1. INTRODUCTION:

The traditional medicine of South and Southeast Asia has employed the perennial tropical shrub Phyllanthus niruri to treat a variety of ailments, including but not limited to jaundice, diarrhea, dyspepsia, genitourinary infections, and kidney stones. P. niruri formulations are used as traditional treatments for renal and vesicular calculi in Brazil, where the plant is known as "Chanca Piedra" or "stone breaker."¹

The fruit and leaves have been used as a treatment for gallstones and jaundice in traditional medical systems like Ayurvedic and Unani medicine. P. niruri, also referred to as "dukonganak" in colloquial Malay, is used to treat renal problems and coughs.² The herb, known as Bhumyamalaki in South India, is thought to be effective in treating syphilis, gonorrhea, and constipation.³

This plant, colloquially referred to as "pitirishi," has developed a reputation in northern India as a go-to treatment for bronchitis, asthma, and even tuberculosis.⁴ This herb's young shoots might occasionally be used as an infusion to treat chronic diarrhoea.⁵P. niruri, also known as "zhu zi cao," has long been used in traditional Chinese medicine to treat liver damage brought on by a variety of hepatotoxic substances. In fact, even since Venkateswaran and colleagues' seminal animal work, which showed for the first time in vivo that P. niruri may have anti-hepatitis B activity,⁶There have been a number of research looking at the varied therapeutic potential of this plant species as a result of the substantial scientific interest in this herb.

Since Ottow first isolated the lignan phyllanthin from this plant in 1861⁷ until as recently as the isolation of the potential anti-HBV phytochemicals nirtetralin and niranthin^8,9, phytochemical studies on this plant have shown that it is rich in tannins, flavonoids, alkaloids, terpenes, coumarins, lignans, and phenylpropanoids, which are responsible for the numerous chemicals that have been identified from this herb employed in study are listed in Table 1 in brief. Although it has a wide variety of ethnomedicinal uses, most of these possible therapeutic uses have not been the subject of research that has advanced to the point of clinical trials. In fact, there is a lack of synthesis in the field of P. niruri studies about the current level of knowledge. The heterogeneity of the initial research on P. niruri has also prevented an objective evaluation of the plant's potential, and the mechanisms underlying the majority of this herb's medicinal effect are still unknown. As it should be emphasized, natural compounds from herbs are still essential sources of innovative therapeutic agents and new chemical entities, P. niruri may potentially be a significant medication lead. The relevance of researching natural products has also been reemphasized due to the past over-reliance on combinatorial chemistry and the fact that it does not always produce large and pharmacologically viable libraries. Exploring these natural products could result in the creation of novel natural product-like libraries, which, when combined with high-throughput screening assays, could produce new therapeutic candidates for further research. P. niruri, a common herb with several uses, can be used to create more affordable and accessible medications that target a variety of chronic conditions and have less adverse effects than synthetic pharmaceuticals. Consolidation of the scientific evidence and potential knowledge gaps must be addressed in order to facilitate more focused future study on this species. The current study aims to compile and synthesize the most recent body of research on the pharmacological properties of P. niruri that has been published in PubMed between 1980 and 2015. It will point out potential directions for additional research into the creation of new Phyllanthus-based medications as well as places where this herb could be improved as an affordable adjunct or perhaps a cutting-edge alternative therapeutic agent.

2. MATERIALS AND METHODS:

2.1. Platform for molecular docking:

Using AutoDock Vina software, a computational docking analysis of all the phytoconstituents chosen as ligands with anticancer action as the target was carried out. ¹⁰

2.2. Protein preparation:

The 2.00 crystal structure of anticancer with inhibitor, (PDB ID:5OTE, having resolution: 1.68Å, R-Value Free: 0.218, R-Value Work: 0.196, R-Value Observed: 0.197), which was retrieved from the protein data bank (https://www.rcsb.org), was subjected to in-silico analysis of a few phytoconstituents. 5OTE is used to treat prostate cancer. Using Discovery Studio, all additional molecules were eliminated, including undesirable chains, nonstandard residues, and co-crystallized water molecules.¹¹

2.3. Ligand preparation:

Using the Avogadro programme, all constituents' three-dimensional (3D) structures were extracted from the PubChem database on the NCBI website (https://pubchem.ncbi.nlm.nih.gov/). However, the ChemSketch application was used to sketch the geometrical 2D structure. The ligand structures were saved in the PDB format and the two-dimensional (2D) structures were converted into 3D models using the Avogadro software. Figure 1 depicts each chemical structure.

