/IAEC/2024-040.

Table: 3. Animal groups having 6 New Zealand White Rabbits each group for set of experiments of either sex.

Groups	Dose	No. of Animals
Group 1	Normal Control	6
Group 2	EGCG-Au/NP/RU dose 1 (400 mg/kg bw)	6
Group 3	EGCG-Au/NP/RU dose 2 (200 mg/kg bw)	6
Group 4	EGCG-Au/NP/RU dose 3 (100 mg/kg bw)	6

Study Plan:

LC-MS/MS System Condition:

Samples were analysed through HPLC (Agilent 1200 series) and MS/MS (Agilent 6410 triple quadrupole) systems on a Hypurity C18 column (50 × 4.6 mm) at Indian Institute of Chemical Biology (IICB) Kolkata & NIPER Kolkata, using an injection volume of 5 µL. The mobile phases consisted of water (A) and acetonitrile (B). The gradient elution system was optimized as follows: 0–3.0 min 20% - 100%B, 3.0–4.5 min 100% - 100% B, 4.5–4.6 min 100% - 20% B, 4.6–7.0 min20% B. The flow rate was 0.6 mL/min. MS/MS data acquisition was performed under negative electrospray ionization (ESI) mode [38,40]. The Multiple Reaction Monitoring (MRM) mode was employed to monitor EGCG, EGCG-Au-NP and Rutin RU (IS) with the precursor-to-product ion transition of m / z 457.3 / 169.3, m / z 481.3 / 125.0 , m/ z 367.3 / 149.3[41] and m / z 609.4 / 300.2 [42], respectively. The parameters were optimized as follows: a capillary voltage of−4000 V, a gas flow rate of 12 L / min, and the dry gas temperature of 350^?C. For EGCG, EGCG-Au and IS, the fragmentary voltages were 100 V, 150 V, 120 V, 220 V, respectively, and the collision energies were 12 eV, 15 eV, 15 eV, and 30 eV, respectively. The collected mass spectrometric data were processed jointly by Mass Hunter software (B.02.01, Agilent Technologies, USA) at IICB Kolkata & NIPER Kolkata.

Preparation of Standard Solutions and Calibration Curves: The appropriate amounts of the chromatographic standards of EGCG, EGCG-Au-NP and the IS were accurately weighed and dissolved in 10% methanol to prepare the stock solutions (1 mg/mL) of different analytes [43]. The working solutions for calibration curve and the quality control (QC) samples were prepared by diluting the stock solutions with 90% methanol. IS was diluted to 5 µg/mL. All the stock and working solutions were stored at 4?C, away from direct light [43,44]. For analysing the plasma and tissue homogenates, calibration curve samples were prepared at the concentrations of 2, 10, 50,250, 500, and 1000 ng/mL. The QC samples were prepared by spiking into plasma at the appropriate volumes of different stock solutions to achieve the concentrations of 4, 400 and 800 ng/mL [45].

RESULT AND DISCUSSION

Molecular docking and Network Pharmacology study: Metal-containing ligands (like an EGCG–Au complex) are a frequent headache in docking. Because the docking setup or the scoring method can’t handle a metal center properly. Gold has multiple oxidation states (Au(I), Au (III)) and its coordination chemistry strongly depends on that. If the charge on the complex is wrong, docking will fail or give unrealistic results. Docking tool can't assign a van der Waals / atom type for Au, producing errors or skipped ligand atoms. If the Au forms a coordination (partial covalent) bond with a ligand atom (S, N, O), the docking scoring that assumes only non-bonded interactions will be wrong.

To investigate its binding affinity and any inhibitory interactions that might support its anticancer efficacy against breast cancer, Epigallocatechin-3-Gallate (EGCG) was molecularly docked with the BRCA1 protein. The three-dimensional structure of EGCG was taken from the PubChem database and refined before docking, while the three-dimensional structure of BRCA1 (obtained from the Protein Data Bank, PDB ID: e.g., 1JM7 or 1T15) was utilized as the receptor.

