INTRODUCTION:

Cancer is a disease caused by uncontrolled cell proliferation, which can result in dangerous tissue damage for cancer patients¹. Bcl-2 influences both anti-apoptosis and pro-apoptosis proteins²^,³. The occurrence of cancer is due to failures in pathways during the apoptosis process. Failures are more frequent in the intrinsic pathway compared to the extrinsic pathway, as the intrinsic pathway is more sensitive and often caused by mutations in the p53 gene. Mutations in the p53 gene lead to gene dysregulation and apoptosis failure, resulting in cancer⁴^,⁵. α-Mangostin has exhibited an anti-metastatic role against prostate cancer. Treating prostate cancer cells with α-MG might decrease the expression of MMP-2/9 and u-PA activator⁶. α-Mangostin can suppress carcinogenesis at all stages (cell division, cell proliferation, apoptosis, inflammation, and metastasis)⁷. The expression levels of the B-cell lymphoma 2-associated X protein (BAX) and B-cell lymphoma 2 (Bcl-2) were measured using Western blot analysis⁸, revealing that the expression of the pro-apoptosis factor Bax increased, while Bcl-2 expression decreased in cells treated with specific concentrations of α-mangostin⁹. It exhibits anti-migratory and anti-proliferative actions in breast cancer cells through STAT3 inhibition. Additionally, the study significantly introduces a new nutraceutical for therapeutic intervention in invasive breast cancer¹⁰.

Previous studies indicate that α-Mangostin induces apoptosis, hindering migration and invasion of breast cancer cells through the phosphoinositide-3-kinase (PI3K)/serine-threonine kinase (AKT) signaling pathway by targeting retinoid X receptor α (RXRα), and cyclin D1 participates in this process¹¹. α-Mangostin also possesses potent binding properties and inhibits Bcl-xL based on high binding energy values through a well-validated molecular docking protocol¹². However, the study did not utilize α-Mangostin derivatives targeting Bcl-2, which influences cancer. The aim of this research is to predict the binding interactions of several α-Mangostin derivatives with the Bcl-2 receptor, as well as to determine their binding affinity and stability. It is hoped that these derivatives can be further developed into potential therapeutic drugs in the future.

METHODS:

Preparation Receptor, Ligand and ADMET Prediction:

Prediction of the physicochemical and pharmacokinetic properties of α-Mangostin and its derivatives was conducted. The chemical structures of the six α-Mangostin derivatives utilized in this study were from as described by Chi et al. (2018)¹³. The method employed for predicting physicochemical properties involved SWISS-ADME¹⁴. Evaluating absorption, distribution, metabolism, excretion (ADME) properties, and toxicity, the pkCSM tool was employed. This in silico analysis was conducted using SMILES data as input, allowing for the prediction of ADMET profiles for the compounds¹⁵. The physicochemical parameters required, such as Log P, molecules, number of rotatable bonds, hydrogen bond acceptors, hydrogen bond donors (HBD), and polar surface activity (PSA), were predicted by inputting the SMILES code into the pkCSM Online Tool application.

Homology modeling and Molecular Docking:

Homology modeling using SWISS-MODEL follows a straightforward process. First, acquire the target protein sequence in FASTA format from databases such as UniProt or NCBI¹⁶. Access the SWISS-MODEL website, input the sequence, and initiate the search for homologous templates from the Protein Data Bank (PDB). Select the most suitable template based on sequence identity and resolution. The tool aligns the target sequence with the template, it generates the 3D structure. Evaluate the model using GMQE and QMEAN scores to assess its quality. The validation of the homology modeling that we carried out can be found in our previous research¹⁷. The three-dimensional structure of the Bcl-2 protein was downloaded from protein data (www.rcsb.org) with the Structure of human Bcl-2 in complex (PDB ID: 4IEH)¹⁸. Receptor and native ligand preparation were conducted using Gnina¹⁹, followed by saving the format in pdb. Gridbox was utilized to determine the receptor area for ligand docking based on x, y, and z coordinates. The grid box values on the protein PDB ID: 4IEH were determined using autobox with the Convolutional Neural Network algorithm.