2.4. Molecular docking:

In order to determine the scoring function based on geometry and forecast the binding affinity of the ligand molecule,^12,13 molecular docking analyses the interactions between the protein and the ligand. We used molecular docking experiments to examine the interactions between specific phytoconstituents (Fig.1), the conventional medication, and the crystal structure of a macromolecule with anticancer activity (PDB ID: 5OTE). PyRx software was used to carry out the molecular docking investigation, and the Vina wizard tool was used to investigate binding affinity. With bound ligands as the benchmark, the final data were analysed and presented using Discovery Studio 2020 Client.¹⁴The number of contacts and active residues responsible for significant binding at the target enzyme's active site are reflected in the protein-ligand interaction visualisation.

2.5. Absorption, distribution, metabolism, and excretion (ADME) and toxicity prediction:

The chosen phytoconstituents and the reference medication were then examined for drug-like characteristics in accordance with Lipinski's rule. The tolerability of phytochemicals must be predicted during therapeutic development before they are consumed by people and animal models. SwissADME (http://www.swissadme.ch) and pkCSM (an online server database predicting small-molecule pharmacokinetic features using graph-based signatures, http://biosig.unimelb.edu.au/pkcsm/prediction) were used to determine the pharmacokinetic profile (ADME) and toxicity predictions of ligands. Simplified Molecular Input Line Entry System (SMILES) notations or PDB files were uploaded to examine the toxicological qualities of ligands, and then the necessary models were chosen to generate a wealth of information regarding effects associated to structure.^15,16

2.6 Standard Preparation:

The actin–myosin cytoskeleton provides the structural framework that determines cell shape, and also is the source of physical force, which directly powers biological activities including adhesion, migration, and cell division. In addition, numerous processes are promoted by the actin–myosin cytoskeleton via less direct routes, such as gene transcription and proliferation, which collectively contribute to cancer.¹⁷Although unlikely to be a primary cancer driver, accumulating evidence indicates that the actin–myosin cytoskeleton provides a critically important ancillary role in tumor growth and spread, which makes actin–myosin cytoskeleton regulators potential targets for cancer chemotherapy.¹⁸ The myotonic dystrophy–related Cdc42-binding kinases MRCKa and MRCKb contribute to the regulation of actin–myosin cytoskeleton organization and dynamics, acting in concert with the Rho-associated coiled-coil kinases ROCK1 and ROCK2. The absence of highly potent and selective MRCK inhibitors has resulted in relatively little knowledge of the potential roles of these kinases in cancer.

The standard is created in a series of phases, such as creating the 2D structure of the standard medicine using the chemsketch tool, then converting the 2D structure into a 3D model using the Avogadro Software, and finally saving it in PDB format. Lenvatinib mesylate's molecular docking with 5OTE was carried out utilising PyRx.

3. RESULTS AND DISCUSSION:

The objective of the current study was to investigate the phytoconstituents found in P. niruri's anticancer activity's inhibitory capacity. Using PyRx, we conducted molecular docking studies of all the phytoconstituents present in P. niruri for this investigation. We next looked at the interactions between the amino acid residues and how they affected the inhibitory potentials of the active components. Using SwissADME and pkCSM servers, selected phytoconstituents with the best fit were further assessed for their absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics.

3.1 Molecular docking:

The docking scores and binding energies of all chemical constituents of P. niruri targeting anticancer activity (PDB ID: 5OTE) and binding interactions with amino acid residues are presented in Table 1.

