INTRODUCTION:

Non-alcoholic fatty liver disease (NAFLD) is currently one of the most prevalent liver disorders in the world, accounting for around 25% of all cases worldwide.

According to current statistics, the prevalence of this disorder ranges from 14% to 21% in European and Asian countries, whereas it is 11% in the United States (male 14%, female 8%)^1,2,3This disorder's etiology includes a wide range of factors, starting from simple steatosis (lipid build-up in the liver parenchyma) to non-alcoholic steatohepatitis (NASH), liver fibrosis, and end-stage liver disease. As a result, NAFLD has received more attention; yet, there is still no viable medicine for the treatment of NAFLD.Current treatment options are still restricted to dietary and lifestyle changes, while chronic patient diagnosis and long-term therapy imposes a massive financial burden. Nonetheless, medication research is progressing, with several molecules entering clinical trials II and III. However, neither the US Food and Drug Administration (FDA) nor the European Medicines Agency (EMA) has authorized any of them ^4-7. Computational and experimental approaches have led the path for successful drug repurposing, reducing the time, cost and risk associated with the new drug development from scratch. These methods provide a comprehensive approach by identifying and comparing a drug's mode of action, a disease's pathways, or both. Medicinal plants, in addition to synthetic chemistry, have been a key source of novel compounds having bioactive potential^8-18.

Herbal medications being regarded as "nature's pharmacy", play an important role in all indigenous traditional therapies. The sophisticated use of herbal treatments around the world varies with the technological progress of the countries from which they originated^{. 19, 20}. Herbal medications have long been used to treat liver disorders. There are several herbal remedies on the market for their hepatoprotective action, there is however little research about the effect of these medicinal herbs on NAFLD and factors leading to the same. We used selected NAFLD target proteins as reference materials in this study, including PPAR- α and PPAR- ℽ, and explored the mechanism of the same utilizing in-silico analysis and molecular docking technology^21-25, so as to provide a basis to offer a scientific foundation for future formulation and administration of such phyto-constituents. The most well-known anti-obesity transcription factor in adipose tissue and the liver is the peroxisome proliferator-activated receptor (PPAR).Hepatic expression of PPAR is highly reduced in NAFLD animals and humans. However, both human and animal research have shown that PPAR has a protective impact against obesity and NAFLD, which has been related to its hypolipidemic,hypoglycaemic,and anti-inflammatory actions, as well as their potential to enhance insulin sensitivity^25-29Furthermore, PPAR-deficient animals were obese, with inflammation, steatosis, oxidative stress, and apoptosis in their livers^29-32.

For selecting natural product molecules with the potential for efficacy in future in vitro and in vivo studies, in-silico methods are an important alternative tool which can aid in the development of safety guidelines for the use of phyto-constituents from medicinal plants^{(26, 27)}.Thus the current investigation attempted to establish the use of such phyto-constituents on PPAR targets for their use in NAFLD and identify the potential leads.

MATERIAL AND METHODS:

Selection of constituents and in silico ADME predictions:

Following the review of literature, PubChem database (http://pubchem.ncbi.nih.gov)was used to confirm all the phyto-constituents. Each of its canonical SMILES and PubChem ids were collected. The SWISS ADME and ADMET lab software were used for the in-silico ADME predictions for these phyto-constituents. The screening was done on the basis of six indicators, namely, Lipinski’s rule of five, solubility, bioavailability, Ames, hERG and metabolic stability. Lipinski’s rule of five lays down the basis for identifying the drug-like molecules from the others. Bioavailability is essential criterion for establishing the oral applicability of the phyto-constituents.

NAFLD Target Prediction:

The data from canonical SMILES was collected from Pubchem.The disease genes were selected from Gene Cards and OMIM databases. Out of more than 100 target genes found for NAFLD,finally two main target genes were selected as target proteins for subsequent docking analysis i.e. PPAR-α and PPAR- ℽ.

In-silico ADME analysis:

The properties of the phyto-constituents were predicted and explored based on data generated from the SWISS-ADME, pkcsm and ADMET lab.