The chemical structures of α-Mangostin and its derivatives were drawn using the MarvinSketch software. Optimization was performed using the Avogadro application, employing the General Amber Force Field molecular mechanics force field²⁰. We utilized deep learning-based software, specifically the convolutional neural network Gnina 1.0.3²¹^,²²^,¹⁹. Additionally, software tools such as Discovery Studio Visualizer Version 2020, Protein Data Bank, PubChem, Marvin Sketch, and Avogadro Version 1.2.0 were used²³. The interactions resulting from docking the ligands of α-Mangostin derivatives were visualized using the Discovery Studio Visualizer.

Molecular Dynamics Simulation:

Praparation protein using pdb2gmx. Ionization was carried out with NaCl with netral pH. A periodic Boundary Condition system with Particle-Mesh Ewald (PME) and Fast Fourier Transform (FFT) was established. amber99sb-ILDN²⁴ force fields were applied for protein and Acpype using for prepare the ligands²⁵, alongside the TIP3P water model, incorporating Hydrogen mass repartitioning²⁶. The equilibration process involved an NVT ensemble, followed by production simulation with an NPT ensemble at a temperature of 310 K. All simulations were conducted using Gromacs 2022.2 software, with a total simulation time of 100 nanoseconds²⁷^,²⁸. Free energy calculations and residue decomposition from molecular dynamics simulations were carried out using all frames with the gmxMMPBSA tool²⁹^,³⁰.

RESULTS AND DISCUSSION:

Preparation Receptor, Ligand and ADME Prediction:

The physicochemical properties of the α-Mangostin compound (Figure 1) and its derivatives are presented in Table 1. This table provides comprehensive details regarding various physical and chemical characteristics, such as solubility, molecular weight, polarity, and other essential attributes, offering a comparative analysis of these properties among α-Mangostin and its derivative compounds. This information contributes to a deeper understanding of their structural characteristics, aiding in the assessment of their potential biological activities and pharmaceutical applications.

Table 2. Determining the pharmacokinetic properties of derivate (D) α-Mangostin compounds

Category	Derivates (D)	α-Mangostin	D1	D2	D3	D4	D5	D6
Absorption	Water solubility (log mol/L)	-4.497	-4.085	-3.183	-4.17	-4.244	-3.699	-4.321
	CaCo₂ permeability (nm/second)	0.662	0.187	0.297	0.132	0.387	0.558	0.213
	Human intestinal absorbtion (%)	89.792	71.296	51.766	71.77	84.066	63.881	84.519
	P-glycoprotein	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Distribution	VDss (log L/kg)	-0.225	-0.097	0.712	-0.418	-0.446	0.202	-0.735
	Blood-brain barrier (BBB) permeability	-1.118	-1.394	-1.597	-1.38	-1.092	-1.329	-1.076
	CNS permeability	-2.114	96,119	-3.882	-3.181	-2.988	-3.698	-2.927
Metabolism	CYP2D6 inhibition	No	No	No	No	No	No	No
	CYP3A4 inhibition	No	No	No	No	No	No	No
	CYP1A2 inhibition	Yes	Yes	Yes	Yes	Yes	No	Yes
	CYP2C19 inhibition	Yes	No	No	No	Yes	No	Yes
Excretion	Total clearance (log ml/ min/kg)	0.482	-0.13	-0.033	0.051	0.51	-0.32	0.495
Excretion	Renal OCT2 substrate	No	No	No	No	No	No	No

The results of the HIA parameter are derived from the summation of bioavailability and absorption, evaluated from the excretion ratio through urine, bile, and feces. The categories for the HIA parameter are 70-100% (good category), 20-70% (moderate category), 0-20% (low category)³².

In the table, α-Mangostin compound and its derivatives fall into the good category except for derivative 5, which falls into the moderate category. P-glycoprotein serves as a biological barrier transporter by expelling toxins and xenobiotics from cells. Screening for P-glycoprotein transport was conducted using MDR-Knockout transgenic mice and in vitro cell systems³¹. The results indicate that α-Mangostin compound and its derivatives are absorbed by P-glycoprotein.

The distribution analysis encompassed evaluations of volume of distribution at steady state (VDss) (log L/kg), Plasma Protein Binding (PPB), and Blood-Brain Barrier (BBB) parameters. The Volume of Distribution signifies the hypothetical volume required for uniform drug dosage distribution to attain an equivalent concentration as that in blood plasma. A VDss below 0.71 L/kg (log VDss<-0.15) is considered low, while above 2.81 L/kg (log VDss>0.45) is considered high. Notably, only derivative 2 had a value of 0.712, considered high, indicating increased drug distribution at the same concentration as in the blood plasma³³^,³⁴.