Table 1: Binding interaction of ligands from Phyllanthus niruri targeting prostate cancer activity (PDB ID:5OTE)

Sr. No.	Chemical constituent	Pub Chem ID	Docking Score 5OTE
1.	Caffeic acid	689043	-6.5
2.	Quinic acid	6508	-6.3
3.	Vanillic acid	8468	-5.8
4.	Protocatechuic acid	528594	-5.8
5.	Fenulic acid	445858	-6.3
6.	Luteolin	5280445	-8.2
Standard Drug
7.	Lenvatinib mesylate	11237762	-3.4

The binding affinities of phytoconstituents ranged from -8.2 to -5.8kcal/mol for 5OTEmacromolecule. From the docked results, it is evident that the compounds, luteolin and caffeic acid for 5OTE exhibit the most favourable binding affinity (-8.2 and -6.5kcal/mol respectively) in complex with anticancer activity, as compared to other docked compounds i.e., fenulic acid (-6.3kcal/mol), quinic acid (-6.3kcal/mol), protocatechuic acid (-5.8 kcal/mol) and vanillic acid (-5.8kcal/mol).

The binding affinity of the standard (lenvatinib mesylate) for 5OTE is -3.4 kcal/mol.¹⁹

In addition, an analysis of the interactions of the 5OTE protein complex and ligand lenvatinib mesylate was performed, which showed that the ligand molecule is oriented due to one unfavorable donor-donor with LYS 105(A) amino acid, two Pi-alkyl with MET 153(A), ALA 217(A), five alkyl interaction with LEU 128(A), ALA 103(A), PHE 219(A), LEU 162(A), LEU 207(A), three conventional hydrogen bonds with ASP 160(A), ASP 218(A) and GLU 124(A) and nine van der waals interactions with amino acid residues GLY 220(A), VAL 90(A), ILE 82(A), PHE 370(A), ASP 371(A), TYR 155(A), ASP 154(A), TYR 156(A), and THR 137(A) were also found. (Fig.2)

An analysis of the interactions between the 5OTE protein complex and the ligand luteolin was also carried out, and it was discovered that the ligand molecule is oriented as a result of three Pi-Alkyl interactions with ALA 103(A), ALA 217(A), MET 153(A), one Pi-Pi stacked interactions with PHE 370(A), one Pi-sigma with LEU 207(A), three conventional hydrogen bond with TYR 156(A), ASP 154(A) and ASP 218(A), one unfavourable donor-donor interactions with ASP 76(A), one carbon hydrogen bond with GLY 50(A) and six van der waals interaction with THR 137(A), ILE 82(A), ASP 371(A), ASP 160(A), ASP 204(A), TYR 155(A) were also found. (Fig.3.a).

Additionally, an analysis of the interactions between the 5OTE protein complex and the ligand caffeic acid was carried out, and it was discovered that the ligand molecule is oriented as a result of one Pi-alkyl with VAL 35(A), one unfavourable interaction with ASP 32(A), and twelve van der waals interaction with THR 402(A), GLN 144(A), PHE 143(A), LEU 71(A), HIS 72(A), GLN 70(A), LYS 67(A), PHE 398(A), GLY 400(A), ILE 399(A), HIS 395(A), CYS 36(A) were also found. (Fig.3.b).

Table 2: Binding interactions of ligands with the binding site of MRCK Kinase

No.	Inhibitor	Binding energy (kcal/mol)	Amino acids interaction with hydrogen bond	Amino acids with hydrophobic interaction
1	Caffeic acid	-6.5	No interaction	THR 402(A), GLN 144(A), PHE 143(A), LEU 71(A), HIS 72(A), GLN 70(A), LYS 67(A), PHE 398(A), GLY 400(A), ILE 399(A), HIS 395(A), CYS 36(A)
2	Quinic acid	-6.3	LYS 109(A), ASP 32(A), GLN 144(A)	PHE 143(A), THR 402(A), CYS 36(A), HIS 395(A), VAL 35(A), LYS 67(A), ASP 145(A), GLU 146(A)
3	Vanillic acid	-5.8	THR 402(A), GLN 144(A), HIS 395(A)	VAL 35(A), CYS 36(A), ILE 399(A)
4	Protocatechuic acid	-5.8	LYS 67(A), GLN 144(A), GLN 70(A)	ASP 32(A), THR 402(A), LEU 71(A), PHE 143(A)
5	Fenulic acid	-6.3	LEU 71(A), LYS 109(A)	GLU 146(A), ASP 145(A), LYS 67(A), GLN 144(A), CYS 36(A), ASP 32(A), PHE 143(A), GLN 70(A)
6	Luteolin	-8.2	ASP 160(A), ASP 218(A) and GLU 124(A)	THR 137(A), ILE 82(A), ASP 371(A), ASP 160(A), ASP 204(A), TYR 155 (A)
7	Lenvatinib mesylate	-3.4	ASP 218(A), GLU 124(A), ASP 160(A), ASP 204(A)	GLY 220(A), THR 137(A), VAL 90(A), ILE 82(A), PHE 370(A), ASP 371(A), TYR 155(A), ASP 154(A), TYR 156(A)

