INTRODUCTION:

The cancer mortality rate is one of the main causes and a serious concern for the world community¹^,2,³. Breast cancer ranks second in the world, and the increase in incidence is getting higher every year^4,5. Based on data from the International Agency for Research on Cancer (WHO) in 2020, 15.5% of deaths occurred in women⁶.

The onset of breast cancer originates from cells in the ductal and lobular layers, as well as some tissues in small amounts^7,8. Overexpression of estrogen receptor α (ERα) causes 70–70% of breast cancers. Estrogen binds to ERα, which forms an active receptor complex, causing cell proliferation^9,10. In general, breast cancer treatment can be achieved through the processes of radiotherapy, surgery, hormone administration, and chemotherapy^11,12. The various side effects of breast cancer therapy and the limitations of anti-breast cancer drugs create challenges and urgent needs in the development of drugs that are more selective and have high activity as therapy¹³. The development of bioactive compounds derived from herbal plants can be one solution to obtaining safer active compounds¹⁴.

Nowadays, many structural modifications of pinostrobin (P) have been developed as a marker compound of Boesenbergia pandurata rhizome. The activities of pinostrobin include anti-cancer, antibacterial, anti-inflammatory, antioxidant, antiviral, and others. The development of pinostrobin has increased over the decades as an anti-cancer drug candidate¹⁵. However, the research progress remained slow, and the breast cancer activity was low when compared to currently available drugs¹⁶. The existence of structural modifications aimed at improving the lipophilic, electronic, and steric properties of pinostrobin, which increase its interaction with the target receptor. Several studies have proved that the addition of prenyl groups to pinostrobin can inhibit MCF-7 cancer cells better than pinostrobin¹⁷. Modification of pinostrobin in the hydroxyl group with the acyl group was predicted to have activity as a breast anticancer through HER-2 inhibition¹⁸. Meanwhile, there has been no research related to the potential modification of pinostrobin hydroxyl groups with acyl groups in producing 5-O-acetylpinostrobin derivatives. Therefore, computational chemistry strategies that reduce the cost and time of laboratory activities are highly attractive to be carried out in obtaining new compounds that are strong and selective¹⁹.

Computational chemistry models are one of the most efficient and powerful methods for studying the prediction of activity, pharmacokinetics, and toxicity^20,21. Activity prediction by molecular docking to predict the accuracy of the ligand with the active side of the target protein by indicating the quantitative prediction of the rerank score between the ligand-receptor complex²². Increasing drug failures in phase 1 clinical trials make pharmacokinetic and toxicity studies important^23,24. The goal is to provide a selection of new compounds with good pharmacokinetic properties that are safer at the onset of drug discovery²⁵. In this study, computational chemistry studies, including molecular docking, pharmacokinetic prediction, and toxicity, were performed. Therefore, the aim of this study was to evaluate the predicted activity, pharmacokinetics, and toxicity of twenty potential 5-O-acetylpinostrobin derivatives as ERα inhibitors.

MATERIALS AND METHODS:

Materials:

The tools used were a Fujitsu AH554 computer, NVIDIA®, Core(TM) i7, CPU @ 2.20 GHz, and 16 GB of RAM. The software used included Molegro Virtual Docker (MVD) v. 6, ChemBio Draw 2D, and 3D v. 20.1.1. The websites used were the pkCSM web (https://biosig.lab.uq.edu.au/pkcsm/) and Smile Translator (https://cactus.nci.nih.gov/translate/). The research materials consisted of twenty 5-O-acetylpinostrobin derivatives such as 5-O-4-(dimethylamino) benzoylpinostrobin (AP-1), 5-O-benzoylpinostrobin (AP-2), 5-O-cyclohexanecarbonylpinostrobin (AP-3), 5-O-2-phenylacetylylpinostrobin (AP-4), 5-O-cyclopentanecarbonylpinostrobin (AP-5), 5-O-3-phenylpropanoylpinostrobin (AP-6), 5-O-cyclobutanecarbonylpinostrobin (AP-7), 5-O-2-(methylsulfonyl) acetylpinostrobin (AP-8), 5-O-cyclopropanecarbonylpinostrobin (AP-9), 5-O-pivalylpinostrobin (AP-10), 5-O-2-(methylthio) acetylpinostrobin (AP-11), 5-O-2,2-dichloroacetylpinostrobin (AP-12), 5-O-isobutonylpinostrobin (AP-13), 5-O-propionylpinostrobin (AP-14), 5-O-3,3,3-trifluoropropanoylpinostrobin (AP-15), 5-O-formylpinostrobin (AP-16), 5-O-dimethylcarbamylpinostrobin (AP-17), 5-O-methoxyformylpinostrobin (AP-18), 5-O-2,2,2-trifluoroacetylpinostrobin (AP-19), and 5-O-acetylpinostrobin (AP-20).