Table 1: Pharmacokinetic predictions of the phyto-constituents

Phytoconstituents	Predicted oral bioavailability	Predicted drug likeness	Pharmacokinetics
Phytoconstituents	Predicted oral bioavailability	Predicted drug likeness	CYP	P-gp substrate	BBB permeability	GI absorptionn	Log Kp (cm/s)
1)Ellagic acid	Not orally bioavailable (high polarity and saturation)	Yes	Only CYP1A2	No	No	Low	-7.81
2)Glycyrrhizic acid	Not orally bioavailable (high polarity and saturation)	No	No	Yes	No	Low	-9.33
3)Gallic acid	Not orally bioavailable (saturation)	Yes	No	No	No	High	-6.84
4)Tannic acid	Not orally bioavailable (high polarity,molecular weight,saturation.)	No	No	Yes	No	Low	-12.28
5)Quercetin	Not orally bioavailable (high saturation)	Yes	Only CYP1A2 CYP2D6 CYP3A4	No	No	High	-7.05
6)Piperine	Orally bioavailable	Yes	Only CYP2D6 CYP3A4	No	Yes	High	-5.58
7)6-Gingerol	Not orally bioavailable (high flexibility)	Yes	Only CYP1A2 CYP2D6	No	Yes	High	-6.14
8)Nerolidol	Orally bioavailable	Yes	Only CYP1A2 CYP2C9	No	Yes	High	-4.23
9)Silymarin	Not orally bioavailable (Polarity)	Yes	No	No	No	Low	-7.89
10)Berberine	Orally bioavailable	Yes	CYP1A2	Yes	Yes	High	-5.78
11)Curcumin	Not orally bioavailable (Saturation)	Yes	CYP2C9 and CYP3A4	No	No	High	-6.28
12)Co Q10	Not orally bioavailable (size,solubility,lipophilicity and fleixibility)	No	No	Yes	No	Low	2.22
13)Resveratrol	Not orally bioavailable (Saturation)	Yes	CYP1A2 and CYP2C9 and CYP3A4	No	Yes	High	-5.47
14)Naringin	Not orally bioavailable (Polarity and size)	No	No	Yes	No	Low	-10.15
15)Rutin	Not orally bioavailable (Polarity and size)	No	No	Yes	No	Low	-10.26
16)Andrographolide	Orally bioavailable	Yes	No	Yes	N	High	-6.9
17)Embelin	Not orally bioavailable (flexibility and lipophilicity)	Yes	CYP2C19 CYP2C9 CYP2D6	No	Yes	High	-4.25
18)Picroside	Not orally bioavailable (Polarity)	No	No	No	No	Low	-10.4
19)Plumbagin	Not orally bioavailable saturation	Yes	CYP1A2	No	Yes	High	-5.82
20)Ammonium Glycrrhizate	Not orally bioavailable (Polarity and size)	No	No	Yes	No	Low	-11.57
21)Mangiferin	Not orally bioavailable (Polarity)	No	No	No	No	Low	-9.14
22)Crocin	Not orally bioavailable (Polarity)	No	No	Yes	No	Low	-8.32
23)Paclotaxel	Not orally bioavailable (Polarity,size and flexibility)	No	No	Yes	No	Low	-8.91
24)Lycorine	Orally bioavailable	Yes	Only CYP2D6	Yes	No	High	-8.07
25)Oleocanthal	Not orally bioavailable (flexibility)	Yes	No	No	No	High	-7.11
26)Xanthorrhizol	Not orally bioavailable (lipophilicity)	Yes	Only CYP2C9 CYP2D6	No	Yes	High	-3.76
27)Tanshinone IIA	Orally bioavailable	Yes	Yes	Yes	Yes	High	-5.02

Preparation of protein structure and docking methodology:

Protein PPAR-α crystal structure (PDB ID: 3K8S) and PPAR-ℽ (PDB ID: 3ET1) were explored for the docking simulation studies. In order to optimize the docking protocol, the co-crystal ligands was docked, ascertaining the binding pose, which was aligned over the crystal ligand, which was acceptable as depicted in the figures (Fig 1-4).