3.2. ADMET study:

Pharmacokinetic profile (ADME) and toxicity predictions of the ligands are important attentive parameters during the transformation of a molecule into a potent drug. In the present study, these parameters were assessed using Swiss ADME and pkCSM. The absorption potential and lipophilicity are characterized by the partition coefficient (Log P) and topological polar surface area (TPSA), respectively. For better penetration of a drug molecule into a cell membrane, the TPSA should be less than 140 Å. However, the value of Log P differs based on the drug target. The ideal Log P value for various drugs are as follows: oral and intestinal absorption, 1.35 − 1.80; sublingual absorption, > 5; and central nervous system (CNS). The aqueous solubility of ligands ideally ranges from –6.5 to 0.5, while the blood brain barrier (BBB) value ranges between –3.0 and 1.2 ²⁰. In addition, non-substrate P-glycoprotein causes drug resistance²¹.

In our study, all the selected ligands followed the TPSA parameter, P-glycoprotein non-inhibition, thereby showing good intestinal absorption and an acceptable range of BBB values. All the compounds showed aqueous solubility values within the range. Further, it was predicted that the selected ligands do not show AMES toxicity, hepatotoxicity, and skin sensitivity. In addition, it did not inhibit hERG-I (low risk of cardiac toxicity). Lipinski’s rule violations, T. pyriformis toxicity, minnow toxicity, maximum tolerated dose, rat acute oral toxicity, and chronic toxicity are depicted in table 3.²²

Table 3: ADME and toxicity predicted profile of ligands with superior docking scores

ADMET Properties	Formula	MW (g/mol)	Log P	TPSA (Ȧ²)	HB donor	Hb acceptor	Aqueous solubility (Log mol/L)	Human intestinal absorption (%)	Blood-brain barrier
Caffeic acid	C₉H₈O₄	180.16	1.19	77.76	3	3	-2.16	56.503	-0.652
Quinic acid	C₇H₁₂O₆	192.17	-2.32	118.22	5	5	-1.67	14.745	-0.999
Vanillic acid	C₈H₈O₄	168.15	1.09	66.76	2	3	-1.85	77.248	-0.326
Protocatechuic acid	C₇H₆O₄	154.12	0.79	77.76	3	4	-1.99	75.77	-0.692
Fenulic acid	C₁₀H₁₀O₄	194.18	1.49	66.76	2	4	-2.90	93.22	-0.28
Luteolin	C₁₅H₁₀O₆	286.24	2.28	111.13	4	6	-3.26	79.08	-1.101
Lenvatinib mesylate	C₂₂H₂₃ClN₄O₇S	522.96	4.07	178.32	4	8	-3.37	88.88	-1.342

Table 3 Continued

ADMET Properties	P-glycoprotein substrate	Total clearance [Log ml/(min.kg)]	Bioavailability score	AMES toxicity	Max tolerated dose [Log mg/(kg.d)]	hERG I inhibitor	hERG II inhibitor
Caffeic acid	YES	0.52	0.56	NO	0.89	NO	NO
Quinic acid	NO	0.63	0.56	NO	2.08	NO	NO
Vanillic acid	NO	0.61	0.85	NO	1.40	NO	NO
Protocatechuic acid	NO	0.55	0.56	NO	1.37	NO	NO
Fenulic acid	YES	0.61	0.85	NO	1.44	NO	NO
Luteolin	YES	0.60	0.55	NO	0.55	NO	NO
Lenvatinib mesylate	YES	0.21	0.17	NO	0.42	NO	YES