The BBB prediction value delineates a compound capacity to breach the blood-brain barrier. Based on classification, a BBB value >2.0 signifies high absorption into the central nervous system (CNS), while values between 0.1-2.0 denote moderate absorption, and <0.1 indicates low absorption into the CNS³¹. Interestingly, the data obtained indicated no metabolism of derivatives through CYP2D6 and CYP3A4. Derivative 5 specifically showed no metabolism through CYP1A2, while derivatives 1, 2, 3, and 5 lacked metabolism through CYP2C19. There exists variability in the α-Mangostin derivatives ability to inhibit specific metabolic enzymes. This variability holds significant implications in understanding their potential interactions with other compounds within the body and their potential impact on metabolic processes.

The appendix highlights that all α-Mangostin derivative compounds do not influence OCT2 substrates, suggesting that these derivatives are not OCT2 substrates. Organic cation transporter 2 (OCT2) plays a vital role in renal uptake, influencing drug transfer and clearance of endogenous compounds in the kidney. It is worth noting that OCT2 substrates, when combined with OCT2 inhibitors, may lead to potentially adverse interactions³⁵.

Toxicity Analysis:

Toxicity testing plays an essential role in evaluating the safety of new drug candidates¹⁷, especially when developing compounds for therapeutic use. α-Mangostin, a natural compound known for its anticancer properties, has shown significant potential in various studies. To enhance its effectiveness and minimize side effects, researchers have created several derivatives. Examining the toxicity of these derivatives is key to understanding their safety and whether they could become viable drugs. In this study, we analyzed six α-mangostin derivatives, focusing on important toxicity factors like AMES toxicity, hERG inhibition, liver toxicity, and skin sensitization. The table below highlights the results, offering a clear view of the potential risks linked to each derivative (Table 3).

The toxicity analysis of the six α-mangostin derivatives provides a clear understanding of their potential risks and safety concerns. Most of the derivatives (1, 2, 4, 5, and 6) tested positive for AMES toxicity, meaning they could cause genetic mutations, which raises some red flags for their long-term use. Derivative 3, however, stood out as the only one without this mutagenic risk, making it potentially safer in that regard. None of the derivatives inhibited hERG I, which is reassuring since hERG I inhibition is often linked to serious heart issues. However, all six derivatives did inhibit hERG II, indicating they might still pose a moderate risk for heart-related side effects that should be looked into further.

On a more positive note, none of the derivatives caused liver toxicity or skin sensitization, suggesting that they would likely be safe for the liver and not trigger allergic skin reactions. This shows that despite some concerns about their potential mutagenic and heart-related risks, they do have promising safety profiles in other areas.

Table 4. Docking of α-Mangostin compounds and derivatives on macromolecules PDB ID: 4IEH

Compounds	Bond interaction	Residue amino acid	∆G kcal/mol	CNN Pose Score
α -Mangostin	Carbon hydrogen bond	Gly104, Leu160	-7.48	0.5762
	Van der waals	Arg65, Arg66, Arg105, Gly104, Gln58
	Alkil	Phe63, Arg66, Tyr67
	π- alkil	Ala59
D1	Conventional hydrogen bond	Arg66, Tyr67, Tyr161	-6.26	0.4324
D1	π alkil	Ala59, Tyr161	-6.26	0.4324
D2	Conventional hydrogen bond	Arg105, Asn102	-6.56	0.3933
	Van der waals	Tyr161
	π alkil	Ala108, Arg105
	Amide π-Stacked	Gly104
D3	π-Sigma	Tyr161	-7.39	0.4828
	Van der waals	Tyr161
	Alkil	Ala58, Val107
	π - π T-Shaped	Gly104
D4	Conventional hydrogen bond	Asp62, Arg105	-7.5	0.6592
	Alkil	Val107, Ala59, Phe157
	π -Cation	Arg66
	Carbon hydrogen bond	Gly104
D5	Conventional hydrogen bond	Arg105	-6.41	0.5551
D5	π -Alkyl	Arg105	-6.41	0.5551
D6	Conventional hydrogen bond	Asp62, Asn102	-7.95	0.6015
	Carbon hydrogen bond	Gly104
	π -Sigma	Tyr161
	π -cation	Arg66
	Alkyl	Ala59