RESULTS AND DISCUSSION:

In-silico interactions and pharmacokinetic properties:

Reverse pharmacology provides a number of constituents which can be explored as potential leads for assessing their activity in NAFLD.We discovered 40 compounds found in leaves, fruits, stems and various other plants of the plants, belonging to varied classes, including phenols, flavonoids, alkaloids,saponins and triterpenes which might play a role in the management of NAFLD.The SWISS ADME prediction software and pkcsm tool were used to predict the ADME behaviour of the selected phytoconstituents.These predictions are made on the premise that compounds which have similar structures should interact with similar proteins i.e. biological targets, and thus display similar biological activities.Thus,out of the said 40 molecules, 27 compounds were found to meet all criteria of Lipinski's rules, and were hence classified as drug-like candidates. The parameters calculated for assessment of drug-like behaviour of the compound are presented in Table 1.Molecules were filtered and selected and further 27 molecules were taken forward for studying the docking interactions.

Molecular docking:

The docking veriﬁcation studies were performed using molecular docking technology to confirm for the binding forces between the constituents and the target protein. SDF format files of selected active phyto-constituents 3D structures were obtained from the PubChem (https://pubchem.ncbi.nlm.nih.gov/) database, and protein structure files of selected targets were obtained from the PDB (http://www.rcsb.org/) database. All small compounds and major targets were pre-processed with the PyMOL software (https://pymol.org/) to remove water molecules and other contaminants before being stored as PDB files. Finally, AutoDockVina software (http://vina.scripps.edu/) was used for semi flexible molecular docking, and the affinity of the phyto-constituents to the specified disease targets was determined and expressed as a value (denoting affinity).

The best docked conformations of the compounds revealed that nine molecules demonstrated at least one hydrogen bond interaction in binding site which was distant from hinge region as listed in Table2.All the fifteen molecules selected from the ADMET predictions were subjected to molecular docking on PPARα target. The active site of the PPARα constitutes of two pockets A and B surrounded by amino acids namely: Tyr314, His440 and Tyr 464 placed amid helix and respectively. The co-crystallized ligand occupied the active site cavity established interactions with Ser280, Tyr314 and Tyr464 by hydrogen bonds. Molecule SM (Silymarin) portrayed a highest docking energy of -8.6 in which the oxygen of the methoxy and hydroxyl functional group interacted with Cys275 and Cys276. The flavanone moiety of SM occupied the hydrophobic region and demonstrated interactions with Ala333, Val332, Cys275 and Ile339.SM was well placed in the active site of PPARα as shown in Fig.1 Furthermore TA (Tanshinone) also demonstrated similar interactions as that of co-crystallized ligand by forming hydrogen bond interactions with Met355, Cys276, Ile317, Thr279, Leu321, Thr283, Met320, Met220 and Val324 with docking energy of -8.5.All the other natural molecules were docked in similar fashion and their results are stated in Table 2. Furthermore GLYA (Glycyrrhizic acid) also demonstrated similar interactions as that of co-crystallized ligand establishing hydrogen bond interactions with Lys449, Glu462, Arg465, Asp466, Met467, Tyr468 and Gln461 and hydrophobic interactions with Lys448 and Lys688 with its docking energy of -8.4 as portrayed in Fig.2.All the other natural molecules were docked and their results are stated in Table 2.