2D and 3D structure preparation:

Each of the twenty 5-O-acetylpinostrobin compounds were drawn with the ChemBio Draw 2D program and saved in .cdxml format. The 2D structures were copied into the ChemBio Draw 3D program, and energy was minimized with MMFF94 minimization. The structures were saved in .mol2 and .sdf formats^26,27.

Receptor preparation:

The receptor used is ERα with PDB code ID: 3ERT, which was obtained from the RSCB Protein Data Bank website (http://rcsb.org) and downloaded in .pdb format²⁸.

The validation of docking methods:

The Molegro Virtual Docker (MVD) program was used to validate docking the native ligand (4-hydroxytamoxifen) with the 3ERT. The setup was completed by removing water, and then cavity detection was performed as the binding site for the native ligand. Cavity 1 (volume 365.056), grid resolution of 0.30 Å, and binding sites of x: 31.83, y: -1.71, and z: 25.20. Setting the algorithm, namely the number of runs of 10 with the parameters of the maximum iterations of 1500, the maximum population size of 50, and pose generation of 100.00. In addition, simplex evolution was used at max steps 300 and distance factor 1.00²⁹. The Mean Square Deviation (RMSD) score was used as a parameter to assess the validation process. RMSD states the average difference of atomic position from ligand replication with crystallography results. RMSD results < 2 A indicate the position of replication docking results closer to the ligand crystallography³⁰.

Molecular Docking:

Molecular docking on twenty 5-O-acetylpinostrobin derivatives was performed in the same manner as the validation process. The results obtained were the rerank score and ligand-receptor amino acid interactions. Amino acid interactions were recorded based on two types of interactions, namely hydrogen bonds and steric interactions. The rerank score was a linear combination of E-inter (steric, Van der Waals, hydrogen bonding, and electrostatic) between ligand and protein and E-intra. (torsion, sp2-sp2, hydrogen bonding, Van der Waals, and electrostatic) of the ligand that has been measured based on predetermined coefficients³¹.

Determination of pharmacokinetics and toxicity parameters:

The 3D structures of twenty 5-O-acetylpinostrobin derivatives saved in .sdf format were translated into .smile format through the website https://cactus.nci.nih.gov/translate/. All compounds in .smile format were entered into the pkCSM website³². The parameters determined include absorption (Caco-2 permeability, %HIA), distribution (VDss, BBB permeability), metabolism (CYP3A4 and CYP2D6 inhibitor), excretion (Cl_tot, Renal OCT-2 substrate), and toxicity (LD₅₀, hepatotoxicity).

RESULT:

The validation results of the docking method:

The validation results obtained an RMSD value of 0.829 A (<2 A). A comparison between the structure of 4-hydroxytamoxifen (4-OHT) as a native ligand for re-docking results with ERα can be seen in Figure 1. The amino acid interaction of 4-OHT before and after docking are shown in Figure 2.

Figure 1. Redocking result of 4-OHT with RMSD 0.829 Å

Figure 2. Interaction of amino acids with 4-OHT before docking (blue) and after docking (yellow)

Molecular docking results:

Twenty 5-O-acetylpinostrobin derivatives had rerank scores ranging from -113.8310 to -71.7388 kcal/mol. Eight compounds (AP-10-12, 14-16, and 19-20) had a smaller rerank score than pinostrobin and native ligand. The amino acid interaction with the ligand was divided into two categories: hydrogen bond and steric interaction. The rerank score and amino acid interactions are shown in Table 1.