Figure 2. Root Mean Square Deviation (RMSD) from Derivate 6 compare with native ligand

Based on the results of RMSD in Figure 2 analysis from molecular dynamics simulations, the original ligand (black line) shows higher stability compared to Derivative 6 (red line). The native ligand maintained lower and stable RMSD values, ranging from 0.2 to 0.4 nm, with slight fluctuations after 18 ns, indicating strong and consistent binding in the binding site. In contrast, Derivative 6 experiences larger RMSD fluctuations, ranging from 0.4 to 0.8 nm, with several peaks above 0.8 nm, indicating higher structural instability. This indicates that Derivative 6 undergoes more conformational changes and may have weaker binding interactions than the native ligand, indicating a less optimal fit within the binding site.

Figure 3. Root Mean Square Fluctuation (RMSF) in amino acids during the simulation between Derivate 6 and Native ligand

The root mean square fluctuation (RMSF) values (Figure 3) for complexes containing the B-cell lymphoma 2 receptor (Bcl-2), the native ligand, and Derivate 6 were examined over time in a 100-nanosecond molecular dynamics (MD) simulation using Gromacs. Variations in RMSF for individual amino acid residues reveal information about the flexibility and dynamic behavior of ligand receptor interactions (Fig. 3). Derivate 6 have RMSF value 0.1079 and the native ligand have values of 0.0977 at the Pro12 amino acid residue. This implies that there was comparable flexibility in the interactions between the ligands and the Bcl-2 receptor at this particular site at stage of the simulation. Moving on to the Gln27 amino acid residue, derivative 6 has an RMSF value of 0.0786, while the native ligand has a value of 0.0945. This consistency implies that both Derivate 6 and the native ligand generated comparable variations in the receptor structure at this precise residue into the simulation. A difference in RMSF values was observed at the Leu48 amino acid residue, where Derivate 6 exhibited a lower RMSF of 0.3176 compared to the natural ligand, which showed a higher value of 0.3867. This disparity shows that, as compared to the native ligand, Derivate 6 may cause less structural flexibility at this position. Both ligands exhibited identical RMSF values at the Pro70 and Leu75 amino acid residues, showing equal flexibility in the Bcl-2 receptor structure at these sites during the simulation. Derivate 6 consistently demonstrated RMSF values slightly greater than the native ligand shows notable interaction at the Ser95 and Glu110 residues. This shows that Derivate 6 generated comparable or somewhat higher structural changes in the Bcl-2 receptor than the original ligand at these sites.

In conclusion, the RMSF analysis gives a complete knowledge of the dynamic behavior of the Bcl-2 receptor in complex with Derivate 6 and the native ligand over the course of a 100 ns MD simulation. These insights into the flexibility of individual amino acid residues are critical for understanding the stability and conformational changes caused by ligand interaction, which will aid in the design and optimization of prospective medicinal drugs⁴⁶.

Figure 4. Free energy calculations using Molecular Mechanics Generalized Born Surface Area (MMGBSA): A) Native ligand and B) Derivative 6. The data indicate that the native ligand has a lower free energy of -49.30 kcal/mol, while the free energy of derivative 6 is only -28.48kcal/mol.

The Molecular Mechanics Generalized Born Surface Area (MM-GBSA) method is used to calculate the energy contributions from various interaction forces, including molecular mechanical energy (MM), solvation energy using generalized born (GB), and the energy associated with changes in surface area (SA) (Figure 4). The ΔG values yield insights into the stability and relative interactions of these compounds in their respective environments⁴⁷. Based on the MM-GBSA calculation results, the original ligand shows a much more favorable interaction compared to Derivative 6. The original ligand has a van der Waals energy contribution of−73.01 kcal/mol and a significant electrostatic energy of −345.69 kcal/mol, indicating strong interaction with the binding site. Although it has a polar solvation energy penalty of 378.27 kcal/mol, the total free energy of the original ligand remains, −49.30 kcal/mol, reflecting high stability. In contrast, Derivative 6 has weaker van der Waals interactions −36.58 kcal/mol and a very small electrostatic contribution −1.31 kcal/mol, with a total free energy of only −28.48 kcal/mol, indicating lower binding affinity. Overall, the native ligand is more stable and has stronger binding in the binding site compared to Derivative 6 (Figure 4).