Auto Dock Vina or another program was used for docking, and the binding energy (ΔG, kcal/mol) was used to measure the binding affinity. With a docking score of roughly -6.9 kcal/mol, EGCG demonstrated a robust binding affinity with BRCA1, indicating the creation of a stable and energetically advantageous complex. A crucial tumor suppressor gene, BRCA1 promotes homologous recombination for DNA repair, preserving genomic stability. Breast and ovarian cancers are closely associated with BRCA1 mutations or loss of function. Green tea's main catechin, EGCG, has been extensively researched for its anti-inflammatory, antioxidant, and anti-cancer effects. According to the current docking data, EGCG may interact with BRCA1 and stabilize its active conformation or modify its DNA repair function in order to partially exercise its anticancer activity. The effectiveness of BRCA1-mediated DNA repair may be improved by such binding, or it may disrupt faulty signaling pathways that aid in the development of tumors. According to the molecular docking study, EGCG has a favorable binding energy and several stabilizing interactions when it comes to the BRCA1 protein. The strong hydrogen bonding network, together with π–π stacking and hydrophobic contacts, indicates a high affinity of EGCG for the BRCT domain of BRCA1. This interaction suggests that EGCG could serve as a natural modulator of BRCA1, offering a promising avenue for breast cancer chemoprevention or therapy.

Pharmacokinetics Study:

Optimization of LC–MS/MS Conditions: Many studies have published the quantitative analyses of EGCG, EGCG-Au-NP in biological matrices [45], though; these methods have been compromised with poor sensitivity [46]. In this study, we developed a novel analytical method to resolve the above-mentioned problems. Several combinations of mobile phases (methanol-water or acetonitrile-water) were tested to optimize the chromatographic conditions [46,47]. The analytes showed nicely-shaped peaks and higher responses in the negative ion mode when the acetonitrile-water system was used as the mobile phase. We resorted to a gradient elution program for minimizing the interferences between the analytes and endogenous constituents of the biological matrices. The peaks of EGCG, EGCG-Au-NP and IS were eluted before 4.5 min. The column was then equilibrated for 2.5 min. RU was employed as the IS in this analysis. The stock solutions of EGCG, EGCG-Au-NP and RU (IS) were scanned in the positive or negative ESI modes. The peak intensities of the analytes were the highest under the negative ion mode, which might be due to the presence of several easily ionisable hydroxyl groups in the analytes under that mode. After several trials, the m/z 367.3/149.3 (EGCG-Au-NP), 457.3/169.3 (EGCG), and 609.4/300.2 (RU) showed high sensitivities in the negative ion mode [47].

Table: 4. The intra-day and Inter-day values of accuracy & precision, the recovery & matrix effect of ECGC, ECGC-Au-NP and IS in rabbit plasma (Stock 4,400,800ng/mL) (n=6) (#p<0.01, significant difference when compared with the control group), (*p<0.05, significant difference when compared with the control group).

Nominal concentrations (ng/mL)	compound	Intra-day Mean± SD (ng/mL)	Intra-day Precision (RE %)	Intra-day Accuracy (RSD %)	Intraday Mean± SD (ng/mL)	Intra-day Precision (RE %)	Intra-day Accuracy (RSD %)	Recovery Mean±SD	Matrix effect Mean±SD
4.0	EGCG	3.8±0.1	-3.7	4.4	3.9±0.2	-2.9	1.9	82.5±4.2	92.6±5.3
400.0	EGCG	397.2±3.1	-1.2	2.3	399.3±9.6*	-0.6	1.7	82.5±4.2*	102.5±3.4#
800.0	EGCG	804.7±56.8	0.9	1.7	805.7±13.6	1.2	0.9	77.3±1.6	96.4±2.7
4.0	EGCG-Au	3.7±0.5	-4.2	5.7	3.9±0.2	-2.4	1.9	81.1±4.7	90.5±5.2
400.0	EGCG-Au	403.3±5.4	1.2	2.2	404.5±8.9*	2.3	4.2	79.7±3.6*	94.6±4.5#
800.0	EGCG-Au	802.5±43.1	0.4	1.4	806.8±23.2	2.4	1.2	83.1±3.1	101.6±3.3
4.0	IS (Rutin)	4.1±0.2	2.7	1.4	4.1±0.1	3.9	2.8	78.2±3.7	94.3±3.1
400.0	IS (Rutin)	399.6±6.3	-0.7	1.1	397.3±11.4*	-1.7	2.6	81.3±5.1*	98.7±5.8#
800.0	IS (Rutin)	800.8±43.2	0.3	1.0	798.5±74.6	-1.2	1.2	82.3±5.2*	103.6±4.7