Table 1. Rerank scores and amino acid interactions of 5-O-acetylpinostrobin derivatives

No. AP	Amino acid interaction		Rerank score (kcal/mol)	No. AP	Amino acid interaction		Rerank score (kcal/mol)
No. AP	Hydrogen bond	Steric interaction	Rerank score (kcal/mol)	No. AP	Hydrogen bond	Steric interaction	Rerank score (kcal/mol)
1	Thr 347	Asp 351, Leu 354, and Leu 536	-97.2996	11	Leu 327, Arg 394, and Lys 449	Glu 353, Pro 325, Pro 324, and Arg 394	-100.0770
2	Cys 530	Lys 529, Val 533, and Trp 383	-71.7388	12	Leu 327 and Lys 449	Arg 394, Glu 353, Leu 327, and Pro 324	-96.6048
3	Thr 347	Met 343, Leu 346, Leu 387, Trp 383, and Ala 350	-87.4017	13	-	Ala 350, Leu 384, and Leu 349	-77.7985
4	Cys 530	Met 528 and Val 533	-95.2128	14	Leu 327, Lys 449, and Arg 394	Ile 326, Glu 353, Pro 324, and Arg 394	-105.5050
5	Thr 347	Met 421, Thr 347, Leu 384, and Leu 349	-96.5739	15	Leu 327, Lys 449, and Arg 394	Arg 394, Ile 326, Glu 353, Pro 325, Pro 324, and Met 357	-113.8310
6	Leu 536 and Cys 530	Lys 529	-86.7957	16	Lys 449 and Arg 394	Pro 325, Glu 353, His 356, Pro 324, and Gly 390	-88.7383
7	Leu 327 and Lys 449	Arg 394, Glu 353, Pro 324, and Met 357	-95.2809	17	Thr 347	Leu 384 and Leu 349	-81.2168
8	Lys 449, Arg 394, and Leu 327	Pro 324, Glu 353, Leu 327, Pro 325, Ile 386, Gly 390, Lys 449, and Met 357	-83.5465	18	Arg 394 and Lys 449	Pro 325, Pro 324, and Gly 390	-100.0020
9	Lys 449, Arg 394, and Leu 327	Ile 326, Arg 394, Glu 353, Pro 325, Gly 390, Ile 386, Lys 449, and Met 357	-88.1631	19	Leu 327, Arg 394, and Lys 449	Arg 394, Glu 353, Pro 325, Pro 324, and Leu 287	-107.7860
10	Leu 327, Arg 394, and Lys 449	Glu 353, Met 357, Pro 324, Arg 394, and Ile 326	-100.9870	20	Leu 327, Arg 394, and Lys 449	Arg 394, Leu 327, Ile 326, Glu 353, and Pro 324	-99.2747
				P	Lys 449	His 356, Pro 324, Lys 449, Ile 386, Gly 390, Glu 323, and Glu 353	-81.2292
				4-OHT	Glu 353 and Arg 394	Glu 353	-91.3077

Table 2. Pharmacokinetics and toxicity prediction results for 5-O-acetylpinostrobin derivatives

No. AP	Absorption		Distribution		Metabolism		Excretion		Toxicity
No. AP	Caco2 permeability	%HIA	VD_ss	BBB permeability	CYP2D6 inhibitior	CYP3A4 inhibitior	Cl_tot	Renal OCT2 substrate	LD₅₀	Hepatotoxicity
1	1.318	97.498	-0.801	-0.189	No	Yes	0.453	No	1169.305	Yes
2	1.184	97.101	-0.882	-0.142	No	No	0.567	No	1050.288	No
3	1.243	95.984	-0.638	-0.229	No	Yes	1.049	No	838.490	No
4	1.216	97.471	-0.891	-0.154	No	Yes	0.332	No	1038.635	Yes
5	1.224	96.274	-0.600	-0.234	No	Yes	0.246	No	828.087	No
6	1.231	97.067	-0.867	-0.203	No	Yes	0.410	No	1058.444	Yes
7	1.223	97.003	-0.554	-0.220	No	Yes	0.243	No	825.650	No
8	1.511	97.720	-0.794	-1.084	No	No	0.602	No	1047.080	Yes
No. AP	Absorption		Distribution		Metabolism		Excretion		Toxicity
No. AP	Caco2 permeability	%HIA	VD_ss	BBB permeability	CYP2D6 inhibitior	CYP3A4 inhibitior	Cl_tot	Renal OCT2 substrate	LD₅₀	Hepatotoxicity
9	1.213	96.972	-0.513	-0.248	No	Yes	0.322	No	813.669	No
10	1.259	96.724	-0.698	-0.160	No	Yes	0.239	No	835.321	No
11	1.156	96.627	-0.445	-0.190	No	No	0.496	No	951.605	No
12	1.164	94.990	-0.520	-0.174	No	No	0.342	No	906.517	No
13	1.477	97.007	-0.608	-0.189	No	Yes	0.314	No	809.764	No
14	1.406	96.889	-0.412	-0.199	No	No	0.408	No	829.908	No
15	1.173	94.042	-0.556	-0.158	No	Yes	0.136	No	1145.144	No
16	1.348	97.244	-0.479	-0.148	No	No	0.185	No	753.182	No
17	1.267	94.006	-0.486	-0.087	No	No	0.508	No	869.785	Yes
18	1.404	95.883	-0.735	-0.675	No	No	0.296	No	887.777	No
19	1.438	94.198	-0.572	-0.110	No	No	0.099	No	1134.766	No
20	1.379	96.877	-0.482	-0.385	No	No	0.386	No	827.960	No
P	1.303	94.104	-0.187	0.229	No	No	0.206	No	801.779	No
4-OHT	1.120	95.378	0.108	-0.288	Yes	Yes	0.441	No	637.591	No