Table 3 Continued

ADMET Properties	Acute oral rat toxicity, LD50 (mol/kg)	Oral rat chronic toxicity (Log mg/kg bw/day)	Hepatotoxicity	Skin sensitisation	T. Pyriformis toxicity (Log µg/L)	Minnow toxicity (Log mmol/L)	Lipinski’s rule violations
Caffeic acid	2.22	1.847	NO	NO	0.135	2.33	YES (0)
Quinic acid	1.28	3.481	NO	NO	0.285	4.37	YES (0)
Vanillic acid	2.20	1.982	NO	NO	0.158	2.14	YES (0)
Protocatechuic acid	2.18	1.95	NO	NO	0.267	2.53	YES (0)
Fenulic acid	2.32	1.79	NO	NO	0.255	2.07	YES (0)
Luteolin	2.37	1.67	NO	NO	0.459	1.51	YES (0)
Lenvatinib mesylate	2.22	1.7	YES	NO	0.309	-0.005	NO (2)

Standard Drug:

1. Lenvatinib mesylate, 5OTE

Fig. 2: Docking scores and binding interaction of lenvatinib mesylate (PDB ID: 5OTE)

The ligand is shown in line and stick representation along with its 2D diagram and hydrogen bond interaction.

1. 5OTE- Drugs to be considered

Fig. 3: Docking scores and binding interaction for prostate cancer activity (PDB ID: 5OTE). The ligand is shown in line and stick representation along with its 2D diagram and hydrogen bond interaction.

Combine Boiled Egg Diagram:

Fig. 4: Combined boiled egg diagram of all phytoconstituents with standard.

Table 4: Molecule names in boiled egg diagram.

Molecule No.	Drug Name
1	Caffeic acid
2	Quinic acid
3	Vanillic acid
4	Protocatechuic acid
5	Fenulic acid
6	Luteolin
7	Lenvatinib mesylate

Boiled means Brain Or IntestinaL Estimate D permeation predictive model.

The boiled egg diagram shows two regions white and yellow.

The white region is the physicochemical space of molecules with highest probability ofbeing absorbed by the gastrointestinal tract, and the yellow region (yolk) is the physicochemical space of molecules with highest probability to permeate to the brain.

In addition, the points are coloured in blue if predicted as actively effluxed by P-gp(PGP⁺) and in red if predicted as non-substrate of P-gp(PGP^-).

Previously, flow cytometry and caspase-3 immunostaining were used to demonstrate that Phyllanthus niruri extracts significantly inhibited human hepatocellular carcinoma cells (HepG2, Huh-7), colorectal carcinoma cells (Ht29), and keratinocytes (HaCaT). These findings suggest that the spray-dried extract of Phyllanthus niruri is protective of normal cells while selectively harmful to cancer cell lines. ²³

Phyllanthus niruri extract was also tested for its impact on hospitalised colorectal cancer patients as well as its impact on cell growth by assessing granzyme expression. On patients with colorectal cancer, Phyllanthus niruri extract boosted granzyme expression, pointing to its potential as an anticancer drug. ²⁴

This study support our in-silico investigation that luteolin and caffeic acid, with the lowest binding energy (-8.2kcal/mol and -6.5kcal/mol) in complex with MRCK could be potential for treatment of cancer. However, the previously reported activity corresponds to the collective activity of the extract, irrespective of the nature of the phytoconstituents and cancer mediators involved. Thus, it is evident from our study that the screened phytoconstituents displayed higher docking scores, stronger binding energies, and better interaction with the conserved catalytic residues, leading to the inhibition/blockade of the MRCK in cancer. Therefore, our study provides supportive and conclusive evidence that amongst the phytoconstituents, luteolin and caffeic acid are responsible for anti-cancer potential by targeting MERK kinases.

4. CONCLUSION:

The development of selective small-molecule inhibitors of the Cdc42-binding MRCK kinases reveals their essential roles in cancer cell viability, migration, and invasive character. In this study, we have carried out an in-silico screening of the phytoconstituents of Phyllanthus niruri. This study demonstrate that six compounds from selected phytoconstituents showed docking results from -8.2 to -5.9 kcal/mol. Among all, luteolin gave the lowest binding energy (-8.3 kcal/mol) with 5OTE macromolecule, whereas the reference compound, lenvatinib mesylate showing a docking score with a binding energy -4.8 kcal/mol.

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Received on 28.12.2022 Modified on 04.02.2023

Research J. Pharm. and Tech 2023; 16(9):4105-4111.

DOI: 10.52711/0974-360X.2023.00671