Table: 5. The Stability of ECGC, ECGC-Au-NP and IS (Stock 4 & 800ng/mL) in rabbit plasma (n=6) (#p<0.01, significant difference when compared with the control group), (*p<0.05, significant difference when compared with the control group).

Nominal concentrations (ng/mL)

Auto sampler for 24 hr.

Room temperature for 5 hr.

-70? for 21 days

Three freeze-thaw cycles

Mean±SD

RSD (%)

Mean±SD

RSD (%)

Mean±SD

RSD (%)

Mean±SD

RSD (%)

EGCG

4.0

800

4.1±0.2

800.2±12.3

1.3

0.5

3.7±0.1*

803.1±35.5

4.7

2.2

3.6±0.1

790.3±91.4

5.7

4.3

3.8±0.1

793.6±56.2

4.8

5.7

EGCG-Au-NP

4.0

800

4.1±0.4

807.3±32.7

1.5

1.3

4.2±0.1*

815.3±84.5

2.5

2.9

4.3±0.3

804.6±73.1

4.3

1.2

3.7±0.1

782.4±78.1

4.2

5.9

IS (Rutin)

4.0

800

4.0±0.2

803.6±46.7

2.2

1.3

3.8±0.1*

812.4±23.7#

3.2

2.4

4.2±0.2

783.2±78.9#

3.3

7.4

3.8±0.2

792.3±93.5#

3.9

4.3

Fig: 5. Mean plasma concentration time profile of EGCG in plasma following a single dose and multiple dose administration in rabbits (n=6)

Fig: 6. Mean plasma concentration time profile of EGCG-Au-NP in plasma following a single dose and multiple dose administration in rabbits (n=6)

Validation of the Assay:

The peaks of EGCG, EGCG-Au-NP, and IS were eluted at 1.9, 3.0, 3.8 and2.0 min, respectively. The endogenous substances in the plasma did not interfere with the detection of the analytes and IS. The response of the blank sample was found to be lower than 20% of the LLOQ response, which characteristically confirmed the absence of any carry-over [48, 49,50].

Linearity and Lower Limit of Quantification:

For all the biological matrices, the calibration curves of EGCG, EGCG-Au-NP ranged from 2 to 1000 ng/mL (R2> 0.995). The accuracy (RSD, %) and precision (RE, %) of the calibration standards were within 15% and ± 15%, respectively [50]. The LLOQs of EGCG, EGCG-Au-NP were calculated to be 2 ng/mL in different biological matrices, and the values of both RSD and RE were within ± 20%. The LLOQs were suitable for performing the pharmacokinetics and tissue distribution studies with an intragastric administration [49, 50].

Table: 6. Pharmacokinetic Parameters of EGCG-Au-NP, EGCG, and Rutin Following Single and Multiple Oral Doses in Rabbits

Precision and Accuracy:

The results of the intra-day (n = 6) and inter-day (n = 6) accuracies and precisions have been depicted here above. Accuracy (RE, %) for all analytes in different matrices ranged from -8.2–9.2 %, and precision (RSD, %) of all analytes in different matrices were below 10.3%. All RE and RSD values fulfilled the FDA guidance Bio-analytical Method Validation [51].

The Recovery and Matrix Effect:

The recoveries and matrix effects of EGCG, ECGC-Au-NP and Rutin have been shown above [52]. The recovery of all analytes ranged from 71.5% to 90.6%. No significant inhibition/enhancement was observed in the mass spectrometric signal of the rat plasma and tissue homogenates. Under the current analytical conditions, the matrix effects showed negligible values [53].