Note: % HIA: Human Intestinal Absorption (%), VDss: Steady State of Volume Distribution (log L/kg)), BBB: Blood Brain Barrier (log BB), CYP2D6: Cytochrome P2D6, CYP3A4: Cytochrome P3A4, Cl_tot: total clearance (mL/min/kg), LD₅₀: Oral rat acute toxicity (g/kgBW).

DISCUSSION:

Drug development using molecular docking, pharmacokinetic prediction, and toxicity studies of twenty 5-O-acetylpinostrobin derivatives is a first step in the discovery of breast cancer drug candidates. Derivatives were obtained by substituting OH-pinostrobin with acyl groups. Twenty acyl groups improve the lipophilic (π), electronic (δ*), and steric (Es) properties based on the Topliss tree³³. Molecular docking was performed to determine the binding affinity and ligand-protein interaction³⁴. The selected target receptor is ERα (PDB ID: 3ERT), which is based on X-ray high resolution of 1.98 Å (< 2 Å), the conditions under which the crystal structure of the protein was obtained, widely used in international journals, and bound with 4-hydroxytamoxifen as a native ligand for ERα^35,36. Before molecular docking, the validation of the docking method was measured by the RMSD score. The RMSD score shows the level of difference from the atomic position before and after the docking proces³⁷. Since the RMSD threshold is less than 2 Å, the average value of the three replications of the re-docking process is valid^38,39. The RMSD score of re-docking in the research was 0.829 Å. The interaction of amino acid residues with 4-OHT before and after docking was similar. The interaction before docking consists of two donor hydrogen bond interactions (Arg 394, Glu 353) and an electrostatic interaction (Asp 351). Docking consists of three donor hydrogen bond interactions (Arg 394, Glu 353, and Thr 347) and an electrostatic interaction (Asp 351). The molecular docking value obtained was the rerank score, which was the value of binding energy needed to form a bond between the ligand and the receptor^40,41. Eight compounds (AP-10-12, 14-16, and 19-20) had a smaller rerank score than pinostrobin and native ligand. The similarity of amino acid residue interactions, such as a donor hydrogen bond between the oxygen atom of the chroman-4-on ring and Arg 394, as well as a steric interaction between Glu 353 and the C-aromatic of the chroman-4-on ring.

For the pharmacokinetic prediction of twenty 5-O-acetylpinostrobin derivatives, the selected absorption parameters were Caco2 permeability and %HIA. All derivatives showed Caco2 permeability values > 0.90 (1.156–1.511), and AP 1–20 had higher values than 4-OHT (1.120). Caco2 permeability is the ability of drug absorption using an in vitro model of the human intestinal mucosa^42,43. %HIA in all compounds showed values above 30% (94.006–97.720%); compounds AP-1 to AP-14, AP-16, and AP-18 to AP-20 had higher %HIA values than pinostrobin (94.104%) and 4-OHT (95.378%). %HIA is used to predict the proportion of compounds absorbed by the human small intestine because the intestine is the main organ for oral route drug absorption⁴³. The results of the analysis of Caco2 permeability and %HIA parameters revealed that all compounds had good absorption.