Table 2. Interaction data and docking scores of the derivatives for PPARα

Sr. No	Molecule Id	Hydrogen bond Interactions	Hydrophobic and Electrostatic Interactions	Docking Score(kcal/mol)
1	EA	Leu 254,Asn 336	Ala 250,Ala 333,Thr253 Gly 337	-6.9
2	GA	Thr 283,Glu 286,Tyr 334	Met 220,Asn 219	-5.7
3	GLYA	Lys 449,Glu 462,Arg 465,Asp 466,Met 467,Tyr 468,Gln 461	Lys 448, Lys 688	-8.4
4	AN	Leu 254,Cys 276,Thr 279	Ala 333,Ala 250,Ile 339,Leu 247,Ile 241,Val 332	-6.8
5	EM	Thr 283,Glu 286	Met 220,asn 219,val 324,ile 317,leu 321,tyr 314,phe 318	-7.0
6	BN	-	Ala 333,ala 250,val 332,cys 275,val 255,leu 254	-7.2
7	GO	Asn 219,met 220,ser 280	Phe 318,leu 321,met 355,met 320,glu 286	-6.9
8	LC	-	Ile 339,val 332,ala 333,ala 250,tyr 334,cys 275,asn 219,ile 272,ile 241	-6.9
9	NO	-	Met 320,phe 218,met 220,leu 331,val 324	-6.2
10	OL	Ser 280	Cys 276,phe 273,val 444,ile 354	-6.8
11	PG	Ser 280,Tyr 314	His 440,cys 276,met 355,leu 460,phe 273	-7.0
12	QC	Thr 253,Cys 276	Ala 250,ala 333,val 332,ile 339,cys 275	-7.4
13	SM	Cys 275,Cys 276	Ala 333,val 332,cys 275,ile 339	-8.6
14	TA	-	Met 355,cys 276,ile 317,thr 279,leu 321,thr 283,met 320,met 220,val 324	-8.5
15	XO	-	Tyr 334,met 22o,val 324,phe 218,met 320,ile 317,thr 283,leu 321	-6.5

Table 3. Interaction data and docking scores of the derivatives for PPAR ℽ

Sr. No	Molecule Id	Hydrogen bond Interactions	Hydrophobic and Electrostatic Interactions	Docking Score(kcal/mol)
1	EA	Tyr 320,his 323	Val 446,val 450`	-6.6
2	GA	Cys 285,arg 288,ser 342	Ile 341,phe 264	-5.9
3	GLYA	Asp 396,thr 440,thr 447	Pro 398,tyr 320,His 323,val 450,val 446,arg 397	-8.9
4	AN	Gln 271,thr 268,his 266,tyr 473	Phe 287,leu 469	-6.5
5	EM	-	Lys 457,tyr 473,leu 465,leu 469,ile 456,phe 282	-6.3
6	BN	-		-
7	GO	Asp 380,ser 382,asn 424,arg 212,lys 216	Lys 216,ala 215,leu 423	-5.0
8	LC	Glu 378	Lys 240,ala 231,arg 234,lys 230	-6.5
9	NO	-	Arg 350,leu 340,pro 246,leu 237,phe 347,val 248	-4.7
10	OL	Asp 337,arg 350,glu 351	Thr 349,phe 347,leu 237	-5.8
11	PG	Leu 237,asn 335	Phe 347	-6.2
12	QC	His 323,asp 475	Tyr 320,val 446,val 450	-6.4
13	SM	His 323,asn 396,thr 447,gln 454	Asp 475,leu 453,val 450,val 446,arg 443,tyr 320	-8.3
14	TA	Gln 294	Leu 318,leu 476,val 293,val 315	-7.3
15	XO	-	Arg 234,ala 231,lys 230,glu 378	-4.9

Fig. 3.Docked view of receptor inhibitor SM with receptor PPARℽ (Docking score - 8.3)

Fig. 4. Docked view of receptor inhibitor GLYA with receptor PPARℽ (Docking score -8.9)

CONCLUSION:

In this paper, in-silico ADME predictions and molecular docking studies were used to explore the efficacy of the phyto-constituents, to be further formulated and evaluated in the treatment of NAFLD.Our study involved studying the effects of the phytoconstituents,concentrated on PPAR receptors, and evaluating their effect on NAFLD.Computational docking exercises helped in understanding the interactions, if any, between the phyto-constituents and the predicted proteins. In conclusion, our results thus revealed that glycyrrhizic acid and silymarin can be used for further applications as they showed a significant interaction on the PPARs,which was proved by the molecular docking results. Further research would help establish exact mechanisms in confirming in-vivo effectiveness. The results thus provided an evidence and basis for the identification, use and application of such suitable phyto-constituents for further formulation and evaluation. These results will further provide a lead in the future exploration for NAFLD treatment.

CONFLICT OF INTEREST:

The authors declare there is no conflict of interest for publication of this work.

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Received on 31.08.2023 Revised on 16.12.2023

Accepted on 14.03.2024 Published on 20.01.2025

Available online from January 27, 2025

Research J. Pharmacy and Technology. 2025;18(1):232-238.

DOI: 10.52711/0974-360X.2025.00036

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