Figure 5. Decomposition energy for each residue is illustrated in the figures: A) for the native ligand, and B) for Derivative 6. These figures show the energy contributions from each residue, highlighting the differences in binding interactions between the two ligands.

Decomposition energy provides a detailed breakdown of the energy contributions from each residue, offering valuable insights into the stability and key interactions within the protein-ligand complex. The figure 5 presented shows the comparison of interaction energies between native ligand (B:LIG:170) and derivative 6 with related protein residues. Derivative 6 exhibited strong interactions, with interaction energies of-17.37 kcal/mol, indicating high stability in the protein-ligand complex. However, the native ligand demonstrated even stronger interactions, with an energy of -21.42 kcal/mol. However, further analysis revealed that residue Tyr 70 plays a more significant role in the interaction with derivative 6, providing an energy contribution of -4.14 kcal/mol, which is the same as the contribution from Tyr 164 in the native ligand. This suggests that structural changes in ligand derivative 6 might increase the affinity and stability of interactions with these residues, potentially contributing to improved biological activity. Meanwhile, other residues such as Phe66 and Arg69 show smaller energy contributions, but are still important in building a complex interaction network around the ligand.

CONCLUSION:

The α-Mangostin compound and its derivatives mostly comply with Lipinski rules, except for derivative 2. Among these, α-Mangostin exhibited the best Gibbs free energy value of -7.95 kcal/mol, forming interacction π -Sigma bonds at the active site of Bcl-2, particularly at the amino acid residue Tyr 161. A molecular dynamics simulation using Gromacs showed temporal fluctuations in complexes involving B-cell lymphoma 2 receptor (Bcl-2) with a native ligand and Derivate 6. Derivate 6 showed higher RMSD values, suggesting potential flexibility in binding. The analysis of root mean square fluctuation (RMSF) for specific amino acid residues revealed that Derivate 6 and the native ligand had varying effects on the Bcl-2 receptor flexibility.

ACKNOWLEDGMENT:

We sincerely appreciate the support and guidance from the Laboratory of Computational Chemistry and Artificial Intelligence, Faculty of Pharmacy Universitas Andalas.

FUNDING:

This research was self-funded.

AUTHOR CONTRIBUTIONS:

Regina Andayani: Conceptualizing the research, editing, and review. Ajuanda Puteri: Conducting ADME tests and writing the article. Aiyi Asnawi: Performing molecular dynamics analysis and interpreting data. Purnawan Pontana Putra: Writing the article, conceptualizing the research flow, conducting ADME tests, performing molecular dynamics simulations, and interpreting the results.

CONFLICT OF INTEREST:

The authors have no known competing interests that could call into question the objectivity of this research.

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Received on 20.07.2024 Revised on 07.12.2024

Accepted on 27.02.2025 Published on 01.07.2025

Available online from July 05, 2025

Research J. Pharmacy and Technology. 2025;18(7):3212-3220.

DOI: 10.52711/0974-360X.2025.00462

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Compounds	Physicochemical parameters						Application of Lipinski five laws
Compounds	Molecular weight (g/mol)	LogP	Rotatable bond	H-bond acceptors	H-bond donors	TPSA (A²⁾	Application of Lipinski five laws
α-Mangostin	410.46	4.64	5	6	3	100.13	Yes
Derivate 1	444.47	3.15	6	8	5	140.59	Yes
Derivate 2	478.49	1.72	7	10	7	181.05	No
Derivate 3	446.49	3.29	7	8	5	140.59	Yes
Derivate 4	384.38	3.09	3	7	3	109.36	Yes
Derivate 5	418.39	1.61	4	9	5	149.82	Yes
Derivate 6	386.40	3.23	4	7	3	109.36	Yes

Toxicity Parameter	Derivative 1	Derivative 2	Derivative 3	Derivative 4	Derivative 5	Derivative 6
AMES toxicity	Yes	Yes	No	Yes	Yes	Yes
hERG I inhibitor	No	No	No	No	No	No
hERG II inhibitor	Yes	Yes	Yes	Yes	Yes	Yes
Hepatotoxicity	No	No	No	No	No	No
Skin Sensitisation	No	No	No	No	No	No