The selected distribution parameters were VDss and BBB permeability. All derivative compounds had log VDss values in the low category, < -0.15 (-0.891 to -0.412). All compounds had lower VDss values compared to pinostrobin (-0.187). VDss, or steady-state volume of distribution, is the volume required for the total dose of a drug to be evenly distributed to produce the same concentration as in blood plasma⁴⁴. The BBB permeability values of all derivatives were < -1, which meant they had a low distribution in the brain. BBB permeability indicates the ability of a drug to enter the brain, this is considered to help reduce side effects and toxicity or to increase the effectiveness of drugs whose pharmacological activity is in the brain^44,45. The analysis of VDss and BBB permeability parameters were analyzed, and all compounds had a good distribution.

The selected metabolism parameters are inhibition of the cytochromes CYP2D6 and CYP3A4. All compounds were shown not to be CYP2D6 inhibitors, and compounds AP-2, 8, 11, 12, 14, and AP-16 to AP-20 were not CYP3A4 inhibitors. According to Cronin-Fenton and Lash (2011), CYP2D6 and CYP3A4 show a relationship in their binding affinity to selective esterogen receptors (ER), the strength of ER affinity predicts the anti-cancer response⁴⁶. Analysis of CYP2D6 and CYP3A4 inhibitor parameters showed that all compounds had good metabolism.

Excretion parameters consist of Cl_tot and Renal OCT2 substrate. Cl_tot indicates an estimate of how much of the compound is cleared by the liver and kidneys⁴⁷. The Cl_tot of all derivatives was <5 mL/min/kg, which means the value is low. All compounds showed that none were Renal OCT2 substrates. The results of the Cl_tot and Renal OCT2 substrate parameter analyses showed that all compounds had good excretion.

Toxicity parameters consist of LD₅₀ and hepatotoxicity, which are the two toxicity parameters. LD₅₀, or lethal dose, is used to assess the relative toxicity of a compound. All compounds have an LD₅₀ > 5000 mg/kg, which means they belong to class VI, which is not toxic. The LD₅₀ of all compounds shows that compound code AP-16 has the lowest LD₅₀, while AP-1 has the highest. The higher the LD₅₀, the greater the dose limit to reach the lethal dose⁴⁸. Compounds that showed no hepatotoxicity were AP-2, 3, 5, 7, 9-16, 19, and AP-20. Hepatotoxicity has been linked with liver safety. The results of the analysis of LD₅₀and hepatotoxicity parameters revealed that all compounds had low toxicity, even though compounds AP-1, 4, 6, 8, and AP-17 were in the hepatotoxic category.

The six compounds (AP-10, 11, 14, 15, 19, and 20) were selected based on these three predictions (molecular docking, pharmacokinetics, and toxicity prediction). Based on molecular docking results, the six selected compounds have the smallest rerank score compared to pinostrobin and 4-hydroxytamoxifen, as well as a similarity of amino acid residues with 4-hydroxytamoxifen. Based on the results of pharmacokinetic prediction, the six compounds have good absorption (higher value of Caco-2 permeability and % HIA), distribution (lower value of VDss and BBB permeability), metabolism (not to be CYP3A4 and partly as CYP 2D6 inhibitors), and excretion (low Cltot, not to be Renal OCT-2 substrate) compared to pinostrobin and 4-hydroxytamoxifen. Based on the toxicity prediction results, the six compounds had low toxicity and were not hepatotoxic.

CONCLUSION:

Molecular docking, pharmacokinetics, and toxicity prediction were used as the basis of drug development. Based on three predictions, it concluded that compounds AP-10, 11, 14, 15, 19, and 20 had greater predicted anti-breastcancer activity, better pharmacokinetics, and lower toxicity than pinostrobin and 4-hydroxytamoxifen. Therefore, these six compounds have the potential to be developed further.

CONFLICT OF INTEREST:

There was no conflict of interest from any of the authors in this manuscript.

ACKNOWLEDGMENTS:

The authors would like to thank the Faculty of Medicine at Universitas Islam Malang for providing support and access to the lab.

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Received on 23.02.2024 Revised on 17.05.2024

Accepted on 16.07.2024 Published on 24.12.2024

Available online from December 27, 2024

Research J. Pharmacy and Technology. 2024;17(12):5996-6002.

DOI: 10.52711/0974-360X.2024.00910