Application of the LC–MS/MS Method in the Pk Studies:

The developed LC–MS/MS method was applied to study the pharmacokinetic profiles plasma concentration-time profiles of EGCG, EGCG-Au-NP & Rutin in the New Zealand white rabbits after single or multiple doses of intragastric administration have been shown here. All pharmacokinetic parameters have been summarized [54,55]. After a single dose intragastric administration, the AUC and Cmax of EGCG-Au-NP in the plasma were 274.9 ng h/mL and 101.2 ±12.3 ng/mL, the AUC and Cmax increased significantly, as the other ingredients inhibited UDP-glucuronosyltransferase (UGT) enzyme or replaced a hydroxyl group on EGCG-Au-NP molecule, reduced the first-pass metabolism, and increased the oral bioavailability of EGCG-Au-NP [54,56]. After multiple doses, the AUC and Cmax of EGCG-Au-NP were not significantly different compared to the parameters of the single dose. R was 1.08, so no significant accumulation was observed [57, 58, 59].

Peak area isolated:

When EGCG-Au-NP was administered to rabbits in the form of the pharmacokinetic behaviour was significantly different from that of EGCG alone [60, 61]. AUC and Cmaxwere1604.6 ng·h/mL and 631.3 ± 17.5 ng/mL, respectively, which significantly increased compared to EGCG alone, as the glucuronidation and sulfation of tea polyphenols are the major elimination path-ways, after multiple administrations, R was0.99, indicating no significant cumulative effect [62,63].

Table: 7. Relative % area demonstration of EGCG and EGCG-Au-NP with respect to m/z resolution value

Resolution (m/z)	EGCG (% area)	ECGC-Au-NP (% area)
571	21.78	22.78
457	22.28	26.26
604	27.09	30.35
647	49.64	50.69
677	57.81	59.81
816	77.77	85.55
822	20.22	59.45
861	9.6	9.6

Fig: 7. C_max plasma concentration of EGCG and EGCG-Au-NP with respect to resolution (m/z).

Fig: 8. C_max plasma concentration of EGCG and EGCG-Au-NP

Although EGCG has a higher Cmax the overall shaded area (AUC) for EGCG-Au-NP is larger, indicating better bioavailability. EGCG-Au maintains higher concentrations for a longer duration, supporting its sustained release and improved systemic retention. This study successfully developed a simple, sensitive, and cost-effective LC–MS/MS method for the simultaneous quantification of EGCG and its gold nanoparticle conjugate (EGCG-Au-NP) in rabbit plasma and tissues. The pharmacokinetic analysis demonstrated consistent absorption and elimination profiles, with no evidence of significant accumulation, indicating the potential for safe repeated dosing [63]. Tissue distribution studies revealed preferential accumulation in the small intestine and moderate levels in key metabolic organs, while minimal distribution was observed in the brain and skin. These findings underscore the potential of EGCG and EGCG-Au-NP as viable candidates for targeted therapeutic applications and warrant further investigation for clinical translation. This analytical approach enables the determination of the relative bioavailability (BA) of a single compound. The accuracy and relevance of this assessment can be further enhanced by including tissue distribution studies involving organs such as the small intestine, liver, lungs, heart, spleen, and skin. As drug distribution patterns may vary across species and target organs, analyzing tissue extracts from these sites will provide deeper insight into organ-specific bioavailability and support translational applicability of the formulation.

ACKNOWLEDGEMENTS: The authors are indebted to the Management of Bengal School of Technology, Delhi Road, Sugandha, Hooghly-712102, for the financial support to carry this work. I am thankful to Mr. Sateesh Kumar Shaw and Mr. Sunil Saha from NIPER Kolkata & IICB Kolkata for performing LC-MS/MS study fully professionally. Also thankful to Mrs. Rituparna Das for helping in Synthesis, extensive & tedious animal study and Mr. Sudipto Modak from BCDA campus-2, Kolkata for Molecular simulation & docking study. I also grateful to my co-authors cum my Research Advisory Committee (RAC) members and my Supervisor and Co-Supervisor for guiding my research work motive.

Conflict of interest: This research work is ethically permitted by IAEC-CPCSEA Bengal School of Technology, West Bengal, India. The authors declare that there is no conflict of interest.

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